It lets you publish and subscribe to streams of records. In this respect it is similar to a message queue or enterprise messaging system.
It lets you store streams of records in a fault-tolerant way.
It lets you process streams of records as they occur.

What is Kafka good for?

It gets used for two broad classes of application:

Building real-time streaming data pipelines that reliably get data between systems or applications
Building real-time streaming applications that transform or react to the streams of data

To understand how Kafka does these things, let’s dive in and explore Kafka’s capabilities from the bottom up.

First a few concepts:

Kafka is run as a cluster on one or more servers.
The Kafka cluster stores streams of records in categories called topics.
Each record consists of a key, a value, and a timestamp.

Kafka has four core APIs:

The Producer API allows an application to publish a stream records to one or more Kafka topics.
The Consumer API allows an application to subscribe to one or more topics and process the stream of records produced to them.
The Streams API allows an application to act as a stream processor , consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams to output streams.
The Connector API allows building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a connector to a relational database might capture every change to a table.

In Kafka the communication between the clients and the servers is done with a simple, high-performance, language agnostic TCP protocol. This protocol is versioned and maintains backwards compatibility with older version. We provide a Java client for Kafka, but clients are available in many languages.

Topics and Logs

Let’s first dive into the core abstraction Kafka provides for a stream of records–the topic.

A topic is a category or feed name to which records are published. Topics in Kafka are always multi-subscriber; that is, a topic can have zero, one, or many consumers that subscribe to the data written to it.

For each topic, the Kafka cluster maintains a partitioned log that looks like this:

Each partition is an ordered, immutable sequence of records that is continually appended to–a structured commit log. The records in the partitions are each assigned a sequential id number called the offset that uniquely identifies each record within the partition.

The Kafka cluster retains all published records–whether or not they have been consumed–using a configurable retention period. For example, if the retention policy is set to two days, then for the two days after a record is published, it is available for consumption, after which it will be discarded to free up space. Kafka’s performance is effectively constant with respect to data size so storing data for a long time is not a problem.

In fact, the only metadata retained on a per-consumer basis is the offset or position of that consumer in the log. This offset is controlled by the consumer: normally a consumer will advance its offset linearly as it reads records, but, in fact, since the position is controlled by the consumer it can consume records in any order it likes. For example a consumer can reset to an older offset to reprocess data from the past or skip ahead to the most recent record and start consuming from “now”.

This combination of features means that Kafka consumers are very cheap–they can come and go without much impact on the cluster or on other consumers. For example, you can use our command line tools to “tail” the contents of any topic without changing what is consumed by any existing consumers.

The partitions in the log serve several purposes. First, they allow the log to scale beyond a size that will fit on a single server. Each individual partition must fit on the servers that host it, but a topic may have many partitions so it can handle an arbitrary amount of data. Second they act as the unit of parallelism–more on that in a bit.

Distribution

The partitions of the log are distributed over the servers in the Kafka cluster with each server handling data and requests for a share of the partitions. Each partition is replicated across a configurable number of servers for fault tolerance.

Each partition has one server which acts as the “leader” and zero or more servers which act as “followers”. The leader handles all read and write requests for the partition while the followers passively replicate the leader. If the leader fails, one of the followers will automatically become the new leader. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster.

Producers

Producers publish data to the topics of their choice. The producer is responsible for choosing which record to assign to which partition within the topic. This can be done in a round-robin fashion simply to balance load or it can be done according to some semantic partition function (say based on some key in the record). More on the use of partitioning in a second!

Consumers

Consumers label themselves with a consumer group name, and each record published to a topic is delivered to one consumer instance within each subscribing consumer group. Consumer instances can be in separate processes or on separate machines.

If all the consumer instances have the same consumer group, then the records will effectively be load balanced over the consumer instances.

If all the consumer instances have different consumer groups, then each record will be broadcast to all the consumer processes.

A two server Kafka cluster hosting four partitions (P0-P3) with two consumer groups. Consumer group A has two consumer instances and group B has four.

More commonly, however, we have found that topics have a small number of consumer groups, one for each “logical subscriber”. Each group is composed of many consumer instances for scalability and fault tolerance. This is nothing more than publish-subscribe semantics where the subscriber is a cluster of consumers instead of a single process.

The way consumption is implemented in Kafka is by dividing up the partitions in the log over the consumer instances so that each instance is the exclusive consumer of a “fair share” of partitions at any point in time. This process of maintaining membership in the group is handled by the Kafka protocol dynamically. If new instances join the group they will take over some partitions from other members of the group; if an instance dies, its partitions will be distributed to the remaining instances.

Kafka only provides a total order over records within a partition, not between different partitions in a topic. Per-partition ordering combined with the ability to partition data by key is sufficient for most applications. However, if you require a total order over records this can be achieved with a topic that has only one partition, though this will mean only one consumer process per consumer group.

Guarantees

At a high-level Kafka gives the following guarantees:

Messages sent by a producer to a particular topic partition will be appended in the order they are sent. That is, if a record M1 is sent by the same producer as a record M2, and M1 is sent first, then M1 will have a lower offset than M2 and appear earlier in the log.
A consumer instance sees records in the order they are stored in the log.
For a topic with replication factor N, we will tolerate up to N-1 server failures without losing any records committed to the log.

More details on these guarantees are given in the design section of the documentation.

Kafka as a Messaging System

How does Kafka’s notion of streams compare to a traditional enterprise messaging system?

Messaging traditionally has two models: queuing and publish-subscribe. In a queue, a pool of consumers may read from a server and each record goes to one of them; in publish-subscribe the record is broadcast to all consumers. Each of these two models has a strength and a weakness. The strength of queuing is that it allows you to divide up the processing of data over multiple consumer instances, which lets you scale your processing. Unfortunately, queues aren’t multi-subscriber–once one process reads the data it’s gone. Publish-subscribe allows you broadcast data to multiple processes, but has no way of scaling processing since every message goes to every subscriber.

The consumer group concept in Kafka generalizes these two concepts. As with a queue the consumer group allows you to divide up processing over a collection of processes (the members of the consumer group). As with publish-subscribe, Kafka allows you to broadcast messages to multiple consumer groups.

The advantage of Kafka’s model is that every topic has both these properties–it can scale processing and is also multi-subscriber–there is no need to choose one or the other.

Kafka has stronger ordering guarantees than a traditional messaging system, too.

A traditional queue retains records in-order on the server, and if multiple consumers consume from the queue then the server hands out records in the order they are stored. However, although the server hands out records in order, the records are delivered asynchronously to consumers, so they may arrive out of order on different consumers. This effectively means the ordering of the records is lost in the presence of parallel consumption. Messaging systems often work around this by having a notion of “exclusive consumer” that allows only one process to consume from a queue, but of course this means that there is no parallelism in processing.

Kafka does it better. By having a notion of parallelism–the partition–within the topics, Kafka is able to provide both ordering guarantees and load balancing over a pool of consumer processes. This is achieved by assigning the partitions in the topic to the consumers in the consumer group so that each partition is consumed by exactly one consumer in the group. By doing this we ensure that the consumer is the only reader of that partition and consumes the data in order. Since there are many partitions this still balances the load over many consumer instances. Note however that there cannot be more consumer instances in a consumer group than partitions.

Kafka as a Storage System

Any message queue that allows publishing messages decoupled from consuming them is effectively acting as a storage system for the in-flight messages. What is different about Kafka is that it is a very good storage system.

Data written to Kafka is written to disk and replicated for fault-tolerance. Kafka allows producers to wait on acknowledgement so that a write isn’t considered complete until it is fully replicated and guaranteed to persist even if the server written to fails.

The disk structures Kafka uses scale well–Kafka will perform the same whether you have 50 KB or 50 TB of persistent data on the server.

As a result of taking storage seriously and allowing the clients to control their read position, you can think of Kafka as a kind of special purpose distributed filesystem dedicated to high-performance, low-latency commit log storage, replication, and propagation.

Kafka for Stream Processing

It isn’t enough to just read, write, and store streams of data, the purpose is to enable real-time processing of streams.

In Kafka a stream processor is anything that takes continual streams of data from input topics, performs some processing on this input, and produces continual streams of data to output topics.

For example, a retail application might take in input streams of sales and shipments, and output a stream of reorders and price adjustments computed off this data.

It is possible to do simple processing directly using the producer and consumer APIs. However for more complex transformations Kafka provides a fully integrated Streams API. This allows building applications that do non-trivial processing that compute aggregations off of streams or join streams together.

This facility helps solve the hard problems this type of application faces: handling out-of-order data, reprocessing input as code changes, performing stateful computations, etc.

The streams API builds on the core primitives Kafka provides: it uses the producer and consumer APIs for input, uses Kafka for stateful storage, and uses the same group mechanism for fault tolerance among the stream processor instances.

Putting the Pieces Together

This combination of messaging, storage, and stream processing may seem unusual but it is essential to Kafka’s role as a streaming platform.

A distributed file system like HDFS allows storing static files for batch processing. Effectively a system like this allows storing and processing historical data from the past.

A traditional enterprise messaging system allows processing future messages that will arrive after you subscribe. Applications built in this way process future data as it arrives.

Kafka combines both of these capabilities, and the combination is critical both for Kafka usage as a platform for streaming applications as well as for streaming data pipelines.

By combining storage and low-latency subscriptions, streaming applications can treat both past and future data the same way. That is a single application can process historical, stored data but rather than ending when it reaches the last record it can keep processing as future data arrives. This is a generalized notion of stream processing that subsumes batch processing as well as message-driven applications.

Likewise for streaming data pipelines the combination of subscription to real-time events make it possible to use Kafka for very low-latency pipelines; but the ability to store data reliably make it possible to use it for critical data where the delivery of data must be guaranteed or for integration with offline systems that load data only periodically or may go down for extended periods of time for maintenance. The stream processing facilities make it possible to transform data as it arrives.

For more information on the guarantees, apis, and capabilities Kafka provides see the rest of the documentation.

1.2 - Use Cases

Here is a description of a few of the popular use cases for Apache Kafka®. For an overview of a number of these areas in action, see this blog post.

Messaging

Kafka works well as a replacement for a more traditional message broker. Message brokers are used for a variety of reasons (to decouple processing from data producers, to buffer unprocessed messages, etc). In comparison to most messaging systems Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good solution for large scale message processing applications.

In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strong durability guarantees Kafka provides.

In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ or RabbitMQ.

Website Activity Tracking

The original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds. This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type. These feeds are available for subscription for a range of use cases including real-time processing, real-time monitoring, and loading into Hadoop or offline data warehousing systems for offline processing and reporting.

Activity tracking is often very high volume as many activity messages are generated for each user page view.

Metrics

Kafka is often used for operational monitoring data. This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.

Log Aggregation

Many people use Kafka as a replacement for a log aggregation solution. Log aggregation typically collects physical log files off servers and puts them in a central place (a file server or HDFS perhaps) for processing. Kafka abstracts away the details of files and gives a cleaner abstraction of log or event data as a stream of messages. This allows for lower-latency processing and easier support for multiple data sources and distributed data consumption. In comparison to log-centric systems like Scribe or Flume, Kafka offers equally good performance, stronger durability guarantees due to replication, and much lower end-to-end latency.

Stream Processing

Many users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and then aggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing. For example, a processing pipeline for recommending news articles might crawl article content from RSS feeds and publish it to an “articles” topic; further processing might normalize or deduplicate this content and publish the cleansed article content to a new topic; a final processing stage might attempt to recommend this content to users. Such processing pipelines create graphs of real-time data flows based on the individual topics. Starting in 0.10.0.0, a light-weight but powerful stream processing library called Kafka Streams is available in Apache Kafka to perform such data processing as described above. Apart from Kafka Streams, alternative open source stream processing tools include Apache Storm and Apache Samza.

Event Sourcing

Event sourcing is a style of application design where state changes are logged as a time-ordered sequence of records. Kafka’s support for very large stored log data makes it an excellent backend for an application built in this style.

Commit Log

Kafka can serve as a kind of external commit-log for a distributed system. The log helps replicate data between nodes and acts as a re-syncing mechanism for failed nodes to restore their data. The log compaction feature in Kafka helps support this usage. In this usage Kafka is similar to Apache BookKeeper project.

1.3 - Quick Start

This tutorial assumes you are starting fresh and have no existing Kafka® or ZooKeeper data. Since Kafka console scripts are different for Unix-based and Windows platforms, on Windows platforms use bin\\windows\ instead of bin/, and change the script extension to .bat.

Step 1: Download the code

Download the 0.10.1.0 release and un-tar it.

> **tar -xzf kafka_2.11-0.10.1.0.tgz**
> **cd kafka_2.11-0.10.1.0**

Step 2: Start the server

Kafka uses ZooKeeper so you need to first start a ZooKeeper server if you don’t already have one. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node ZooKeeper instance.

> **bin/zookeeper-server-start.sh config/zookeeper.properties**
[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)
...

Now start the Kafka server:

> **bin/kafka-server-start.sh config/server.properties**
[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)
...

Step 3: Create a topic

Let’s create a topic named “test” with a single partition and only one replica:

> **bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test**

We can now see that topic if we run the list topic command:

> **bin/kafka-topics.sh --list --zookeeper localhost:2181**
test

Alternatively, instead of manually creating topics you can also configure your brokers to auto-create topics when a non-existent topic is published to.

Step 4: Send some messages

Kafka comes with a command line client that will take input from a file or from standard input and send it out as messages to the Kafka cluster. By default, each line will be sent as a separate message.

Run the producer and then type a few messages into the console to send to the server.

> **bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test**
**This is a message**
**This is another message**

Step 5: Start a consumer

Kafka also has a command line consumer that will dump out messages to standard output.

> **bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning**
This is a message
This is another message

If you have each of the above commands running in a different terminal then you should now be able to type messages into the producer terminal and see them appear in the consumer terminal.

All of the command line tools have additional options; running the command with no arguments will display usage information documenting them in more detail.

Step 6: Setting up a multi-broker cluster

So far we have been running against a single broker, but that’s no fun. For Kafka, a single broker is just a cluster of size one, so nothing much changes other than starting a few more broker instances. But just to get feel for it, let’s expand our cluster to three nodes (still all on our local machine).

First we make a config file for each of the brokers (on Windows use the copy command instead):

> **cp config/server.properties config/server-1.properties**
> **cp config/server.properties config/server-2.properties**

Now edit these new files and set the following properties:

config/server-1.properties:
    broker.id=1
    listeners=PLAINTEXT://:9093
    log.dir=/tmp/kafka-logs-1

config/server-2.properties:
    broker.id=2
    listeners=PLAINTEXT://:9094
    log.dir=/tmp/kafka-logs-2

The broker.id property is the unique and permanent name of each node in the cluster. We have to override the port and log directory only because we are running these all on the same machine and we want to keep the brokers from all trying to register on the same port or overwrite each other’s data.

We already have Zookeeper and our single node started, so we just need to start the two new nodes:

> **bin/kafka-server-start.sh config/server-1.properties &**
...
> **bin/kafka-server-start.sh config/server-2.properties &**
...

Now create a new topic with a replication factor of three:

> **bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 3 --partitions 1 --topic my-replicated-topic**

Okay but now that we have a cluster how can we know which broker is doing what? To see that run the “describe topics” command:

> **bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-replicated-topic**
Topic:my-replicated-topic	PartitionCount:1	ReplicationFactor:3	Configs:
	Topic: my-replicated-topic	Partition: 0	Leader: 1	Replicas: 1,2,0	Isr: 1,2,0

Here is an explanation of output. The first line gives a summary of all the partitions, each additional line gives information about one partition. Since we have only one partition for this topic there is only one line.

“leader” is the node responsible for all reads and writes for the given partition. Each node will be the leader for a randomly selected portion of the partitions.
“replicas” is the list of nodes that replicate the log for this partition regardless of whether they are the leader or even if they are currently alive.
“isr” is the set of “in-sync” replicas. This is the subset of the replicas list that is currently alive and caught-up to the leader.

Note that in my example node 1 is the leader for the only partition of the topic.

We can run the same command on the original topic we created to see where it is:

> **bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic test**
Topic:test	PartitionCount:1	ReplicationFactor:1	Configs:
	Topic: test	Partition: 0	Leader: 0	Replicas: 0	Isr: 0

So there is no surprise there–the original topic has no replicas and is on server 0, the only server in our cluster when we created it.

Let’s publish a few messages to our new topic:

> **bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-replicated-topic**
...
**my test message 1**
**my test message 2**
**^C**

Now let’s consume these messages:

> **bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning --topic my-replicated-topic**
...
my test message 1
my test message 2
**^C**

Now let’s test out fault-tolerance. Broker 1 was acting as the leader so let’s kill it:

> **ps aux | grep server-1.properties**
_7564_ ttys002    0:15.91 /System/Library/Frameworks/JavaVM.framework/Versions/1.8/Home/bin/java...
> **kill -9 7564**

On Windows use:

> **wmic process get processid,caption,commandline | find "java.exe" | find "server-1.properties"**
java.exe    java  -Xmx1G -Xms1G -server -XX:+UseG1GC ... build\\libs\\kafka_2.10-0.10.1.0.jar"  kafka.Kafka config\\server-1.properties    _644_
> **taskkill /pid 644 /f**

Leadership has switched to one of the slaves and node 1 is no longer in the in-sync replica set:

> **bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-replicated-topic**
Topic:my-replicated-topic	PartitionCount:1	ReplicationFactor:3	Configs:
	Topic: my-replicated-topic	Partition: 0	Leader: 2	Replicas: 1,2,0	Isr: 2,0

But the messages are still available for consumption even though the leader that took the writes originally is down:

> **bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning --topic my-replicated-topic**
...
my test message 1
my test message 2
**^C**

Step 7: Use Kafka Connect to import/export data

Writing data from the console and writing it back to the console is a convenient place to start, but you’ll probably want to use data from other sources or export data from Kafka to other systems. For many systems, instead of writing custom integration code you can use Kafka Connect to import or export data.

Kafka Connect is a tool included with Kafka that imports and exports data to Kafka. It is an extensible tool that runs connectors , which implement the custom logic for interacting with an external system. In this quickstart we’ll see how to run Kafka Connect with simple connectors that import data from a file to a Kafka topic and export data from a Kafka topic to a file.

First, we’ll start by creating some seed data to test with:

> **echo -e "foo\nbar" > test.txt**

Next, we’ll start two connectors running in standalone mode, which means they run in a single, local, dedicated process. We provide three configuration files as parameters. The first is always the configuration for the Kafka Connect process, containing common configuration such as the Kafka brokers to connect to and the serialization format for data. The remaining configuration files each specify a connector to create. These files include a unique connector name, the connector class to instantiate, and any other configuration required by the connector.

> **bin/connect-standalone.sh config/connect-standalone.properties config/connect-file-source.properties config/connect-file-sink.properties**

These sample configuration files, included with Kafka, use the default local cluster configuration you started earlier and create two connectors: the first is a source connector that reads lines from an input file and produces each to a Kafka topic and the second is a sink connector that reads messages from a Kafka topic and produces each as a line in an output file.

During startup you’ll see a number of log messages, including some indicating that the connectors are being instantiated. Once the Kafka Connect process has started, the source connector should start reading lines from test.txt and producing them to the topic connect-test, and the sink connector should start reading messages from the topic connect-test and write them to the file test.sink.txt. We can verify the data has been delivered through the entire pipeline by examining the contents of the output file:

> **cat test.sink.txt**
foo
bar

Note that the data is being stored in the Kafka topic connect-test, so we can also run a console consumer to see the data in the topic (or use custom consumer code to process it):

> **bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic connect-test --from-beginning**
{"schema":{"type":"string","optional":false},"payload":"foo"}
{"schema":{"type":"string","optional":false},"payload":"bar"}
...

The connectors continue to process data, so we can add data to the file and see it move through the pipeline:

> **echo "Another line" >> test.txt**

You should see the line appear in the console consumer output and in the sink file.

Step 8: Use Kafka Streams to process data

Kafka Streams is a client library of Kafka for real-time stream processing and analyzing data stored in Kafka brokers. This quickstart example will demonstrate how to run a streaming application coded in this library. Here is the gist of the WordCountDemo example code (converted to use Java 8 lambda expressions for easy reading).

KTable wordCounts = textLines
    // Split each text line, by whitespace, into words.
    .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))

    // Ensure the words are available as record keys for the next aggregate operation.
    .map((key, value) -> new KeyValue<>(value, value))

    // Count the occurrences of each word (record key) and store the results into a table named "Counts".
    .countByKey("Counts")

It implements the WordCount algorithm, which computes a word occurrence histogram from the input text. However, unlike other WordCount examples you might have seen before that operate on bounded data, the WordCount demo application behaves slightly differently because it is designed to operate on an infinite, unbounded stream of data. Similar to the bounded variant, it is a stateful algorithm that tracks and updates the counts of words. However, since it must assume potentially unbounded input data, it will periodically output its current state and results while continuing to process more data because it cannot know when it has processed “all” the input data.

We will now prepare input data to a Kafka topic, which will subsequently be processed by a Kafka Streams application.

> **echo -e "all streams lead to kafka\nhello kafka streams\njoin kafka summit" > file-input.txt**

Or on Windows:

> **echo all streams lead to kafka> file-input.txt**
> **echo hello kafka streams>> file-input.txt**
> **echo|set /p=join kafka summit>> file-input.txt**

Next, we send this input data to the input topic named streams-file-input using the console producer (in practice, stream data will likely be flowing continuously into Kafka where the application will be up and running):

> **bin/kafka-topics.sh --create \**
            **--zookeeper localhost:2181 \**
            **--replication-factor 1 \**
            **--partitions 1 \**
            **--topic streams-file-input**



> **bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-file-input < file-input.txt**

We can now run the WordCount demo application to process the input data:

> **bin/kafka-run-class.sh org.apache.kafka.streams.examples.wordcount.WordCountDemo**

There won’t be any STDOUT output except log entries as the results are continuously written back into another topic named streams-wordcount-output in Kafka. The demo will run for a few seconds and then, unlike typical stream processing applications, terminate automatically.

We can now inspect the output of the WordCount demo application by reading from its output topic:

> **bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 \**
            **--topic streams-wordcount-output \**
            **--from-beginning \**
            **--formatter kafka.tools.DefaultMessageFormatter \**
            **--property print.key=true \**
            **--property print.value=true \**
            **--property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \**
            **--property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer**

with the following output data being printed to the console:

all     1
lead    1
to      1
hello   1
streams 2
join    1
kafka   3
summit  1

Here, the first column is the Kafka message key, and the second column is the message value, both in in java.lang.String format. Note that the output is actually a continuous stream of updates, where each data record (i.e. each line in the original output above) is an updated count of a single word, aka record key such as “kafka”. For multiple records with the same key, each later record is an update of the previous one.

Now you can write more input messages to the streams-file-input topic and observe additional messages added to streams-wordcount-output topic, reflecting updated word counts (e.g., using the console producer and the console consumer, as described above).

You can stop the console consumer via Ctrl-C.

1.4 - Ecosystem

There are a plethora of tools that integrate with Kafka outside the main distribution. The ecosystem page lists many of these, including stream processing systems, Hadoop integration, monitoring, and deployment tools.

1.5 - Upgrading

Upgrading from 0.8.x, 0.9.x or 0.10.0.X to 0.10.1.0

0.10.1.0 has wire protocol changes. By following the recommended rolling upgrade plan below, you guarantee no downtime during the upgrade. However, please notice the Potential breaking changes in 0.10.1.0 before upgrade.
Note: Because new protocols are introduced, it is important to upgrade your Kafka clusters before upgrading your clients (i.e. 0.10.1.x clients only support 0.10.1.x or later brokers while 0.10.1.x brokers also support older clients).

For a rolling upgrade:

Update server.properties file on all brokers and add the following properties:
- inter.broker.protocol.version=CURRENT_KAFKA_VERSION (e.g. 0.8.2.0, 0.9.0.0 or 0.10.0.0).
- log.message.format.version=CURRENT_KAFKA_VERSION (See potential performance impact following the upgrade for the details on what this configuration does.)
Upgrade the brokers one at a time: shut down the broker, update the code, and restart it.
Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.10.1.0.
If your previous message format is 0.10.0, change log.message.format.version to 0.10.1 (this is a no-op as the message format is the same for both 0.10.0 and 0.10.1). If your previous message format version is lower than 0.10.0, do not change log.message.format.version yet - this parameter should only change once all consumers have been upgraded to 0.10.0.0 or later.
Restart the brokers one by one for the new protocol version to take effect.
If log.message.format.version is still lower than 0.10.0 at this point, wait until all consumers have been upgraded to 0.10.0 or later, then change log.message.format.version to 0.10.1 on each broker and restart them one by one.

Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.

Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.

Potential breaking changes in 0.10.1.0

The log retention time is no longer based on last modified time of the log segments. Instead it will be based on the largest timestamp of the messages in a log segment.
The log rolling time is no longer depending on log segment create time. Instead it is now based on the timestamp in the messages. More specifically. if the timestamp of the first message in the segment is T, the log will be rolled out when a new message has a timestamp greater than or equal to T + log.roll.ms
The open file handlers of 0.10.0 will increase by ~33% because of the addition of time index files for each segment.
The time index and offset index share the same index size configuration. Since each time index entry is 1.5x the size of offset index entry. User may need to increase log.index.size.max.bytes to avoid potential frequent log rolling.
Due to the increased number of index files, on some brokers with large amount the log segments (e.g. >15K), the log loading process during the broker startup could be longer. Based on our experiment, setting the num.recovery.threads.per.data.dir to one may reduce the log loading time.

Notable changes in 0.10.1.0

The new Java consumer is no longer in beta and we recommend it for all new development. The old Scala consumers are still supported, but they will be deprecated in the next release and will be removed in a future major release.
The --new-consumer/--new.consumer switch is no longer required to use tools like MirrorMaker and the Console Consumer with the new consumer; one simply needs to pass a Kafka broker to connect to instead of the ZooKeeper ensemble. In addition, usage of the Console Consumer with the old consumer has been deprecated and it will be removed in a future major release.
Kafka clusters can now be uniquely identified by a cluster id. It will be automatically generated when a broker is upgraded to 0.10.1.0. The cluster id is available via the kafka.server:type=KafkaServer,name=ClusterId metric and it is part of the Metadata response. Serializers, client interceptors and metric reporters can receive the cluster id by implementing the ClusterResourceListener interface.
The BrokerState “RunningAsController” (value 4) has been removed. Due to a bug, a broker would only be in this state briefly before transitioning out of it and hence the impact of the removal should be minimal. The recommended way to detect if a given broker is the controller is via the kafka.controller:type=KafkaController,name=ActiveControllerCount metric.
The new Java Consumer now allows users to search offsets by timestamp on partitions.
The new Java Consumer now supports heartbeating from a background thread. There is a new configuration max.poll.interval.ms which controls the maximum time between poll invocations before the consumer will proactively leave the group (5 minutes by default). The value of the configuration request.timeout.ms must always be larger than max.poll.interval.ms because this is the maximum time that a JoinGroup request can block on the server while the consumer is rebalancing, so we have changed its default value to just above 5 minutes. Finally, the default value of session.timeout.ms has been adjusted down to 10 seconds, and the default value of max.poll.records has been changed to 500.
When using an Authorizer and a user doesn’t have Describe authorization on a topic, the broker will no longer return TOPIC_AUTHORIZATION_FAILED errors to requests since this leaks topic names. Instead, the UNKNOWN_TOPIC_OR_PARTITION error code will be returned. This may cause unexpected timeouts or delays when using the producer and consumer since Kafka clients will typically retry automatically on unknown topic errors. You should consult the client logs if you suspect this could be happening.
Fetch responses have a size limit by default (50 MB for consumers and 10 MB for replication). The existing per partition limits also apply (1 MB for consumers and replication). Note that neither of these limits is an absolute maximum as explained in the next point.
Consumers and replicas can make progress if a message larger than the response/partition size limit is found. More concretely, if the first message in the first non-empty partition of the fetch is larger than either or both limits, the message will still be returned.
Overloaded constructors were added to kafka.api.FetchRequest and kafka.javaapi.FetchRequest to allow the caller to specify the order of the partitions (since order is significant in v3). The previously existing constructors were deprecated and the partitions are shuffled before the request is sent to avoid starvation issues.

New Protocol Versions

ListOffsetRequest v1 supports accurate offset search based on timestamps.
MetadataResponse v2 introduces a new field: “cluster_id”.
FetchRequest v3 supports limiting the response size (in addition to the existing per partition limit), it returns messages bigger than the limits if required to make progress and the order of partitions in the request is now significant.
JoinGroup v1 introduces a new field: “rebalance_timeout”.

Upgrading from 0.8.x or 0.9.x to 0.10.0.0

0.10.0.0 has potential breaking changes (please review before upgrading) and possible performance impact following the upgrade. By following the recommended rolling upgrade plan below, you guarantee no downtime and no performance impact during and following the upgrade.
Note: Because new protocols are introduced, it is important to upgrade your Kafka clusters before upgrading your clients.

Notes to clients with version 0.9.0.0: Due to a bug introduced in 0.9.0.0, clients that depend on ZooKeeper (old Scala high-level Consumer and MirrorMaker if used with the old consumer) will not work with 0.10.0.x brokers. Therefore, 0.9.0.0 clients should be upgraded to 0.9.0.1 before brokers are upgraded to 0.10.0.x. This step is not necessary for 0.8.X or 0.9.0.1 clients.

For a rolling upgrade:

Update server.properties file on all brokers and add the following properties:
- inter.broker.protocol.version=CURRENT_KAFKA_VERSION (e.g. 0.8.2 or 0.9.0.0).
- log.message.format.version=CURRENT_KAFKA_VERSION (See potential performance impact following the upgrade for the details on what this configuration does.)
Upgrade the brokers. This can be done a broker at a time by simply bringing it down, updating the code, and restarting it.
Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.10.0.0. NOTE: You shouldn’t touch log.message.format.version yet - this parameter should only change once all consumers have been upgraded to 0.10.0.0
Restart the brokers one by one for the new protocol version to take effect.
Once all consumers have been upgraded to 0.10.0, change log.message.format.version to 0.10.0 on each broker and restart them one by one.

Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.

Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.

Potential performance impact following upgrade to 0.10.0.0

The message format in 0.10.0 includes a new timestamp field and uses relative offsets for compressed messages. The on disk message format can be configured through log.message.format.version in the server.properties file. The default on-disk message format is 0.10.0. If a consumer client is on a version before 0.10.0.0, it only understands message formats before 0.10.0. In this case, the broker is able to convert messages from the 0.10.0 format to an earlier format before sending the response to the consumer on an older version. However, the broker can’t use zero-copy transfer in this case. Reports from the Kafka community on the performance impact have shown CPU utilization going from 20% before to 100% after an upgrade, which forced an immediate upgrade of all clients to bring performance back to normal. To avoid such message conversion before consumers are upgraded to 0.10.0.0, one can set log.message.format.version to 0.8.2 or 0.9.0 when upgrading the broker to 0.10.0.0. This way, the broker can still use zero-copy transfer to send the data to the old consumers. Once consumers are upgraded, one can change the message format to 0.10.0 on the broker and enjoy the new message format that includes new timestamp and improved compression. The conversion is supported to ensure compatibility and can be useful to support a few apps that have not updated to newer clients yet, but is impractical to support all consumer traffic on even an overprovisioned cluster. Therefore, it is critical to avoid the message conversion as much as possible when brokers have been upgraded but the majority of clients have not.

For clients that are upgraded to 0.10.0.0, there is no performance impact.

Note: By setting the message format version, one certifies that all existing messages are on or below that message format version. Otherwise consumers before 0.10.0.0 might break. In particular, after the message format is set to 0.10.0, one should not change it back to an earlier format as it may break consumers on versions before 0.10.0.0.

Note: Due to the additional timestamp introduced in each message, producers sending small messages may see a message throughput degradation because of the increased overhead. Likewise, replication now transmits an additional 8 bytes per message. If you’re running close to the network capacity of your cluster, it’s possible that you’ll overwhelm the network cards and see failures and performance issues due to the overload.

Note: If you have enabled compression on producers, you may notice reduced producer throughput and/or lower compression rate on the broker in some cases. When receiving compressed messages, 0.10.0 brokers avoid recompressing the messages, which in general reduces the latency and improves the throughput. In certain cases, however, this may reduce the batching size on the producer, which could lead to worse throughput. If this happens, users can tune linger.ms and batch.size of the producer for better throughput. In addition, the producer buffer used for compressing messages with snappy is smaller than the one used by the broker, which may have a negative impact on the compression ratio for the messages on disk. We intend to make this configurable in a future Kafka release.

Potential breaking changes in 0.10.0.0

Starting from Kafka 0.10.0.0, the message format version in Kafka is represented as the Kafka version. For example, message format 0.9.0 refers to the highest message version supported by Kafka 0.9.0.
Message format 0.10.0 has been introduced and it is used by default. It includes a timestamp field in the messages and relative offsets are used for compressed messages.
ProduceRequest/Response v2 has been introduced and it is used by default to support message format 0.10.0
FetchRequest/Response v2 has been introduced and it is used by default to support message format 0.10.0
MessageFormatter interface was changed from def writeTo(key: Array[Byte], value: Array[Byte], output: PrintStream) to def writeTo(consumerRecord: ConsumerRecord[Array[Byte], Array[Byte]], output: PrintStream)
MessageReader interface was changed from def readMessage(): KeyedMessage[Array[Byte], Array[Byte]] to def readMessage(): ProducerRecord[Array[Byte], Array[Byte]]
MessageFormatter’s package was changed from kafka.tools to kafka.common
MessageReader’s package was changed from kafka.tools to kafka.common
MirrorMakerMessageHandler no longer exposes the handle(record: MessageAndMetadata[Array[Byte], Array[Byte]]) method as it was never called.
The 0.7 KafkaMigrationTool is no longer packaged with Kafka. If you need to migrate from 0.7 to 0.10.0, please migrate to 0.8 first and then follow the documented upgrade process to upgrade from 0.8 to 0.10.0.
The new consumer has standardized its APIs to accept java.util.Collection as the sequence type for method parameters. Existing code may have to be updated to work with the 0.10.0 client library.
LZ4-compressed message handling was changed to use an interoperable framing specification (LZ4f v1.5.1). To maintain compatibility with old clients, this change only applies to Message format 0.10.0 and later. Clients that Produce/Fetch LZ4-compressed messages using v0/v1 (Message format 0.9.0) should continue to use the 0.9.0 framing implementation. Clients that use Produce/Fetch protocols v2 or later should use interoperable LZ4f framing. A list of interoperable LZ4 libraries is available at http://www.lz4.org/

Notable changes in 0.10.0.0

Starting from Kafka 0.10.0.0, a new client library named Kafka Streams is available for stream processing on data stored in Kafka topics. This new client library only works with 0.10.x and upward versioned brokers due to message format changes mentioned above. For more information please read this section.
The default value of the configuration parameter receive.buffer.bytes is now 64K for the new consumer.
The new consumer now exposes the configuration parameter exclude.internal.topics to restrict internal topics (such as the consumer offsets topic) from accidentally being included in regular expression subscriptions. By default, it is enabled.
The old Scala producer has been deprecated. Users should migrate their code to the Java producer included in the kafka-clients JAR as soon as possible.
The new consumer API has been marked stable.

Upgrading from 0.8.0, 0.8.1.X or 0.8.2.X to 0.9.0.0

0.9.0.0 has potential breaking changes (please review before upgrading) and an inter-broker protocol change from previous versions. This means that upgraded brokers and clients may not be compatible with older versions. It is important that you upgrade your Kafka cluster before upgrading your clients. If you are using MirrorMaker downstream clusters should be upgraded first as well.

For a rolling upgrade:

Update server.properties file on all brokers and add the following property: inter.broker.protocol.version=0.8.2.X
Upgrade the brokers. This can be done a broker at a time by simply bringing it down, updating the code, and restarting it.
Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.9.0.0.
Restart the brokers one by one for the new protocol version to take effect

Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.

Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.

Potential breaking changes in 0.9.0.0

Java 1.6 is no longer supported.
Scala 2.9 is no longer supported.
Broker IDs above 1000 are now reserved by default to automatically assigned broker IDs. If your cluster has existing broker IDs above that threshold make sure to increase the reserved.broker.max.id broker configuration property accordingly.
Configuration parameter replica.lag.max.messages was removed. Partition leaders will no longer consider the number of lagging messages when deciding which replicas are in sync.
Configuration parameter replica.lag.time.max.ms now refers not just to the time passed since last fetch request from replica, but also to time since the replica last caught up. Replicas that are still fetching messages from leaders but did not catch up to the latest messages in replica.lag.time.max.ms will be considered out of sync.
Compacted topics no longer accept messages without key and an exception is thrown by the producer if this is attempted. In 0.8.x, a message without key would cause the log compaction thread to subsequently complain and quit (and stop compacting all compacted topics).
MirrorMaker no longer supports multiple target clusters. As a result it will only accept a single –consumer.config parameter. To mirror multiple source clusters, you will need at least one MirrorMaker instance per source cluster, each with its own consumer configuration.
Tools packaged under org.apache.kafka.clients.tools.* have been moved to org.apache.kafka.tools.*. All included scripts will still function as usual, only custom code directly importing these classes will be affected.
The default Kafka JVM performance options (KAFKA_JVM_PERFORMANCE_OPTS) have been changed in kafka-run-class.sh.
The kafka-topics.sh script (kafka.admin.TopicCommand) now exits with non-zero exit code on failure.
The kafka-topics.sh script (kafka.admin.TopicCommand) will now print a warning when topic names risk metric collisions due to the use of a ‘.’ or ‘_’ in the topic name, and error in the case of an actual collision.
The kafka-console-producer.sh script (kafka.tools.ConsoleProducer) will use the Java producer instead of the old Scala producer be default, and users have to specify ‘old-producer’ to use the old producer.
By default, all command line tools will print all logging messages to stderr instead of stdout.

Notable changes in 0.9.0.1

The new broker id generation feature can be disabled by setting broker.id.generation.enable to false.
Configuration parameter log.cleaner.enable is now true by default. This means topics with a cleanup.policy=compact will now be compacted by default, and 128 MB of heap will be allocated to the cleaner process via log.cleaner.dedupe.buffer.size. You may want to review log.cleaner.dedupe.buffer.size and the other log.cleaner configuration values based on your usage of compacted topics.
Default value of configuration parameter fetch.min.bytes for the new consumer is now 1 by default.

Deprecations in 0.9.0.0

Altering topic configuration from the kafka-topics.sh script (kafka.admin.TopicCommand) has been deprecated. Going forward, please use the kafka-configs.sh script (kafka.admin.ConfigCommand) for this functionality.
The kafka-consumer-offset-checker.sh (kafka.tools.ConsumerOffsetChecker) has been deprecated. Going forward, please use kafka-consumer-groups.sh (kafka.admin.ConsumerGroupCommand) for this functionality.
The kafka.tools.ProducerPerformance class has been deprecated. Going forward, please use org.apache.kafka.tools.ProducerPerformance for this functionality (kafka-producer-perf-test.sh will also be changed to use the new class).
The producer config block.on.buffer.full has been deprecated and will be removed in future release. Currently its default value has been changed to false. The KafkaProducer will no longer throw BufferExhaustedException but instead will use max.block.ms value to block, after which it will throw a TimeoutException. If block.on.buffer.full property is set to true explicitly, it will set the max.block.ms to Long.MAX_VALUE and metadata.fetch.timeout.ms will not be honoured

Upgrading from 0.8.1 to 0.8.2

0.8.2 is fully compatible with 0.8.1. The upgrade can be done one broker at a time by simply bringing it down, updating the code, and restarting it.

Upgrading from 0.8.0 to 0.8.1

0.8.1 is fully compatible with 0.8. The upgrade can be done one broker at a time by simply bringing it down, updating the code, and restarting it.

Upgrading from 0.7

Release 0.7 is incompatible with newer releases. Major changes were made to the API, ZooKeeper data structures, and protocol, and configuration in order to add replication (Which was missing in 0.7). The upgrade from 0.7 to later versions requires a special tool for migration. This migration can be done without downtime.

2 - APIs

2.1 - API

Kafka includes four core apis:

The Producer API allows applications to send streams of data to topics in the Kafka cluster.
The Consumer API allows applications to read streams of data from topics in the Kafka cluster.
The Streams API allows transforming streams of data from input topics to output topics.
The Connect API allows implementing connectors that continually pull from some source system or application into Kafka or push from Kafka into some sink system or application. Kafka exposes all its functionality over a language independent protocol which has clients available in many programming languages. However only the Java clients are maintained as part of the main Kafka project, the others are available as independent open source projects. A list of non-Java clients is available here.

Producer API

The Producer API allows applications to send streams of data to topics in the Kafka cluster.

Examples showing how to use the producer are given in the javadocs.

To use the producer, you can use the following maven dependency:

		<dependency>
			<groupId>org.apache.kafka</groupId>
			<artifactId>kafka-clients</artifactId>
			<version>0.10.1.0</version>
		</dependency>

Consumer API

The Consumer API allows applications to read streams of data from topics in the Kafka cluster.

Examples showing how to use the consumer are given in the javadocs.

To use the consumer, you can use the following maven dependency:

		<dependency>
			<groupId>org.apache.kafka</groupId>
			<artifactId>kafka-clients</artifactId>
			<version>0.10.1.0</version>
		</dependency>

Streams API

The Streams API allows transforming streams of data from input topics to output topics.

Examples showing how to use this library are given in the javadocs

Additional documentation on using the Streams API is available here.

To use Kafka Streams you can use the following maven dependency:

		<dependency>
			<groupId>org.apache.kafka</groupId>
			<artifactId>kafka-streams</artifactId>
			<version>0.10.1.0</version>
		</dependency>

Connect API

The Connect API allows implementing connectors that continually pull from some source data system into Kafka or push from Kafka into some sink data system.

Many users of Connect won’t need to use this API directly, though, they can use pre-built connectors without needing to write any code. Additional information on using Connect is available here.

Those who want to implement custom connectors can see the javadoc.

Legacy APIs

A more limited legacy producer and consumer api is also included in Kafka. These old Scala APIs are deprecated and only still available for compatibility purposes. Information on them can be found here here.

3 - Configuration

3.1 - Configuration

Kafka uses key-value pairs in the property file format for configuration. These values can be supplied either from a file or programmatically.

Broker Configs

The essential configurations are the following:

broker.id
log.dirs

zookeeper.connect Topic-level configurations and defaults are discussed in more detail below.

Name	Description	Type	Default	Valid Values	Importance
zookeeper.connect	Zookeeper host string	string			high
advertised.host.name	DEPRECATED: only used when `advertised.listeners` or `listeners` are not set. Use `advertised.listeners` instead. Hostname to publish to ZooKeeper for clients to use. In IaaS environments, this may need to be different from the interface to which the broker binds. If this is not set, it will use the value for `host.name` if configured. Otherwise it will use the value returned from java.net.InetAddress.getCanonicalHostName().	string	null		high
advertised.listeners	Listeners to publish to ZooKeeper for clients to use, if different than the listeners above. In IaaS environments, this may need to be different from the interface to which the broker binds. If this is not set, the value for `listeners` will be used.	string	null		high
advertised.port	DEPRECATED: only used when `advertised.listeners` or `listeners` are not set. Use `advertised.listeners` instead. The port to publish to ZooKeeper for clients to use. In IaaS environments, this may need to be different from the port to which the broker binds. If this is not set, it will publish the same port that the broker binds to.	int	null		high
auto.create.topics.enable	Enable auto creation of topic on the server	boolean	true		high
auto.leader.rebalance.enable	Enables auto leader balancing. A background thread checks and triggers leader balance if required at regular intervals	boolean	true		high
background.threads	The number of threads to use for various background processing tasks	int	10	[1,...]	high
broker.id	The broker id for this server. If unset, a unique broker id will be generated.To avoid conflicts between zookeeper generated broker id's and user configured broker id's, generated broker idsstart from reserved.broker.max.id + 1.	int	-1		high
compression.type	Specify the final compression type for a given topic. This configuration accepts the standard compression codecs ('gzip', 'snappy', 'lz4'). It additionally accepts 'uncompressed' which is equivalent to no compression; and 'producer' which means retain the original compression codec set by the producer.	string	producer		high
delete.topic.enable	Enables delete topic. Delete topic through the admin tool will have no effect if this config is turned off	boolean	false		high
host.name	DEPRECATED: only used when `listeners` is not set. Use `listeners` instead. hostname of broker. If this is set, it will only bind to this address. If this is not set, it will bind to all interfaces	string	""		high
leader.imbalance.check.interval.seconds	The frequency with which the partition rebalance check is triggered by the controller	long	300		high
leader.imbalance.per.broker.percentage	The ratio of leader imbalance allowed per broker. The controller would trigger a leader balance if it goes above this value per broker. The value is specified in percentage.	int	10		high
listeners	Listener List - Comma-separated list of URIs we will listen on and their protocols. Specify hostname as 0.0.0.0 to bind to all interfaces. Leave hostname empty to bind to default interface. Examples of legal listener lists: PLAINTEXT://myhost:9092,TRACE://:9091 PLAINTEXT://0.0.0.0:9092, TRACE://localhost:9093	string	null		high
log.dir	The directory in which the log data is kept (supplemental for log.dirs property)	string	/tmp/kafka-logs		high
log.dirs	The directories in which the log data is kept. If not set, the value in log.dir is used	string	null		high
log.flush.interval.messages	The number of messages accumulated on a log partition before messages are flushed to disk	long	9223372036854775807	[1,...]	high
log.flush.interval.ms	The maximum time in ms that a message in any topic is kept in memory before flushed to disk. If not set, the value in log.flush.scheduler.interval.ms is used	long	null		high
log.flush.offset.checkpoint.interval.ms	The frequency with which we update the persistent record of the last flush which acts as the log recovery point	int	60000	[0,...]	high
log.flush.scheduler.interval.ms	The frequency in ms that the log flusher checks whether any log needs to be flushed to disk	long	9223372036854775807		high
log.retention.bytes	The maximum size of the log before deleting it	long	-1		high
log.retention.hours	The number of hours to keep a log file before deleting it (in hours), tertiary to log.retention.ms property	int	168		high
log.retention.minutes	The number of minutes to keep a log file before deleting it (in minutes), secondary to log.retention.ms property. If not set, the value in log.retention.hours is used	int	null		high
log.retention.ms	The number of milliseconds to keep a log file before deleting it (in milliseconds), If not set, the value in log.retention.minutes is used	long	null		high
log.roll.hours	The maximum time before a new log segment is rolled out (in hours), secondary to log.roll.ms property	int	168	[1,...]	high
log.roll.jitter.hours	The maximum jitter to subtract from logRollTimeMillis (in hours), secondary to log.roll.jitter.ms property	int	0	[0,...]	high
log.roll.jitter.ms	The maximum jitter to subtract from logRollTimeMillis (in milliseconds). If not set, the value in log.roll.jitter.hours is used	long	null		high
log.roll.ms	The maximum time before a new log segment is rolled out (in milliseconds). If not set, the value in log.roll.hours is used	long	null		high
log.segment.bytes	The maximum size of a single log file	int	1073741824	[14,...]	high
log.segment.delete.delay.ms	The amount of time to wait before deleting a file from the filesystem	long	60000	[0,...]	high
message.max.bytes	The maximum size of message that the server can receive	int	1000012	[0,...]	high
min.insync.replicas	When a producer sets acks to "all" (or "-1"), min.insync.replicas specifies the minimum number of replicas that must acknowledge a write for the write to be considered successful. If this minimum cannot be met, then the producer will raise an exception (either NotEnoughReplicas or NotEnoughReplicasAfterAppend). When used together, min.insync.replicas and acks allow you to enforce greater durability guarantees. A typical scenario would be to create a topic with a replication factor of 3, set min.insync.replicas to 2, and produce with acks of "all". This will ensure that the producer raises an exception if a majority of replicas do not receive a write.	int	1	[1,...]	high
num.io.threads	The number of io threads that the server uses for carrying out network requests	int	8	[1,...]	high
num.network.threads	the number of network threads that the server uses for handling network requests	int	3	[1,...]	high
num.recovery.threads.per.data.dir	The number of threads per data directory to be used for log recovery at startup and flushing at shutdown	int	1	[1,...]	high
num.replica.fetchers	Number of fetcher threads used to replicate messages from a source broker. Increasing this value can increase the degree of I/O parallelism in the follower broker.	int	1		high
offset.metadata.max.bytes	The maximum size for a metadata entry associated with an offset commit	int	4096		high
offsets.commit.required.acks	The required acks before the commit can be accepted. In general, the default (-1) should not be overridden	short	-1		high
offsets.commit.timeout.ms	Offset commit will be delayed until all replicas for the offsets topic receive the commit or this timeout is reached. This is similar to the producer request timeout.	int	5000	[1,...]	high
offsets.load.buffer.size	Batch size for reading from the offsets segments when loading offsets into the cache.	int	5242880	[1,...]	high
offsets.retention.check.interval.ms	Frequency at which to check for stale offsets	long	600000	[1,...]	high
offsets.retention.minutes	Log retention window in minutes for offsets topic	int	1440	[1,...]	high
offsets.topic.compression.codec	Compression codec for the offsets topic - compression may be used to achieve "atomic" commits	int	0		high
offsets.topic.num.partitions	The number of partitions for the offset commit topic (should not change after deployment)	int	50	[1,...]	high
offsets.topic.replication.factor	The replication factor for the offsets topic (set higher to ensure availability). To ensure that the effective replication factor of the offsets topic is the configured value, the number of alive brokers has to be at least the replication factor at the time of the first request for the offsets topic. If not, either the offsets topic creation will fail or it will get a replication factor of min(alive brokers, configured replication factor)	short	3	[1,...]	high
offsets.topic.segment.bytes	The offsets topic segment bytes should be kept relatively small in order to facilitate faster log compaction and cache loads	int	104857600	[1,...]	high
port	DEPRECATED: only used when `listeners` is not set. Use `listeners` instead. the port to listen and accept connections on	int	9092		high
queued.max.requests	The number of queued requests allowed before blocking the network threads	int	500	[1,...]	high
quota.consumer.default	DEPRECATED: Used only when dynamic default quotas are not configured for or in Zookeeper. Any consumer distinguished by clientId/consumer group will get throttled if it fetches more bytes than this value per-second	long	9223372036854775807	[1,...]	high
quota.producer.default	DEPRECATED: Used only when dynamic default quotas are not configured for , or in Zookeeper. Any producer distinguished by clientId will get throttled if it produces more bytes than this value per-second	long	9223372036854775807	[1,...]	high
replica.fetch.min.bytes	Minimum bytes expected for each fetch response. If not enough bytes, wait up to replicaMaxWaitTimeMs	int	1		high
replica.fetch.wait.max.ms	max wait time for each fetcher request issued by follower replicas. This value should always be less than the replica.lag.time.max.ms at all times to prevent frequent shrinking of ISR for low throughput topics	int	500		high
replica.high.watermark.checkpoint.interval.ms	The frequency with which the high watermark is saved out to disk	long	5000		high
replica.lag.time.max.ms	If a follower hasn't sent any fetch requests or hasn't consumed up to the leaders log end offset for at least this time, the leader will remove the follower from isr	long	10000		high
replica.socket.receive.buffer.bytes	The socket receive buffer for network requests	int	65536		high
replica.socket.timeout.ms	The socket timeout for network requests. Its value should be at least replica.fetch.wait.max.ms	int	30000		high
request.timeout.ms	The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.	int	30000		high
socket.receive.buffer.bytes	The SO_RCVBUF buffer of the socket sever sockets. If the value is -1, the OS default will be used.	int	102400		high
socket.request.max.bytes	The maximum number of bytes in a socket request	int	104857600	[1,...]	high
socket.send.buffer.bytes	The SO_SNDBUF buffer of the socket sever sockets. If the value is -1, the OS default will be used.	int	102400		high
unclean.leader.election.enable	Indicates whether to enable replicas not in the ISR set to be elected as leader as a last resort, even though doing so may result in data loss	boolean	true		high
zookeeper.connection.timeout.ms	The max time that the client waits to establish a connection to zookeeper. If not set, the value in zookeeper.session.timeout.ms is used	int	null		high
zookeeper.session.timeout.ms	Zookeeper session timeout	int	6000		high
zookeeper.set.acl	Set client to use secure ACLs	boolean	false		high
broker.id.generation.enable	Enable automatic broker id generation on the server. When enabled the value configured for reserved.broker.max.id should be reviewed.	boolean	true		medium
broker.rack	Rack of the broker. This will be used in rack aware replication assignment for fault tolerance. Examples: `RACK1`, `us-east-1d`	string	null		medium
connections.max.idle.ms	Idle connections timeout: the server socket processor threads close the connections that idle more than this	long	600000		medium
controlled.shutdown.enable	Enable controlled shutdown of the server	boolean	true		medium
controlled.shutdown.max.retries	Controlled shutdown can fail for multiple reasons. This determines the number of retries when such failure happens	int	3		medium
controlled.shutdown.retry.backoff.ms	Before each retry, the system needs time to recover from the state that caused the previous failure (Controller fail over, replica lag etc). This config determines the amount of time to wait before retrying.	long	5000		medium
controller.socket.timeout.ms	The socket timeout for controller-to-broker channels	int	30000		medium
default.replication.factor	default replication factors for automatically created topics	int	1		medium
fetch.purgatory.purge.interval.requests	The purge interval (in number of requests) of the fetch request purgatory	int	1000		medium
group.max.session.timeout.ms	The maximum allowed session timeout for registered consumers. Longer timeouts give consumers more time to process messages in between heartbeats at the cost of a longer time to detect failures.	int	300000		medium
group.min.session.timeout.ms	The minimum allowed session timeout for registered consumers. Shorter timeouts result in quicker failure detection at the cost of more frequent consumer heartbeating, which can overwhelm broker resources.	int	6000		medium
inter.broker.protocol.version	Specify which version of the inter-broker protocol will be used. This is typically bumped after all brokers were upgraded to a new version. Example of some valid values are: 0.8.0, 0.8.1, 0.8.1.1, 0.8.2, 0.8.2.0, 0.8.2.1, 0.9.0.0, 0.9.0.1 Check ApiVersion for the full list.	string	0.10.1-IV2		medium
log.cleaner.backoff.ms	The amount of time to sleep when there are no logs to clean	long	15000	[0,...]	medium
log.cleaner.dedupe.buffer.size	The total memory used for log deduplication across all cleaner threads	long	134217728		medium
log.cleaner.delete.retention.ms	How long are delete records retained?	long	86400000		medium
log.cleaner.enable	Enable the log cleaner process to run on the server? Should be enabled if using any topics with a cleanup.policy=compact including the internal offsets topic. If disabled those topics will not be compacted and continually grow in size.	boolean	true		medium
log.cleaner.io.buffer.load.factor	Log cleaner dedupe buffer load factor. The percentage full the dedupe buffer can become. A higher value will allow more log to be cleaned at once but will lead to more hash collisions	double	0.9		medium
log.cleaner.io.buffer.size	The total memory used for log cleaner I/O buffers across all cleaner threads	int	524288	[0,...]	medium
log.cleaner.io.max.bytes.per.second	The log cleaner will be throttled so that the sum of its read and write i/o will be less than this value on average	double	1.7976931348623157E308		medium
log.cleaner.min.cleanable.ratio	The minimum ratio of dirty log to total log for a log to eligible for cleaning	double	0.5		medium
log.cleaner.min.compaction.lag.ms	The minimum time a message will remain uncompacted in the log. Only applicable for logs that are being compacted.	long	0		medium
log.cleaner.threads	The number of background threads to use for log cleaning	int	1	[0,...]	medium
log.cleanup.policy	The default cleanup policy for segments beyond the retention window. A comma separated list of valid policies. Valid policies are: "delete" and "compact"	list	[delete]	[compact, delete]	medium
log.index.interval.bytes	The interval with which we add an entry to the offset index	int	4096	[0,...]	medium
log.index.size.max.bytes	The maximum size in bytes of the offset index	int	10485760	[4,...]	medium
log.message.format.version	Specify the message format version the broker will use to append messages to the logs. The value should be a valid ApiVersion. Some examples are: 0.8.2, 0.9.0.0, 0.10.0, check ApiVersion for more details. By setting a particular message format version, the user is certifying that all the existing messages on disk are smaller or equal than the specified version. Setting this value incorrectly will cause consumers with older versions to break as they will receive messages with a format that they don't understand.	string	0.10.1-IV2		medium
log.message.timestamp.difference.max.ms	The maximum difference allowed between the timestamp when a broker receives a message and the timestamp specified in the message. If log.message.timestamp.type=CreateTime, a message will be rejected if the difference in timestamp exceeds this threshold. This configuration is ignored if log.message.timestamp.type=LogAppendTime.	long	9223372036854775807	[0,...]	medium
log.message.timestamp.type	Define whether the timestamp in the message is message create time or log append time. The value should be either `CreateTime` or `LogAppendTime`	string	CreateTime	[CreateTime, LogAppendTime]	medium
log.preallocate	Should pre allocate file when create new segment? If you are using Kafka on Windows, you probably need to set it to true.	boolean	false		medium
log.retention.check.interval.ms	The frequency in milliseconds that the log cleaner checks whether any log is eligible for deletion	long	300000	[1,...]	medium
max.connections.per.ip	The maximum number of connections we allow from each ip address	int	2147483647	[1,...]	medium
max.connections.per.ip.overrides	Per-ip or hostname overrides to the default maximum number of connections	string	""		medium
num.partitions	The default number of log partitions per topic	int	1	[1,...]	medium
principal.builder.class	The fully qualified name of a class that implements the PrincipalBuilder interface, which is currently used to build the Principal for connections with the SSL SecurityProtocol.	class	class org.apache.kafka.common.security.auth.DefaultPrincipalBuilder		medium
producer.purgatory.purge.interval.requests	The purge interval (in number of requests) of the producer request purgatory	int	1000		medium
replica.fetch.backoff.ms	The amount of time to sleep when fetch partition error occurs.	int	1000	[0,...]	medium
replica.fetch.max.bytes	The number of bytes of messages to attempt to fetch for each partition. This is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that progress can be made. The maximum message size accepted by the broker is defined via `message.max.bytes` (broker config) or `max.message.bytes` (topic config).	int	1048576	[0,...]	medium
replica.fetch.response.max.bytes	Maximum bytes expected for the entire fetch response. This is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that progress can be made. The maximum message size accepted by the broker is defined via `message.max.bytes` (broker config) or `max.message.bytes` (topic config).	int	10485760	[0,...]	medium
reserved.broker.max.id	Max number that can be used for a broker.id	int	1000	[0,...]	medium
sasl.enabled.mechanisms	The list of SASL mechanisms enabled in the Kafka server. The list may contain any mechanism for which a security provider is available. Only GSSAPI is enabled by default.	list	[GSSAPI]		medium
sasl.kerberos.kinit.cmd	Kerberos kinit command path.	string	/usr/bin/kinit		medium
sasl.kerberos.min.time.before.relogin	Login thread sleep time between refresh attempts.	long	60000		medium
sasl.kerberos.principal.to.local.rules	A list of rules for mapping from principal names to short names (typically operating system usernames). The rules are evaluated in order and the first rule that matches a principal name is used to map it to a short name. Any later rules in the list are ignored. By default, principal names of the form {username}/{hostname}@{REALM} are mapped to {username}. For more details on the format please see security authorization and acls.	list	[DEFAULT]		medium
sasl.kerberos.service.name	The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.	string	null		medium
sasl.kerberos.ticket.renew.jitter	Percentage of random jitter added to the renewal time.	double	0.05		medium
sasl.kerberos.ticket.renew.window.factor	Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.	double	0.8		medium
sasl.mechanism.inter.broker.protocol	SASL mechanism used for inter-broker communication. Default is GSSAPI.	string	GSSAPI		medium
security.inter.broker.protocol	Security protocol used to communicate between brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.	string	PLAINTEXT		medium
ssl.cipher.suites	A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.	list	null		medium
ssl.client.auth	Configures kafka broker to request client authentication. The following settings are common: `ssl.client.auth=required` If set to required client authentication is required. `ssl.client.auth=requested` This means client authentication is optional. unlike requested , if this option is set client can choose not to provide authentication information about itself `ssl.client.auth=none` This means client authentication is not needed.	string	none	[required, requested, none]	medium
ssl.enabled.protocols	The list of protocols enabled for SSL connections.	list	[TLSv1.2, TLSv1.1, TLSv1]		medium
ssl.key.password	The password of the private key in the key store file. This is optional for client.	password	null		medium
ssl.keymanager.algorithm	The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.	string	SunX509		medium
ssl.keystore.location	The location of the key store file. This is optional for client and can be used for two-way authentication for client.	string	null		medium
ssl.keystore.password	The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.	password	null		medium
ssl.keystore.type	The file format of the key store file. This is optional for client.	string	JKS		medium
ssl.protocol	The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.	string	TLS		medium
ssl.provider	The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.	string	null		medium
ssl.trustmanager.algorithm	The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.	string	PKIX		medium
ssl.truststore.location	The location of the trust store file.	string	null		medium
ssl.truststore.password	The password for the trust store file.	password	null		medium
ssl.truststore.type	The file format of the trust store file.	string	JKS		medium
authorizer.class.name	The authorizer class that should be used for authorization	string	""		low
metric.reporters	A list of classes to use as metrics reporters. Implementing the `MetricReporter` interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.	list	[]		low
metrics.num.samples	The number of samples maintained to compute metrics.	int	2	[1,...]	low
metrics.sample.window.ms	The window of time a metrics sample is computed over.	long	30000	[1,...]	low
quota.window.num	The number of samples to retain in memory for client quotas	int	11	[1,...]	low
quota.window.size.seconds	The time span of each sample for client quotas	int	1	[1,...]	low
replication.quota.window.num	The number of samples to retain in memory for replication quotas	int	11	[1,...]	low
replication.quota.window.size.seconds	The time span of each sample for replication quotas	int	1	[1,...]	low
ssl.endpoint.identification.algorithm	The endpoint identification algorithm to validate server hostname using server certificate.	string	null		low
ssl.secure.random.implementation	The SecureRandom PRNG implementation to use for SSL cryptography operations.	string	null		low
zookeeper.sync.time.ms	How far a ZK follower can be behind a ZK leader	int	2000		low

More details about broker configuration can be found in the scala class kafka.server.KafkaConfig.

Topic-level configuration Configurations pertinent to topics have both a server default as well an optional per-topic override. If no per-topic configuration is given the server default is used. The override can be set at topic creation time by giving one or more --config options. This example creates a topic named my-topic with a custom max message size and flush rate:

  ** > bin/kafka-topics.sh --zookeeper localhost:2181 --create --topic my-topic --partitions 1
          --replication-factor 1 --config max.message.bytes=64000 --config flush.messages=1**

Overrides can also be changed or set later using the alter configs command. This example updates the max message size for my-topic :

  ** > bin/kafka-configs.sh --zookeeper localhost:2181 --entity-type topics --entity-name my-topic --alter --add-config max.message.bytes=128000**

To check overrides set on the topic you can do

  ** > bin/kafka-configs.sh --zookeeper localhost:2181 --entity-type topics --entity-name my-topic --describe**

To remove an override you can do

  ** > bin/kafka-configs.sh --zookeeper localhost:2181  --entity-type topics --entity-name my-topic --alter --delete-config max.message.bytes**

The following are the topic-level configurations. The server’s default configuration for this property is given under the Server Default Property heading. A given server default config value only applies to a topic if it does not have an explicit topic config override.

Name	Description	Type	Default	Valid Values	Server Default Property	Importance
cleanup.policy	A string that is either "delete" or "compact". This string designates the retention policy to use on old log segments. The default policy ("delete") will discard old segments when their retention time or size limit has been reached. The "compact" setting will enable log compaction on the topic.	list	[delete]	[compact, delete]	log.cleanup.policy	medium
compression.type	Specify the final compression type for a given topic. This configuration accepts the standard compression codecs ('gzip', 'snappy', lz4). It additionally accepts 'uncompressed' which is equivalent to no compression; and 'producer' which means retain the original compression codec set by the producer.	string	producer	[uncompressed, snappy, lz4, gzip, producer]	compression.type	medium
delete.retention.ms	The amount of time to retain delete tombstone markers for log compacted topics. This setting also gives a bound on the time in which a consumer must complete a read if they begin from offset 0 to ensure that they get a valid snapshot of the final stage (otherwise delete tombstones may be collected before they complete their scan).	long	86400000	[0,...]	log.cleaner.delete.retention.ms	medium
file.delete.delay.ms	The time to wait before deleting a file from the filesystem	long	60000	[0,...]	log.segment.delete.delay.ms	medium
flush.messages	This setting allows specifying an interval at which we will force an fsync of data written to the log. For example if this was set to 1 we would fsync after every message; if it were 5 we would fsync after every five messages. In general we recommend you not set this and use replication for durability and allow the operating system's background flush capabilities as it is more efficient. This setting can be overridden on a per-topic basis (see the per-topic configuration section).	long	9223372036854775807	[0,...]	log.flush.interval.messages	medium
flush.ms	This setting allows specifying a time interval at which we will force an fsync of data written to the log. For example if this was set to 1000 we would fsync after 1000 ms had passed. In general we recommend you not set this and use replication for durability and allow the operating system's background flush capabilities as it is more efficient.	long	9223372036854775807	[0,...]	log.flush.interval.ms	medium
follower.replication.throttled.replicas	A list of replicas for which log replication should be throttled on the follower side. The list should describe a set of replicas in the form [PartitionId]:[BrokerId],[PartitionId]:[BrokerId]:... or alternatively the wildcard '*' can be used to throttle all replicas for this topic.	list	[]	kafka.server.ThrottledReplicaListValidator$@59d57c39	follower.replication.throttled.replicas	medium
index.interval.bytes	This setting controls how frequently Kafka adds an index entry to it's offset index. The default setting ensures that we index a message roughly every 4096 bytes. More indexing allows reads to jump closer to the exact position in the log but makes the index larger. You probably don't need to change this.	int	4096	[0,...]	log.index.interval.bytes	medium
leader.replication.throttled.replicas	A list of replicas for which log replication should be throttled on the leader side. The list should describe a set of replicas in the form [PartitionId]:[BrokerId],[PartitionId]:[BrokerId]:... or alternatively the wildcard '*' can be used to throttle all replicas for this topic.	list	[]	kafka.server.ThrottledReplicaListValidator$@59d57c39	leader.replication.throttled.replicas	medium
max.message.bytes	This is largest message size Kafka will allow to be appended. Note that if you increase this size you must also increase your consumer's fetch size so they can fetch messages this large.	int	1000012	[0,...]	message.max.bytes	medium
message.format.version	Specify the message format version the broker will use to append messages to the logs. The value should be a valid ApiVersion. Some examples are: 0.8.2, 0.9.0.0, 0.10.0, check ApiVersion for more details. By setting a particular message format version, the user is certifying that all the existing messages on disk are smaller or equal than the specified version. Setting this value incorrectly will cause consumers with older versions to break as they will receive messages with a format that they don't understand.	string	0.10.1-IV2		log.message.format.version	medium
message.timestamp.difference.max.ms	The maximum difference allowed between the timestamp when a broker receives a message and the timestamp specified in the message. If message.timestamp.type=CreateTime, a message will be rejected if the difference in timestamp exceeds this threshold. This configuration is ignored if message.timestamp.type=LogAppendTime.	long	9223372036854775807	[0,...]	log.message.timestamp.difference.max.ms	medium
message.timestamp.type	Define whether the timestamp in the message is message create time or log append time. The value should be either `CreateTime` or `LogAppendTime`	string	CreateTime		log.message.timestamp.type	medium
min.cleanable.dirty.ratio	This configuration controls how frequently the log compactor will attempt to clean the log (assuming log compaction is enabled). By default we will avoid cleaning a log where more than 50% of the log has been compacted. This ratio bounds the maximum space wasted in the log by duplicates (at 50% at most 50% of the log could be duplicates). A higher ratio will mean fewer, more efficient cleanings but will mean more wasted space in the log.	double	0.5	[0,...,1]	log.cleaner.min.cleanable.ratio	medium
min.compaction.lag.ms	The minimum time a message will remain uncompacted in the log. Only applicable for logs that are being compacted.	long	0	[0,...]	log.cleaner.min.compaction.lag.ms	medium
min.insync.replicas	When a producer sets acks to "all" (or "-1"), min.insync.replicas specifies the minimum number of replicas that must acknowledge a write for the write to be considered successful. If this minimum cannot be met, then the producer will raise an exception (either NotEnoughReplicas or NotEnoughReplicasAfterAppend). When used together, min.insync.replicas and acks allow you to enforce greater durability guarantees. A typical scenario would be to create a topic with a replication factor of 3, set min.insync.replicas to 2, and produce with acks of "all". This will ensure that the producer raises an exception if a majority of replicas do not receive a write.	int	1	[1,...]	min.insync.replicas	medium
preallocate	Should pre allocate file when create new segment?	boolean	false		log.preallocate	medium
retention.bytes	This configuration controls the maximum size a log can grow to before we will discard old log segments to free up space if we are using the "delete" retention policy. By default there is no size limit only a time limit.	long	-1		log.retention.bytes	medium
retention.ms	This configuration controls the maximum time we will retain a log before we will discard old log segments to free up space if we are using the "delete" retention policy. This represents an SLA on how soon consumers must read their data.	long	604800000		log.retention.ms	medium
segment.bytes	This configuration controls the segment file size for the log. Retention and cleaning is always done a file at a time so a larger segment size means fewer files but less granular control over retention.	int	1073741824	[14,...]	log.segment.bytes	medium
segment.index.bytes	This configuration controls the size of the index that maps offsets to file positions. We preallocate this index file and shrink it only after log rolls. You generally should not need to change this setting.	int	10485760	[0,...]	log.index.size.max.bytes	medium
segment.jitter.ms	The maximum random jitter subtracted from the scheduled segment roll time to avoid thundering herds of segment rolling	long	0	[0,...]	log.roll.jitter.ms	medium
segment.ms	This configuration controls the period of time after which Kafka will force the log to roll even if the segment file isn't full to ensure that retention can delete or compact old data.	long	604800000	[0,...]	log.roll.ms	medium
unclean.leader.election.enable	Indicates whether to enable replicas not in the ISR set to be elected as leader as a last resort, even though doing so may result in data loss	boolean	true		unclean.leader.election.enable	medium

Producer Configs

Below is the configuration of the Java producer:

Name	Description	Type	Default	Valid Values	Importance
bootstrap.servers	A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form `host1:port1,host2:port2,...`. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).	list			high
key.serializer	Serializer class for key that implements the `Serializer` interface.	class			high
value.serializer	Serializer class for value that implements the `Serializer` interface.	class			high
acks	The number of acknowledgments the producer requires the leader to have received before considering a request complete. This controls the durability of records that are sent. The following settings are allowed: `acks=0` If set to zero then the producer will not wait for any acknowledgment from the server at all. The record will be immediately added to the socket buffer and considered sent. No guarantee can be made that the server has received the record in this case, and the `retries` configuration will not take effect (as the client won't generally know of any failures). The offset given back for each record will always be set to -1. `acks=1` This will mean the leader will write the record to its local log but will respond without awaiting full acknowledgement from all followers. In this case should the leader fail immediately after acknowledging the record but before the followers have replicated it then the record will be lost. `acks=all` This means the leader will wait for the full set of in-sync replicas to acknowledge the record. This guarantees that the record will not be lost as long as at least one in-sync replica remains alive. This is the strongest available guarantee. This is equivalent to the acks=-1 setting.	string	1	[all, -1, 0, 1]	high
buffer.memory	The total bytes of memory the producer can use to buffer records waiting to be sent to the server. If records are sent faster than they can be delivered to the server the producer will block for `max.block.ms` after which it will throw an exception. This setting should correspond roughly to the total memory the producer will use, but is not a hard bound since not all memory the producer uses is used for buffering. Some additional memory will be used for compression (if compression is enabled) as well as for maintaining in-flight requests.	long	33554432	[0,...]	high
compression.type	The compression type for all data generated by the producer. The default is none (i.e. no compression). Valid values are `none`, `gzip`, `snappy`, or `lz4`. Compression is of full batches of data, so the efficacy of batching will also impact the compression ratio (more batching means better compression).	string	none		high
retries	Setting a value greater than zero will cause the client to resend any record whose send fails with a potentially transient error. Note that this retry is no different than if the client resent the record upon receiving the error. Allowing retries without setting `max.in.flight.requests.per.connection` to 1 will potentially change the ordering of records because if two batches are sent to a single partition, and the first fails and is retried but the second succeeds, then the records in the second batch may appear first.	int	0	[0,...,2147483647]	high
ssl.key.password	The password of the private key in the key store file. This is optional for client.	password	null		high
ssl.keystore.location	The location of the key store file. This is optional for client and can be used for two-way authentication for client.	string	null		high
ssl.keystore.password	The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.	password	null		high
ssl.truststore.location	The location of the trust store file.	string	null		high
ssl.truststore.password	The password for the trust store file.	password	null		high
batch.size	The producer will attempt to batch records together into fewer requests whenever multiple records are being sent to the same partition. This helps performance on both the client and the server. This configuration controls the default batch size in bytes. No attempt will be made to batch records larger than this size. Requests sent to brokers will contain multiple batches, one for each partition with data available to be sent. A small batch size will make batching less common and may reduce throughput (a batch size of zero will disable batching entirely). A very large batch size may use memory a bit more wastefully as we will always allocate a buffer of the specified batch size in anticipation of additional records.	int	16384	[0,...]	medium
client.id	An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.	string	""		medium
connections.max.idle.ms	Close idle connections after the number of milliseconds specified by this config.	long	540000		medium
linger.ms	The producer groups together any records that arrive in between request transmissions into a single batched request. Normally this occurs only under load when records arrive faster than they can be sent out. However in some circumstances the client may want to reduce the number of requests even under moderate load. This setting accomplishes this by adding a small amount of artificial delay—that is, rather than immediately sending out a record the producer will wait for up to the given delay to allow other records to be sent so that the sends can be batched together. This can be thought of as analogous to Nagle's algorithm in TCP. This setting gives the upper bound on the delay for batching: once we get `batch.size` worth of records for a partition it will be sent immediately regardless of this setting, however if we have fewer than this many bytes accumulated for this partition we will 'linger' for the specified time waiting for more records to show up. This setting defaults to 0 (i.e. no delay). Setting `linger.ms=5`, for example, would have the effect of reducing the number of requests sent but would add up to 5ms of latency to records sent in the absense of load.	long	0	[0,...]	medium
max.block.ms	The configuration controls how long `KafkaProducer.send()` and `KafkaProducer.partitionsFor()` will block.These methods can be blocked either because the buffer is full or metadata unavailable.Blocking in the user-supplied serializers or partitioner will not be counted against this timeout.	long	60000	[0,...]	medium
max.request.size	The maximum size of a request in bytes. This is also effectively a cap on the maximum record size. Note that the server has its own cap on record size which may be different from this. This setting will limit the number of record batches the producer will send in a single request to avoid sending huge requests.	int	1048576	[0,...]	medium
partitioner.class	Partitioner class that implements the `Partitioner` interface.	class	class org.apache.kafka.clients.producer.internals.DefaultPartitioner		medium
receive.buffer.bytes	The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.	int	32768	[-1,...]	medium
request.timeout.ms	The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.	int	30000	[0,...]	medium
sasl.kerberos.service.name	The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.	string	null		medium
sasl.mechanism	SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.	string	GSSAPI		medium
security.protocol	Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.	string	PLAINTEXT		medium
send.buffer.bytes	The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.	int	131072	[-1,...]	medium
ssl.enabled.protocols	The list of protocols enabled for SSL connections.	list	[TLSv1.2, TLSv1.1, TLSv1]		medium
ssl.keystore.type	The file format of the key store file. This is optional for client.	string	JKS		medium
ssl.protocol	The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.	string	TLS		medium
ssl.provider	The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.	string	null		medium
ssl.truststore.type	The file format of the trust store file.	string	JKS		medium
timeout.ms	The configuration controls the maximum amount of time the server will wait for acknowledgments from followers to meet the acknowledgment requirements the producer has specified with the `acks` configuration. If the requested number of acknowledgments are not met when the timeout elapses an error will be returned. This timeout is measured on the server side and does not include the network latency of the request.	int	30000	[0,...]	medium
block.on.buffer.full	When our memory buffer is exhausted we must either stop accepting new records (block) or throw errors. By default this setting is false and the producer will no longer throw a BufferExhaustException but instead will use the `max.block.ms` value to block, after which it will throw a TimeoutException. Setting this property to true will set the `max.block.ms` to Long.MAX_VALUE. Also if this property is set to true, parameter `metadata.fetch.timeout.ms` is no longer honored. This parameter is deprecated and will be removed in a future release. Parameter `max.block.ms` should be used instead.	boolean	false		low
interceptor.classes	A list of classes to use as interceptors. Implementing the `ProducerInterceptor` interface allows you to intercept (and possibly mutate) the records received by the producer before they are published to the Kafka cluster. By default, there are no interceptors.	list	null		low
max.in.flight.requests.per.connection	The maximum number of unacknowledged requests the client will send on a single connection before blocking. Note that if this setting is set to be greater than 1 and there are failed sends, there is a risk of message re-ordering due to retries (i.e., if retries are enabled).	int	5	[1,...]	low
metadata.fetch.timeout.ms	The first time data is sent to a topic we must fetch metadata about that topic to know which servers host the topic's partitions. This config specifies the maximum time, in milliseconds, for this fetch to succeed before throwing an exception back to the client.	long	60000	[0,...]	low
metadata.max.age.ms	The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.	long	300000	[0,...]	low
metric.reporters	A list of classes to use as metrics reporters. Implementing the `MetricReporter` interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.	list	[]		low
metrics.num.samples	The number of samples maintained to compute metrics.	int	2	[1,...]	low
metrics.sample.window.ms	The window of time a metrics sample is computed over.	long	30000	[0,...]	low
reconnect.backoff.ms	The amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all requests sent by the consumer to the broker.	long	50	[0,...]	low
retry.backoff.ms	The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.	long	100	[0,...]	low
sasl.kerberos.kinit.cmd	Kerberos kinit command path.	string	/usr/bin/kinit		low
sasl.kerberos.min.time.before.relogin	Login thread sleep time between refresh attempts.	long	60000		low
sasl.kerberos.ticket.renew.jitter	Percentage of random jitter added to the renewal time.	double	0.05		low
sasl.kerberos.ticket.renew.window.factor	Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.	double	0.8		low
ssl.cipher.suites	A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.	list	null		low
ssl.endpoint.identification.algorithm	The endpoint identification algorithm to validate server hostname using server certificate.	string	null		low
ssl.keymanager.algorithm	The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.	string	SunX509		low
ssl.secure.random.implementation	The SecureRandom PRNG implementation to use for SSL cryptography operations.	string	null		low
ssl.trustmanager.algorithm	The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.	string	PKIX		low

For those interested in the legacy Scala producer configs, information can be found here.

Consumer Configs

In 0.9.0.0 we introduced the new Java consumer as a replacement for the older Scala-based simple and high-level consumers. The configs for both new and old consumers are described below.

New Consumer Configs

Below is the configuration for the new consumer:

Name	Description	Type	Default	Valid Values	Importance
bootstrap.servers	A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form `host1:port1,host2:port2,...`. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).	list			high
key.deserializer	Deserializer class for key that implements the `Deserializer` interface.	class			high
value.deserializer	Deserializer class for value that implements the `Deserializer` interface.	class			high
fetch.min.bytes	The minimum amount of data the server should return for a fetch request. If insufficient data is available the request will wait for that much data to accumulate before answering the request. The default setting of 1 byte means that fetch requests are answered as soon as a single byte of data is available or the fetch request times out waiting for data to arrive. Setting this to something greater than 1 will cause the server to wait for larger amounts of data to accumulate which can improve server throughput a bit at the cost of some additional latency.	int	1	[0,...]	high
group.id	A unique string that identifies the consumer group this consumer belongs to. This property is required if the consumer uses either the group management functionality by using `subscribe(topic)` or the Kafka-based offset management strategy.	string	""		high
heartbeat.interval.ms	The expected time between heartbeats to the consumer coordinator when using Kafka's group management facilities. Heartbeats are used to ensure that the consumer's session stays active and to facilitate rebalancing when new consumers join or leave the group. The value must be set lower than `session.timeout.ms`, but typically should be set no higher than 1/3 of that value. It can be adjusted even lower to control the expected time for normal rebalances.	int	3000		high
max.partition.fetch.bytes	The maximum amount of data per-partition the server will return. If the first message in the first non-empty partition of the fetch is larger than this limit, the message will still be returned to ensure that the consumer can make progress. The maximum message size accepted by the broker is defined via `message.max.bytes` (broker config) or `max.message.bytes` (topic config). See fetch.max.bytes for limiting the consumer request size	int	1048576	[0,...]	high
session.timeout.ms	The timeout used to detect consumer failures when using Kafka's group management facility. The consumer sends periodic heartbeats to indicate its liveness to the broker. If no heartbeats are received by the broker before the expiration of this session timeout, then the broker will remove this consumer from the group and initiate a rebalance. Note that the value must be in the allowable range as configured in the broker configuration by `group.min.session.timeout.ms` and `group.max.session.timeout.ms`.	int	10000		high
ssl.key.password	The password of the private key in the key store file. This is optional for client.	password	null		high
ssl.keystore.location	The location of the key store file. This is optional for client and can be used for two-way authentication for client.	string	null		high
ssl.keystore.password	The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.	password	null		high
ssl.truststore.location	The location of the trust store file.	string	null		high
ssl.truststore.password	The password for the trust store file.	password	null		high
auto.offset.reset	What to do when there is no initial offset in Kafka or if the current offset does not exist any more on the server (e.g. because that data has been deleted): earliest: automatically reset the offset to the earliest offset latest: automatically reset the offset to the latest offset none: throw exception to the consumer if no previous offset is found for the consumer's group anything else: throw exception to the consumer.	string	latest	[latest, earliest, none]	medium
connections.max.idle.ms	Close idle connections after the number of milliseconds specified by this config.	long	540000		medium
enable.auto.commit	If true the consumer's offset will be periodically committed in the background.	boolean	true		medium
exclude.internal.topics	Whether records from internal topics (such as offsets) should be exposed to the consumer. If set to `true` the only way to receive records from an internal topic is subscribing to it.	boolean	true		medium
fetch.max.bytes	The maximum amount of data the server should return for a fetch request. This is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that the consumer can make progress. The maximum message size accepted by the broker is defined via `message.max.bytes` (broker config) or `max.message.bytes` (topic config). Note that the consumer performs multiple fetches in parallel.	int	52428800	[0,...]	medium
max.poll.interval.ms	The maximum delay between invocations of poll() when using consumer group management. This places an upper bound on the amount of time that the consumer can be idle before fetching more records. If poll() is not called before expiration of this timeout, then the consumer is considered failed and the group will rebalance in order to reassign the partitions to another member.	int	300000	[1,...]	medium
max.poll.records	The maximum number of records returned in a single call to poll().	int	500	[1,...]	medium
partition.assignment.strategy	The class name of the partition assignment strategy that the client will use to distribute partition ownership amongst consumer instances when group management is used	list	[class org.apache.kafka.clients.consumer.RangeAssignor]		medium
receive.buffer.bytes	The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.	int	65536	[-1,...]	medium
request.timeout.ms	The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.	int	305000	[0,...]	medium
sasl.kerberos.service.name	The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.	string	null		medium
sasl.mechanism	SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.	string	GSSAPI		medium
security.protocol	Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.	string	PLAINTEXT		medium
send.buffer.bytes	The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.	int	131072	[-1,...]	medium
ssl.enabled.protocols	The list of protocols enabled for SSL connections.	list	[TLSv1.2, TLSv1.1, TLSv1]		medium
ssl.keystore.type	The file format of the key store file. This is optional for client.	string	JKS		medium
ssl.protocol	The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.	string	TLS		medium
ssl.provider	The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.	string	null		medium
ssl.truststore.type	The file format of the trust store file.	string	JKS		medium
auto.commit.interval.ms	The frequency in milliseconds that the consumer offsets are auto-committed to Kafka if `enable.auto.commit` is set to `true`.	int	5000	[0,...]	low
check.crcs	Automatically check the CRC32 of the records consumed. This ensures no on-the-wire or on-disk corruption to the messages occurred. This check adds some overhead, so it may be disabled in cases seeking extreme performance.	boolean	true		low
client.id	An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.	string	""		low
fetch.max.wait.ms	The maximum amount of time the server will block before answering the fetch request if there isn't sufficient data to immediately satisfy the requirement given by fetch.min.bytes.	int	500	[0,...]	low
interceptor.classes	A list of classes to use as interceptors. Implementing the `ConsumerInterceptor` interface allows you to intercept (and possibly mutate) records received by the consumer. By default, there are no interceptors.	list	null		low
metadata.max.age.ms	The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.	long	300000	[0,...]	low
metric.reporters	A list of classes to use as metrics reporters. Implementing the `MetricReporter` interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.	list	[]		low
metrics.num.samples	The number of samples maintained to compute metrics.	int	2	[1,...]	low
metrics.sample.window.ms	The window of time a metrics sample is computed over.	long	30000	[0,...]	low
reconnect.backoff.ms	The amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all requests sent by the consumer to the broker.	long	50	[0,...]	low
retry.backoff.ms	The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.	long	100	[0,...]	low
sasl.kerberos.kinit.cmd	Kerberos kinit command path.	string	/usr/bin/kinit		low
sasl.kerberos.min.time.before.relogin	Login thread sleep time between refresh attempts.	long	60000		low
sasl.kerberos.ticket.renew.jitter	Percentage of random jitter added to the renewal time.	double	0.05		low
sasl.kerberos.ticket.renew.window.factor	Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.	double	0.8		low
ssl.cipher.suites	A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.	list	null		low
ssl.endpoint.identification.algorithm	The endpoint identification algorithm to validate server hostname using server certificate.	string	null		low
ssl.keymanager.algorithm	The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.	string	SunX509		low
ssl.secure.random.implementation	The SecureRandom PRNG implementation to use for SSL cryptography operations.	string	null		low
ssl.trustmanager.algorithm	The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.	string	PKIX		low

Old Consumer Configs

The essential old consumer configurations are the following:

group.id

zookeeper.connect

Property	Default	Description
group.id		A string that uniquely identifies the group of consumer processes to which this consumer belongs. By setting the same group id multiple processes indicate that they are all part of the same consumer group.
zookeeper.connect		Specifies the ZooKeeper connection string in the form `hostname:port` where host and port are the host and port of a ZooKeeper server. To allow connecting through other ZooKeeper nodes when that ZooKeeper machine is down you can also specify multiple hosts in the form `hostname1:port1,hostname2:port2,hostname3:port3`. The server may also have a ZooKeeper chroot path as part of its ZooKeeper connection string which puts its data under some path in the global ZooKeeper namespace. If so the consumer should use the same chroot path in its connection string. For example to give a chroot path of `/chroot/path` you would give the connection string as `hostname1:port1,hostname2:port2,hostname3:port3/chroot/path`.
consumer.id	null	Generated automatically if not set.
socket.timeout.ms	30 * 1000	The socket timeout for network requests. The actual timeout set will be max.fetch.wait + socket.timeout.ms.
socket.receive.buffer.bytes	64 * 1024	The socket receive buffer for network requests
fetch.message.max.bytes	1024 * 1024	The number of bytes of messages to attempt to fetch for each topic-partition in each fetch request. These bytes will be read into memory for each partition, so this helps control the memory used by the consumer. The fetch request size must be at least as large as the maximum message size the server allows or else it is possible for the producer to send messages larger than the consumer can fetch.
num.consumer.fetchers	1	The number fetcher threads used to fetch data.
auto.commit.enable	true	If true, periodically commit to ZooKeeper the offset of messages already fetched by the consumer. This committed offset will be used when the process fails as the position from which the new consumer will begin.
auto.commit.interval.ms	60 * 1000	The frequency in ms that the consumer offsets are committed to zookeeper.
queued.max.message.chunks	2	Max number of message chunks buffered for consumption. Each chunk can be up to fetch.message.max.bytes.
rebalance.max.retries	4	When a new consumer joins a consumer group the set of consumers attempt to “rebalance” the load to assign partitions to each consumer. If the set of consumers changes while this assignment is taking place the rebalance will fail and retry. This setting controls the maximum number of attempts before giving up.
fetch.min.bytes	1	The minimum amount of data the server should return for a fetch request. If insufficient data is available the request will wait for that much data to accumulate before answering the request.
fetch.wait.max.ms	100	The maximum amount of time the server will block before answering the fetch request if there isn’t sufficient data to immediately satisfy fetch.min.bytes
rebalance.backoff.ms	2000	Backoff time between retries during rebalance. If not set explicitly, the value in zookeeper.sync.time.ms is used.
refresh.leader.backoff.ms	200	Backoff time to wait before trying to determine the leader of a partition that has just lost its leader.
auto.offset.reset	largest	What to do when there is no initial offset in ZooKeeper or if an offset is out of range:

smallest : automatically reset the offset to the smallest offset
largest : automatically reset the offset to the largest offset
anything else: throw exception to the consumer
consumer.timeout.ms | -1 | Throw a timeout exception to the consumer if no message is available for consumption after the specified interval
exclude.internal.topics | true | Whether messages from internal topics (such as offsets) should be exposed to the consumer.
client.id | group id value | The client id is a user-specified string sent in each request to help trace calls. It should logically identify the application making the request.
zookeeper.session.timeout.ms | 6000 | ZooKeeper session timeout. If the consumer fails to heartbeat to ZooKeeper for this period of time it is considered dead and a rebalance will occur.
zookeeper.connection.timeout.ms | 6000 | The max time that the client waits while establishing a connection to zookeeper.
zookeeper.sync.time.ms | 2000 | How far a ZK follower can be behind a ZK leader
offsets.storage | zookeeper | Select where offsets should be stored (zookeeper or kafka).
offsets.channel.backoff.ms | 1000 | The backoff period when reconnecting the offsets channel or retrying failed offset fetch/commit requests.
offsets.channel.socket.timeout.ms | 10000 | Socket timeout when reading responses for offset fetch/commit requests. This timeout is also used for ConsumerMetadata requests that are used to query for the offset manager.
offsets.commit.max.retries | 5 | Retry the offset commit up to this many times on failure. This retry count only applies to offset commits during shut-down. It does not apply to commits originating from the auto-commit thread. It also does not apply to attempts to query for the offset coordinator before committing offsets. i.e., if a consumer metadata request fails for any reason, it will be retried and that retry does not count toward this limit.
dual.commit.enabled | true | If you are using “kafka” as offsets.storage, you can dual commit offsets to ZooKeeper (in addition to Kafka). This is required during migration from zookeeper-based offset storage to kafka-based offset storage. With respect to any given consumer group, it is safe to turn this off after all instances within that group have been migrated to the new version that commits offsets to the broker (instead of directly to ZooKeeper).
partition.assignment.strategy | range | Select between the “range” or “roundrobin” strategy for assigning partitions to consumer streams.The round-robin partition assignor lays out all the available partitions and all the available consumer threads. It then proceeds to do a round-robin assignment from partition to consumer thread. If the subscriptions of all consumer instances are identical, then the partitions will be uniformly distributed. (i.e., the partition ownership counts will be within a delta of exactly one across all consumer threads.) Round-robin assignment is permitted only if: (a) Every topic has the same number of streams within a consumer instance (b) The set of subscribed topics is identical for every consumer instance within the group. Range partitioning works on a per-topic basis. For each topic, we lay out the available partitions in numeric order and the consumer threads in lexicographic order. We then divide the number of partitions by the total number of consumer streams (threads) to determine the number of partitions to assign to each consumer. If it does not evenly divide, then the first few consumers will have one extra partition.

More details about consumer configuration can be found in the scala class kafka.consumer.ConsumerConfig.

Kafka Connect Configs

Below is the configuration of the Kafka Connect framework.

Name	Description	Type	Default	Valid Values	Importance
config.storage.topic	kafka topic to store configs	string			high
group.id	A unique string that identifies the Connect cluster group this worker belongs to.	string			high
key.converter	Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the keys in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.	class			high
offset.storage.topic	kafka topic to store connector offsets in	string			high
status.storage.topic	kafka topic to track connector and task status	string			high
value.converter	Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the values in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.	class			high
internal.key.converter	Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the keys in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro. This setting controls the format used for internal bookkeeping data used by the framework, such as configs and offsets, so users can typically use any functioning Converter implementation.	class			low
internal.value.converter	Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the values in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro. This setting controls the format used for internal bookkeeping data used by the framework, such as configs and offsets, so users can typically use any functioning Converter implementation.	class			low
bootstrap.servers	A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form `host1:port1,host2:port2,...`. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).	list	[localhost:9092]		high
heartbeat.interval.ms	The expected time between heartbeats to the group coordinator when using Kafka's group management facilities. Heartbeats are used to ensure that the worker's session stays active and to facilitate rebalancing when new members join or leave the group. The value must be set lower than `session.timeout.ms`, but typically should be set no higher than 1/3 of that value. It can be adjusted even lower to control the expected time for normal rebalances.	int	3000		high
rebalance.timeout.ms	The maximum allowed time for each worker to join the group once a rebalance has begun. This is basically a limit on the amount of time needed for all tasks to flush any pending data and commit offsets. If the timeout is exceeded, then the worker will be removed from the group, which will cause offset commit failures.	int	60000		high
session.timeout.ms	The timeout used to detect worker failures. The worker sends periodic heartbeats to indicate its liveness to the broker. If no heartbeats are received by the broker before the expiration of this session timeout, then the broker will remove the worker from the group and initiate a rebalance. Note that the value must be in the allowable range as configured in the broker configuration by `group.min.session.timeout.ms` and `group.max.session.timeout.ms`.	int	10000		high
ssl.key.password	The password of the private key in the key store file. This is optional for client.	password	null		high
ssl.keystore.location	The location of the key store file. This is optional for client and can be used for two-way authentication for client.	string	null		high
ssl.keystore.password	The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.	password	null		high
ssl.truststore.location	The location of the trust store file.	string	null		high
ssl.truststore.password	The password for the trust store file.	password	null		high
connections.max.idle.ms	Close idle connections after the number of milliseconds specified by this config.	long	540000		medium
receive.buffer.bytes	The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.	int	32768	[0,...]	medium
request.timeout.ms	The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.	int	40000	[0,...]	medium
sasl.kerberos.service.name	The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.	string	null		medium
sasl.mechanism	SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.	string	GSSAPI		medium
security.protocol	Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.	string	PLAINTEXT		medium
send.buffer.bytes	The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.	int	131072	[0,...]	medium
ssl.enabled.protocols	The list of protocols enabled for SSL connections.	list	[TLSv1.2, TLSv1.1, TLSv1]		medium
ssl.keystore.type	The file format of the key store file. This is optional for client.	string	JKS		medium
ssl.protocol	The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.	string	TLS		medium
ssl.provider	The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.	string	null		medium
ssl.truststore.type	The file format of the trust store file.	string	JKS		medium
worker.sync.timeout.ms	When the worker is out of sync with other workers and needs to resynchronize configurations, wait up to this amount of time before giving up, leaving the group, and waiting a backoff period before rejoining.	int	3000		medium
worker.unsync.backoff.ms	When the worker is out of sync with other workers and fails to catch up within worker.sync.timeout.ms, leave the Connect cluster for this long before rejoining.	int	300000		medium
access.control.allow.methods	Sets the methods supported for cross origin requests by setting the Access-Control-Allow-Methods header. The default value of the Access-Control-Allow-Methods header allows cross origin requests for GET, POST and HEAD.	string	""		low
access.control.allow.origin	Value to set the Access-Control-Allow-Origin header to for REST API requests.To enable cross origin access, set this to the domain of the application that should be permitted to access the API, or '*' to allow access from any domain. The default value only allows access from the domain of the REST API.	string	""		low
client.id	An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.	string	""		low
metadata.max.age.ms	The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.	long	300000	[0,...]	low
metric.reporters	A list of classes to use as metrics reporters. Implementing the `MetricReporter` interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.	list	[]		low
metrics.num.samples	The number of samples maintained to compute metrics.	int	2	[1,...]	low
metrics.sample.window.ms	The window of time a metrics sample is computed over.	long	30000	[0,...]	low
offset.flush.interval.ms	Interval at which to try committing offsets for tasks.	long	60000		low
offset.flush.timeout.ms	Maximum number of milliseconds to wait for records to flush and partition offset data to be committed to offset storage before cancelling the process and restoring the offset data to be committed in a future attempt.	long	5000		low
reconnect.backoff.ms	The amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all requests sent by the consumer to the broker.	long	50	[0,...]	low
rest.advertised.host.name	If this is set, this is the hostname that will be given out to other workers to connect to.	string	null		low
rest.advertised.port	If this is set, this is the port that will be given out to other workers to connect to.	int	null		low
rest.host.name	Hostname for the REST API. If this is set, it will only bind to this interface.	string	null		low
rest.port	Port for the REST API to listen on.	int	8083		low
retry.backoff.ms	The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.	long	100	[0,...]	low
sasl.kerberos.kinit.cmd	Kerberos kinit command path.	string	/usr/bin/kinit		low
sasl.kerberos.min.time.before.relogin	Login thread sleep time between refresh attempts.	long	60000		low
sasl.kerberos.ticket.renew.jitter	Percentage of random jitter added to the renewal time.	double	0.05		low
sasl.kerberos.ticket.renew.window.factor	Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.	double	0.8		low
ssl.cipher.suites	A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.	list	null		low
ssl.endpoint.identification.algorithm	The endpoint identification algorithm to validate server hostname using server certificate.	string	null		low
ssl.keymanager.algorithm	The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.	string	SunX509		low
ssl.secure.random.implementation	The SecureRandom PRNG implementation to use for SSL cryptography operations.	string	null		low
ssl.trustmanager.algorithm	The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.	string	PKIX		low
task.shutdown.graceful.timeout.ms	Amount of time to wait for tasks to shutdown gracefully. This is the total amount of time, not per task. All task have shutdown triggered, then they are waited on sequentially.	long	5000		low

Kafka Streams Configs

Below is the configuration of the Kafka Streams client library.

Name	Description	Type	Default	Valid Values	Importance
application.id	An identifier for the stream processing application. Must be unique within the Kafka cluster. It is used as 1) the default client-id prefix, 2) the group-id for membership management, 3) the changelog topic prefix.	string			high
bootstrap.servers	A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form `host1:port1,host2:port2,...`. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).	list			high
client.id	An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.	string	""		high
zookeeper.connect	Zookeeper connect string for Kafka topics management.	string	""		high
key.serde	Serializer / deserializer class for key that implements the `Serde` interface.	class	class org.apache.kafka.common.serialization.Serdes$ByteArraySerde		medium
partition.grouper	Partition grouper class that implements the `PartitionGrouper` interface.	class	class org.apache.kafka.streams.processor.DefaultPartitionGrouper		medium
replication.factor	The replication factor for change log topics and repartition topics created by the stream processing application.	int	1		medium
state.dir	Directory location for state store.	string	/tmp/kafka-streams		medium
timestamp.extractor	Timestamp extractor class that implements the `TimestampExtractor` interface.	class	class org.apache.kafka.streams.processor.ConsumerRecordTimestampExtractor		medium
value.serde	Serializer / deserializer class for value that implements the `Serde` interface.	class	class org.apache.kafka.common.serialization.Serdes$ByteArraySerde		medium
windowstore.changelog.additional.retention.ms	Added to a windows maintainMs to ensure data is not deleted from the log prematurely. Allows for clock drift. Default is 1 day	long	86400000		medium
application.server	A host:port pair pointing to an embedded user defined endpoint that can be used for discovering the locations of state stores within a single KafkaStreams application	string	""		low
buffered.records.per.partition	The maximum number of records to buffer per partition.	int	1000		low
cache.max.bytes.buffering	Maximum number of memory bytes to be used for buffering across all threads	long	10485760	[0,...]	low
commit.interval.ms	The frequency with which to save the position of the processor.	long	30000		low
metric.reporters	A list of classes to use as metrics reporters. Implementing the `MetricReporter` interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.	list	[]		low
metrics.num.samples	The number of samples maintained to compute metrics.	int	2	[1,...]	low
metrics.sample.window.ms	The window of time a metrics sample is computed over.	long	30000	[0,...]	low
num.standby.replicas	The number of standby replicas for each task.	int	0		low
num.stream.threads	The number of threads to execute stream processing.	int	1		low
poll.ms	The amount of time in milliseconds to block waiting for input.	long	100		low
rocksdb.config.setter	A Rocks DB config setter class that implements the `RocksDBConfigSetter` interface	class	null		low
state.cleanup.delay.ms	The amount of time in milliseconds to wait before deleting state when a partition has migrated.	long	60000		low

4 - Design

4.1 - Design

Motivation

We designed Kafka to be able to act as a unified platform for handling all the real-time data feeds a large company might have. To do this we had to think through a fairly broad set of use cases.

It would have to have high-throughput to support high volume event streams such as real-time log aggregation.

It would need to deal gracefully with large data backlogs to be able to support periodic data loads from offline systems.

It also meant the system would have to handle low-latency delivery to handle more traditional messaging use-cases.

We wanted to support partitioned, distributed, real-time processing of these feeds to create new, derived feeds. This motivated our partitioning and consumer model.

Finally in cases where the stream is fed into other data systems for serving, we knew the system would have to be able to guarantee fault-tolerance in the presence of machine failures.

Supporting these uses led us to a design with a number of unique elements, more akin to a database log than a traditional messaging system. We will outline some elements of the design in the following sections.

Persistence

Don’t fear the filesystem!

Kafka relies heavily on the filesystem for storing and caching messages. There is a general perception that “disks are slow” which makes people skeptical that a persistent structure can offer competitive performance. In fact disks are both much slower and much faster than people expect depending on how they are used; and a properly designed disk structure can often be as fast as the network.

The key fact about disk performance is that the throughput of hard drives has been diverging from the latency of a disk seek for the last decade. As a result the performance of linear writes on a JBOD configuration with six 7200rpm SATA RAID-5 array is about 600MB/sec but the performance of random writes is only about 100k/sec–a difference of over 6000X. These linear reads and writes are the most predictable of all usage patterns, and are heavily optimized by the operating system. A modern operating system provides read-ahead and write-behind techniques that prefetch data in large block multiples and group smaller logical writes into large physical writes. A further discussion of this issue can be found in this ACM Queue article; they actually find that sequential disk access can in some cases be faster than random memory access!

To compensate for this performance divergence, modern operating systems have become increasingly aggressive in their use of main memory for disk caching. A modern OS will happily divert all free memory to disk caching with little performance penalty when the memory is reclaimed. All disk reads and writes will go through this unified cache. This feature cannot easily be turned off without using direct I/O, so even if a process maintains an in-process cache of the data, this data will likely be duplicated in OS pagecache, effectively storing everything twice.

Furthermore, we are building on top of the JVM, and anyone who has spent any time with Java memory usage knows two things:

The memory overhead of objects is very high, often doubling the size of the data stored (or worse).
Java garbage collection becomes increasingly fiddly and slow as the in-heap data increases.

As a result of these factors using the filesystem and relying on pagecache is superior to maintaining an in-memory cache or other structure–we at least double the available cache by having automatic access to all free memory, and likely double again by storing a compact byte structure rather than individual objects. Doing so will result in a cache of up to 28-30GB on a 32GB machine without GC penalties. Furthermore, this cache will stay warm even if the service is restarted, whereas the in-process cache will need to be rebuilt in memory (which for a 10GB cache may take 10 minutes) or else it will need to start with a completely cold cache (which likely means terrible initial performance). This also greatly simplifies the code as all logic for maintaining coherency between the cache and filesystem is now in the OS, which tends to do so more efficiently and more correctly than one-off in-process attempts. If your disk usage favors linear reads then read-ahead is effectively pre-populating this cache with useful data on each disk read.

This suggests a design which is very simple: rather than maintain as much as possible in-memory and flush it all out to the filesystem in a panic when we run out of space, we invert that. All data is immediately written to a persistent log on the filesystem without necessarily flushing to disk. In effect this just means that it is transferred into the kernel’s pagecache.

This style of pagecache-centric design is described in an article on the design of Varnish here (along with a healthy dose of arrogance).

Constant Time Suffices

The persistent data structure used in messaging systems are often a per-consumer queue with an associated BTree or other general-purpose random access data structures to maintain metadata about messages. BTrees are the most versatile data structure available, and make it possible to support a wide variety of transactional and non-transactional semantics in the messaging system. They do come with a fairly high cost, though: Btree operations are O(log N). Normally O(log N) is considered essentially equivalent to constant time, but this is not true for disk operations. Disk seeks come at 10 ms a pop, and each disk can do only one seek at a time so parallelism is limited. Hence even a handful of disk seeks leads to very high overhead. Since storage systems mix very fast cached operations with very slow physical disk operations, the observed performance of tree structures is often superlinear as data increases with fixed cache–i.e. doubling your data makes things much worse than twice as slow.

Intuitively a persistent queue could be built on simple reads and appends to files as is commonly the case with logging solutions. This structure has the advantage that all operations are O(1) and reads do not block writes or each other. This has obvious performance advantages since the performance is completely decoupled from the data size–one server can now take full advantage of a number of cheap, low-rotational speed 1+TB SATA drives. Though they have poor seek performance, these drives have acceptable performance for large reads and writes and come at 1/3 the price and 3x the capacity.

Having access to virtually unlimited disk space without any performance penalty means that we can provide some features not usually found in a messaging system. For example, in Kafka, instead of attempting to delete messages as soon as they are consumed, we can retain messages for a relatively long period (say a week). This leads to a great deal of flexibility for consumers, as we will describe.

Efficiency

We have put significant effort into efficiency. One of our primary use cases is handling web activity data, which is very high volume: each page view may generate dozens of writes. Furthermore, we assume each message published is read by at least one consumer (often many), hence we strive to make consumption as cheap as possible.

We have also found, from experience building and running a number of similar systems, that efficiency is a key to effective multi-tenant operations. If the downstream infrastructure service can easily become a bottleneck due to a small bump in usage by the application, such small changes will often create problems. By being very fast we help ensure that the application will tip-over under load before the infrastructure. This is particularly important when trying to run a centralized service that supports dozens or hundreds of applications on a centralized cluster as changes in usage patterns are a near-daily occurrence.

We discussed disk efficiency in the previous section. Once poor disk access patterns have been eliminated, there are two common causes of inefficiency in this type of system: too many small I/O operations, and excessive byte copying.

The small I/O problem happens both between the client and the server and in the server’s own persistent operations.

To avoid this, our protocol is built around a “message set” abstraction that naturally groups messages together. This allows network requests to group messages together and amortize the overhead of the network roundtrip rather than sending a single message at a time. The server in turn appends chunks of messages to its log in one go, and the consumer fetches large linear chunks at a time.

This simple optimization produces orders of magnitude speed up. Batching leads to larger network packets, larger sequential disk operations, contiguous memory blocks, and so on, all of which allows Kafka to turn a bursty stream of random message writes into linear writes that flow to the consumers.

The other inefficiency is in byte copying. At low message rates this is not an issue, but under load the impact is significant. To avoid this we employ a standardized binary message format that is shared by the producer, the broker, and the consumer (so data chunks can be transferred without modification between them).

The message log maintained by the broker is itself just a directory of files, each populated by a sequence of message sets that have been written to disk in the same format used by the producer and consumer. Maintaining this common format allows optimization of the most important operation: network transfer of persistent log chunks. Modern unix operating systems offer a highly optimized code path for transferring data out of pagecache to a socket; in Linux this is done with the sendfile system call.

To understand the impact of sendfile, it is important to understand the common data path for transfer of data from file to socket:

The operating system reads data from the disk into pagecache in kernel space
The application reads the data from kernel space into a user-space buffer
The application writes the data back into kernel space into a socket buffer
The operating system copies the data from the socket buffer to the NIC buffer where it is sent over the network

This is clearly inefficient, there are four copies and two system calls. Using sendfile, this re-copying is avoided by allowing the OS to send the data from pagecache to the network directly. So in this optimized path, only the final copy to the NIC buffer is needed.

We expect a common use case to be multiple consumers on a topic. Using the zero-copy optimization above, data is copied into pagecache exactly once and reused on each consumption instead of being stored in memory and copied out to kernel space every time it is read. This allows messages to be consumed at a rate that approaches the limit of the network connection.

This combination of pagecache and sendfile means that on a Kafka cluster where the consumers are mostly caught up you will see no read activity on the disks whatsoever as they will be serving data entirely from cache.

For more background on the sendfile and zero-copy support in Java, see this article.

End-to-end Batch Compression

In some cases the bottleneck is actually not CPU or disk but network bandwidth. This is particularly true for a data pipeline that needs to send messages between data centers over a wide-area network. Of course, the user can always compress its messages one at a time without any support needed from Kafka, but this can lead to very poor compression ratios as much of the redundancy is due to repetition between messages of the same type (e.g. field names in JSON or user agents in web logs or common string values). Efficient compression requires compressing multiple messages together rather than compressing each message individually.

Kafka supports this by allowing recursive message sets. A batch of messages can be clumped together compressed and sent to the server in this form. This batch of messages will be written in compressed form and will remain compressed in the log and will only be decompressed by the consumer.

Kafka supports GZIP, Snappy and LZ4 compression protocols. More details on compression can be found here.

The Producer

Load balancing

The producer sends data directly to the broker that is the leader for the partition without any intervening routing tier. To help the producer do this all Kafka nodes can answer a request for metadata about which servers are alive and where the leaders for the partitions of a topic are at any given time to allow the producer to appropriately direct its requests.

The client controls which partition it publishes messages to. This can be done at random, implementing a kind of random load balancing, or it can be done by some semantic partitioning function. We expose the interface for semantic partitioning by allowing the user to specify a key to partition by and using this to hash to a partition (there is also an option to override the partition function if need be). For example if the key chosen was a user id then all data for a given user would be sent to the same partition. This in turn will allow consumers to make locality assumptions about their consumption. This style of partitioning is explicitly designed to allow locality-sensitive processing in consumers.

Asynchronous send

Batching is one of the big drivers of efficiency, and to enable batching the Kafka producer will attempt to accumulate data in memory and to send out larger batches in a single request. The batching can be configured to accumulate no more than a fixed number of messages and to wait no longer than some fixed latency bound (say 64k or 10 ms). This allows the accumulation of more bytes to send, and few larger I/O operations on the servers. This buffering is configurable and gives a mechanism to trade off a small amount of additional latency for better throughput.

Details on configuration and the api for the producer can be found elsewhere in the documentation.

The Consumer

The Kafka consumer works by issuing “fetch” requests to the brokers leading the partitions it wants to consume. The consumer specifies its offset in the log with each request and receives back a chunk of log beginning from that position. The consumer thus has significant control over this position and can rewind it to re-consume data if need be.

Push vs. pull

An initial question we considered is whether consumers should pull data from brokers or brokers should push data to the consumer. In this respect Kafka follows a more traditional design, shared by most messaging systems, where data is pushed to the broker from the producer and pulled from the broker by the consumer. Some logging-centric systems, such as Scribe and Apache Flume, follow a very different push-based path where data is pushed downstream. There are pros and cons to both approaches. However, a push-based system has difficulty dealing with diverse consumers as the broker controls the rate at which data is transferred. The goal is generally for the consumer to be able to consume at the maximum possible rate; unfortunately, in a push system this means the consumer tends to be overwhelmed when its rate of consumption falls below the rate of production (a denial of service attack, in essence). A pull-based system has the nicer property that the consumer simply falls behind and catches up when it can. This can be mitigated with some kind of backoff protocol by which the consumer can indicate it is overwhelmed, but getting the rate of transfer to fully utilize (but never over-utilize) the consumer is trickier than it seems. Previous attempts at building systems in this fashion led us to go with a more traditional pull model.

Another advantage of a pull-based system is that it lends itself to aggressive batching of data sent to the consumer. A push-based system must choose to either send a request immediately or accumulate more data and then send it later without knowledge of whether the downstream consumer will be able to immediately process it. If tuned for low latency, this will result in sending a single message at a time only for the transfer to end up being buffered anyway, which is wasteful. A pull-based design fixes this as the consumer always pulls all available messages after its current position in the log (or up to some configurable max size). So one gets optimal batching without introducing unnecessary latency.

The deficiency of a naive pull-based system is that if the broker has no data the consumer may end up polling in a tight loop, effectively busy-waiting for data to arrive. To avoid this we have parameters in our pull request that allow the consumer request to block in a “long poll” waiting until data arrives (and optionally waiting until a given number of bytes is available to ensure large transfer sizes).

You could imagine other possible designs which would be only pull, end-to-end. The producer would locally write to a local log, and brokers would pull from that with consumers pulling from them. A similar type of “store-and-forward” producer is often proposed. This is intriguing but we felt not very suitable for our target use cases which have thousands of producers. Our experience running persistent data systems at scale led us to feel that involving thousands of disks in the system across many applications would not actually make things more reliable and would be a nightmare to operate. And in practice we have found that we can run a pipeline with strong SLAs at large scale without a need for producer persistence.

Consumer Position

Keeping track of what has been consumed is, surprisingly, one of the key performance points of a messaging system.

Most messaging systems keep metadata about what messages have been consumed on the broker. That is, as a message is handed out to a consumer, the broker either records that fact locally immediately or it may wait for acknowledgement from the consumer. This is a fairly intuitive choice, and indeed for a single machine server it is not clear where else this state could go. Since the data structures used for storage in many messaging systems scale poorly, this is also a pragmatic choice–since the broker knows what is consumed it can immediately delete it, keeping the data size small.

What is perhaps not obvious is that getting the broker and consumer to come into agreement about what has been consumed is not a trivial problem. If the broker records a message as consumed immediately every time it is handed out over the network, then if the consumer fails to process the message (say because it crashes or the request times out or whatever) that message will be lost. To solve this problem, many messaging systems add an acknowledgement feature which means that messages are only marked as sent not consumed when they are sent; the broker waits for a specific acknowledgement from the consumer to record the message as consumed. This strategy fixes the problem of losing messages, but creates new problems. First of all, if the consumer processes the message but fails before it can send an acknowledgement then the message will be consumed twice. The second problem is around performance, now the broker must keep multiple states about every single message (first to lock it so it is not given out a second time, and then to mark it as permanently consumed so that it can be removed). Tricky problems must be dealt with, like what to do with messages that are sent but never acknowledged.

Kafka handles this differently. Our topic is divided into a set of totally ordered partitions, each of which is consumed by exactly one consumer within each subscribing consumer group at any given time. This means that the position of a consumer in each partition is just a single integer, the offset of the next message to consume. This makes the state about what has been consumed very small, just one number for each partition. This state can be periodically checkpointed. This makes the equivalent of message acknowledgements very cheap.

There is a side benefit of this decision. A consumer can deliberately rewind back to an old offset and re-consume data. This violates the common contract of a queue, but turns out to be an essential feature for many consumers. For example, if the consumer code has a bug and is discovered after some messages are consumed, the consumer can re-consume those messages once the bug is fixed.

Offline Data Load

Scalable persistence allows for the possibility of consumers that only periodically consume such as batch data loads that periodically bulk-load data into an offline system such as Hadoop or a relational data warehouse.

In the case of Hadoop we parallelize the data load by splitting the load over individual map tasks, one for each node/topic/partition combination, allowing full parallelism in the loading. Hadoop provides the task management, and tasks which fail can restart without danger of duplicate data–they simply restart from their original position.

Message Delivery Semantics

Now that we understand a little about how producers and consumers work, let’s discuss the semantic guarantees Kafka provides between producer and consumer. Clearly there are multiple possible message delivery guarantees that could be provided:

At most once –Messages may be lost but are never redelivered.
At least once –Messages are never lost but may be redelivered.
Exactly once –this is what people actually want, each message is delivered once and only once.

It’s worth noting that this breaks down into two problems: the durability guarantees for publishing a message and the guarantees when consuming a message.

Many systems claim to provide “exactly once” delivery semantics, but it is important to read the fine print, most of these claims are misleading (i.e. they don’t translate to the case where consumers or producers can fail, cases where there are multiple consumer processes, or cases where data written to disk can be lost).

Kafka’s semantics are straight-forward. When publishing a message we have a notion of the message being “committed” to the log. Once a published message is committed it will not be lost as long as one broker that replicates the partition to which this message was written remains “alive”. The definition of alive as well as a description of which types of failures we attempt to handle will be described in more detail in the next section. For now let’s assume a perfect, lossless broker and try to understand the guarantees to the producer and consumer. If a producer attempts to publish a message and experiences a network error it cannot be sure if this error happened before or after the message was committed. This is similar to the semantics of inserting into a database table with an autogenerated key.

These are not the strongest possible semantics for publishers. Although we cannot be sure of what happened in the case of a network error, it is possible to allow the producer to generate a sort of “primary key” that makes retrying the produce request idempotent. This feature is not trivial for a replicated system because of course it must work even (or especially) in the case of a server failure. With this feature it would suffice for the producer to retry until it receives acknowledgement of a successfully committed message at which point we would guarantee the message had been published exactly once. We hope to add this in a future Kafka version.

Not all use cases require such strong guarantees. For uses which are latency sensitive we allow the producer to specify the durability level it desires. If the producer specifies that it wants to wait on the message being committed this can take on the order of 10 ms. However the producer can also specify that it wants to perform the send completely asynchronously or that it wants to wait only until the leader (but not necessarily the followers) have the message.

Now let’s describe the semantics from the point-of-view of the consumer. All replicas have the exact same log with the same offsets. The consumer controls its position in this log. If the consumer never crashed it could just store this position in memory, but if the consumer fails and we want this topic partition to be taken over by another process the new process will need to choose an appropriate position from which to start processing. Let’s say the consumer reads some messages – it has several options for processing the messages and updating its position.

It can read the messages, then save its position in the log, and finally process the messages. In this case there is a possibility that the consumer process crashes after saving its position but before saving the output of its message processing. In this case the process that took over processing would start at the saved position even though a few messages prior to that position had not been processed. This corresponds to “at-most-once” semantics as in the case of a consumer failure messages may not be processed.
It can read the messages, process the messages, and finally save its position. In this case there is a possibility that the consumer process crashes after processing messages but before saving its position. In this case when the new process takes over the first few messages it receives will already have been processed. This corresponds to the “at-least-once” semantics in the case of consumer failure. In many cases messages have a primary key and so the updates are idempotent (receiving the same message twice just overwrites a record with another copy of itself).
So what about exactly once semantics (i.e. the thing you actually want)? The limitation here is not actually a feature of the messaging system but rather the need to co-ordinate the consumer’s position with what is actually stored as output. The classic way of achieving this would be to introduce a two-phase commit between the storage for the consumer position and the storage of the consumers output. But this can be handled more simply and generally by simply letting the consumer store its offset in the same place as its output. This is better because many of the output systems a consumer might want to write to will not support a two-phase commit. As an example of this, our Hadoop ETL that populates data in HDFS stores its offsets in HDFS with the data it reads so that it is guaranteed that either data and offsets are both updated or neither is. We follow similar patterns for many other data systems which require these stronger semantics and for which the messages do not have a primary key to allow for deduplication.

So effectively Kafka guarantees at-least-once delivery by default and allows the user to implement at most once delivery by disabling retries on the producer and committing its offset prior to processing a batch of messages. Exactly-once delivery requires co-operation with the destination storage system but Kafka provides the offset which makes implementing this straight-forward.

Replication

Kafka replicates the log for each topic’s partitions across a configurable number of servers (you can set this replication factor on a topic-by-topic basis). This allows automatic failover to these replicas when a server in the cluster fails so messages remain available in the presence of failures.

Other messaging systems provide some replication-related features, but, in our (totally biased) opinion, this appears to be a tacked-on thing, not heavily used, and with large downsides: slaves are inactive, throughput is heavily impacted, it requires fiddly manual configuration, etc. Kafka is meant to be used with replication by default–in fact we implement un-replicated topics as replicated topics where the replication factor is one.

The unit of replication is the topic partition. Under non-failure conditions, each partition in Kafka has a single leader and zero or more followers. The total number of replicas including the leader constitute the replication factor. All reads and writes go to the leader of the partition. Typically, there are many more partitions than brokers and the leaders are evenly distributed among brokers. The logs on the followers are identical to the leader’s log–all have the same offsets and messages in the same order (though, of course, at any given time the leader may have a few as-yet unreplicated messages at the end of its log).

Followers consume messages from the leader just as a normal Kafka consumer would and apply them to their own log. Having the followers pull from the leader has the nice property of allowing the follower to naturally batch together log entries they are applying to their log.

As with most distributed systems automatically handling failures requires having a precise definition of what it means for a node to be “alive”. For Kafka node liveness has two conditions

A node must be able to maintain its session with ZooKeeper (via ZooKeeper’s heartbeat mechanism)
If it is a slave it must replicate the writes happening on the leader and not fall “too far” behind We refer to nodes satisfying these two conditions as being “in sync” to avoid the vagueness of “alive” or “failed”. The leader keeps track of the set of “in sync” nodes. If a follower dies, gets stuck, or falls behind, the leader will remove it from the list of in sync replicas. The determination of stuck and lagging replicas is controlled by the replica.lag.time.max.ms configuration.

In distributed systems terminology we only attempt to handle a “fail/recover” model of failures where nodes suddenly cease working and then later recover (perhaps without knowing that they have died). Kafka does not handle so-called “Byzantine” failures in which nodes produce arbitrary or malicious responses (perhaps due to bugs or foul play).

A message is considered “committed” when all in sync replicas for that partition have applied it to their log. Only committed messages are ever given out to the consumer. This means that the consumer need not worry about potentially seeing a message that could be lost if the leader fails. Producers, on the other hand, have the option of either waiting for the message to be committed or not, depending on their preference for tradeoff between latency and durability. This preference is controlled by the acks setting that the producer uses.

The guarantee that Kafka offers is that a committed message will not be lost, as long as there is at least one in sync replica alive, at all times.

Kafka will remain available in the presence of node failures after a short fail-over period, but may not remain available in the presence of network partitions.

Replicated Logs: Quorums, ISRs, and State Machines (Oh my!)

At its heart a Kafka partition is a replicated log. The replicated log is one of the most basic primitives in distributed data systems, and there are many approaches for implementing one. A replicated log can be used by other systems as a primitive for implementing other distributed systems in the state-machine style.

A replicated log models the process of coming into consensus on the order of a series of values (generally numbering the log entries 0, 1, 2, …). There are many ways to implement this, but the simplest and fastest is with a leader who chooses the ordering of values provided to it. As long as the leader remains alive, all followers need to only copy the values and ordering the leader chooses.

Of course if leaders didn’t fail we wouldn’t need followers! When the leader does die we need to choose a new leader from among the followers. But followers themselves may fall behind or crash so we must ensure we choose an up-to-date follower. The fundamental guarantee a log replication algorithm must provide is that if we tell the client a message is committed, and the leader fails, the new leader we elect must also have that message. This yields a tradeoff: if the leader waits for more followers to acknowledge a message before declaring it committed then there will be more potentially electable leaders.

If you choose the number of acknowledgements required and the number of logs that must be compared to elect a leader such that there is guaranteed to be an overlap, then this is called a Quorum.

A common approach to this tradeoff is to use a majority vote for both the commit decision and the leader election. This is not what Kafka does, but let’s explore it anyway to understand the tradeoffs. Let’s say we have 2 f +1 replicas. If f +1 replicas must receive a message prior to a commit being declared by the leader, and if we elect a new leader by electing the follower with the most complete log from at least f +1 replicas, then, with no more than f failures, the leader is guaranteed to have all committed messages. This is because among any f +1 replicas, there must be at least one replica that contains all committed messages. That replica’s log will be the most complete and therefore will be selected as the new leader. There are many remaining details that each algorithm must handle (such as precisely defined what makes a log more complete, ensuring log consistency during leader failure or changing the set of servers in the replica set) but we will ignore these for now.

This majority vote approach has a very nice property: the latency is dependent on only the fastest servers. That is, if the replication factor is three, the latency is determined by the faster slave not the slower one.

There are a rich variety of algorithms in this family including ZooKeeper’s Zab, Raft, and Viewstamped Replication. The most similar academic publication we are aware of to Kafka’s actual implementation is PacificA from Microsoft.

The downside of majority vote is that it doesn’t take many failures to leave you with no electable leaders. To tolerate one failure requires three copies of the data, and to tolerate two failures requires five copies of the data. In our experience having only enough redundancy to tolerate a single failure is not enough for a practical system, but doing every write five times, with 5x the disk space requirements and 1/5th the throughput, is not very practical for large volume data problems. This is likely why quorum algorithms more commonly appear for shared cluster configuration such as ZooKeeper but are less common for primary data storage. For example in HDFS the namenode’s high-availability feature is built on a majority-vote-based journal, but this more expensive approach is not used for the data itself.

Kafka takes a slightly different approach to choosing its quorum set. Instead of majority vote, Kafka dynamically maintains a set of in-sync replicas (ISR) that are caught-up to the leader. Only members of this set are eligible for election as leader. A write to a Kafka partition is not considered committed until all in-sync replicas have received the write. This ISR set is persisted to ZooKeeper whenever it changes. Because of this, any replica in the ISR is eligible to be elected leader. This is an important factor for Kafka’s usage model where there are many partitions and ensuring leadership balance is important. With this ISR model and f+1 replicas, a Kafka topic can tolerate f failures without losing committed messages.

For most use cases we hope to handle, we think this tradeoff is a reasonable one. In practice, to tolerate f failures, both the majority vote and the ISR approach will wait for the same number of replicas to acknowledge before committing a message (e.g. to survive one failure a majority quorum needs three replicas and one acknowledgement and the ISR approach requires two replicas and one acknowledgement). The ability to commit without the slowest servers is an advantage of the majority vote approach. However, we think it is ameliorated by allowing the client to choose whether they block on the message commit or not, and the additional throughput and disk space due to the lower required replication factor is worth it.

Another important design distinction is that Kafka does not require that crashed nodes recover with all their data intact. It is not uncommon for replication algorithms in this space to depend on the existence of “stable storage” that cannot be lost in any failure-recovery scenario without potential consistency violations. There are two primary problems with this assumption. First, disk errors are the most common problem we observe in real operation of persistent data systems and they often do not leave data intact. Secondly, even if this were not a problem, we do not want to require the use of fsync on every write for our consistency guarantees as this can reduce performance by two to three orders of magnitude. Our protocol for allowing a replica to rejoin the ISR ensures that before rejoining, it must fully re-sync again even if it lost unflushed data in its crash.

Unclean leader election: What if they all die?

Note that Kafka’s guarantee with respect to data loss is predicated on at least one replica remaining in sync. If all the nodes replicating a partition die, this guarantee no longer holds.

However a practical system needs to do something reasonable when all the replicas die. If you are unlucky enough to have this occur, it is important to consider what will happen. There are two behaviors that could be implemented:

Wait for a replica in the ISR to come back to life and choose this replica as the leader (hopefully it still has all its data).
Choose the first replica (not necessarily in the ISR) that comes back to life as the leader.

This is a simple tradeoff between availability and consistency. If we wait for replicas in the ISR, then we will remain unavailable as long as those replicas are down. If such replicas were destroyed or their data was lost, then we are permanently down. If, on the other hand, a non-in-sync replica comes back to life and we allow it to become leader, then its log becomes the source of truth even though it is not guaranteed to have every committed message. By default, Kafka chooses the second strategy and favor choosing a potentially inconsistent replica when all replicas in the ISR are dead. This behavior can be disabled using configuration property unclean.leader.election.enable, to support use cases where downtime is preferable to inconsistency.

This dilemma is not specific to Kafka. It exists in any quorum-based scheme. For example in a majority voting scheme, if a majority of servers suffer a permanent failure, then you must either choose to lose 100% of your data or violate consistency by taking what remains on an existing server as your new source of truth.

Availability and Durability Guarantees

When writing to Kafka, producers can choose whether they wait for the message to be acknowledged by 0,1 or all (-1) replicas. Note that “acknowledgement by all replicas” does not guarantee that the full set of assigned replicas have received the message. By default, when acks=all, acknowledgement happens as soon as all the current in-sync replicas have received the message. For example, if a topic is configured with only two replicas and one fails (i.e., only one in sync replica remains), then writes that specify acks=all will succeed. However, these writes could be lost if the remaining replica also fails. Although this ensures maximum availability of the partition, this behavior may be undesirable to some users who prefer durability over availability. Therefore, we provide two topic-level configurations that can be used to prefer message durability over availability:

Disable unclean leader election - if all replicas become unavailable, then the partition will remain unavailable until the most recent leader becomes available again. This effectively prefers unavailability over the risk of message loss. See the previous section on Unclean Leader Election for clarification.
Specify a minimum ISR size - the partition will only accept writes if the size of the ISR is above a certain minimum, in order to prevent the loss of messages that were written to just a single replica, which subsequently becomes unavailable. This setting only takes effect if the producer uses acks=all and guarantees that the message will be acknowledged by at least this many in-sync replicas. This setting offers a trade-off between consistency and availability. A higher setting for minimum ISR size guarantees better consistency since the message is guaranteed to be written to more replicas which reduces the probability that it will be lost. However, it reduces availability since the partition will be unavailable for writes if the number of in-sync replicas drops below the minimum threshold.

Replica Management

The above discussion on replicated logs really covers only a single log, i.e. one topic partition. However a Kafka cluster will manage hundreds or thousands of these partitions. We attempt to balance partitions within a cluster in a round-robin fashion to avoid clustering all partitions for high-volume topics on a small number of nodes. Likewise we try to balance leadership so that each node is the leader for a proportional share of its partitions.

It is also important to optimize the leadership election process as that is the critical window of unavailability. A naive implementation of leader election would end up running an election per partition for all partitions a node hosted when that node failed. Instead, we elect one of the brokers as the “controller”. This controller detects failures at the broker level and is responsible for changing the leader of all affected partitions in a failed broker. The result is that we are able to batch together many of the required leadership change notifications which makes the election process far cheaper and faster for a large number of partitions. If the controller fails, one of the surviving brokers will become the new controller.

Log Compaction

Log compaction ensures that Kafka will always retain at least the last known value for each message key within the log of data for a single topic partition. It addresses use cases and scenarios such as restoring state after application crashes or system failure, or reloading caches after application restarts during operational maintenance. Let’s dive into these use cases in more detail and then describe how compaction works.

So far we have described only the simpler approach to data retention where old log data is discarded after a fixed period of time or when the log reaches some predetermined size. This works well for temporal event data such as logging where each record stands alone. However an important class of data streams are the log of changes to keyed, mutable data (for example, the changes to a database table).

Let’s discuss a concrete example of such a stream. Say we have a topic containing user email addresses; every time a user updates their email address we send a message to this topic using their user id as the primary key. Now say we send the following messages over some time period for a user with id 123, each message corresponding to a change in email address (messages for other ids are omitted):

        123 => bill@microsoft.com
                .
                .
                .
        123 => bill@gatesfoundation.org
                .
                .
                .
        123 => bill@gmail.com

Log compaction gives us a more granular retention mechanism so that we are guaranteed to retain at least the last update for each primary key (e.g. bill@gmail.com). By doing this we guarantee that the log contains a full snapshot of the final value for every key not just keys that changed recently. This means downstream consumers can restore their own state off this topic without us having to retain a complete log of all changes.

Let’s start by looking at a few use cases where this is useful, then we’ll see how it can be used.

Database change subscription. It is often necessary to have a data set in multiple data systems, and often one of these systems is a database of some kind (either a RDBMS or perhaps a new-fangled key-value store). For example you might have a database, a cache, a search cluster, and a Hadoop cluster. Each change to the database will need to be reflected in the cache, the search cluster, and eventually in Hadoop. In the case that one is only handling the real-time updates you only need recent log. But if you want to be able to reload the cache or restore a failed search node you may need a complete data set.
Event sourcing. This is a style of application design which co-locates query processing with application design and uses a log of changes as the primary store for the application.
Journaling for high-availability. A process that does local computation can be made fault-tolerant by logging out changes that it makes to its local state so another process can reload these changes and carry on if it should fail. A concrete example of this is handling counts, aggregations, and other “group by”-like processing in a stream query system. Samza, a real-time stream-processing framework, uses this feature for exactly this purpose. In each of these cases one needs primarily to handle the real-time feed of changes, but occasionally, when a machine crashes or data needs to be re-loaded or re-processed, one needs to do a full load. Log compaction allows feeding both of these use cases off the same backing topic. This style of usage of a log is described in more detail in this blog post.

The general idea is quite simple. If we had infinite log retention, and we logged each change in the above cases, then we would have captured the state of the system at each time from when it first began. Using this complete log, we could restore to any point in time by replaying the first N records in the log. This hypothetical complete log is not very practical for systems that update a single record many times as the log will grow without bound even for a stable dataset. The simple log retention mechanism which throws away old updates will bound space but the log is no longer a way to restore the current state–now restoring from the beginning of the log no longer recreates the current state as old updates may not be captured at all.

Log compaction is a mechanism to give finer-grained per-record retention, rather than the coarser-grained time-based retention. The idea is to selectively remove records where we have a more recent update with the same primary key. This way the log is guaranteed to have at least the last state for each key.

This retention policy can be set per-topic, so a single cluster can have some topics where retention is enforced by size or time and other topics where retention is enforced by compaction.

This functionality is inspired by one of LinkedIn’s oldest and most successful pieces of infrastructure–a database changelog caching service called Databus. Unlike most log-structured storage systems Kafka is built for subscription and organizes data for fast linear reads and writes. Unlike Databus, Kafka acts as a source-of-truth store so it is useful even in situations where the upstream data source would not otherwise be replayable.

Log Compaction Basics

Here is a high-level picture that shows the logical structure of a Kafka log with the offset for each message.

The head of the log is identical to a traditional Kafka log. It has dense, sequential offsets and retains all messages. Log compaction adds an option for handling the tail of the log. The picture above shows a log with a compacted tail. Note that the messages in the tail of the log retain the original offset assigned when they were first written–that never changes. Note also that all offsets remain valid positions in the log, even if the message with that offset has been compacted away; in this case this position is indistinguishable from the next highest offset that does appear in the log. For example, in the picture above the offsets 36, 37, and 38 are all equivalent positions and a read beginning at any of these offsets would return a message set beginning with 38.

Compaction also allows for deletes. A message with a key and a null payload will be treated as a delete from the log. This delete marker will cause any prior message with that key to be removed (as would any new message with that key), but delete markers are special in that they will themselves be cleaned out of the log after a period of time to free up space. The point in time at which deletes are no longer retained is marked as the “delete retention point” in the above diagram.

The compaction is done in the background by periodically recopying log segments. Cleaning does not block reads and can be throttled to use no more than a configurable amount of I/O throughput to avoid impacting producers and consumers. The actual process of compacting a log segment looks something like this:

What guarantees does log compaction provide?

Log compaction guarantees the following:

Any consumer that stays caught-up to within the head of the log will see every message that is written; these messages will have sequential offsets. The topic’s min.compaction.lag.ms can be used to guarantee the minimum length of time must pass after a message is written before it could be compacted. I.e. it provides a lower bound on how long each message will remain in the (uncompacted) head.
Ordering of messages is always maintained. Compaction will never re-order messages, just remove some.
The offset for a message never changes. It is the permanent identifier for a position in the log.
Any consumer progressing from the start of the log will see at least the final state of all records in the order they were written. All delete markers for deleted records will be seen provided the consumer reaches the head of the log in a time period less than the topic’s delete.retention.ms setting (the default is 24 hours). This is important as delete marker removal happens concurrently with read, and thus it is important that we do not remove any delete marker prior to the consumer seeing it.

Log Compaction Details

Log compaction is handled by the log cleaner, a pool of background threads that recopy log segment files, removing records whose key appears in the head of the log. Each compactor thread works as follows:

It chooses the log that has the highest ratio of log head to log tail
It creates a succinct summary of the last offset for each key in the head of the log
It recopies the log from beginning to end removing keys which have a later occurrence in the log. New, clean segments are swapped into the log immediately so the additional disk space required is just one additional log segment (not a fully copy of the log).
The summary of the log head is essentially just a space-compact hash table. It uses exactly 24 bytes per entry. As a result with 8GB of cleaner buffer one cleaner iteration can clean around 366GB of log head (assuming 1k messages).

Configuring The Log Cleaner

The log cleaner is enabled by default. This will start the pool of cleaner threads. To enable log cleaning on a particular topic you can add the log-specific property

  log.cleanup.policy=compact

This can be done either at topic creation time or using the alter topic command.

The log cleaner can be configured to retain a minimum amount of the uncompacted “head” of the log. This is enabled by setting the compaction time lag.

  log.cleaner.min.compaction.lag.ms

This can be used to prevent messages newer than a minimum message age from being subject to compaction. If not set, all log segments are eligible for compaction except for the last segment, i.e. the one currently being written to. The active segment will not be compacted even if all of its messages are older than the minimum compaction time lag.

Further cleaner configurations are described here.

Quotas

Starting in 0.9, the Kafka cluster has the ability to enforce quotas on produce and fetch requests. Quotas are basically byte-rate thresholds defined per group of clients sharing a quota.

Why are quotas necessary?

It is possible for producers and consumers to produce/consume very high volumes of data and thus monopolize broker resources, cause network saturation and generally DOS other clients and the brokers themselves. Having quotas protects against these issues and is all the more important in large multi-tenant clusters where a small set of badly behaved clients can degrade user experience for the well behaved ones. In fact, when running Kafka as a service this even makes it possible to enforce API limits according to an agreed upon contract.

Client groups

The identity of Kafka clients is the user principal which represents an authenticated user in a secure cluster. In a cluster that supports unauthenticated clients, user principal is a grouping of unauthenticated users chosen by the broker using a configurable PrincipalBuilder. Client-id is a logical grouping of clients with a meaningful name chosen by the client application. The tuple (user, client-id) defines a secure logical group of clients that share both user principal and client-id.

Quotas can be applied to (user, client-id), user or client-id groups. For a given connection, the most specific quota matching the connection is applied. All connections of a quota group share the quota configured for the group. For example, if (user=“test-user”, client-id=“test-client”) has a produce quota of 10MB/sec, this is shared across all producer instances of user “test-user” with the client-id “test-client”.

Quota Configuration

Quota configuration may be defined for (user, client-id), user and client-id groups. It is possible to override the default quota at any of the quota levels that needs a higher (or even lower) quota. The mechanism is similar to the per-topic log config overrides. User and (user, client-id) quota overrides are written to ZooKeeper under /config/users and client-id quota overrides are written under /config/clients. These overrides are read by all brokers and are effective immediately. This lets us change quotas without having to do a rolling restart of the entire cluster. See here for details. Default quotas for each group may also be updated dynamically using the same mechanism.

The order of precedence for quota configuration is:

/config/users//clients/
/config/users//clients/
/config/users/
/config/users//clients/
/config/users//clients/
/config/users/
/config/clients/
/config/clients/

Broker properties (quota.producer.default, quota.consumer.default) can also be used to set defaults for client-id groups. These properties are being deprecated and will be removed in a later release. Default quotas for client-id can be set in Zookeeper similar to the other quota overrides and defaults.

Enforcement

By default, each unique client group receives a fixed quota in bytes/sec as configured by the cluster. This quota is defined on a per-broker basis. Each client can publish/fetch a maximum of X bytes/sec per broker before it gets throttled. We decided that defining these quotas per broker is much better than having a fixed cluster wide bandwidth per client because that would require a mechanism to share client quota usage among all the brokers. This can be harder to get right than the quota implementation itself!

How does a broker react when it detects a quota violation? In our solution, the broker does not return an error rather it attempts to slow down a client exceeding its quota. It computes the amount of delay needed to bring a guilty client under its quota and delays the response for that time. This approach keeps the quota violation transparent to clients (outside of client-side metrics). This also keeps them from having to implement any special backoff and retry behavior which can get tricky. In fact, bad client behavior (retry without backoff) can exacerbate the very problem quotas are trying to solve.

Client byte rate is measured over multiple small windows (e.g. 30 windows of 1 second each) in order to detect and correct quota violations quickly. Typically, having large measurement windows (for e.g. 10 windows of 30 seconds each) leads to large bursts of traffic followed by long delays which is not great in terms of user experience.

4.2 - Protocol

Kafka protocol guide

This document covers the wire protocol implemented in Kafka. It is meant to give a readable guide to the protocol that covers the available requests, their binary format, and the proper way to make use of them to implement a client. This document assumes you understand the basic design and terminology described here

Preliminaries
- Network
- Partitioning and bootstrapping
- Partitioning Strategies
- Batching
- Versioning and Compatibility
The Protocol
- Protocol Primitive Types
- Notes on reading the request format grammars
- Common Request and Response Structure
- Message Sets
Constants
- Error Codes
- Api Keys
The Messages
Some Common Philosophical Questions

Preliminaries

Network

Kafka uses a binary protocol over TCP. The protocol defines all apis as request response message pairs. All messages are size delimited and are made up of the following primitive types.

The client initiates a socket connection and then writes a sequence of request messages and reads back the corresponding response message. No handshake is required on connection or disconnection. TCP is happier if you maintain persistent connections used for many requests to amortize the cost of the TCP handshake, but beyond this penalty connecting is pretty cheap.

The client will likely need to maintain a connection to multiple brokers, as data is partitioned and the clients will need to talk to the server that has their data. However it should not generally be necessary to maintain multiple connections to a single broker from a single client instance (i.e. connection pooling).

The server guarantees that on a single TCP connection, requests will be processed in the order they are sent and responses will return in that order as well. The broker’s request processing allows only a single in-flight request per connection in order to guarantee this ordering. Note that clients can (and ideally should) use non-blocking IO to implement request pipelining and achieve higher throughput. i.e., clients can send requests even while awaiting responses for preceding requests since the outstanding requests will be buffered in the underlying OS socket buffer. All requests are initiated by the client, and result in a corresponding response message from the server except where noted.

The server has a configurable maximum limit on request size and any request that exceeds this limit will result in the socket being disconnected.

Partitioning and bootstrapping

Kafka is a partitioned system so not all servers have the complete data set. Instead recall that topics are split into a pre-defined number of partitions, P, and each partition is replicated with some replication factor, N. Topic partitions themselves are just ordered “commit logs” numbered 0, 1, …, P.

All systems of this nature have the question of how a particular piece of data is assigned to a particular partition. Kafka clients directly control this assignment, the brokers themselves enforce no particular semantics of which messages should be published to a particular partition. Rather, to publish messages the client directly addresses messages to a particular partition, and when fetching messages, fetches from a particular partition. If two clients want to use the same partitioning scheme they must use the same method to compute the mapping of key to partition.

These requests to publish or fetch data must be sent to the broker that is currently acting as the leader for a given partition. This condition is enforced by the broker, so a request for a particular partition to the wrong broker will result in an the NotLeaderForPartition error code (described below).

How can the client find out which topics exist, what partitions they have, and which brokers currently host those partitions so that it can direct its requests to the right hosts? This information is dynamic, so you can’t just configure each client with some static mapping file. Instead all Kafka brokers can answer a metadata request that describes the current state of the cluster: what topics there are, which partitions those topics have, which broker is the leader for those partitions, and the host and port information for these brokers.

In other words, the client needs to somehow find one broker and that broker will tell the client about all the other brokers that exist and what partitions they host. This first broker may itself go down so the best practice for a client implementation is to take a list of two or three urls to bootstrap from. The user can then choose to use a load balancer or just statically configure two or three of their kafka hosts in the clients.

The client does not need to keep polling to see if the cluster has changed; it can fetch metadata once when it is instantiated cache that metadata until it receives an error indicating that the metadata is out of date. This error can come in two forms: (1) a socket error indicating the client cannot communicate with a particular broker, (2) an error code in the response to a request indicating that this broker no longer hosts the partition for which data was requested.

Cycle through a list of “bootstrap” kafka urls until we find one we can connect to. Fetch cluster metadata.
Process fetch or produce requests, directing them to the appropriate broker based on the topic/partitions they send to or fetch from.
If we get an appropriate error, refresh the metadata and try again.

Partitioning Strategies

As mentioned above the assignment of messages to partitions is something the producing client controls. That said, how should this functionality be exposed to the end-user?

Partitioning really serves two purposes in Kafka:

It balances data and request load over brokers
It serves as a way to divvy up processing among consumer processes while allowing local state and preserving order within the partition. We call this semantic partitioning.

For a given use case you may care about only one of these or both.

To accomplish simple load balancing a simple approach would be for the client to just round robin requests over all brokers. Another alternative, in an environment where there are many more producers than brokers, would be to have each client chose a single partition at random and publish to that. This later strategy will result in far fewer TCP connections.

Semantic partitioning means using some key in the message to assign messages to partitions. For example if you were processing a click message stream you might want to partition the stream by the user id so that all data for a particular user would go to a single consumer. To accomplish this the client can take a key associated with the message and use some hash of this key to choose the partition to which to deliver the message.

Batching

Our apis encourage batching small things together for efficiency. We have found this is a very significant performance win. Both our API to send messages and our API to fetch messages always work with a sequence of messages not a single message to encourage this. A clever client can make use of this and support an “asynchronous” mode in which it batches together messages sent individually and sends them in larger clumps. We go even further with this and allow the batching across multiple topics and partitions, so a produce request may contain data to append to many partitions and a fetch request may pull data from many partitions all at once.

The client implementer can choose to ignore this and send everything one at a time if they like.

Versioning and Compatibility

The protocol is designed to enable incremental evolution in a backward compatible fashion. Our versioning is on a per-api basis, each version consisting of a request and response pair. Each request contains an API key that identifies the API being invoked and a version number that indicates the format of the request and the expected format of the response.

The intention is that clients would implement a particular version of the protocol, and indicate this version in their requests. Our goal is primarily to allow API evolution in an environment where downtime is not allowed and clients and servers cannot all be changed at once.

The server will reject requests with a version it does not support, and will always respond to the client with exactly the protocol format it expects based on the version it included in its request. The intended upgrade path is that new features would first be rolled out on the server (with the older clients not making use of them) and then as newer clients are deployed these new features would gradually be taken advantage of.

Currently all versions are baselined at 0, as we evolve these APIs we will indicate the format for each version individually.

Retrieving Supported API versions

In order for a client to successfully talk to a broker, it must use request versions supported by the broker. Clients may work against multiple broker versions, however to do so the clients need to know what versions of various APIs a broker supports. Starting from 0.10.0.0, brokers provide information on various versions of APIs they support. Details of this new capability can be found here. Clients may use the supported API versions information to take appropriate actions such as propagating an unsupported API version error to application or choose an API request/response version supported by both the client and broker. The following sequence maybe used by a client to obtain supported API versions from a broker.

Client sends ApiVersionsRequest to a broker after connection has been established with the broker. If SSL is enabled, this happens after SSL connection has been established.
On receiving ApiVersionsRequest, a broker returns its full list of supported ApiKeys and versions regardless of current authentication state (e.g., before SASL authentication on an SASL listener, do note that no Kafka protocol requests may take place on a SSL listener before the SSL handshake is finished). If this is considered to leak information about the broker version a workaround is to use SSL with client authentication which is performed at an earlier stage of the connection where the ApiVersionRequest is not available. Also, note that broker versions older than 0.10.0.0 do not support this API and will either ignore the request or close connection in response to the request.
If multiple versions of an API are supported by broker and client, clients are recommended to use the latest version supported by the broker and itself.
Deprecation of a protocol version is done by marking an API version as deprecated in protocol documentation.
Supported API versions obtained from a broker, is valid only for current connection on which that information is obtained. In the event of disconnection, the client should obtain the information from broker again, as the broker might have upgraded/downgraded in the mean time.

SASL Authentication Sequence

The following sequence is used for SASL authentication:

Kafka ApiVersionsRequest may be sent by the client to obtain the version ranges of requests supported by the broker. This is optional.
Kafka SaslHandshakeRequest containing the SASL mechanism for authentication is sent by the client. If the requested mechanism is not enabled in the server, the server responds with the list of supported mechanisms and closes the client connection. If the mechanism is enabled in the server, the server sends a successful response and continues with SASL authentication.
The actual SASL authentication is now performed. A series of SASL client and server tokens corresponding to the mechanism are sent as opaque packets. These packets contain a 32-bit size followed by the token as defined by the protocol for the SASL mechanism.
If authentication succeeds, subsequent packets are handled as Kafka API requests. Otherwise, the client connection is closed.

For interoperability with 0.9.0.x clients, the first packet received by the server is handled as a SASL/GSSAPI client token if it is not a valid Kafka request. SASL/GSSAPI authentication is performed starting with this packet, skipping the first two steps above.

The Protocol

Protocol Primitive Types

The protocol is built out of the following primitive types.

Fixed Width Primitives

int8, int16, int32, int64 - Signed integers with the given precision (in bits) stored in big endian order.

Variable Length Primitives

bytes, string - These types consist of a signed integer giving a length N followed by N bytes of content. A length of -1 indicates null. string uses an int16 for its size, and bytes uses an int32.

Arrays

This is a notation for handling repeated structures. These will always be encoded as an int32 size containing the length N followed by N repetitions of the structure which can itself be made up of other primitive types. In the BNF grammars below we will show an array of a structure foo as [foo].

Notes on reading the request format grammars

The BNFs below give an exact context free grammar for the request and response binary format. The BNF is intentionally not compact in order to give human-readable name. As always in a BNF a sequence of productions indicates concatenation. When there are multiple possible productions these are separated with ‘|’ and may be enclosed in parenthesis for grouping. The top-level definition is always given first and subsequent sub-parts are indented.

Common Request and Response Structure

All requests and responses originate from the following grammar which will be incrementally describe through the rest of this document:

RequestOrResponse => Size (RequestMessage | ResponseMessage)
Size => int32

Field	Description
message_size	The message_size field gives the size of the subsequent request or response message in bytes. The client can read requests by first reading this 4 byte size as an integer N, and then reading and parsing the subsequent N bytes of the request.

Message Sets

A description of the message set format can be found here. (KAFKA-3368)

Constants

Error Codes

We use numeric codes to indicate what problem occurred on the server. These can be translated by the client into exceptions or whatever the appropriate error handling mechanism in the client language. Here is a table of the error codes currently in use:

Error	Code	Retriable	Description
UNKNOWN	-1	False	The server experienced an unexpected error when processing the request
NONE	0	False
OFFSET_OUT_OF_RANGE	1	False	The requested offset is not within the range of offsets maintained by the server.
CORRUPT_MESSAGE	2	True	This message has failed its CRC checksum, exceeds the valid size, or is otherwise corrupt.
UNKNOWN_TOPIC_OR_PARTITION	3	True	This server does not host this topic-partition.
INVALID_FETCH_SIZE	4	False	The requested fetch size is invalid.
LEADER_NOT_AVAILABLE	5	True	There is no leader for this topic-partition as we are in the middle of a leadership election.
NOT_LEADER_FOR_PARTITION	6	True	This server is not the leader for that topic-partition.
REQUEST_TIMED_OUT	7	True	The request timed out.
BROKER_NOT_AVAILABLE	8	False	The broker is not available.
REPLICA_NOT_AVAILABLE	9	False	The replica is not available for the requested topic-partition
MESSAGE_TOO_LARGE	10	False	The request included a message larger than the max message size the server will accept.
STALE_CONTROLLER_EPOCH	11	False	The controller moved to another broker.
OFFSET_METADATA_TOO_LARGE	12	False	The metadata field of the offset request was too large.
NETWORK_EXCEPTION	13	True	The server disconnected before a response was received.
GROUP_LOAD_IN_PROGRESS	14	True	The coordinator is loading and hence can't process requests for this group.
GROUP_COORDINATOR_NOT_AVAILABLE	15	True	The group coordinator is not available.
NOT_COORDINATOR_FOR_GROUP	16	True	This is not the correct coordinator for this group.
INVALID_TOPIC_EXCEPTION	17	False	The request attempted to perform an operation on an invalid topic.
RECORD_LIST_TOO_LARGE	18	False	The request included message batch larger than the configured segment size on the server.
NOT_ENOUGH_REPLICAS	19	True	Messages are rejected since there are fewer in-sync replicas than required.
NOT_ENOUGH_REPLICAS_AFTER_APPEND	20	True	Messages are written to the log, but to fewer in-sync replicas than required.
INVALID_REQUIRED_ACKS	21	False	Produce request specified an invalid value for required acks.
ILLEGAL_GENERATION	22	False	Specified group generation id is not valid.
INCONSISTENT_GROUP_PROTOCOL	23	False	The group member's supported protocols are incompatible with those of existing members.
INVALID_GROUP_ID	24	False	The configured groupId is invalid
UNKNOWN_MEMBER_ID	25	False	The coordinator is not aware of this member.
INVALID_SESSION_TIMEOUT	26	False	The session timeout is not within the range allowed by the broker (as configured by group.min.session.timeout.ms and group.max.session.timeout.ms).
REBALANCE_IN_PROGRESS	27	False	The group is rebalancing, so a rejoin is needed.
INVALID_COMMIT_OFFSET_SIZE	28	False	The committing offset data size is not valid
TOPIC_AUTHORIZATION_FAILED	29	False	Not authorized to access topics: [Topic authorization failed.]
GROUP_AUTHORIZATION_FAILED	30	False	Not authorized to access group: Group authorization failed.
CLUSTER_AUTHORIZATION_FAILED	31	False	Cluster authorization failed.
INVALID_TIMESTAMP	32	False	The timestamp of the message is out of acceptable range.
UNSUPPORTED_SASL_MECHANISM	33	False	The broker does not support the requested SASL mechanism.
ILLEGAL_SASL_STATE	34	False	Request is not valid given the current SASL state.
UNSUPPORTED_VERSION	35	False	The version of API is not supported.
TOPIC_ALREADY_EXISTS	36	False	Topic with this name already exists.
INVALID_PARTITIONS	37	False	Number of partitions is invalid.
INVALID_REPLICATION_FACTOR	38	False	Replication-factor is invalid.
INVALID_REPLICA_ASSIGNMENT	39	False	Replica assignment is invalid.
INVALID_CONFIG	40	False	Configuration is invalid.
NOT_CONTROLLER	41	True	This is not the correct controller for this cluster.
INVALID_REQUEST	42	False	This most likely occurs because of a request being malformed by the client library or the message was sent to an incompatible broker. See the broker logs for more details.
UNSUPPORTED_FOR_MESSAGE_FORMAT	43	False	The message format version on the broker does not support the request.

Api Keys

The following are the numeric codes that the ApiKey in the request can take for each of the below request types.

Name	Key
Produce	0
Fetch	1
Offsets	2
Metadata	3
LeaderAndIsr	4
StopReplica	5
UpdateMetadata	6
ControlledShutdown	7
OffsetCommit	8
OffsetFetch	9
GroupCoordinator	10
JoinGroup	11
Heartbeat	12
LeaveGroup	13
SyncGroup	14
DescribeGroups	15
ListGroups	16
SaslHandshake	17
ApiVersions	18
CreateTopics	19
DeleteTopics	20

The Messages

This section gives details on each of the individual API Messages, their usage, their binary format, and the meaning of their fields.

Headers:

Request Header => api_key api_version correlation_id client_id 
  api_key => INT16
  api_version => INT16
  correlation_id => INT32
  client_id => NULLABLE_STRING

Field	Description
api_key	The id of the request type.
api_version	The version of the API.
correlation_id	A user-supplied integer value that will be passed back with the response
client_id	A user specified identifier for the client making the request.

Response Header => correlation_id 
  correlation_id => INT32

Field	Description
correlation_id	The user-supplied value passed in with the request

Produce API (Key: 0):

Requests:

Produce Request (Version: 0) => acks timeout [topic_data] 
  acks => INT16
  timeout => INT32
  topic_data => topic [data] 
    topic => STRING
    data => partition record_set 
      partition => INT32
      record_set => BYTES

Field	Description
acks	The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.
timeout	The time to await a response in ms.
topic_data
topic
data
partition
record_set

Produce Request (Version: 1) => acks timeout [topic_data] 
  acks => INT16
  timeout => INT32
  topic_data => topic [data] 
    topic => STRING
    data => partition record_set 
      partition => INT32
      record_set => BYTES

Field	Description
acks	The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.
timeout	The time to await a response in ms.
topic_data
topic
data
partition
record_set

Produce Request (Version: 2) => acks timeout [topic_data] 
  acks => INT16
  timeout => INT32
  topic_data => topic [data] 
    topic => STRING
    data => partition record_set 
      partition => INT32
      record_set => BYTES

Field	Description
acks	The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.
timeout	The time to await a response in ms.
topic_data
topic
data
partition
record_set

Responses:

Produce Response (Version: 0) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code base_offset 
      partition => INT32
      error_code => INT16
      base_offset => INT64

Field	Description
responses
topic
partition_responses
partition
error_code
base_offset

Produce Response (Version: 1) => [responses] throttle_time_ms 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code base_offset 
      partition => INT32
      error_code => INT16
      base_offset => INT64
  throttle_time_ms => INT32

Field	Description
responses
topic
partition_responses
partition
error_code
base_offset
throttle_time_ms	Duration in milliseconds for which the request was throttled due to quota violation. (Zero if the request did not violate any quota.)

Produce Response (Version: 2) => [responses] throttle_time_ms 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code base_offset timestamp 
      partition => INT32
      error_code => INT16
      base_offset => INT64
      timestamp => INT64
  throttle_time_ms => INT32

Field	Description
responses
topic
partition_responses
partition
error_code
base_offset
timestamp	The timestamp returned by broker after appending the messages. If CreateTime is used for the topic, the timestamp will be -1. If LogAppendTime is used for the topic, the timestamp will be the broker local time when the messages are appended.
throttle_time_ms	Duration in milliseconds for which the request was throttled due to quota violation. (Zero if the request did not violate any quota.)

Fetch API (Key: 1):

Requests:

Fetch Request (Version: 0) => replica_id max_wait_time min_bytes [topics] 
  replica_id => INT32
  max_wait_time => INT32
  min_bytes => INT32
  topics => topic [partitions] 
    topic => STRING
    partitions => partition fetch_offset max_bytes 
      partition => INT32
      fetch_offset => INT64
      max_bytes => INT32

Field	Description
replica_id	Broker id of the follower. For normal consumers, use -1.
max_wait_time	Maximum time in ms to wait for the response.
min_bytes	Minimum bytes to accumulate in the response.
topics	Topics to fetch.
topic	Topic to fetch.
partitions	Partitions to fetch.
partition	Topic partition id.
fetch_offset	Message offset.
max_bytes	Maximum bytes to fetch.

Fetch Request (Version: 1) => replica_id max_wait_time min_bytes [topics] 
  replica_id => INT32
  max_wait_time => INT32
  min_bytes => INT32
  topics => topic [partitions] 
    topic => STRING
    partitions => partition fetch_offset max_bytes 
      partition => INT32
      fetch_offset => INT64
      max_bytes => INT32

Field	Description
replica_id	Broker id of the follower. For normal consumers, use -1.
max_wait_time	Maximum time in ms to wait for the response.
min_bytes	Minimum bytes to accumulate in the response.
topics	Topics to fetch.
topic	Topic to fetch.
partitions	Partitions to fetch.
partition	Topic partition id.
fetch_offset	Message offset.
max_bytes	Maximum bytes to fetch.

Fetch Request (Version: 2) => replica_id max_wait_time min_bytes [topics] 
  replica_id => INT32
  max_wait_time => INT32
  min_bytes => INT32
  topics => topic [partitions] 
    topic => STRING
    partitions => partition fetch_offset max_bytes 
      partition => INT32
      fetch_offset => INT64
      max_bytes => INT32

Field	Description
replica_id	Broker id of the follower. For normal consumers, use -1.
max_wait_time	Maximum time in ms to wait for the response.
min_bytes	Minimum bytes to accumulate in the response.
topics	Topics to fetch.
topic	Topic to fetch.
partitions	Partitions to fetch.
partition	Topic partition id.
fetch_offset	Message offset.
max_bytes	Maximum bytes to fetch.

Fetch Request (Version: 3) => replica_id max_wait_time min_bytes max_bytes [topics] 
  replica_id => INT32
  max_wait_time => INT32
  min_bytes => INT32
  max_bytes => INT32
  topics => topic [partitions] 
    topic => STRING
    partitions => partition fetch_offset max_bytes 
      partition => INT32
      fetch_offset => INT64
      max_bytes => INT32

Field	Description
replica_id	Broker id of the follower. For normal consumers, use -1.
max_wait_time	Maximum time in ms to wait for the response.
min_bytes	Minimum bytes to accumulate in the response.
max_bytes	Maximum bytes to accumulate in the response. Note that this is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that progress can be made.
topics	Topics to fetch in the order provided.
topic	Topic to fetch.
partitions	Partitions to fetch.
partition	Topic partition id.
fetch_offset	Message offset.
max_bytes	Maximum bytes to fetch.

Responses:

Fetch Response (Version: 0) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code high_watermark record_set 
      partition => INT32
      error_code => INT16
      high_watermark => INT64
      record_set => BYTES

Field	Description
responses
topic
partition_responses
partition	Topic partition id.
error_code
high_watermark	Last committed offset.
record_set

Fetch Response (Version: 1) => throttle_time_ms [responses] 
  throttle_time_ms => INT32
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code high_watermark record_set 
      partition => INT32
      error_code => INT16
      high_watermark => INT64
      record_set => BYTES

Field	Description
throttle_time_ms	Duration in milliseconds for which the request was throttled due to quota violation. (Zero if the request did not violate any quota.)
responses
topic
partition_responses
partition	Topic partition id.
error_code
high_watermark	Last committed offset.
record_set

Fetch Response (Version: 2) => throttle_time_ms [responses] 
  throttle_time_ms => INT32
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code high_watermark record_set 
      partition => INT32
      error_code => INT16
      high_watermark => INT64
      record_set => BYTES

Field	Description
throttle_time_ms	Duration in milliseconds for which the request was throttled due to quota violation. (Zero if the request did not violate any quota.)
responses
topic
partition_responses
partition	Topic partition id.
error_code
high_watermark	Last committed offset.
record_set

Fetch Response (Version: 3) => throttle_time_ms [responses] 
  throttle_time_ms => INT32
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code high_watermark record_set 
      partition => INT32
      error_code => INT16
      high_watermark => INT64
      record_set => BYTES

Field	Description
throttle_time_ms	Duration in milliseconds for which the request was throttled due to quota violation. (Zero if the request did not violate any quota.)
responses
topic
partition_responses
partition	Topic partition id.
error_code
high_watermark	Last committed offset.
record_set

Offsets API (Key: 2):

Requests:

Offsets Request (Version: 0) => replica_id [topics] 
  replica_id => INT32
  topics => topic [partitions] 
    topic => STRING
    partitions => partition timestamp max_num_offsets 
      partition => INT32
      timestamp => INT64
      max_num_offsets => INT32

Field	Description
replica_id	Broker id of the follower. For normal consumers, use -1.
topics	Topics to list offsets.
topic	Topic to list offset.
partitions	Partitions to list offset.
partition	Topic partition id.
timestamp	Timestamp.
max_num_offsets	Maximum offsets to return.

Offsets Request (Version: 1) => replica_id [topics] 
  replica_id => INT32
  topics => topic [partitions] 
    topic => STRING
    partitions => partition timestamp 
      partition => INT32
      timestamp => INT64

Field	Description
replica_id	Broker id of the follower. For normal consumers, use -1.
topics	Topics to list offsets.
topic	Topic to list offset.
partitions	Partitions to list offset.
partition	Topic partition id.
timestamp	The target timestamp for the partition.

Responses:

Offsets Response (Version: 0) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code [offsets] 
      partition => INT32
      error_code => INT16
      offsets => INT64

Field	Description
responses
topic
partition_responses
partition	Topic partition id.
error_code
offsets	A list of offsets.

Offsets Response (Version: 1) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code timestamp offset 
      partition => INT32
      error_code => INT16
      timestamp => INT64
      offset => INT64

Field	Description
responses
topic
partition_responses
partition	Topic partition id.
error_code
timestamp	The timestamp associated with the returned offset
offset	offset found

Metadata API (Key: 3):

Requests:

Metadata Request (Version: 0) => [topics] 
  topics => STRING

Field	Description
topics	An array of topics to fetch metadata for. If no topics are specified fetch metadata for all topics.

Metadata Request (Version: 1) => [topics] 
  topics => STRING

Field	Description
topics	An array of topics to fetch metadata for. If the topics array is null fetch metadata for all topics.

Metadata Request (Version: 2) => [topics] 
  topics => STRING

Field	Description
topics	An array of topics to fetch metadata for. If the topics array is null fetch metadata for all topics.

Responses:

Metadata Response (Version: 0) => [brokers] [topic_metadata] 
  brokers => node_id host port 
    node_id => INT32
    host => STRING
    port => INT32
  topic_metadata => topic_error_code topic [partition_metadata] 
    topic_error_code => INT16
    topic => STRING
    partition_metadata => partition_error_code partition_id leader [replicas] [isr] 
      partition_error_code => INT16
      partition_id => INT32
      leader => INT32
      replicas => INT32
      isr => INT32

Field	Description
brokers	Host and port information for all brokers.
node_id	The broker id.
host	The hostname of the broker.
port	The port on which the broker accepts requests.
topic_metadata
topic_error_code	The error code for the given topic.
topic	The name of the topic
partition_metadata	Metadata for each partition of the topic.
partition_error_code	The error code for the partition, if any.
partition_id	The id of the partition.
leader	The id of the broker acting as leader for this partition.
replicas	The set of all nodes that host this partition.
isr	The set of nodes that are in sync with the leader for this partition.

Metadata Response (Version: 1) => [brokers] controller_id [topic_metadata] 
  brokers => node_id host port rack 
    node_id => INT32
    host => STRING
    port => INT32
    rack => NULLABLE_STRING
  controller_id => INT32
  topic_metadata => topic_error_code topic is_internal [partition_metadata] 
    topic_error_code => INT16
    topic => STRING
    is_internal => BOOLEAN
    partition_metadata => partition_error_code partition_id leader [replicas] [isr] 
      partition_error_code => INT16
      partition_id => INT32
      leader => INT32
      replicas => INT32
      isr => INT32

Field	Description
brokers	Host and port information for all brokers.
node_id	The broker id.
host	The hostname of the broker.
port	The port on which the broker accepts requests.
rack	The rack of the broker.
controller_id	The broker id of the controller broker.
topic_metadata
topic_error_code	The error code for the given topic.
topic	The name of the topic
is_internal	Indicates if the topic is considered a Kafka internal topic
partition_metadata	Metadata for each partition of the topic.
partition_error_code	The error code for the partition, if any.
partition_id	The id of the partition.
leader	The id of the broker acting as leader for this partition.
replicas	The set of all nodes that host this partition.
isr	The set of nodes that are in sync with the leader for this partition.

Metadata Response (Version: 2) => [brokers] cluster_id controller_id [topic_metadata] 
  brokers => node_id host port rack 
    node_id => INT32
    host => STRING
    port => INT32
    rack => NULLABLE_STRING
  cluster_id => NULLABLE_STRING
  controller_id => INT32
  topic_metadata => topic_error_code topic is_internal [partition_metadata] 
    topic_error_code => INT16
    topic => STRING
    is_internal => BOOLEAN
    partition_metadata => partition_error_code partition_id leader [replicas] [isr] 
      partition_error_code => INT16
      partition_id => INT32
      leader => INT32
      replicas => INT32
      isr => INT32

Field	Description
brokers	Host and port information for all brokers.
node_id	The broker id.
host	The hostname of the broker.
port	The port on which the broker accepts requests.
rack	The rack of the broker.
cluster_id	The cluster id that this broker belongs to.
controller_id	The broker id of the controller broker.
topic_metadata
topic_error_code	The error code for the given topic.
topic	The name of the topic
is_internal	Indicates if the topic is considered a Kafka internal topic
partition_metadata	Metadata for each partition of the topic.
partition_error_code	The error code for the partition, if any.
partition_id	The id of the partition.
leader	The id of the broker acting as leader for this partition.
replicas	The set of all nodes that host this partition.
isr	The set of nodes that are in sync with the leader for this partition.

LeaderAndIsr API (Key: 4):

Requests:

LeaderAndIsr Request (Version: 0) => controller_id controller_epoch [partition_states] [live_leaders] 
  controller_id => INT32
  controller_epoch => INT32
  partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas] 
    topic => STRING
    partition => INT32
    controller_epoch => INT32
    leader => INT32
    leader_epoch => INT32
    isr => INT32
    zk_version => INT32
    replicas => INT32
  live_leaders => id host port 
    id => INT32
    host => STRING
    port => INT32

Field	Description
controller_id	The controller id.
controller_epoch	The controller epoch.
partition_states
topic	Topic name.
partition	Topic partition id.
controller_epoch	The controller epoch.
leader	The broker id for the leader.
leader_epoch	The leader epoch.
isr	The in sync replica ids.
zk_version	The ZK version.
replicas	The replica ids.
live_leaders
id	The broker id.
host	The hostname of the broker.
port	The port on which the broker accepts requests.

Responses:

LeaderAndIsr Response (Version: 0) => error_code [partitions] 
  error_code => INT16
  partitions => topic partition error_code 
    topic => STRING
    partition => INT32
    error_code => INT16

Field	Description
error_code	Error code.
partitions
topic	Topic name.
partition	Topic partition id.
error_code	Error code.

StopReplica API (Key: 5):

Requests:

StopReplica Request (Version: 0) => controller_id controller_epoch delete_partitions [partitions] 
  controller_id => INT32
  controller_epoch => INT32
  delete_partitions => BOOLEAN
  partitions => topic partition 
    topic => STRING
    partition => INT32

Field	Description
controller_id	The controller id.
controller_epoch	The controller epoch.
delete_partitions	Boolean which indicates if replica's partitions must be deleted.
partitions
topic	Topic name.
partition	Topic partition id.

Responses:

StopReplica Response (Version: 0) => error_code [partitions] 
  error_code => INT16
  partitions => topic partition error_code 
    topic => STRING
    partition => INT32
    error_code => INT16

Field	Description
error_code	Error code.
partitions
topic	Topic name.
partition	Topic partition id.
error_code	Error code.

UpdateMetadata API (Key: 6):

Requests:

UpdateMetadata Request (Version: 0) => controller_id controller_epoch [partition_states] [live_brokers] 
  controller_id => INT32
  controller_epoch => INT32
  partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas] 
    topic => STRING
    partition => INT32
    controller_epoch => INT32
    leader => INT32
    leader_epoch => INT32
    isr => INT32
    zk_version => INT32
    replicas => INT32
  live_brokers => id host port 
    id => INT32
    host => STRING
    port => INT32

Field	Description
controller_id	The controller id.
controller_epoch	The controller epoch.
partition_states
topic	Topic name.
partition	Topic partition id.
controller_epoch	The controller epoch.
leader	The broker id for the leader.
leader_epoch	The leader epoch.
isr	The in sync replica ids.
zk_version	The ZK version.
replicas	The replica ids.
live_brokers
id	The broker id.
host	The hostname of the broker.
port	The port on which the broker accepts requests.

UpdateMetadata Request (Version: 1) => controller_id controller_epoch [partition_states] [live_brokers] 
  controller_id => INT32
  controller_epoch => INT32
  partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas] 
    topic => STRING
    partition => INT32
    controller_epoch => INT32
    leader => INT32
    leader_epoch => INT32
    isr => INT32
    zk_version => INT32
    replicas => INT32
  live_brokers => id [end_points] 
    id => INT32
    end_points => port host security_protocol_type 
      port => INT32
      host => STRING
      security_protocol_type => INT16

Field	Description
controller_id	The controller id.
controller_epoch	The controller epoch.
partition_states
topic	Topic name.
partition	Topic partition id.
controller_epoch	The controller epoch.
leader	The broker id for the leader.
leader_epoch	The leader epoch.
isr	The in sync replica ids.
zk_version	The ZK version.
replicas	The replica ids.
live_brokers
id	The broker id.
end_points
port	The port on which the broker accepts requests.
host	The hostname of the broker.
security_protocol_type	The security protocol type.

UpdateMetadata Request (Version: 2) => controller_id controller_epoch [partition_states] [live_brokers] 
  controller_id => INT32
  controller_epoch => INT32
  partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas] 
    topic => STRING
    partition => INT32
    controller_epoch => INT32
    leader => INT32
    leader_epoch => INT32
    isr => INT32
    zk_version => INT32
    replicas => INT32
  live_brokers => id [end_points] rack 
    id => INT32
    end_points => port host security_protocol_type 
      port => INT32
      host => STRING
      security_protocol_type => INT16
    rack => NULLABLE_STRING

Field	Description
controller_id	The controller id.
controller_epoch	The controller epoch.
partition_states
topic	Topic name.
partition	Topic partition id.
controller_epoch	The controller epoch.
leader	The broker id for the leader.
leader_epoch	The leader epoch.
isr	The in sync replica ids.
zk_version	The ZK version.
replicas	The replica ids.
live_brokers
id	The broker id.
end_points
port	The port on which the broker accepts requests.
host	The hostname of the broker.
security_protocol_type	The security protocol type.
rack	The rack

Responses:

UpdateMetadata Response (Version: 0) => error_code 
  error_code => INT16

Field	Description
error_code	Error code.

UpdateMetadata Response (Version: 1) => error_code 
  error_code => INT16

Field	Description
error_code	Error code.

UpdateMetadata Response (Version: 2) => error_code 
  error_code => INT16

Field	Description
error_code	Error code.

ControlledShutdown API (Key: 7):

Requests:

ControlledShutdown Request (Version: 1) => broker_id 
  broker_id => INT32

Field	Description
broker_id	The id of the broker for which controlled shutdown has been requested.

Responses:

ControlledShutdown Response (Version: 1) => error_code [partitions_remaining] 
  error_code => INT16
  partitions_remaining => topic partition 
    topic => STRING
    partition => INT32

Field	Description
error_code
partitions_remaining	The partitions that the broker still leads.
topic
partition	Topic partition id.

OffsetCommit API (Key: 8):

Requests:

OffsetCommit Request (Version: 0) => group_id [topics] 
  group_id => STRING
  topics => topic [partitions] 
    topic => STRING
    partitions => partition offset metadata 
      partition => INT32
      offset => INT64
      metadata => NULLABLE_STRING

Field	Description
group_id	The group id.
topics	Topics to commit offsets.
topic	Topic to commit.
partitions	Partitions to commit offsets.
partition	Topic partition id.
offset	Message offset to be committed.
metadata	Any associated metadata the client wants to keep.

OffsetCommit Request (Version: 1) => group_id group_generation_id member_id [topics] 
  group_id => STRING
  group_generation_id => INT32
  member_id => STRING
  topics => topic [partitions] 
    topic => STRING
    partitions => partition offset timestamp metadata 
      partition => INT32
      offset => INT64
      timestamp => INT64
      metadata => NULLABLE_STRING

Field	Description
group_id	The group id.
group_generation_id	The generation of the group.
member_id	The member id assigned by the group coordinator.
topics	Topics to commit offsets.
topic	Topic to commit.
partitions	Partitions to commit offsets.
partition	Topic partition id.
offset	Message offset to be committed.
timestamp	Timestamp of the commit
metadata	Any associated metadata the client wants to keep.

OffsetCommit Request (Version: 2) => group_id group_generation_id member_id retention_time [topics] 
  group_id => STRING
  group_generation_id => INT32
  member_id => STRING
  retention_time => INT64
  topics => topic [partitions] 
    topic => STRING
    partitions => partition offset metadata 
      partition => INT32
      offset => INT64
      metadata => NULLABLE_STRING

Field	Description
group_id	The group id.
group_generation_id	The generation of the consumer group.
member_id	The consumer id assigned by the group coordinator.
retention_time	Time period in ms to retain the offset.
topics	Topics to commit offsets.
topic	Topic to commit.
partitions	Partitions to commit offsets.
partition	Topic partition id.
offset	Message offset to be committed.
metadata	Any associated metadata the client wants to keep.

Responses:

OffsetCommit Response (Version: 0) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code 
      partition => INT32
      error_code => INT16

Field	Description
responses
topic
partition_responses
partition	Topic partition id.
error_code

OffsetCommit Response (Version: 1) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code 
      partition => INT32
      error_code => INT16

Field	Description
responses
topic
partition_responses
partition	Topic partition id.
error_code

OffsetCommit Response (Version: 2) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition error_code 
      partition => INT32
      error_code => INT16

Field	Description
responses
topic
partition_responses
partition	Topic partition id.
error_code

OffsetFetch API (Key: 9):

Requests:

OffsetFetch Request (Version: 0) => group_id [topics] 
  group_id => STRING
  topics => topic [partitions] 
    topic => STRING
    partitions => partition 
      partition => INT32

Field	Description
group_id	The consumer group id.
topics	Topics to fetch offsets.
topic	Topic to fetch offset.
partitions	Partitions to fetch offsets.
partition	Topic partition id.

OffsetFetch Request (Version: 1) => group_id [topics] 
  group_id => STRING
  topics => topic [partitions] 
    topic => STRING
    partitions => partition 
      partition => INT32

Field	Description
group_id	The consumer group id.
topics	Topics to fetch offsets.
topic	Topic to fetch offset.
partitions	Partitions to fetch offsets.
partition	Topic partition id.

Responses:

OffsetFetch Response (Version: 0) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition offset metadata error_code 
      partition => INT32
      offset => INT64
      metadata => NULLABLE_STRING
      error_code => INT16

Field	Description
responses
topic
partition_responses
partition	Topic partition id.
offset	Last committed message offset.
metadata	Any associated metadata the client wants to keep.
error_code

OffsetFetch Response (Version: 1) => [responses] 
  responses => topic [partition_responses] 
    topic => STRING
    partition_responses => partition offset metadata error_code 
      partition => INT32
      offset => INT64
      metadata => NULLABLE_STRING
      error_code => INT16

Field	Description
responses
topic
partition_responses
partition	Topic partition id.
offset	Last committed message offset.
metadata	Any associated metadata the client wants to keep.
error_code

GroupCoordinator API (Key: 10):

Requests:

GroupCoordinator Request (Version: 0) => group_id 
  group_id => STRING

Field	Description
group_id	The unique group id.

Responses:

GroupCoordinator Response (Version: 0) => error_code coordinator 
  error_code => INT16
  coordinator => node_id host port 
    node_id => INT32
    host => STRING
    port => INT32

Field	Description
error_code
coordinator	Host and port information for the coordinator for a consumer group.
node_id	The broker id.
host	The hostname of the broker.
port	The port on which the broker accepts requests.

JoinGroup API (Key: 11):

Requests:

JoinGroup Request (Version: 0) => group_id session_timeout member_id protocol_type [group_protocols] 
  group_id => STRING
  session_timeout => INT32
  member_id => STRING
  protocol_type => STRING
  group_protocols => protocol_name protocol_metadata 
    protocol_name => STRING
    protocol_metadata => BYTES

Field	Description
group_id	The group id.
session_timeout	The coordinator considers the consumer dead if it receives no heartbeat after this timeout in ms.
member_id	The assigned consumer id or an empty string for a new consumer.
protocol_type	Unique name for class of protocols implemented by group
group_protocols	List of protocols that the member supports
protocol_name
protocol_metadata

JoinGroup Request (Version: 1) => group_id session_timeout rebalance_timeout member_id protocol_type [group_protocols] 
  group_id => STRING
  session_timeout => INT32
  rebalance_timeout => INT32
  member_id => STRING
  protocol_type => STRING
  group_protocols => protocol_name protocol_metadata 
    protocol_name => STRING
    protocol_metadata => BYTES

Field	Description
group_id	The group id.
session_timeout	The coordinator considers the consumer dead if it receives no heartbeat after this timeout in ms.
rebalance_timeout	The maximum time that the coordinator will wait for each member to rejoin when rebalancing the group
member_id	The assigned consumer id or an empty string for a new consumer.
protocol_type	Unique name for class of protocols implemented by group
group_protocols	List of protocols that the member supports
protocol_name
protocol_metadata

Responses:

JoinGroup Response (Version: 0) => error_code generation_id group_protocol leader_id member_id [members] 
  error_code => INT16
  generation_id => INT32
  group_protocol => STRING
  leader_id => STRING
  member_id => STRING
  members => member_id member_metadata 
    member_id => STRING
    member_metadata => BYTES

Field	Description
error_code
generation_id	The generation of the consumer group.
group_protocol	The group protocol selected by the coordinator
leader_id	The leader of the group
member_id	The consumer id assigned by the group coordinator.
members
member_id
member_metadata

JoinGroup Response (Version: 1) => error_code generation_id group_protocol leader_id member_id [members] 
  error_code => INT16
  generation_id => INT32
  group_protocol => STRING
  leader_id => STRING
  member_id => STRING
  members => member_id member_metadata 
    member_id => STRING
    member_metadata => BYTES

Field	Description
error_code
generation_id	The generation of the consumer group.
group_protocol	The group protocol selected by the coordinator
leader_id	The leader of the group
member_id	The consumer id assigned by the group coordinator.
members
member_id
member_metadata

Heartbeat API (Key: 12):

Requests:

Heartbeat Request (Version: 0) => group_id group_generation_id member_id 
  group_id => STRING
  group_generation_id => INT32
  member_id => STRING

Field	Description
group_id	The group id.
group_generation_id	The generation of the group.
member_id	The member id assigned by the group coordinator.

Responses:

Heartbeat Response (Version: 0) => error_code 
  error_code => INT16

Field	Description
error_code

LeaveGroup API (Key: 13):

Requests:

LeaveGroup Request (Version: 0) => group_id member_id 
  group_id => STRING
  member_id => STRING

Field	Description
group_id	The group id.
member_id	The member id assigned by the group coordinator.

Responses:

LeaveGroup Response (Version: 0) => error_code 
  error_code => INT16

Field	Description
error_code

SyncGroup API (Key: 14):

Requests:

SyncGroup Request (Version: 0) => group_id generation_id member_id [group_assignment] 
  group_id => STRING
  generation_id => INT32
  member_id => STRING
  group_assignment => member_id member_assignment 
    member_id => STRING
    member_assignment => BYTES

Field	Description
group_id
generation_id
member_id
group_assignment
member_id
member_assignment

Responses:

SyncGroup Response (Version: 0) => error_code member_assignment 
  error_code => INT16
  member_assignment => BYTES

Field	Description
error_code
member_assignment

DescribeGroups API (Key: 15):

Requests:

DescribeGroups Request (Version: 0) => [group_ids] 
  group_ids => STRING

Field	Description
group_ids	List of groupIds to request metadata for (an empty groupId array will return empty group metadata).

Responses:

DescribeGroups Response (Version: 0) => [groups] 
  groups => error_code group_id state protocol_type protocol [members] 
    error_code => INT16
    group_id => STRING
    state => STRING
    protocol_type => STRING
    protocol => STRING
    members => member_id client_id client_host member_metadata member_assignment 
      member_id => STRING
      client_id => STRING
      client_host => STRING
      member_metadata => BYTES
      member_assignment => BYTES

Field	Description
groups
error_code
group_id
state	The current state of the group (one of: Dead, Stable, AwaitingSync, or PreparingRebalance, or empty if there is no active group)
protocol_type	The current group protocol type (will be empty if there is no active group)
protocol	The current group protocol (only provided if the group is Stable)
members	Current group members (only provided if the group is not Dead)
member_id	The memberId assigned by the coordinator
client_id	The client id used in the member's latest join group request
client_host	The client host used in the request session corresponding to the member's join group.
member_metadata	The metadata corresponding to the current group protocol in use (will only be present if the group is stable).
member_assignment	The current assignment provided by the group leader (will only be present if the group is stable).

ListGroups API (Key: 16):

Requests:

ListGroups Request (Version: 0) =>

Field	Description

Responses:

ListGroups Response (Version: 0) => error_code [groups] 
  error_code => INT16
  groups => group_id protocol_type 
    group_id => STRING
    protocol_type => STRING

Field	Description
error_code
groups
group_id
protocol_type

SaslHandshake API (Key: 17):

Requests:

SaslHandshake Request (Version: 0) => mechanism 
  mechanism => STRING

Field	Description
mechanism	SASL Mechanism chosen by the client.

Responses:

SaslHandshake Response (Version: 0) => error_code [enabled_mechanisms] 
  error_code => INT16
  enabled_mechanisms => STRING

Field	Description
error_code
enabled_mechanisms	Array of mechanisms enabled in the server.

ApiVersions API (Key: 18):

Requests:

ApiVersions Request (Version: 0) =>

Field	Description

Responses:

ApiVersions Response (Version: 0) => error_code [api_versions] 
  error_code => INT16
  api_versions => api_key min_version max_version 
    api_key => INT16
    min_version => INT16
    max_version => INT16

Field	Description
error_code	Error code.
api_versions	API versions supported by the broker.
api_key	API key.
min_version	Minimum supported version.
max_version	Maximum supported version.

CreateTopics API (Key: 19):

Requests:

CreateTopics Request (Version: 0) => [create_topic_requests] timeout 
  create_topic_requests => topic num_partitions replication_factor [replica_assignment] [configs] 
    topic => STRING
    num_partitions => INT32
    replication_factor => INT16
    replica_assignment => partition_id [replicas] 
      partition_id => INT32
      replicas => INT32
    configs => config_key config_value 
      config_key => STRING
      config_value => STRING
  timeout => INT32

Field	Description
create_topic_requests	An array of single topic creation requests. Can not have multiple entries for the same topic.
topic	Name for newly created topic.
num_partitions	Number of partitions to be created. -1 indicates unset.
replication_factor	Replication factor for the topic. -1 indicates unset.
replica_assignment	Replica assignment among kafka brokers for this topic partitions. If this is set num_partitions and replication_factor must be unset.
partition_id
replicas	The set of all nodes that should host this partition. The first replica in the list is the preferred leader.
configs	Topic level configuration for topic to be set.
config_key	Configuration key name
config_value	Configuration value
timeout	The time in ms to wait for a topic to be completely created on the controller node. Values <= 0 will trigger topic creation and return immediately

Responses:

CreateTopics Response (Version: 0) => [topic_error_codes] 
  topic_error_codes => topic error_code 
    topic => STRING
    error_code => INT16

Field	Description
topic_error_codes	An array of per topic error codes.
topic
error_code

DeleteTopics API (Key: 20):

Requests:

DeleteTopics Request (Version: 0) => [topics] timeout 
  topics => STRING
  timeout => INT32

Field	Description
topics	An array of topics to be deleted.
timeout	The time in ms to wait for a topic to be completely deleted on the controller node. Values <= 0 will trigger topic deletion and return immediately

Responses:

DeleteTopics Response (Version: 0) => [topic_error_codes] 
  topic_error_codes => topic error_code 
    topic => STRING
    error_code => INT16

Field	Description
topic_error_codes	An array of per topic error codes.
topic
error_code

Some Common Philosophical Questions

Some people have asked why we don’t use HTTP. There are a number of reasons, the best is that client implementors can make use of some of the more advanced TCP features–the ability to multiplex requests, the ability to simultaneously poll many connections, etc. We have also found HTTP libraries in many languages to be surprisingly shabby.

Others have asked if maybe we shouldn’t support many different protocols. Prior experience with this was that it makes it very hard to add and test new features if they have to be ported across many protocol implementations. Our feeling is that most users don’t really see multiple protocols as a feature, they just want a good reliable client in the language of their choice.

Another question is why we don’t adopt XMPP, STOMP, AMQP or an existing protocol. The answer to this varies by protocol, but in general the problem is that the protocol does determine large parts of the implementation and we couldn’t do what we are doing if we didn’t have control over the protocol. Our belief is that it is possible to do better than existing messaging systems have in providing a truly distributed messaging system, and to do this we need to build something that works differently.

A final question is why we don’t use a system like Protocol Buffers or Thrift to define our request messages. These packages excel at helping you to managing lots and lots of serialized messages. However we have only a few messages. Support across languages is somewhat spotty (depending on the package). Finally the mapping between binary log format and wire protocol is something we manage somewhat carefully and this would not be possible with these systems. Finally we prefer the style of versioning APIs explicitly and checking this to inferring new values as nulls as it allows more nuanced control of compatibility.

5 - Implementation

5.1 - API Design

API Design

Producer APIs

The Producer API that wraps the 2 low-level producers - kafka.producer.SyncProducer and kafka.producer.async.AsyncProducer.

    class Producer {

    /* Sends the data, partitioned by key to the topic using either the */
    /* synchronous or the asynchronous producer */
    public void send(kafka.javaapi.producer.ProducerData<K,V> producerData);

    /* Sends a list of data, partitioned by key to the topic using either */
    /* the synchronous or the asynchronous producer */
    public void send(java.util.List<kafka.javaapi.producer.ProducerData<K,V>> producerData);

    /* Closes the producer and cleans up */
    public void close();

    }

The goal is to expose all the producer functionality through a single API to the client. The Kafka producer

can handle queueing/buffering of multiple producer requests and asynchronous dispatch of the batched data:

kafka.producer.Producer provides the ability to batch multiple produce requests (producer.type=async), before serializing and dispatching them to the appropriate kafka broker partition. The size of the batch can be controlled by a few config parameters. As events enter a queue, they are buffered in a queue, until either queue.time or batch.size is reached. A background thread (kafka.producer.async.ProducerSendThread) dequeues the batch of data and lets the kafka.producer.EventHandler serialize and send the data to the appropriate kafka broker partition. A custom event handler can be plugged in through the event.handler config parameter. At various stages of this producer queue pipeline, it is helpful to be able to inject callbacks, either for plugging in custom logging/tracing code or custom monitoring logic. This is possible by implementing the kafka.producer.async.CallbackHandler interface and setting callback.handler config parameter to that class.

handles the serialization of data through a user-specified Encoder:

    interface Encoder<T> {
public Message toMessage(T data);
}

The default is the no-op kafka.serializer.DefaultEncoder

provides software load balancing through an optionally user-specified Partitioner:

The routing decision is influenced by the kafka.producer.Partitioner.

        interface Partitioner<T> {
    int partition(T key, int numPartitions);
    }

The partition API uses the key and the number of available broker partitions to return a partition id. This id is used as an index into a sorted list of broker_ids and partitions to pick a broker partition for the producer request. The default partitioning strategy is hash(key)%numPartitions. If the key is null, then a random broker partition is picked. A custom partitioning strategy can also be plugged in using the partitioner.class config parameter.

Consumer APIs

We have 2 levels of consumer APIs. The low-level “simple” API maintains a connection to a single broker and has a close correspondence to the network requests sent to the server. This API is completely stateless, with the offset being passed in on every request, allowing the user to maintain this metadata however they choose.

The high-level API hides the details of brokers from the consumer and allows consuming off the cluster of machines without concern for the underlying topology. It also maintains the state of what has been consumed. The high-level API also provides the ability to subscribe to topics that match a filter expression (i.e., either a whitelist or a blacklist regular expression).

Low-level API

    class SimpleConsumer {

    /* Send fetch request to a broker and get back a set of messages. */
    public ByteBufferMessageSet fetch(FetchRequest request);

    /* Send a list of fetch requests to a broker and get back a response set. */
    public MultiFetchResponse multifetch(List<FetchRequest> fetches);

    /**
    * Get a list of valid offsets (up to maxSize) before the given time.
    * The result is a list of offsets, in descending order.
    * @param time: time in millisecs,
    *              if set to OffsetRequest$.MODULE$.LATEST_TIME(), get from the latest offset available.
    *              if set to OffsetRequest$.MODULE$.EARLIEST_TIME(), get from the earliest offset available.
    */
    public long[] getOffsetsBefore(String topic, int partition, long time, int maxNumOffsets);
    }

The low-level API is used to implement the high-level API as well as being used directly for some of our offline consumers which have particular requirements around maintaining state.

High-level API

    /* create a connection to the cluster */
    ConsumerConnector connector = Consumer.create(consumerConfig);

    interface ConsumerConnector {

    /**
    * This method is used to get a list of KafkaStreams, which are iterators over
    * MessageAndMetadata objects from which you can obtain messages and their
    * associated metadata (currently only topic).
    *  Input: a map of <topic, #streams>
    *  Output: a map of <topic, list of message streams>
    */
    public Map<String,List<KafkaStream>> createMessageStreams(Map<String,Int> topicCountMap);

    /**
    * You can also obtain a list of KafkaStreams, that iterate over messages
    * from topics that match a TopicFilter. (A TopicFilter encapsulates a
    * whitelist or a blacklist which is a standard Java regex.)
    */
    public List<KafkaStream> createMessageStreamsByFilter(
        TopicFilter topicFilter, int numStreams);

    /* Commit the offsets of all messages consumed so far. */
    public commitOffsets()

    /* Shut down the connector */
    public shutdown()
    }

This API is centered around iterators, implemented by the KafkaStream class. Each KafkaStream represents the stream of messages from one or more partitions on one or more servers. Each stream is used for single threaded processing, so the client can provide the number of desired streams in the create call. Thus a stream may represent the merging of multiple server partitions (to correspond to the number of processing threads), but each partition only goes to one stream.

The createMessageStreams call registers the consumer for the topic, which results in rebalancing the consumer/broker assignment. The API encourages creating many topic streams in a single call in order to minimize this rebalancing. The createMessageStreamsByFilter call (additionally) registers watchers to discover new topics that match its filter. Note that each stream that createMessageStreamsByFilter returns may iterate over messages from multiple topics (i.e., if multiple topics are allowed by the filter).

5.2 - Network Layer

Network Layer

The network layer is a fairly straight-forward NIO server, and will not be described in great detail. The sendfile implementation is done by giving the MessageSet interface a writeTo method. This allows the file-backed message set to use the more efficient transferTo implementation instead of an in-process buffered write. The threading model is a single acceptor thread and N processor threads which handle a fixed number of connections each. This design has been pretty thoroughly tested elsewhere and found to be simple to implement and fast. The protocol is kept quite simple to allow for future implementation of clients in other languages.

5.3 - Messages

Messages

Messages consist of a fixed-size header, a variable length opaque key byte array and a variable length opaque value byte array. The header contains the following fields:

A CRC32 checksum to detect corruption or truncation.
- A format version.
An attributes identifier
A timestamp

Leaving the key and value opaque is the right decision: there is a great deal of progress being made on serialization libraries right now, and any particular choice is unlikely to be right for all uses. Needless to say a particular application using Kafka would likely mandate a particular serialization type as part of its usage. The MessageSet interface is simply an iterator over messages with specialized methods for bulk reading and writing to an NIO Channel.

5.4 - Message Format

Message Format

        /**
        * 1. 4 byte CRC32 of the message
        * 2. 1 byte "magic" identifier to allow format changes, value is 0 or 1
        * 3. 1 byte "attributes" identifier to allow annotations on the message independent of the version
        *    bit 0 ~ 2 : Compression codec.
        *      0 : no compression
        *      1 : gzip
        *      2 : snappy
        *      3 : lz4
        *    bit 3 : Timestamp type
        *      0 : create time
        *      1 : log append time
        *    bit 4 ~ 7 : reserved
        * 4. (Optional) 8 byte timestamp only if "magic" identifier is greater than 0
        * 5. 4 byte key length, containing length K
        * 6. K byte key
        * 7. 4 byte payload length, containing length V
        * 8. V byte payload
        */

5.5 - Log

Log

A log for a topic named “my_topic” with two partitions consists of two directories (namely my_topic_0 and my_topic_1) populated with data files containing the messages for that topic. The format of the log files is a sequence of “log entries”"; each log entry is a 4 byte integer N storing the message length which is followed by the N message bytes. Each message is uniquely identified by a 64-bit integer offset giving the byte position of the start of this message in the stream of all messages ever sent to that topic on that partition. The on-disk format of each message is given below. Each log file is named with the offset of the first message it contains. So the first file created will be 00000000000.kafka, and each additional file will have an integer name roughly S bytes from the previous file where S is the max log file size given in the configuration.

The exact binary format for messages is versioned and maintained as a standard interface so message sets can be transferred between producer, broker, and client without recopying or conversion when desirable. This format is as follows:

    On-disk format of a message

    offset         : 8 bytes 
    message length : 4 bytes (value: 4 + 1 + 1 + 8(if magic value > 0) + 4 + K + 4 + V)
    crc            : 4 bytes
    magic value    : 1 byte
    attributes     : 1 byte
    timestamp      : 8 bytes (Only exists when magic value is greater than zero)
    key length     : 4 bytes
    key            : K bytes
    value length   : 4 bytes
    value          : V bytes

The use of the message offset as the message id is unusual. Our original idea was to use a GUID generated by the producer, and maintain a mapping from GUID to offset on each broker. But since a consumer must maintain an ID for each server, the global uniqueness of the GUID provides no value. Furthermore, the complexity of maintaining the mapping from a random id to an offset requires a heavy weight index structure which must be synchronized with disk, essentially requiring a full persistent random-access data structure. Thus to simplify the lookup structure we decided to use a simple per-partition atomic counter which could be coupled with the partition id and node id to uniquely identify a message; this makes the lookup structure simpler, though multiple seeks per consumer request are still likely. However once we settled on a counter, the jump to directly using the offset seemed natural–both after all are monotonically increasing integers unique to a partition. Since the offset is hidden from the consumer API this decision is ultimately an implementation detail and we went with the more efficient approach.

Writes

The log allows serial appends which always go to the last file. This file is rolled over to a fresh file when it reaches a configurable size (say 1GB). The log takes two configuration parameters: M , which gives the number of messages to write before forcing the OS to flush the file to disk, and S , which gives a number of seconds after which a flush is forced. This gives a durability guarantee of losing at most M messages or S seconds of data in the event of a system crash.

Reads

Reads are done by giving the 64-bit logical offset of a message and an S -byte max chunk size. This will return an iterator over the messages contained in the S -byte buffer. S is intended to be larger than any single message, but in the event of an abnormally large message, the read can be retried multiple times, each time doubling the buffer size, until the message is read successfully. A maximum message and buffer size can be specified to make the server reject messages larger than some size, and to give a bound to the client on the maximum it needs to ever read to get a complete message. It is likely that the read buffer ends with a partial message, this is easily detected by the size delimiting.

The actual process of reading from an offset requires first locating the log segment file in which the data is stored, calculating the file-specific offset from the global offset value, and then reading from that file offset. The search is done as a simple binary search variation against an in-memory range maintained for each file.

The log provides the capability of getting the most recently written message to allow clients to start subscribing as of “right now”. This is also useful in the case the consumer fails to consume its data within its SLA-specified number of days. In this case when the client attempts to consume a non-existent offset it is given an OutOfRangeException and can either reset itself or fail as appropriate to the use case.

The following is the format of the results sent to the consumer.

    MessageSetSend (fetch result)

    total length     : 4 bytes
    error code       : 2 bytes
    message 1        : x bytes
    ...
    message n        : x bytes
    


    MultiMessageSetSend (multiFetch result)

    total length       : 4 bytes
    error code         : 2 bytes
    messageSetSend 1
    ...
    messageSetSend n

Deletes

Data is deleted one log segment at a time. The log manager allows pluggable delete policies to choose which files are eligible for deletion. The current policy deletes any log with a modification time of more than N days ago, though a policy which retained the last N GB could also be useful. To avoid locking reads while still allowing deletes that modify the segment list we use a copy-on-write style segment list implementation that provides consistent views to allow a binary search to proceed on an immutable static snapshot view of the log segments while deletes are progressing.

Guarantees

The log provides a configuration parameter M which controls the maximum number of messages that are written before forcing a flush to disk. On startup a log recovery process is run that iterates over all messages in the newest log segment and verifies that each message entry is valid. A message entry is valid if the sum of its size and offset are less than the length of the file AND the CRC32 of the message payload matches the CRC stored with the message. In the event corruption is detected the log is truncated to the last valid offset.

Note that two kinds of corruption must be handled: truncation in which an unwritten block is lost due to a crash, and corruption in which a nonsense block is ADDED to the file. The reason for this is that in general the OS makes no guarantee of the write order between the file inode and the actual block data so in addition to losing written data the file can gain nonsense data if the inode is updated with a new size but a crash occurs before the block containing that data is written. The CRC detects this corner case, and prevents it from corrupting the log (though the unwritten messages are, of course, lost).

5.6 - Distribution

Distribution

Consumer Offset Tracking

The high-level consumer tracks the maximum offset it has consumed in each partition and periodically commits its offset vector so that it can resume from those offsets in the event of a restart. Kafka provides the option to store all the offsets for a given consumer group in a designated broker (for that group) called the offset manager. i.e., any consumer instance in that consumer group should send its offset commits and fetches to that offset manager (broker). The high-level consumer handles this automatically. If you use the simple consumer you will need to manage offsets manually. This is currently unsupported in the Java simple consumer which can only commit or fetch offsets in ZooKeeper. If you use the Scala simple consumer you can discover the offset manager and explicitly commit or fetch offsets to the offset manager. A consumer can look up its offset manager by issuing a GroupCoordinatorRequest to any Kafka broker and reading the GroupCoordinatorResponse which will contain the offset manager. The consumer can then proceed to commit or fetch offsets from the offsets manager broker. In case the offset manager moves, the consumer will need to rediscover the offset manager. If you wish to manage your offsets manually, you can take a look at these code samples that explain how to issue OffsetCommitRequest and OffsetFetchRequest.

When the offset manager receives an OffsetCommitRequest, it appends the request to a special compacted Kafka topic named __consumer_offsets. The offset manager sends a successful offset commit response to the consumer only after all the replicas of the offsets topic receive the offsets. In case the offsets fail to replicate within a configurable timeout, the offset commit will fail and the consumer may retry the commit after backing off. (This is done automatically by the high-level consumer.) The brokers periodically compact the offsets topic since it only needs to maintain the most recent offset commit per partition. The offset manager also caches the offsets in an in-memory table in order to serve offset fetches quickly.

When the offset manager receives an offset fetch request, it simply returns the last committed offset vector from the offsets cache. In case the offset manager was just started or if it just became the offset manager for a new set of consumer groups (by becoming a leader for a partition of the offsets topic), it may need to load the offsets topic partition into the cache. In this case, the offset fetch will fail with an OffsetsLoadInProgress exception and the consumer may retry the OffsetFetchRequest after backing off. (This is done automatically by the high-level consumer.)

Migrating offsets from ZooKeeper to Kafka

Kafka consumers in earlier releases store their offsets by default in ZooKeeper. It is possible to migrate these consumers to commit offsets into Kafka by following these steps:

Set offsets.storage=kafka and dual.commit.enabled=true in your consumer config.
Do a rolling bounce of your consumers and then verify that your consumers are healthy.
Set dual.commit.enabled=false in your consumer config.
Do a rolling bounce of your consumers and then verify that your consumers are healthy.

A roll-back (i.e., migrating from Kafka back to ZooKeeper) can also be performed using the above steps if you set offsets.storage=zookeeper.

ZooKeeper Directories

The following gives the ZooKeeper structures and algorithms used for co-ordination between consumers and brokers.

Notation

When an element in a path is denoted [xyz], that means that the value of xyz is not fixed and there is in fact a ZooKeeper znode for each possible value of xyz. For example /topics/[topic] would be a directory named /topics containing a sub-directory for each topic name. Numerical ranges are also given such as [0…5] to indicate the subdirectories 0, 1, 2, 3, 4. An arrow -> is used to indicate the contents of a znode. For example /hello -> world would indicate a znode /hello containing the value “world”.

Broker Node Registry

    /brokers/ids/[0...N] --> {"jmx_port":...,"timestamp":...,"endpoints":[...],"host":...,"version":...,"port":...} (ephemeral node)

This is a list of all present broker nodes, each of which provides a unique logical broker id which identifies it to consumers (which must be given as part of its configuration). On startup, a broker node registers itself by creating a znode with the logical broker id under /brokers/ids. The purpose of the logical broker id is to allow a broker to be moved to a different physical machine without affecting consumers. An attempt to register a broker id that is already in use (say because two servers are configured with the same broker id) results in an error.

Since the broker registers itself in ZooKeeper using ephemeral znodes, this registration is dynamic and will disappear if the broker is shutdown or dies (thus notifying consumers it is no longer available).

Broker Topic Registry

    /brokers/topics/[topic]/partitions/[0...N]/state --> {"controller_epoch":...,"leader":...,"version":...,"leader_epoch":...,"isr":[...]} (ephemeral node)

Each broker registers itself under the topics it maintains and stores the number of partitions for that topic.

Consumers and Consumer Groups

Consumers of topics also register themselves in ZooKeeper, in order to coordinate with each other and balance the consumption of data. Consumers can also store their offsets in ZooKeeper by setting offsets.storage=zookeeper. However, this offset storage mechanism will be deprecated in a future release. Therefore, it is recommended to migrate offsets storage to Kafka.

Multiple consumers can form a group and jointly consume a single topic. Each consumer in the same group is given a shared group_id. For example if one consumer is your foobar process, which is run across three machines, then you might assign this group of consumers the id “foobar”. This group id is provided in the configuration of the consumer, and is your way to tell the consumer which group it belongs to.

The consumers in a group divide up the partitions as fairly as possible, each partition is consumed by exactly one consumer in a consumer group.

Consumer Id Registry

In addition to the group_id which is shared by all consumers in a group, each consumer is given a transient, unique consumer_id (of the form hostname:uuid) for identification purposes. Consumer ids are registered in the following directory.

    /consumers/[group_id]/ids/[consumer_id] --> {"version":...,"subscription":{...:...},"pattern":...,"timestamp":...} (ephemeral node)

Each of the consumers in the group registers under its group and creates a znode with its consumer_id. The value of the znode contains a map of <topic, #streams>. This id is simply used to identify each of the consumers which is currently active within a group. This is an ephemeral node so it will disappear if the consumer process dies.

Consumer Offsets

Consumers track the maximum offset they have consumed in each partition. This value is stored in a ZooKeeper directory if offsets.storage=zookeeper.

    /consumers/[group_id]/offsets/[topic]/[partition_id] --> offset_counter_value (persistent node)

Partition Owner registry

Each broker partition is consumed by a single consumer within a given consumer group. The consumer must establish its ownership of a given partition before any consumption can begin. To establish its ownership, a consumer writes its own id in an ephemeral node under the particular broker partition it is claiming.

    /consumers/[group_id]/owners/[topic]/[partition_id] --> consumer_node_id (ephemeral node)

Cluster Id

The cluster id is a unique and immutable identifier assigned to a Kafka cluster. The cluster id can have a maximum of 22 characters and the allowed characters are defined by the regular expression [a-zA-Z0-9_\-]+, which corresponds to the characters used by the URL-safe Base64 variant with no padding. Conceptually, it is auto-generated when a cluster is started for the first time.

Implementation-wise, it is generated when a broker with version 0.10.1 or later is successfully started for the first time. The broker tries to get the cluster id from the /cluster/id znode during startup. If the znode does not exist, the broker generates a new cluster id and creates the znode with this cluster id.

Broker node registration

The broker nodes are basically independent, so they only publish information about what they have. When a broker joins, it registers itself under the broker node registry directory and writes information about its host name and port. The broker also register the list of existing topics and their logical partitions in the broker topic registry. New topics are registered dynamically when they are created on the broker.

Consumer registration algorithm

When a consumer starts, it does the following:

Register itself in the consumer id registry under its group.
Register a watch on changes (new consumers joining or any existing consumers leaving) under the consumer id registry. (Each change triggers rebalancing among all consumers within the group to which the changed consumer belongs.)
Register a watch on changes (new brokers joining or any existing brokers leaving) under the broker id registry. (Each change triggers rebalancing among all consumers in all consumer groups.)
If the consumer creates a message stream using a topic filter, it also registers a watch on changes (new topics being added) under the broker topic registry. (Each change will trigger re-evaluation of the available topics to determine which topics are allowed by the topic filter. A new allowed topic will trigger rebalancing among all consumers within the consumer group.)
Force itself to rebalance within in its consumer group.

Consumer rebalancing algorithm

The consumer rebalancing algorithms allows all the consumers in a group to come into consensus on which consumer is consuming which partitions. Consumer rebalancing is triggered on each addition or removal of both broker nodes and other consumers within the same group. For a given topic and a given consumer group, broker partitions are divided evenly among consumers within the group. A partition is always consumed by a single consumer. This design simplifies the implementation. Had we allowed a partition to be concurrently consumed by multiple consumers, there would be contention on the partition and some kind of locking would be required. If there are more consumers than partitions, some consumers won’t get any data at all. During rebalancing, we try to assign partitions to consumers in such a way that reduces the number of broker nodes each consumer has to connect to.

Each consumer does the following during rebalancing:

    1. For each topic T that Ci subscribes to
    2.   let PT be all partitions producing topic T
    3.   let CG be all consumers in the same group as Ci that consume topic T
    4.   sort PT (so partitions on the same broker are clustered together)
    5.   sort CG
    6.   let i be the index position of Ci in CG and let N = size(PT)/size(CG)
    7.   assign partitions from i*N to (i+1)*N - 1 to consumer Ci
    8.   remove current entries owned by Ci from the partition owner registry
    9.   add newly assigned partitions to the partition owner registry
            (we may need to re-try this until the original partition owner releases its ownership)

When rebalancing is triggered at one consumer, rebalancing should be triggered in other consumers within the same group about the same time.

6 - Operations

6.1 - Basic Kafka Operations

Basic Kafka Operations

This section will review the most common operations you will perform on your Kafka cluster. All of the tools reviewed in this section are available under the bin/ directory of the Kafka distribution and each tool will print details on all possible commandline options if it is run with no arguments.

Adding and removing topics

You have the option of either adding topics manually or having them be created automatically when data is first published to a non-existent topic. If topics are auto-created then you may want to tune the default topic configurations used for auto-created topics.

Topics are added and modified using the topic tool:

  > bin/kafka-topics.sh --zookeeper zk_host:port/chroot --create --topic my_topic_name
        --partitions 20 --replication-factor 3 --config x=y

The replication factor controls how many servers will replicate each message that is written. If you have a replication factor of 3 then up to 2 servers can fail before you will lose access to your data. We recommend you use a replication factor of 2 or 3 so that you can transparently bounce machines without interrupting data consumption.

The partition count controls how many logs the topic will be sharded into. There are several impacts of the partition count. First each partition must fit entirely on a single server. So if you have 20 partitions the full data set (and read and write load) will be handled by no more than 20 servers (no counting replicas). Finally the partition count impacts the maximum parallelism of your consumers. This is discussed in greater detail in the concepts section.

Each sharded partition log is placed into its own folder under the Kafka log directory. The name of such folders consists of the topic name, appended by a dash (-) and the partition id. Since a typical folder name can not be over 255 characters long, there will be a limitation on the length of topic names. We assume the number of partitions will not ever be above 100,000. Therefore, topic names cannot be longer than 249 characters. This leaves just enough room in the folder name for a dash and a potentially 5 digit long partition id.

The configurations added on the command line override the default settings the server has for things like the length of time data should be retained. The complete set of per-topic configurations is documented here.

Modifying topics

You can change the configuration or partitioning of a topic using the same topic tool.

To add partitions you can do

  > bin/kafka-topics.sh --zookeeper zk_host:port/chroot --alter --topic my_topic_name
        --partitions 40

Be aware that one use case for partitions is to semantically partition data, and adding partitions doesn’t change the partitioning of existing data so this may disturb consumers if they rely on that partition. That is if data is partitioned by hash(key) % number_of_partitions then this partitioning will potentially be shuffled by adding partitions but Kafka will not attempt to automatically redistribute data in any way.

To add configs:

  > bin/kafka-topics.sh --zookeeper zk_host:port/chroot --alter --topic my_topic_name --config x=y

To remove a config:

  > bin/kafka-topics.sh --zookeeper zk_host:port/chroot --alter --topic my_topic_name --delete-config x

And finally deleting a topic:

  > bin/kafka-topics.sh --zookeeper zk_host:port/chroot --delete --topic my_topic_name

Topic deletion option is disabled by default. To enable it set the server config

delete.topic.enable=true

Kafka does not currently support reducing the number of partitions for a topic.

Instructions for changing the replication factor of a topic can be found here.

Graceful shutdown

The Kafka cluster will automatically detect any broker shutdown or failure and elect new leaders for the partitions on that machine. This will occur whether a server fails or it is brought down intentionally for maintenance or configuration changes. For the latter cases Kafka supports a more graceful mechanism for stopping a server than just killing it. When a server is stopped gracefully it has two optimizations it will take advantage of:

It will sync all its logs to disk to avoid needing to do any log recovery when it restarts (i.e. validating the checksum for all messages in the tail of the log). Log recovery takes time so this speeds up intentional restarts.
It will migrate any partitions the server is the leader for to other replicas prior to shutting down. This will make the leadership transfer faster and minimize the time each partition is unavailable to a few milliseconds. Syncing the logs will happen automatically whenever the server is stopped other than by a hard kill, but the controlled leadership migration requires using a special setting:
```
 controlled.shutdown.enable=true
```

Note that controlled shutdown will only succeed if all the partitions hosted on the broker have replicas (i.e. the replication factor is greater than 1 and at least one of these replicas is alive). This is generally what you want since shutting down the last replica would make that topic partition unavailable.

Balancing leadership

Whenever a broker stops or crashes leadership for that broker’s partitions transfers to other replicas. This means that by default when the broker is restarted it will only be a follower for all its partitions, meaning it will not be used for client reads and writes.

To avoid this imbalance, Kafka has a notion of preferred replicas. If the list of replicas for a partition is 1,5,9 then node 1 is preferred as the leader to either node 5 or 9 because it is earlier in the replica list. You can have the Kafka cluster try to restore leadership to the restored replicas by running the command:

  > bin/kafka-preferred-replica-election.sh --zookeeper zk_host:port/chroot

Since running this command can be tedious you can also configure Kafka to do this automatically by setting the following configuration:

      auto.leader.rebalance.enable=true

Balancing Replicas Across Racks

The rack awareness feature spreads replicas of the same partition across different racks. This extends the guarantees Kafka provides for broker-failure to cover rack-failure, limiting the risk of data loss should all the brokers on a rack fail at once. The feature can also be applied to other broker groupings such as availability zones in EC2.

You can specify that a broker belongs to a particular rack by adding a property to the broker config:

   broker.rack=my-rack-id

When a topic is created, modified or replicas are redistributed, the rack constraint will be honoured, ensuring replicas span as many racks as they can (a partition will span min(#racks, replication-factor) different racks).

The algorithm used to assign replicas to brokers ensures that the number of leaders per broker will be constant, regardless of how brokers are distributed across racks. This ensures balanced throughput.

However if racks are assigned different numbers of brokers, the assignment of replicas will not be even. Racks with fewer brokers will get more replicas, meaning they will use more storage and put more resources into replication. Hence it is sensible to configure an equal number of brokers per rack.

Mirroring data between clusters

We refer to the process of replicating data between Kafka clusters “mirroring” to avoid confusion with the replication that happens amongst the nodes in a single cluster. Kafka comes with a tool for mirroring data between Kafka clusters. The tool consumes from a source cluster and produces to a destination cluster. A common use case for this kind of mirroring is to provide a replica in another datacenter. This scenario will be discussed in more detail in the next section.

You can run many such mirroring processes to increase throughput and for fault-tolerance (if one process dies, the others will take overs the additional load).

Data will be read from topics in the source cluster and written to a topic with the same name in the destination cluster. In fact the mirror maker is little more than a Kafka consumer and producer hooked together.

The source and destination clusters are completely independent entities: they can have different numbers of partitions and the offsets will not be the same. For this reason the mirror cluster is not really intended as a fault-tolerance mechanism (as the consumer position will be different); for that we recommend using normal in-cluster replication. The mirror maker process will, however, retain and use the message key for partitioning so order is preserved on a per-key basis.

Here is an example showing how to mirror a single topic (named my-topic) from an input cluster:

  > bin/kafka-mirror-maker.sh
        --consumer.config consumer.properties
        --producer.config producer.properties --whitelist my-topic

Note that we specify the list of topics with the --whitelist option. This option allows any regular expression using Java-style regular expressions. So you could mirror two topics named A and B using --whitelist 'A|B'. Or you could mirror all topics using --whitelist '*'. Make sure to quote any regular expression to ensure the shell doesn’t try to expand it as a file path. For convenience we allow the use of ‘,’ instead of ‘|’ to specify a list of topics.

Sometimes it is easier to say what it is that you don’t want. Instead of using --whitelist to say what you want to mirror you can use --blacklist to say what to exclude. This also takes a regular expression argument. However, --blacklist is not supported when the new consumer has been enabled (i.e. when bootstrap.servers has been defined in the consumer configuration).

Combining mirroring with the configuration auto.create.topics.enable=true makes it possible to have a replica cluster that will automatically create and replicate all data in a source cluster even as new topics are added.

Checking consumer position

Sometimes it’s useful to see the position of your consumers. We have a tool that will show the position of all consumers in a consumer group as well as how far behind the end of the log they are. To run this tool on a consumer group named my-group consuming a topic named my-topic would look like this:

  > bin/kafka-run-class.sh kafka.tools.ConsumerOffsetChecker --zookeeper localhost:2181 --group test
  Group           Topic                          Pid Offset          logSize         Lag             Owner
  my-group        my-topic                       0   0               0               0               test_jkreps-mn-1394154511599-60744496-0
  my-group        my-topic                       1   0               0               0               test_jkreps-mn-1394154521217-1a0be913-0

NOTE: Since 0.9.0.0, the kafka.tools.ConsumerOffsetChecker tool has been deprecated. You should use the kafka.admin.ConsumerGroupCommand (or the bin/kafka-consumer-groups.sh script) to manage consumer groups, including consumers created with the new consumer API.

Managing Consumer Groups

With the ConsumerGroupCommand tool, we can list, describe, or delete consumer groups. Note that deletion is only available when the group metadata is stored in ZooKeeper. When using the new consumer API (where the broker handles coordination of partition handling and rebalance), the group is deleted when the last committed offset for that group expires. For example, to list all consumer groups across all topics:

  > bin/kafka-consumer-groups.sh --bootstrap-server broker1:9092 --list

  test-consumer-group

To view offsets as in the previous example with the ConsumerOffsetChecker, we “describe” the consumer group like this:

  > bin/kafka-consumer-groups.sh --bootstrap-server broker1:9092 --describe --group test-consumer-group

  GROUP                          TOPIC                          PARTITION  CURRENT-OFFSET  LOG-END-OFFSET  LAG             OWNER
  test-consumer-group            test-foo                       0          1               3               2               consumer-1_/127.0.0.1

If you are using the old high-level consumer and storing the group metadata in ZooKeeper (i.e. offsets.storage=zookeeper), pass --zookeeper instead of bootstrap-server:

  > bin/kafka-consumer-groups.sh --zookeeper localhost:2181 --list

Expanding your cluster

Adding servers to a Kafka cluster is easy, just assign them a unique broker id and start up Kafka on your new servers. However these new servers will not automatically be assigned any data partitions, so unless partitions are moved to them they won’t be doing any work until new topics are created. So usually when you add machines to your cluster you will want to migrate some existing data to these machines.

The process of migrating data is manually initiated but fully automated. Under the covers what happens is that Kafka will add the new server as a follower of the partition it is migrating and allow it to fully replicate the existing data in that partition. When the new server has fully replicated the contents of this partition and joined the in-sync replica one of the existing replicas will delete their partition’s data.

The partition reassignment tool can be used to move partitions across brokers. An ideal partition distribution would ensure even data load and partition sizes across all brokers. The partition reassignment tool does not have the capability to automatically study the data distribution in a Kafka cluster and move partitions around to attain an even load distribution. As such, the admin has to figure out which topics or partitions should be moved around.

The partition reassignment tool can run in 3 mutually exclusive modes:

--generate: In this mode, given a list of topics and a list of brokers, the tool generates a candidate reassignment to move all partitions of the specified topics to the new brokers. This option merely provides a convenient way to generate a partition reassignment plan given a list of topics and target brokers.
--execute: In this mode, the tool kicks off the reassignment of partitions based on the user provided reassignment plan. (using the –reassignment-json-file option). This can either be a custom reassignment plan hand crafted by the admin or provided by using the –generate option
--verify: In this mode, the tool verifies the status of the reassignment for all partitions listed during the last –execute. The status can be either of successfully completed, failed or in progress

Automatically migrating data to new machines

The partition reassignment tool can be used to move some topics off of the current set of brokers to the newly added brokers. This is typically useful while expanding an existing cluster since it is easier to move entire topics to the new set of brokers, than moving one partition at a time. When used to do this, the user should provide a list of topics that should be moved to the new set of brokers and a target list of new brokers. The tool then evenly distributes all partitions for the given list of topics across the new set of brokers. During this move, the replication factor of the topic is kept constant. Effectively the replicas for all partitions for the input list of topics are moved from the old set of brokers to the newly added brokers.

For instance, the following example will move all partitions for topics foo1,foo2 to the new set of brokers 5,6. At the end of this move, all partitions for topics foo1 and foo2 will only exist on brokers 5,6.

Since the tool accepts the input list of topics as a json file, you first need to identify the topics you want to move and create the json file as follows:

  > cat topics-to-move.json
  {"topics": [{"topic": "foo1"},
              {"topic": "foo2"}],
  "version":1
  }

Once the json file is ready, use the partition reassignment tool to generate a candidate assignment:

  > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --topics-to-move-json-file topics-to-move.json --broker-list "5,6" --generate
  Current partition replica assignment

  {"version":1,
  "partitions":[{"topic":"foo1","partition":2,"replicas":[1,2]},
                {"topic":"foo1","partition":0,"replicas":[3,4]},
                {"topic":"foo2","partition":2,"replicas":[1,2]},
                {"topic":"foo2","partition":0,"replicas":[3,4]},
                {"topic":"foo1","partition":1,"replicas":[2,3]},
                {"topic":"foo2","partition":1,"replicas":[2,3]}]
  }

  Proposed partition reassignment configuration

  {"version":1,
  "partitions":[{"topic":"foo1","partition":2,"replicas":[5,6]},
                {"topic":"foo1","partition":0,"replicas":[5,6]},
                {"topic":"foo2","partition":2,"replicas":[5,6]},
                {"topic":"foo2","partition":0,"replicas":[5,6]},
                {"topic":"foo1","partition":1,"replicas":[5,6]},
                {"topic":"foo2","partition":1,"replicas":[5,6]}]
  }

The tool generates a candidate assignment that will move all partitions from topics foo1,foo2 to brokers 5,6. Note, however, that at this point, the partition movement has not started, it merely tells you the current assignment and the proposed new assignment. The current assignment should be saved in case you want to rollback to it. The new assignment should be saved in a json file (e.g. expand-cluster-reassignment.json) to be input to the tool with the –execute option as follows:

  > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file expand-cluster-reassignment.json --execute
  Current partition replica assignment

  {"version":1,
  "partitions":[{"topic":"foo1","partition":2,"replicas":[1,2]},
                {"topic":"foo1","partition":0,"replicas":[3,4]},
                {"topic":"foo2","partition":2,"replicas":[1,2]},
                {"topic":"foo2","partition":0,"replicas":[3,4]},
                {"topic":"foo1","partition":1,"replicas":[2,3]},
                {"topic":"foo2","partition":1,"replicas":[2,3]}]
  }

  Save this to use as the --reassignment-json-file option during rollback
  Successfully started reassignment of partitions
  {"version":1,
  "partitions":[{"topic":"foo1","partition":2,"replicas":[5,6]},
                {"topic":"foo1","partition":0,"replicas":[5,6]},
                {"topic":"foo2","partition":2,"replicas":[5,6]},
                {"topic":"foo2","partition":0,"replicas":[5,6]},
                {"topic":"foo1","partition":1,"replicas":[5,6]},
                {"topic":"foo2","partition":1,"replicas":[5,6]}]
  }

Finally, the –verify option can be used with the tool to check the status of the partition reassignment. Note that the same expand-cluster-reassignment.json (used with the –execute option) should be used with the –verify option:

  > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file expand-cluster-reassignment.json --verify
  Status of partition reassignment:
  Reassignment of partition [foo1,0] completed successfully
  Reassignment of partition [foo1,1] is in progress
  Reassignment of partition [foo1,2] is in progress
  Reassignment of partition [foo2,0] completed successfully
  Reassignment of partition [foo2,1] completed successfully
  Reassignment of partition [foo2,2] completed successfully

Custom partition assignment and migration

The partition reassignment tool can also be used to selectively move replicas of a partition to a specific set of brokers. When used in this manner, it is assumed that the user knows the reassignment plan and does not require the tool to generate a candidate reassignment, effectively skipping the –generate step and moving straight to the –execute step

For instance, the following example moves partition 0 of topic foo1 to brokers 5,6 and partition 1 of topic foo2 to brokers 2,3:

The first step is to hand craft the custom reassignment plan in a json file:

  > cat custom-reassignment.json
  {"version":1,"partitions":[{"topic":"foo1","partition":0,"replicas":[5,6]},{"topic":"foo2","partition":1,"replicas":[2,3]}]}

Then, use the json file with the –execute option to start the reassignment process:

  > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file custom-reassignment.json --execute
  Current partition replica assignment

  {"version":1,
  "partitions":[{"topic":"foo1","partition":0,"replicas":[1,2]},
                {"topic":"foo2","partition":1,"replicas":[3,4]}]
  }

  Save this to use as the --reassignment-json-file option during rollback
  Successfully started reassignment of partitions
  {"version":1,
  "partitions":[{"topic":"foo1","partition":0,"replicas":[5,6]},
                {"topic":"foo2","partition":1,"replicas":[2,3]}]
  }

The –verify option can be used with the tool to check the status of the partition reassignment. Note that the same expand-cluster-reassignment.json (used with the –execute option) should be used with the –verify option:

  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file custom-reassignment.json --verify
  Status of partition reassignment:
  Reassignment of partition [foo1,0] completed successfully
  Reassignment of partition [foo2,1] completed successfully

Decommissioning brokers

The partition reassignment tool does not have the ability to automatically generate a reassignment plan for decommissioning brokers yet. As such, the admin has to come up with a reassignment plan to move the replica for all partitions hosted on the broker to be decommissioned, to the rest of the brokers. This can be relatively tedious as the reassignment needs to ensure that all the replicas are not moved from the decommissioned broker to only one other broker. To make this process effortless, we plan to add tooling support for decommissioning brokers in the future.

Increasing replication factor

Increasing the replication factor of an existing partition is easy. Just specify the extra replicas in the custom reassignment json file and use it with the –execute option to increase the replication factor of the specified partitions.

For instance, the following example increases the replication factor of partition 0 of topic foo from 1 to 3. Before increasing the replication factor, the partition’s only replica existed on broker 5. As part of increasing the replication factor, we will add more replicas on brokers 6 and 7.

The first step is to hand craft the custom reassignment plan in a json file:

  > cat increase-replication-factor.json
  {"version":1,
  "partitions":[{"topic":"foo","partition":0,"replicas":[5,6,7]}]}

Then, use the json file with the –execute option to start the reassignment process:

  > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file increase-replication-factor.json --execute
  Current partition replica assignment

  {"version":1,
  "partitions":[{"topic":"foo","partition":0,"replicas":[5]}]}

  Save this to use as the --reassignment-json-file option during rollback
  Successfully started reassignment of partitions
  {"version":1,
  "partitions":[{"topic":"foo","partition":0,"replicas":[5,6,7]}]}

The –verify option can be used with the tool to check the status of the partition reassignment. Note that the same increase-replication-factor.json (used with the –execute option) should be used with the –verify option:

  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file increase-replication-factor.json --verify
  Status of partition reassignment:
  Reassignment of partition [foo,0] completed successfully

You can also verify the increase in replication factor with the kafka-topics tool:

  > bin/kafka-topics.sh --zookeeper localhost:2181 --topic foo --describe
  Topic:foo	PartitionCount:1	ReplicationFactor:3	Configs:
    Topic: foo	Partition: 0	Leader: 5	Replicas: 5,6,7	Isr: 5,6,7

Limiting Bandwidth Usage during Data Migration

Kafka lets you apply a throttle to replication traffic, setting an upper bound on the bandwidth used to move replicas from machine to machine. This is useful when rebalancing a cluster, bootstrapping a new broker or adding or removing brokers, as it limits the impact these data-intensive operations will have on users.

There are two interfaces that can be used to engage a throttle. The simplest, and safest, is to apply a throttle when invoking the kafka-reassign-partitions.sh, but kafka-configs.sh can also be used to view and alter the throttle values directly.

So for example, if you were to execute a rebalance, with the below command, it would move partitions at no more than 50MB/s.

$ bin/kafka-reassign-partitions.sh --zookeeper myhost:2181--execute --reassignment-json-file bigger-cluster.json —throttle 50000000

When you execute this script you will see the throttle engage:

  The throttle limit was set to 50000000 B/s
  Successfully started reassignment of partitions.

Should you wish to alter the throttle, during a rebalance, say to increase the throughput so it completes quicker, you can do this by re-running the execute command passing the same reassignment-json-file:

$ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181  --execute --reassignment-json-file bigger-cluster.json --throttle 700000000
  There is an existing assignment running.
  The throttle limit was set to 700000000 B/s

Once the rebalance completes the administrator can check the status of the rebalance using the –verify option. If the rebalance has completed, the throttle will be removed via the –verify command. It is important that administrators remove the throttle in a timely manner once rebalancing completes by running the command with the –verify option. Failure to do so could cause regular replication traffic to be throttled.

When the –verify option is executed, and the reassignment has completed, the script will confirm that the throttle was removed:

$ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181  --verify --reassignment-json-file bigger-cluster.json
  Status of partition reassignment:
  Reassignment of partition [my-topic,1] completed successfully
  Reassignment of partition [mytopic,0] completed successfully
  Throttle was removed.

The administrator can also validate the assigned configs using the kafka-configs.sh. There are two pairs of throttle configuration used to manage the throttling process. The throttle value itself. This is configured, at a broker level, using the dynamic properties:

leader.replication.throttled.rate
  follower.replication.throttled.rate

There is also an enumerated set of throttled replicas:

leader.replication.throttled.replicas
  follower.replication.throttled.replicas

Which are configured per topic. All four config values are automatically assigned by kafka-reassign-partitions.sh (discussed below).

To view the throttle limit configuration:

$ bin/kafka-configs.sh --describe --zookeeper localhost:2181 --entity-type brokers
  Configs for brokers '2' are leader.replication.throttled.rate=700000000,follower.replication.throttled.rate=700000000
  Configs for brokers '1' are leader.replication.throttled.rate=700000000,follower.replication.throttled.rate=700000000

This shows the throttle applied to both leader and follower side of the replication protocol. By default both sides are assigned the same throttled throughput value.

To view the list of throttled replicas:

$ bin/kafka-configs.sh --describe --zookeeper localhost:2181 --entity-type topics
  Configs for topic 'my-topic' are leader.replication.throttled.replicas=1:102,0:101,
      follower.replication.throttled.replicas=1:101,0:102

Here we see the leader throttle is applied to partition 1 on broker 102 and partition 0 on broker 101. Likewise the follower throttle is applied to partition 1 on broker 101 and partition 0 on broker 102.

By default kafka-reassign-partitions.sh will apply the leader throttle to all replicas that exist before the rebalance, any one of which might be leader. It will apply the follower throttle to all move destinations. So if there is a partition with replicas on brokers 101,102, being reassigned to 102,103, a leader throttle, for that partition, would be applied to 101,102 and a follower throttle would be applied to 103 only.

If required, you can also use the –alter switch on kafka-configs.sh to alter the throttle configurations manually.

Safe usage of throttled replication

Some care should be taken when using throttled replication. In particular:

(1) Throttle Removal:

The throttle should be removed in a timely manner once reassignment completes (by running kafka-reassign-partitions —verify).

(2) Ensuring Progress:

If the throttle is set too low, in comparison to the incoming write rate, it is possible for replication to not make progress. This occurs when:

max(BytesInPerSec) > throttle

Where BytesInPerSec is the metric that monitors the write throughput of producers into each broker.

The administrator can monitor whether replication is making progress, during the rebalance, using the metric:

kafka.server:type=FetcherLagMetrics,name=ConsumerLag,clientId=([-.\w]+),topic=([-.\w]+),partition=([0-9]+)

The lag should constantly decrease during replication. If the metric does not decrease the administrator should increase the throttle throughput as described above.

Setting quotas

Quotas overrides and defaults may be configured at (user, client-id), user or client-id levels as described here. By default, clients receive an unlimited quota. It is possible to set custom quotas for each (user, client-id), user or client-id group.

Configure custom quota for (user=user1, client-id=clientA):

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048' --entity-type users --entity-name user1 --entity-type clients --entity-name clientA
  Updated config for entity: user-principal 'user1', client-id 'clientA'.

Configure custom quota for user=user1:

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048' --entity-type users --entity-name user1
  Updated config for entity: user-principal 'user1'.

Configure custom quota for client-id=clientA:

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048' --entity-type clients --entity-name clientA
  Updated config for entity: client-id 'clientA'.

It is possible to set default quotas for each (user, client-id), user or client-id group by specifying --entity-default option instead of --entity-name.

Configure default client-id quota for user=userA:

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048' --entity-type users --entity-name user1 --entity-type clients --entity-default
  Updated config for entity: user-principal 'user1', default client-id.

Configure default quota for user:

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048' --entity-type users --entity-default
  Updated config for entity: default user-principal.

Configure default quota for client-id:

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048' --entity-type clients --entity-default
  Updated config for entity: default client-id.

Here’s how to describe the quota for a given (user, client-id):

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --describe --entity-type users --entity-name user1 --entity-type clients --entity-name clientA
  Configs for user-principal 'user1', client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048

Describe quota for a given user:

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --describe --entity-type users --entity-name user1
  Configs for user-principal 'user1' are producer_byte_rate=1024,consumer_byte_rate=2048

Describe quota for a given client-id:

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --describe --entity-type clients --entity-name clientA
  Configs for client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048

If entity name is not specified, all entities of the specified type are described. For example, describe all users:

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --describe --entity-type users
  Configs for user-principal 'user1' are producer_byte_rate=1024,consumer_byte_rate=2048
  Configs for default user-principal are producer_byte_rate=1024,consumer_byte_rate=2048

Similarly for (user, client):

  > bin/kafka-configs.sh  --zookeeper localhost:2181 --describe --entity-type users --entity-type clients
  Configs for user-principal 'user1', default client-id are producer_byte_rate=1024,consumer_byte_rate=2048
  Configs for user-principal 'user1', client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048

It is possible to set default quotas that apply to all client-ids by setting these configs on the brokers. These properties are applied only if quota overrides or defaults are not configured in Zookeeper. By default, each client-id receives an unlimited quota. The following sets the default quota per producer and consumer client-id to 10MB/sec.

    quota.producer.default=10485760
    quota.consumer.default=10485760

Note that these properties are being deprecated and may be removed in a future release. Defaults configured using kafka-configs.sh take precedence over these properties.

6.2 - Datacenters

Datacenters

Some deployments will need to manage a data pipeline that spans multiple datacenters. Our recommended approach to this is to deploy a local Kafka cluster in each datacenter with application instances in each datacenter interacting only with their local cluster and mirroring between clusters (see the documentation on the mirror maker tool for how to do this).

This deployment pattern allows datacenters to act as independent entities and allows us to manage and tune inter-datacenter replication centrally. This allows each facility to stand alone and operate even if the inter-datacenter links are unavailable: when this occurs the mirroring falls behind until the link is restored at which time it catches up.

For applications that need a global view of all data you can use mirroring to provide clusters which have aggregate data mirrored from the local clusters in all datacenters. These aggregate clusters are used for reads by applications that require the full data set.

This is not the only possible deployment pattern. It is possible to read from or write to a remote Kafka cluster over the WAN, though obviously this will add whatever latency is required to get the cluster.

Kafka naturally batches data in both the producer and consumer so it can achieve high-throughput even over a high-latency connection. To allow this though it may be necessary to increase the TCP socket buffer sizes for the producer, consumer, and broker using the socket.send.buffer.bytes and socket.receive.buffer.bytes configurations. The appropriate way to set this is documented here.

It is generally not advisable to run a single Kafka cluster that spans multiple datacenters over a high-latency link. This will incur very high replication latency both for Kafka writes and ZooKeeper writes, and neither Kafka nor ZooKeeper will remain available in all locations if the network between locations is unavailable.

6.3 - Kafka Configuration

Kafka Configuration

Important Client Configurations

The most important producer configurations control

compression
sync vs async production
batch size (for async producers)

The most important consumer configuration is the fetch size.

All configurations are documented in the configuration section.

A Production Server Config

Here is our production server configuration:

  # Replication configurations
  num.replica.fetchers=4
  replica.fetch.max.bytes=1048576
  replica.fetch.wait.max.ms=500
  replica.high.watermark.checkpoint.interval.ms=5000
  replica.socket.timeout.ms=30000
  replica.socket.receive.buffer.bytes=65536
  replica.lag.time.max.ms=10000

  controller.socket.timeout.ms=30000
  controller.message.queue.size=10

  # Log configuration
  num.partitions=8
  message.max.bytes=1000000
  auto.create.topics.enable=true
  log.index.interval.bytes=4096
  log.index.size.max.bytes=10485760
  log.retention.hours=168
  log.flush.interval.ms=10000
  log.flush.interval.messages=20000
  log.flush.scheduler.interval.ms=2000
  log.roll.hours=168
  log.retention.check.interval.ms=300000
  log.segment.bytes=1073741824

  # ZK configuration
  zookeeper.connection.timeout.ms=6000
  zookeeper.sync.time.ms=2000

  # Socket server configuration
  num.io.threads=8
  num.network.threads=8
  socket.request.max.bytes=104857600
  socket.receive.buffer.bytes=1048576
  socket.send.buffer.bytes=1048576
  queued.max.requests=16
  fetch.purgatory.purge.interval.requests=100
  producer.purgatory.purge.interval.requests=100

Our client configuration varies a fair amount between different use cases.

6.4 - Java Version

Java Version

From a security perspective, we recommend you use the latest released version of JDK 1.8 as older freely available versions have disclosed security vulnerabilities. LinkedIn is currently running JDK 1.8 u5 (looking to upgrade to a newer version) with the G1 collector. If you decide to use the G1 collector (the current default) and you are still on JDK 1.7, make sure you are on u51 or newer. LinkedIn tried out u21 in testing, but they had a number of problems with the GC implementation in that version. LinkedIn’s tuning looks like this:

  -Xmx6g -Xms6g -XX:MetaspaceSize=96m -XX:+UseG1GC
  -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:G1HeapRegionSize=16M
  -XX:MinMetaspaceFreeRatio=50 -XX:MaxMetaspaceFreeRatio=80

For reference, here are the stats on one of LinkedIn’s busiest clusters (at peak):

60 brokers
50k partitions (replication factor 2)
800k messages/sec in
300 MB/sec inbound, 1 GB/sec+ outbound

The tuning looks fairly aggressive, but all of the brokers in that cluster have a 90% GC pause time of about 21ms, and they’re doing less than 1 young GC per second.

6.5 - Hardware and OS

Hardware and OS

We are using dual quad-core Intel Xeon machines with 24GB of memory.

You need sufficient memory to buffer active readers and writers. You can do a back-of-the-envelope estimate of memory needs by assuming you want to be able to buffer for 30 seconds and compute your memory need as write_throughput*30.

The disk throughput is important. We have 8x7200 rpm SATA drives. In general disk throughput is the performance bottleneck, and more disks is better. Depending on how you configure flush behavior you may or may not benefit from more expensive disks (if you force flush often then higher RPM SAS drives may be better).

OS

Kafka should run well on any unix system and has been tested on Linux and Solaris.

We have seen a few issues running on Windows and Windows is not currently a well supported platform though we would be happy to change that.

It is unlikely to require much OS-level tuning, but there are two potentially important OS-level configurations:

File descriptor limits: Kafka uses file descriptors for log segments and open connections. If a broker hosts many partitions, consider that the broker needs at least (number_of_partitions)*(partition_size/segment_size) to track all log segments in addition to the number of connections the broker makes. We recommend at least 100000 allowed file descriptors for the broker processes as a starting point.
Max socket buffer size: can be increased to enable high-performance data transfer between data centers as described here.

Disks and Filesystem

We recommend using multiple drives to get good throughput and not sharing the same drives used for Kafka data with application logs or other OS filesystem activity to ensure good latency. You can either RAID these drives together into a single volume or format and mount each drive as its own directory. Since Kafka has replication the redundancy provided by RAID can also be provided at the application level. This choice has several tradeoffs.

If you configure multiple data directories partitions will be assigned round-robin to data directories. Each partition will be entirely in one of the data directories. If data is not well balanced among partitions this can lead to load imbalance between disks.

RAID can potentially do better at balancing load between disks (although it doesn’t always seem to) because it balances load at a lower level. The primary downside of RAID is that it is usually a big performance hit for write throughput and reduces the available disk space.

Another potential benefit of RAID is the ability to tolerate disk failures. However our experience has been that rebuilding the RAID array is so I/O intensive that it effectively disables the server, so this does not provide much real availability improvement.

Application vs. OS Flush Management

Kafka always immediately writes all data to the filesystem and supports the ability to configure the flush policy that controls when data is forced out of the OS cache and onto disk using the flush. This flush policy can be controlled to force data to disk after a period of time or after a certain number of messages has been written. There are several choices in this configuration.

Kafka must eventually call fsync to know that data was flushed. When recovering from a crash for any log segment not known to be fsync’d Kafka will check the integrity of each message by checking its CRC and also rebuild the accompanying offset index file as part of the recovery process executed on startup.

Note that durability in Kafka does not require syncing data to disk, as a failed node will always recover from its replicas.

We recommend using the default flush settings which disable application fsync entirely. This means relying on the background flush done by the OS and Kafka’s own background flush. This provides the best of all worlds for most uses: no knobs to tune, great throughput and latency, and full recovery guarantees. We generally feel that the guarantees provided by replication are stronger than sync to local disk, however the paranoid still may prefer having both and application level fsync policies are still supported.

The drawback of using application level flush settings is that it is less efficient in its disk usage pattern (it gives the OS less leeway to re-order writes) and it can introduce latency as fsync in most Linux filesystems blocks writes to the file whereas the background flushing does much more granular page-level locking.

In general you don’t need to do any low-level tuning of the filesystem, but in the next few sections we will go over some of this in case it is useful.

Understanding Linux OS Flush Behavior

In Linux, data written to the filesystem is maintained in pagecache until it must be written out to disk (due to an application-level fsync or the OS’s own flush policy). The flushing of data is done by a set of background threads called pdflush (or in post 2.6.32 kernels “flusher threads”).

Pdflush has a configurable policy that controls how much dirty data can be maintained in cache and for how long before it must be written back to disk. This policy is described here. When Pdflush cannot keep up with the rate of data being written it will eventually cause the writing process to block incurring latency in the writes to slow down the accumulation of data.

You can see the current state of OS memory usage by doing

    > cat /proc/meminfo

The meaning of these values are described in the link above.

Using pagecache has several advantages over an in-process cache for storing data that will be written out to disk:

The I/O scheduler will batch together consecutive small writes into bigger physical writes which improves throughput.
The I/O scheduler will attempt to re-sequence writes to minimize movement of the disk head which improves throughput.
It automatically uses all the free memory on the machine

Filesystem Selection

Kafka uses regular files on disk, and as such it has no hard dependency on a specific filesystem. The two filesystems which have the most usage, however, are EXT4 and XFS. Historically, EXT4 has had more usage, but recent improvements to the XFS filesystem have shown it to have better performance characteristics for Kafka’s workload with no compromise in stability.

Comparison testing was performed on a cluster with significant message loads, using a variety of filesystem creation and mount options. The primary metric in Kafka that was monitored was the “Request Local Time”, indicating the amount of time append operations were taking. XFS resulted in much better local times (160ms vs. 250ms+ for the best EXT4 configuration), as well as lower average wait times. The XFS performance also showed less variability in disk performance.

General Filesystem Notes

For any filesystem used for data directories, on Linux systems, the following options are recommended to be used at mount time:

noatime: This option disables updating of a file’s atime (last access time) attribute when the file is read. This can eliminate a significant number of filesystem writes, especially in the case of bootstrapping consumers. Kafka does not rely on the atime attributes at all, so it is safe to disable this.

XFS Notes

The XFS filesystem has a significant amount of auto-tuning in place, so it does not require any change in the default settings, either at filesystem creation time or at mount. The only tuning parameters worth considering are:

largeio: This affects the preferred I/O size reported by the stat call. While this can allow for higher performance on larger disk writes, in practice it had minimal or no effect on performance.
nobarrier: For underlying devices that have battery-backed cache, this option can provide a little more performance by disabling periodic write flushes. However, if the underlying device is well-behaved, it will report to the filesystem that it does not require flushes, and this option will have no effect.

EXT4 Notes

EXT4 is a serviceable choice of filesystem for the Kafka data directories, however getting the most performance out of it will require adjusting several mount options. In addition, these options are generally unsafe in a failure scenario, and will result in much more data loss and corruption. For a single broker failure, this is not much of a concern as the disk can be wiped and the replicas rebuilt from the cluster. In a multiple-failure scenario, such as a power outage, this can mean underlying filesystem (and therefore data) corruption that is not easily recoverable. The following options can be adjusted:

data=writeback: Ext4 defaults to data=ordered which puts a strong order on some writes. Kafka does not require this ordering as it does very paranoid data recovery on all unflushed log. This setting removes the ordering constraint and seems to significantly reduce latency.
Disabling journaling: Journaling is a tradeoff: it makes reboots faster after server crashes but it introduces a great deal of additional locking which adds variance to write performance. Those who don’t care about reboot time and want to reduce a major source of write latency spikes can turn off journaling entirely.
commit=num_secs: This tunes the frequency with which ext4 commits to its metadata journal. Setting this to a lower value reduces the loss of unflushed data during a crash. Setting this to a higher value will improve throughput.
nobh: This setting controls additional ordering guarantees when using data=writeback mode. This should be safe with Kafka as we do not depend on write ordering and improves throughput and latency.
delalloc: Delayed allocation means that the filesystem avoid allocating any blocks until the physical write occurs. This allows ext4 to allocate a large extent instead of smaller pages and helps ensure the data is written sequentially. This feature is great for throughput. It does seem to involve some locking in the filesystem which adds a bit of latency variance.

6.6 - Monitoring

Monitoring

Kafka uses Yammer Metrics for metrics reporting in both the server and the client. This can be configured to report stats using pluggable stats reporters to hook up to your monitoring system.

The easiest way to see the available metrics is to fire up jconsole and point it at a running kafka client or server; this will allow browsing all metrics with JMX.

We do graphing and alerting on the following metrics: Description	Mbean name	Normal value
Message in rate	kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec
Byte in rate	kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec
Request rate	kafka.network:type=RequestMetrics,name=RequestsPerSec,request={Produce	FetchConsumer
Byte out rate	kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec
Log flush rate and time	kafka.log:type=LogFlushStats,name=LogFlushRateAndTimeMs

of under replicated partitions (|ISR| < |all replicas|) | kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions | 0

Is controller active on broker | kafka.controller:type=KafkaController,name=ActiveControllerCount | only one broker in the cluster should have 1
Leader election rate | kafka.controller:type=ControllerStats,name=LeaderElectionRateAndTimeMs | non-zero when there are broker failures
Unclean leader election rate | kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec | 0
Partition counts | kafka.server:type=ReplicaManager,name=PartitionCount | mostly even across brokers
Leader replica counts | kafka.server:type=ReplicaManager,name=LeaderCount | mostly even across brokers
ISR shrink rate | kafka.server:type=ReplicaManager,name=IsrShrinksPerSec | If a broker goes down, ISR for some of the partitions will shrink. When that broker is up again, ISR will be expanded once the replicas are fully caught up. Other than that, the expected value for both ISR shrink rate and expansion rate is 0.
ISR expansion rate | kafka.server:type=ReplicaManager,name=IsrExpandsPerSec | See above
Max lag in messages btw follower and leader replicas | kafka.server:type=ReplicaFetcherManager,name=MaxLag,clientId=Replica | lag should be proportional to the maximum batch size of a produce request.
Lag in messages per follower replica | kafka.server:type=FetcherLagMetrics,name=ConsumerLag,clientId=([-.\w]+),topic=([-.\w]+),partition=([0-9]+) | lag should be proportional to the maximum batch size of a produce request.
Requests waiting in the producer purgatory | kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Produce | non-zero if ack=-1 is used
Requests waiting in the fetch purgatory | kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Fetch | size depends on fetch.wait.max.ms in the consumer
Request total time | kafka.network:type=RequestMetrics,name=TotalTimeMs,request={Produce|FetchConsumer|FetchFollower} | broken into queue, local, remote and response send time
Time the request waits in the request queue | kafka.network:type=RequestMetrics,name=RequestQueueTimeMs,request={Produce|FetchConsumer|FetchFollower} |
Time the request is processed at the leader | kafka.network:type=RequestMetrics,name=LocalTimeMs,request={Produce|FetchConsumer|FetchFollower} |
Time the request waits for the follower | kafka.network:type=RequestMetrics,name=RemoteTimeMs,request={Produce|FetchConsumer|FetchFollower} | non-zero for produce requests when ack=-1
Time the request waits in the response queue | kafka.network:type=RequestMetrics,name=ResponseQueueTimeMs,request={Produce|FetchConsumer|FetchFollower} |
Time to send the response | kafka.network:type=RequestMetrics,name=ResponseSendTimeMs,request={Produce|FetchConsumer|FetchFollower} |
Number of messages the consumer lags behind the producer by | kafka.consumer:type=ConsumerFetcherManager,name=MaxLag,clientId=([-.\w]+) |
The average fraction of time the network processors are idle | kafka.network:type=SocketServer,name=NetworkProcessorAvgIdlePercent | between 0 and 1, ideally > 0.3
The average fraction of time the request handler threads are idle | kafka.server:type=KafkaRequestHandlerPool,name=RequestHandlerAvgIdlePercent | between 0 and 1, ideally > 0.3
Quota metrics per (user, client-id), user or client-id | kafka.server:type={Produce|Fetch},user=([-.\w]+),client-id=([-.\w]+) | Two attributes. throttle-time indicates the amount of time in ms the client was throttled. Ideally = 0. byte-rate indicates the data produce/consume rate of the client in bytes/sec. For (user, client-id) quotas, both user and client-id are specified. If per-client-id quota is applied to the client, user is not specified. If per-user quota is applied, client-id is not specified.

Common monitoring metrics for producer/consumer/connect

The following metrics are available on producer/consumer/connector instances. For specific metrics, please see following sections. Metric/Attribute name	Description	Mbean name
connection-close-rate	Connections closed per second in the window.	kafka.[producer
connection-creation-rate	New connections established per second in the window.	kafka.[producer
network-io-rate	The average number of network operations (reads or writes) on all connections per second.	kafka.[producer
outgoing-byte-rate	The average number of outgoing bytes sent per second to all servers.	kafka.[producer
request-rate	The average number of requests sent per second.	kafka.[producer
request-size-avg	The average size of all requests in the window.	kafka.[producer
request-size-max	The maximum size of any request sent in the window.	kafka.[producer
incoming-byte-rate	Bytes/second read off all sockets.	kafka.[producer
response-rate	Responses received sent per second.	kafka.[producer
select-rate	Number of times the I/O layer checked for new I/O to perform per second.	kafka.[producer
io-wait-time-ns-avg	The average length of time the I/O thread spent waiting for a socket ready for reads or writes in nanoseconds.	kafka.[producer
io-wait-ratio	The fraction of time the I/O thread spent waiting.	kafka.[producer
io-time-ns-avg	The average length of time for I/O per select call in nanoseconds.	kafka.[producer
io-ratio	The fraction of time the I/O thread spent doing I/O.	kafka.[producer
connection-count	The current number of active connections.	kafka.[producer

Common Per-broker metrics for producer/consumer/connect

The following metrics are available on producer/consumer/connector instances. For specific metrics, please see following sections. Metric/Attribute name	Description	Mbean name
outgoing-byte-rate	The average number of outgoing bytes sent per second for a node.	kafka.producer:type=[consumer
request-rate	The average number of requests sent per second for a node.	kafka.producer:type=[consumer
request-size-avg	The average size of all requests in the window for a node.	kafka.producer:type=[consumer
request-size-max	The maximum size of any request sent in the window for a node.	kafka.producer:type=[consumer
incoming-byte-rate	The average number of responses received per second for a node.	kafka.producer:type=[consumer
request-latency-avg	The average request latency in ms for a node.	kafka.producer:type=[consumer
request-latency-max	The maximum request latency in ms for a node.	kafka.producer:type=[consumer
response-rate	Responses received sent per second for a node.	kafka.producer:type=[consumer

Producer monitoring

The following metrics are available on producer instances. Metric/Attribute name	Description	Mbean name
waiting-threads	The number of user threads blocked waiting for buffer memory to enqueue their records.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
buffer-total-bytes	The maximum amount of buffer memory the client can use (whether or not it is currently used).	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
buffer-available-bytes	The total amount of buffer memory that is not being used (either unallocated or in the free list).	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
bufferpool-wait-time	The fraction of time an appender waits for space allocation.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
batch-size-avg	The average number of bytes sent per partition per-request.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
batch-size-max	The max number of bytes sent per partition per-request.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
compression-rate-avg	The average compression rate of record batches.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
record-queue-time-avg	The average time in ms record batches spent in the record accumulator.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
record-queue-time-max	The maximum time in ms record batches spent in the record accumulator.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
request-latency-avg	The average request latency in ms.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
request-latency-max	The maximum request latency in ms.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
record-send-rate	The average number of records sent per second.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
records-per-request-avg	The average number of records per request.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
record-retry-rate	The average per-second number of retried record sends.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
record-error-rate	The average per-second number of record sends that resulted in errors.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
record-size-max	The maximum record size.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
record-size-avg	The average record size.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
requests-in-flight	The current number of in-flight requests awaiting a response.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
metadata-age	The age in seconds of the current producer metadata being used.	kafka.producer:type=producer-metrics,client-id=([-.\w]+)
record-send-rate	The average number of records sent per second for a topic.	kafka.producer:type=producer-topic-metrics,client-id=([-.\w]+),topic=([-.\w]+)
byte-rate	The average number of bytes sent per second for a topic.	kafka.producer:type=producer-topic-metrics,client-id=([-.\w]+),topic=([-.\w]+)
compression-rate	The average compression rate of record batches for a topic.	kafka.producer:type=producer-topic-metrics,client-id=([-.\w]+),topic=([-.\w]+)
record-retry-rate	The average per-second number of retried record sends for a topic.	kafka.producer:type=producer-topic-metrics,client-id=([-.\w]+),topic=([-.\w]+)
record-error-rate	The average per-second number of record sends that resulted in errors for a topic.	kafka.producer:type=producer-topic-metrics,client-id=([-.\w]+),topic=([-.\w]+)
produce-throttle-time-max	The maximum time in ms a request was throttled by a broker.	kafka.producer:type=producer-topic-metrics,client-id=([-.\w]+)
produce-throttle-time-avg	The average time in ms a request was throttled by a broker.	kafka.producer:type=producer-topic-metrics,client-id=([-.\w]+)

New consumer monitoring

The following metrics are available on new consumer instances.

Consumer Group Metrics

Metric/Attribute name	Description	Mbean name
commit-latency-avg	The average time taken for a commit request	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
commit-latency-max	The max time taken for a commit request	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
commit-rate	The number of commit calls per second	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
assigned-partitions	The number of partitions currently assigned to this consumer	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
heartbeat-response-time-max	The max time taken to receive a response to a heartbeat request	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
heartbeat-rate	The average number of heartbeats per second	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
join-time-avg	The average time taken for a group rejoin	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
join-time-max	The max time taken for a group rejoin	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
join-rate	The number of group joins per second	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
sync-time-avg	The average time taken for a group sync	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
sync-time-max	The max time taken for a group sync	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
sync-rate	The number of group syncs per second	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)
last-heartbeat-seconds-ago	The number of seconds since the last controller heartbeat	kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)

Consumer Fetch Metrics

Metric/Attribute name	Description	Mbean name
fetch-size-avg	The average number of bytes fetched per request	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
fetch-size-max	The maximum number of bytes fetched per request	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
bytes-consumed-rate	The average number of bytes consumed per second	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
records-per-request-avg	The average number of records in each request	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
records-consumed-rate	The average number of records consumed per second	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
fetch-latency-avg	The average time taken for a fetch request	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
fetch-latency-max	The max time taken for a fetch request	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
fetch-rate	The number of fetch requests per second	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
records-lag-max	The maximum lag in terms of number of records for any partition in this window	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
fetch-throttle-time-avg	The average throttle time in ms	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)
fetch-throttle-time-max	The maximum throttle time in ms	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+)

Topic-level Fetch Metrics

Metric/Attribute name	Description	Mbean name
fetch-size-avg	The average number of bytes fetched per request for a specific topic.	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+),topic=([-.\w]+)
fetch-size-max	The maximum number of bytes fetched per request for a specific topic.	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+),topic=([-.\w]+)
bytes-consumed-rate	The average number of bytes consumed per second for a specific topic.	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+),topic=([-.\w]+)
records-per-request-avg	The average number of records in each request for a specific topic.	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+),topic=([-.\w]+)
records-consumed-rate	The average number of records consumed per second for a specific topic.	kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-.\w]+),topic=([-.\w]+)

Others

We recommend monitoring GC time and other stats and various server stats such as CPU utilization, I/O service time, etc. On the client side, we recommend monitoring the message/byte rate (global and per topic), request rate/size/time, and on the consumer side, max lag in messages among all partitions and min fetch request rate. For a consumer to keep up, max lag needs to be less than a threshold and min fetch rate needs to be larger than 0.

Audit

The final alerting we do is on the correctness of the data delivery. We audit that every message that is sent is consumed by all consumers and measure the lag for this to occur. For important topics we alert if a certain completeness is not achieved in a certain time period. The details of this are discussed in KAFKA-260.

6.7 - ZooKeeper

ZooKeeper

Stable version

The current stable branch is 3.4 and the latest release of that branch is 3.4.8, which is the one ZkClient 0.9 uses. ZkClient is the client layer Kafka uses to interact with ZooKeeper.

Operationalizing ZooKeeper

Operationally, we do the following for a healthy ZooKeeper installation:

Redundancy in the physical/hardware/network layout: try not to put them all in the same rack, decent (but don’t go nuts) hardware, try to keep redundant power and network paths, etc. A typical ZooKeeper ensemble has 5 or 7 servers, which tolerates 2 and 3 servers down, respectively. If you have a small deployment, then using 3 servers is acceptable, but keep in mind that you’ll only be able to tolerate 1 server down in this case.
I/O segregation: if you do a lot of write type traffic you’ll almost definitely want the transaction logs on a dedicated disk group. Writes to the transaction log are synchronous (but batched for performance), and consequently, concurrent writes can significantly affect performance. ZooKeeper snapshots can be one such a source of concurrent writes, and ideally should be written on a disk group separate from the transaction log. Snapshots are written to disk asynchronously, so it is typically ok to share with the operating system and message log files. You can configure a server to use a separate disk group with the dataLogDir parameter.
Application segregation: Unless you really understand the application patterns of other apps that you want to install on the same box, it can be a good idea to run ZooKeeper in isolation (though this can be a balancing act with the capabilities of the hardware).
Use care with virtualization: It can work, depending on your cluster layout and read/write patterns and SLAs, but the tiny overheads introduced by the virtualization layer can add up and throw off ZooKeeper, as it can be very time sensitive
ZooKeeper configuration: It’s java, make sure you give it ’enough’ heap space (We usually run them with 3-5G, but that’s mostly due to the data set size we have here). Unfortunately we don’t have a good formula for it, but keep in mind that allowing for more ZooKeeper state means that snapshots can become large, and large snapshots affect recovery time. In fact, if the snapshot becomes too large (a few gigabytes), then you may need to increase the initLimit parameter to give enough time for servers to recover and join the ensemble.
Monitoring: Both JMX and the 4 letter words (4lw) commands are very useful, they do overlap in some cases (and in those cases we prefer the 4 letter commands, they seem more predictable, or at the very least, they work better with the LI monitoring infrastructure)
Don’t overbuild the cluster: large clusters, especially in a write heavy usage pattern, means a lot of intracluster communication (quorums on the writes and subsequent cluster member updates), but don’t underbuild it (and risk swamping the cluster). Having more servers adds to your read capacity.

Overall, we try to keep the ZooKeeper system as small as will handle the load (plus standard growth capacity planning) and as simple as possible. We try not to do anything fancy with the configuration or application layout as compared to the official release as well as keep it as self contained as possible. For these reasons, we tend to skip the OS packaged versions, since it has a tendency to try to put things in the OS standard hierarchy, which can be ‘messy’, for want of a better way to word it.

7 - Security

7.1 - Security Overview

Security Overview

In release 0.9.0.0, the Kafka community added a number of features that, used either separately or together, increases security in a Kafka cluster. These features are considered to be of beta quality. The following security measures are currently supported:

Authentication of connections to brokers from clients (producers and consumers), other brokers and tools, using either SSL or SASL (Kerberos). SASL/PLAIN can also be used from release 0.10.0.0 onwards.
Authentication of connections from brokers to ZooKeeper
Encryption of data transferred between brokers and clients, between brokers, or between brokers and tools using SSL (Note that there is a performance degradation when SSL is enabled, the magnitude of which depends on the CPU type and the JVM implementation.)
Authorization of read / write operations by clients
Authorization is pluggable and integration with external authorization services is supported

It’s worth noting that security is optional - non-secured clusters are supported, as well as a mix of authenticated, unauthenticated, encrypted and non-encrypted clients. The guides below explain how to configure and use the security features in both clients and brokers.

7.2 - Encryption and Authentication using SSL

Encryption and Authentication using SSL

Apache Kafka allows clients to connect over SSL. By default, SSL is disabled but can be turned on as needed.

Generate SSL key and certificate for each Kafka broker

The first step of deploying HTTPS is to generate the key and the certificate for each machine in the cluster. You can use Java’s keytool utility to accomplish this task. We will generate the key into a temporary keystore initially so that we can export and sign it later with CA.

                keytool -keystore server.keystore.jks -alias localhost -validity {validity} -genkey

You need to specify two parameters in the above command: 1. keystore: the keystore file that stores the certificate. The keystore file contains the private key of the certificate; therefore, it needs to be kept safely. 2. validity: the valid time of the certificate in days.

Note: By default the property ssl.endpoint.identification.algorithm is not defined, so hostname verification is not performed. In order to enable hostname verification, set the following property:

    	ssl.endpoint.identification.algorithm=HTTPS

Once enabled, clients will verify the server’s fully qualified domain name (FQDN) against one of the following two fields: 1. Common Name (CN) 2. Subject Alternative Name (SAN)
Both fields are valid, RFC-2818 recommends the use of SAN however. SAN is also more flexible, allowing for multiple DNS entries to be declared. Another advantage is that the CN can be set to a more meaningful value for authorization purposes. To add a SAN field append the following argument -ext SAN=DNS:{FQDN} to the keytool command:

            keytool -keystore server.keystore.jks -alias localhost -validity {validity} -genkey -ext SAN=DNS:{FQDN}

The following command can be run afterwards to verify the contents of the generated certificate:

            keytool -list -v -keystore server.keystore.jks

Creating your own CA

After the first step, each machine in the cluster has a public-private key pair, and a certificate to identify the machine. The certificate, however, is unsigned, which means that an attacker can create such a certificate to pretend to be any machine.

Therefore, it is important to prevent forged certificates by signing them for each machine in the cluster. A certificate authority (CA) is responsible for signing certificates. CA works likes a government that issues passports—the government stamps (signs) each passport so that the passport becomes difficult to forge. Other governments verify the stamps to ensure the passport is authentic. Similarly, the CA signs the certificates, and the cryptography guarantees that a signed certificate is computationally difficult to forge. Thus, as long as the CA is a genuine and trusted authority, the clients have high assurance that they are connecting to the authentic machines.

                openssl req **-new** -x509 -keyout ca-key -out ca-cert -days 365

The generated CA is simply a public-private key pair and certificate, and it is intended to sign other certificates.
The next step is to add the generated CA to the clients’ truststore so that the clients can trust this CA:

                keytool -keystore client.truststore.jks -alias CARoot -import -file ca-cert

Note: If you configure the Kafka brokers to require client authentication by setting ssl.client.auth to be “requested” or “required” on the Kafka brokers config then you must provide a truststore for the Kafka brokers as well and it should have all the CA certificates that clients’ keys were signed by.

                keytool -keystore server.truststore.jks -alias CARoot **-import** -file ca-cert

In contrast to the keystore in step 1 that stores each machine’s own identity, the truststore of a client stores all the certificates that the client should trust. Importing a certificate into one’s truststore also means trusting all certificates that are signed by that certificate. As the analogy above, trusting the government (CA) also means trusting all passports (certificates) that it has issued. This attribute is called the chain of trust, and it is particularly useful when deploying SSL on a large Kafka cluster. You can sign all certificates in the cluster with a single CA, and have all machines share the same truststore that trusts the CA. That way all machines can authenticate all other machines. 3. #### Signing the certificate

The next step is to sign all certificates generated by step 1 with the CA generated in step 2. First, you need to export the certificate from the keystore:

                keytool -keystore server.keystore.jks -alias localhost -certreq -file cert-file

Then sign it with the CA:

                openssl x509 -req -CA ca-cert -CAkey ca-key -in cert-file -out cert-signed -days {validity} -CAcreateserial -passin pass:{ca-password}

Finally, you need to import both the certificate of the CA and the signed certificate into the keystore:

                keytool -keystore server.keystore.jks -alias CARoot -import -file ca-cert
            keytool -keystore server.keystore.jks -alias localhost -import -file cert-signed

The definitions of the parameters are the following: 1. keystore: the location of the keystore 2. ca-cert: the certificate of the CA 3. ca-key: the private key of the CA 4. ca-password: the passphrase of the CA 5. cert-file: the exported, unsigned certificate of the server 6. cert-signed: the signed certificate of the server Here is an example of a bash script with all above steps. Note that one of the commands assumes a password of test1234, so either use that password or edit the command before running it.

                #!/bin/bash
            #Step 1
            keytool -keystore server.keystore.jks -alias localhost -validity 365 -keyalg RSA -genkey
            #Step 2
            openssl req -new -x509 -keyout ca-key -out ca-cert -days 365
            keytool -keystore server.truststore.jks -alias CARoot -import -file ca-cert
            keytool -keystore client.truststore.jks -alias CARoot -import -file ca-cert
            #Step 3
            keytool -keystore server.keystore.jks -alias localhost -certreq -file cert-file
            openssl x509 -req -CA ca-cert -CAkey ca-key -in cert-file -out cert-signed -days 365 -CAcreateserial -passin pass:test1234
            keytool -keystore server.keystore.jks -alias CARoot -import -file ca-cert
            keytool -keystore server.keystore.jks -alias localhost -import -file cert-signed

Configuring Kafka Brokers

Kafka Brokers support listening for connections on multiple ports. We need to configure the following property in server.properties, which must have one or more comma-separated values:

    listeners

If SSL is not enabled for inter-broker communication (see below for how to enable it), both PLAINTEXT and SSL ports will be necessary.

                listeners=PLAINTEXT://host.name:port,SSL://host.name:port

Following SSL configs are needed on the broker side

                ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
            ssl.keystore.password=test1234
            ssl.key.password=test1234
            ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
            ssl.truststore.password=test1234

Optional settings that are worth considering: 1. ssl.client.auth=none (“required” => client authentication is required, “requested” => client authentication is requested and client without certs can still connect. The usage of “requested” is discouraged as it provides a false sense of security and misconfigured clients will still connect successfully.) 2. ssl.cipher.suites (Optional). A cipher suite is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. (Default is an empty list) 3. ssl.enabled.protocols=TLSv1.2,TLSv1.1,TLSv1 (list out the SSL protocols that you are going to accept from clients. Do note that SSL is deprecated in favor of TLS and using SSL in production is not recommended) 4. ssl.keystore.type=JKS 5. ssl.truststore.type=JKS 6. ssl.secure.random.implementation=SHA1PRNG If you want to enable SSL for inter-broker communication, add the following to the broker properties file (it defaults to PLAINTEXT)

                security.inter.broker.protocol=SSL

Due to import regulations in some countries, the Oracle implementation limits the strength of cryptographic algorithms available by default. If stronger algorithms are needed (for example, AES with 256-bit keys), the JCE Unlimited Strength Jurisdiction Policy Files must be obtained and installed in the JDK/JRE. See the JCA Providers Documentation for more information.

The JRE/JDK will have a default pseudo-random number generator (PRNG) that is used for cryptography operations, so it is not required to configure the implementation used with the

    ssl.secure.random.implementation

. However, there are performance issues with some implementations (notably, the default chosen on Linux systems,

    NativePRNG

, utilizes a global lock). In cases where performance of SSL connections becomes an issue, consider explicitly setting the implementation to be used. The

    SHA1PRNG

implementation is non-blocking, and has shown very good performance characteristics under heavy load (50 MB/sec of produced messages, plus replication traffic, per-broker).

Once you start the broker you should be able to see in the server.log

                with addresses: PLAINTEXT -> EndPoint(192.168.64.1,9092,PLAINTEXT),SSL -> EndPoint(192.168.64.1,9093,SSL)

To check quickly if the server keystore and truststore are setup properly you can run the following command

    openssl s_client -debug -connect localhost:9093 -tls1

(Note: TLSv1 should be listed under ssl.enabled.protocols)
In the output of this command you should see server’s certificate:

If the certificate does not show up or if there are any other error messages then your keystore is not setup properly. 5. #### Configuring Kafka Clients

SSL is supported only for the new Kafka Producer and Consumer, the older API is not supported. The configs for SSL will be the same for both producer and consumer.
If client authentication is not required in the broker, then the following is a minimal configuration example:

                security.protocol=SSL
            ssl.truststore.location=/var/private/ssl/kafka.client.truststore.jks
            ssl.truststore.password=test1234

If client authentication is required, then a keystore must be created like in step 1 and the following must also be configured:

                ssl.keystore.location=/var/private/ssl/kafka.client.keystore.jks
            ssl.keystore.password=test1234
            ssl.key.password=test1234

Other configuration settings that may also be needed depending on our requirements and the broker configuration: 1. ssl.provider (Optional). The name of the security provider used for SSL connections. Default value is the default security provider of the JVM. 2. ssl.cipher.suites (Optional). A cipher suite is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. 3. ssl.enabled.protocols=TLSv1.2,TLSv1.1,TLSv1. It should list at least one of the protocols configured on the broker side 4. ssl.truststore.type=JKS 5. ssl.keystore.type=JKS

Examples using console-producer and console-consumer:

                kafka-console-producer.sh --broker-list localhost:9093 --topic test --producer.config client-ssl.properties
            kafka-console-consumer.sh --bootstrap-server localhost:9093 --topic test --consumer.config client-ssl.properties

7.3 - Authentication using SASL

Authentication using SASL

SASL configuration for Kafka brokers

1. Select one or more supported mechanisms to enable in the broker. `GSSAPI` and `PLAIN` are the mechanisms currently supported in Kafka.
2. Add a JAAS config file for the selected mechanisms as described in the examples for setting up GSSAPI (Kerberos) or PLAIN.
3. Pass the JAAS config file location as JVM parameter to each Kafka broker. For example: 
    
                -Djava.security.auth.login.config=/etc/kafka/kafka_server_jaas.conf

4. Configure a SASL port in server.properties, by adding at least one of SASL_PLAINTEXT or SASL_SSL to the _listeners_ parameter, which contains one or more comma-separated values: 
    
                listeners=SASL_PLAINTEXT://host.name:port

If SASL_SSL is used, then SSL must also be configured. If you are only configuring a SASL port (or if you want the Kafka brokers to authenticate each other using SASL) then make sure you set the same SASL protocol for inter-broker communication:

                security.inter.broker.protocol=SASL_PLAINTEXT (or SASL_SSL)

5. Enable one or more SASL mechanisms in server.properties: 
    
                sasl.enabled.mechanisms=GSSAPI (,PLAIN)

6. Configure the SASL mechanism for inter-broker communication in server.properties if using SASL for inter-broker communication: 
    
                sasl.mechanism.inter.broker.protocol=GSSAPI (or PLAIN)

7. Follow the steps in GSSAPI (Kerberos) or PLAIN to configure SASL for the enabled mechanisms. To enable multiple mechanisms in the broker, follow the steps here.

Important notes: 1. KafkaServer is the section name in the JAAS file used by each KafkaServer/Broker. This section provides SASL configuration options for the broker including any SASL client connections made by the broker for inter-broker communication. 2. Client section is used to authenticate a SASL connection with zookeeper. It also allows the brokers to set SASL ACL on zookeeper nodes which locks these nodes down so that only the brokers can modify it. It is necessary to have the same principal name across all brokers. If you want to use a section name other than Client, set the system property zookeeper.sasl.client to the appropriate name (e.g. , -Dzookeeper.sasl.client=ZkClient). 3. ZooKeeper uses “zookeeper” as the service name by default. If you want to change this, set the system property zookeeper.sasl.client.username to the appropriate name (e.g. , -Dzookeeper.sasl.client.username=zk). 2. #### SASL configuration for Kafka clients

SASL authentication is only supported for the new Java Kafka producer and consumer, the older API is not supported. To configure SASL authentication on the clients: 1. Select a SASL mechanism for authentication. 2. Add a JAAS config file for the selected mechanism as described in the examples for setting up GSSAPI (Kerberos) or PLAIN. KafkaClient is the section name in the JAAS file used by Kafka clients. 3. Pass the JAAS config file location as JVM parameter to each client JVM. For example:

                -Djava.security.auth.login.config=/etc/kafka/kafka_client_jaas.conf

4. Configure the following properties in producer.properties or consumer.properties: 
    
                security.protocol=SASL_PLAINTEXT (or SASL_SSL)
            sasl.mechanism=GSSAPI (or PLAIN)

5. Follow the steps in GSSAPI (Kerberos) or PLAIN to configure SASL for the selected mechanism.

Authentication using SASL/Kerberos

1. ##### Prerequisites

  1. **Kerberos**

If your organization is already using a Kerberos server (for example, by using Active Directory), there is no need to install a new server just for Kafka. Otherwise you will need to install one, your Linux vendor likely has packages for Kerberos and a short guide on how to install and configure it (Ubuntu, Redhat). Note that if you are using Oracle Java, you will need to download JCE policy files for your Java version and copy them to $JAVA_HOME/jre/lib/security. 2. Create Kerberos Principals
If you are using the organization’s Kerberos or Active Directory server, ask your Kerberos administrator for a principal for each Kafka broker in your cluster and for every operating system user that will access Kafka with Kerberos authentication (via clients and tools). If you have installed your own Kerberos, you will need to create these principals yourself using the following commands:

                            sudo /usr/sbin/kadmin.local -q 'addprinc -randkey kafka/{hostname}@{REALM}'
                sudo /usr/sbin/kadmin.local -q "ktadd -k /etc/security/keytabs/{keytabname}.keytab kafka/{hostname}@{REALM}"

  3. **Make sure all hosts can be reachable using hostnames** \- it is a Kerberos requirement that all your hosts can be resolved with their FQDNs.
2. ##### Configuring Kafka Brokers

  1. Add a suitably modified JAAS file similar to the one below to each Kafka broker's config directory, let's call it kafka_server_jaas.conf for this example (note that each broker should have its own keytab): 
        
                            KafkaServer {
                    com.sun.security.auth.module.Krb5LoginModule required
                    useKeyTab=true
                    storeKey=true
                    keyTab="/etc/security/keytabs/kafka_server.keytab"
                    principal="kafka/kafka1.hostname.com@EXAMPLE.COM";
                };
        
                // Zookeeper client authentication
                Client {
                com.sun.security.auth.module.Krb5LoginModule required
                useKeyTab=true
                storeKey=true
                keyTab="/etc/security/keytabs/kafka_server.keytab"
                principal="kafka/kafka1.hostname.com@EXAMPLE.COM";
                };

KafkaServer section in the JAAS file tells the broker which principal to use and the location of the keytab where this principal is stored. It allows the broker to login using the keytab specified in this section. See notes for more details on Zookeeper SASL configuration. 2. Pass the JAAS and optionally the krb5 file locations as JVM parameters to each Kafka broker (see here for more details):

                        -Djava.security.krb5.conf=/etc/kafka/krb5.conf
                -Djava.security.auth.login.config=/etc/kafka/kafka_server_jaas.conf

  3. Make sure the keytabs configured in the JAAS file are readable by the operating system user who is starting kafka broker.
  4. Configure SASL port and SASL mechanisms in server.properties as described here.

For example:

                        listeners=SASL_PLAINTEXT://host.name:port
                security.inter.broker.protocol=SASL_PLAINTEXT
                sasl.mechanism.inter.broker.protocol=GSSAPI
                sasl.enabled.mechanisms=GSSAPI

We must also configure the service name in server.properties, which should match the principal name of the kafka brokers. In the above example, principal is “kafka/kafka1.hostname.com@EXAMPLE.com”, so:

                        sasl.kerberos.service.name=kafka

3. ##### Configuring Kafka Clients

To configure SASL authentication on the clients: 1. Clients (producers, consumers, connect workers, etc) will authenticate to the cluster with their own principal (usually with the same name as the user running the client), so obtain or create these principals as needed. Then create a JAAS file for each principal. The KafkaClient section describes how the clients like producer and consumer can connect to the Kafka Broker. The following is an example configuration for a client using a keytab (recommended for long-running processes):

                            KafkaClient {
                    com.sun.security.auth.module.Krb5LoginModule required
                    useKeyTab=true
                    storeKey=true
                    keyTab="/etc/security/keytabs/kafka_client.keytab"
                    principal="kafka-client-1@EXAMPLE.COM";
                };

For command-line utilities like kafka-console-consumer or kafka-console-producer, kinit can be used along with “useTicketCache=true” as in:

                            KafkaClient {
                    com.sun.security.auth.module.Krb5LoginModule required
                    useTicketCache=true;
                };

  2. Pass the JAAS and optionally krb5 file locations as JVM parameters to each client JVM (see [here](https://docs.oracle.com/javase/8/docs/technotes/guides/security/jgss/tutorials/KerberosReq.html) for more details): 
        
                        -Djava.security.krb5.conf=/etc/kafka/krb5.conf
                -Djava.security.auth.login.config=/etc/kafka/kafka_client_jaas.conf

  3. Make sure the keytabs configured in the kafka_client_jaas.conf are readable by the operating system user who is starting kafka client.
  4. Configure the following properties in producer.properties or consumer.properties: 
        
                        security.protocol=SASL_PLAINTEXT (or SASL_SSL)
                sasl.mechanism=GSSAPI
                sasl.kerberos.service.name=kafka

Authentication using SASL/PLAIN

SASL/PLAIN is a simple username/password authentication mechanism that is typically used with TLS for encryption to implement secure authentication. Kafka supports a default implementation for SASL/PLAIN which can be extended for production use as described here.

The username is used as the authenticated Principal for configuration of ACLs etc. 1. ##### Configuring Kafka Brokers

  1. Add a suitably modified JAAS file similar to the one below to each Kafka broker's config directory, let's call it kafka_server_jaas.conf for this example: 
        
                            KafkaServer {
                    org.apache.kafka.common.security.plain.PlainLoginModule required
                    username="admin"
                    password="admin-secret"
                    user_admin="admin-secret"
                    user_alice="alice-secret";
                };

This configuration defines two users (admin and alice). The properties username and password in the KafkaServer section are used by the broker to initiate connections to other brokers. In this example, admin is the user for inter-broker communication. The set of properties user__userName_ defines the passwords for all users that connect to the broker and the broker validates all client connections including those from other brokers using these properties. 2. Pass the JAAS config file location as JVM parameter to each Kafka broker:

                        -Djava.security.auth.login.config=/etc/kafka/kafka_server_jaas.conf

  3. Configure SASL port and SASL mechanisms in server.properties as described here.

For example:

                        listeners=SASL_SSL://host.name:port
                security.inter.broker.protocol=SASL_SSL
                sasl.mechanism.inter.broker.protocol=PLAIN
                sasl.enabled.mechanisms=PLAIN

2. ##### Configuring Kafka Clients

To configure SASL authentication on the clients: 1. The KafkaClient section describes how the clients like producer and consumer can connect to the Kafka Broker. The following is an example configuration for a client for the PLAIN mechanism:

                            KafkaClient {
                    org.apache.kafka.common.security.plain.PlainLoginModule required
                    username="alice"
                    password="alice-secret";
                };

The properties username and password in the KafkaClient section are used by clients to configure the user for client connections. In this example, clients connect to the broker as user alice. 2. Pass the JAAS config file location as JVM parameter to each client JVM:

                        -Djava.security.auth.login.config=/etc/kafka/kafka_client_jaas.conf

  3. Configure the following properties in producer.properties or consumer.properties: 
        
                        security.protocol=SASL_SSL
                sasl.mechanism=PLAIN

3. ##### Use of SASL/PLAIN in production

   * SASL/PLAIN should be used only with SSL as transport layer to ensure that clear passwords are not transmitted on the wire without encryption.
   * The default implementation of SASL/PLAIN in Kafka specifies usernames and passwords in the JAAS configuration file as shown here. To avoid storing passwords on disk, you can plug in your own implementation of `javax.security.auth.spi.LoginModule` that provides usernames and passwords from an external source. The login module implementation should provide username as the public credential and password as the private credential of the `Subject`. The default implementation `org.apache.kafka.common.security.plain.PlainLoginModule` can be used as an example.
   * In production systems, external authentication servers may implement password authentication. Kafka brokers can be integrated with these servers by adding your own implementation of `javax.security.sasl.SaslServer`. The default implementation included in Kafka in the package `org.apache.kafka.common.security.plain` can be used as an example to get started. 
     * New providers must be installed and registered in the JVM. Providers can be installed by adding provider classes to the normal `CLASSPATH` or bundled as a jar file and added to `_JAVA_HOME_ /lib/ext`.
     * Providers can be registered statically by adding a provider to the security properties file `_JAVA_HOME_ /lib/security/java.security`. 
            
                                security.provider.n=providerClassName

where providerClassName is the fully qualified name of the new provider and n is the preference order with lower numbers indicating higher preference. * Alternatively, you can register providers dynamically at runtime by invoking Security.addProvider at the beginning of the client application or in a static initializer in the login module. For example:

                                Security.addProvider(new PlainSaslServerProvider());

     * For more details, see [JCA Reference](http://docs.oracle.com/javase/8/docs/technotes/guides/security/crypto/CryptoSpec.html).

Enabling multiple SASL mechanisms in a broker

1. Specify configuration for the login modules of all enabled mechanisms in the `KafkaServer` section of the JAAS config file. For example: 
    
                    KafkaServer {
                com.sun.security.auth.module.Krb5LoginModule required
                useKeyTab=true
                storeKey=true
                keyTab="/etc/security/keytabs/kafka_server.keytab"
                principal="kafka/kafka1.hostname.com@EXAMPLE.COM";
    
                org.apache.kafka.common.security.plain.PlainLoginModule required
                username="admin"
                password="admin-secret"
                user_admin="admin-secret"
                user_alice="alice-secret";
            };

2. Enable the SASL mechanisms in server.properties: 
    
                sasl.enabled.mechanisms=GSSAPI,PLAIN

3. Specify the SASL security protocol and mechanism for inter-broker communication in server.properties if required: 
    
                security.inter.broker.protocol=SASL_PLAINTEXT (or SASL_SSL)
            sasl.mechanism.inter.broker.protocol=GSSAPI (or PLAIN)

4. Follow the mechanism-specific steps in GSSAPI (Kerberos) and PLAIN to configure SASL for the enabled mechanisms.

Modifying SASL mechanism in a Running Cluster

SASL mechanism can be modified in a running cluster using the following sequence:

1. Enable new SASL mechanism by adding the mechanism to `sasl.enabled.mechanisms` in server.properties for each broker. Update JAAS config file to include both mechanisms as described here. Incrementally bounce the cluster nodes.
2. Restart clients using the new mechanism.
3. To change the mechanism of inter-broker communication (if this is required), set `sasl.mechanism.inter.broker.protocol` in server.properties to the new mechanism and incrementally bounce the cluster again.
4. To remove old mechanism (if this is required), remove the old mechanism from `sasl.enabled.mechanisms` in server.properties and remove the entries for the old mechanism from JAAS config file. Incrementally bounce the cluster again.

7.4 - Authorization and ACLs

Authorization and ACLs

Kafka ships with a pluggable Authorizer and an out-of-box authorizer implementation that uses zookeeper to store all the acls. Kafka acls are defined in the general format of “Principal P is [Allowed/Denied] Operation O From Host H On Resource R”. You can read more about the acl structure on KIP-11. In order to add, remove or list acls you can use the Kafka authorizer CLI. By default, if a Resource R has no associated acls, no one other than super users is allowed to access R. If you want to change that behavior, you can include the following in broker.properties.

allow.everyone.if.no.acl.found=true

One can also add super users in broker.properties like the following (note that the delimiter is semicolon since SSL user names may contain comma).

super.users=User:Bob;User:Alice

By default, the SSL user name will be of the form “CN=writeuser,OU=Unknown,O=Unknown,L=Unknown,ST=Unknown,C=Unknown”. One can change that by setting a customized PrincipalBuilder in broker.properties like the following.

principal.builder.class=CustomizedPrincipalBuilderClass

By default, the SASL user name will be the primary part of the Kerberos principal. One can change that by setting sasl.kerberos.principal.to.local.rules to a customized rule in broker.properties. The format of sasl.kerberos.principal.to.local.rules is a list where each rule works in the same way as the auth_to_local in Kerberos configuration file (krb5.conf). Each rules starts with RULE: and contains an expression in the format n:strings/pattern/replacement/g. See the kerberos documentation for more details. An example of adding a rule to properly translate user@MYDOMAIN.COM to user while also keeping the default rule in place is:

sasl.kerberos.principal.to.local.rules=RULE:[1:$1@$0](.*@MYDOMAIN.COM)s/@.*//,DEFAULT

Command Line Interface

Kafka Authorization management CLI can be found under bin directory with all the other CLIs. The CLI script is called kafka-acls.sh. Following lists all the options that the script supports:

Option	Description	Default	Option type
--add	Indicates to the script that user is trying to add an acl.		Action
--remove	Indicates to the script that user is trying to remove an acl.		Action
--list	Indicates to the script that user is trying to list acls.		Action
--authorizer	Fully qualified class name of the authorizer.	kafka.security.auth.SimpleAclAuthorizer	Configuration
--authorizer-properties	key=val pairs that will be passed to authorizer for initialization. For the default authorizer the example values are: zookeeper.connect=localhost:2181		Configuration
--cluster	Specifies cluster as resource.		Resource
--topic [topic-name]	Specifies the topic as resource.		Resource
--group [group-name]	Specifies the consumer-group as resource.		Resource
--allow-principal	Principal is in PrincipalType:name format that will be added to ACL with Allow permission.
You can specify multiple –allow-principal in a single command.		Principal
--deny-principal	Principal is in PrincipalType:name format that will be added to ACL with Deny permission.
You can specify multiple –deny-principal in a single command.		Principal
--allow-host	IP address from which principals listed in –allow-principal will have access.	if –allow-principal is specified defaults to * which translates to “all hosts”	Host
--deny-host	IP address from which principals listed in –deny-principal will be denied access.	if –deny-principal is specified defaults to * which translates to “all hosts”	Host
--operation	Operation that will be allowed or denied.
Valid values are : Read, Write, Create, Delete, Alter, Describe, ClusterAction, All	All	Operation
--producer	Convenience option to add/remove acls for producer role. This will generate acls that allows WRITE, DESCRIBE on topic and CREATE on cluster.		Convenience
--consumer	Convenience option to add/remove acls for consumer role. This will generate acls that allows READ, DESCRIBE on topic and READ on consumer-group.		Convenience
--force	Convenience option to assume yes to all queries and do not prompt.		Convenience

Examples

Adding Acls
Suppose you want to add an acl “Principals User:Bob and User:Alice are allowed to perform Operation Read and Write on Topic Test-Topic from IP 198.51.100.0 and IP 198.51.100.1”. You can do that by executing the CLI with following options:
```
bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --add --allow-principal User:Bob --allow-principal User:Alice --allow-host 198.51.100.0 --allow-host 198.51.100.1 --operation Read --operation Write --topic Test-topic
```

By default, all principals that don’t have an explicit acl that allows access for an operation to a resource are denied. In rare cases where an allow acl is defined that allows access to all but some principal we will have to use the –deny-principal and –deny-host option. For example, if we want to allow all users to Read from Test-topic but only deny User:BadBob from IP 198.51.100.3 we can do so using following commands:

    bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --add --allow-principal User:* --allow-host * --deny-principal User:BadBob --deny-host 198.51.100.3 --operation Read --topic Test-topic

Note that --allow-host and deny-host only support IP addresses (hostnames are not supported). Above examples add acls to a topic by specifying –topic [topic-name] as the resource option. Similarly user can add acls to cluster by specifying –cluster and to a consumer group by specifying –group [group-name].

Removing Acls
Removing acls is pretty much the same. The only difference is instead of –add option users will have to specify –remove option. To remove the acls added by the first example above we can execute the CLI with following options:

 bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --remove --allow-principal User:Bob --allow-principal User:Alice --allow-host 198.51.100.0 --allow-host 198.51.100.1 --operation Read --operation Write --topic Test-topic

List Acls
We can list acls for any resource by specifying the –list option with the resource. To list all acls for Test-topic we can execute the CLI with following options:
```
bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --list --topic Test-topic
```
Adding or removing a principal as producer or consumer
The most common use case for acl management are adding/removing a principal as producer or consumer so we added convenience options to handle these cases. In order to add User:Bob as a producer of Test-topic we can execute the following command:
```
 bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --add --allow-principal User:Bob --producer --topic Test-topic
```

Similarly to add Alice as a consumer of Test-topic with consumer group Group-1 we just have to pass –consumer option:

     bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --add --allow-principal User:Bob --consumer --topic test-topic --group Group-1

Note that for consumer option we must also specify the consumer group. In order to remove a principal from producer or consumer role we just need to pass –remove option.

7.5 - Incorporating Security Features in a Running Cluster

Incorporating Security Features in a Running Cluster

You can secure a running cluster via one or more of the supported protocols discussed previously. This is done in phases:

Incrementally bounce the cluster nodes to open additional secured port(s).
Restart clients using the secured rather than PLAINTEXT port (assuming you are securing the client-broker connection).
Incrementally bounce the cluster again to enable broker-to-broker security (if this is required)
A final incremental bounce to close the PLAINTEXT port.

The specific steps for configuring SSL and SASL are described in sections 7.2 and 7.3. Follow these steps to enable security for your desired protocol(s).

The security implementation lets you configure different protocols for both broker-client and broker-broker communication. These must be enabled in separate bounces. A PLAINTEXT port must be left open throughout so brokers and/or clients can continue to communicate.

When performing an incremental bounce stop the brokers cleanly via a SIGTERM. It’s also good practice to wait for restarted replicas to return to the ISR list before moving onto the next node.

As an example, say we wish to encrypt both broker-client and broker-broker communication with SSL. In the first incremental bounce, a SSL port is opened on each node:

            listeners=PLAINTEXT://broker1:9091,SSL://broker1:9092

We then restart the clients, changing their config to point at the newly opened, secured port:

            bootstrap.servers = [broker1:9092,...]
            security.protocol = SSL
            ...etc

In the second incremental server bounce we instruct Kafka to use SSL as the broker-broker protocol (which will use the same SSL port):

            listeners=PLAINTEXT://broker1:9091,SSL://broker1:9092
            security.inter.broker.protocol=SSL

In the final bounce we secure the cluster by closing the PLAINTEXT port:

            listeners=SSL://broker1:9092
            security.inter.broker.protocol=SSL

Alternatively we might choose to open multiple ports so that different protocols can be used for broker-broker and broker-client communication. Say we wished to use SSL encryption throughout (i.e. for broker-broker and broker-client communication) but we’d like to add SASL authentication to the broker-client connection also. We would achieve this by opening two additional ports during the first bounce:

            listeners=PLAINTEXT://broker1:9091,SSL://broker1:9092,SASL_SSL://broker1:9093

We would then restart the clients, changing their config to point at the newly opened, SASL & SSL secured port:

            bootstrap.servers = [broker1:9093,...]
            security.protocol = SASL_SSL
            ...etc

The second server bounce would switch the cluster to use encrypted broker-broker communication via the SSL port we previously opened on port 9092:

            listeners=PLAINTEXT://broker1:9091,SSL://broker1:9092,SASL_SSL://broker1:9093
            security.inter.broker.protocol=SSL

The final bounce secures the cluster by closing the PLAINTEXT port.

        listeners=SSL://broker1:9092,SASL_SSL://broker1:9093
        security.inter.broker.protocol=SSL

ZooKeeper can be secured independently of the Kafka cluster. The steps for doing this are covered in section 7.6.2.

7.6 - ZooKeeper Authentication

ZooKeeper Authentication

New clusters

To enable ZooKeeper authentication on brokers, there are two necessary steps:

Create a JAAS login file and set the appropriate system property to point to it as described above
Set the configuration property zookeeper.set.acl in each broker to true

The metadata stored in ZooKeeper for the Kafka cluster is world-readable, but can only be modified by the brokers. The rationale behind this decision is that the data stored in ZooKeeper is not sensitive, but inappropriate manipulation of that data can cause cluster disruption. We also recommend limiting the access to ZooKeeper via network segmentation (only brokers and some admin tools need access to ZooKeeper if the new Java consumer and producer clients are used).

Migrating clusters

If you are running a version of Kafka that does not support security or simply with security disabled, and you want to make the cluster secure, then you need to execute the following steps to enable ZooKeeper authentication with minimal disruption to your operations:

Perform a rolling restart setting the JAAS login file, which enables brokers to authenticate. At the end of the rolling restart, brokers are able to manipulate znodes with strict ACLs, but they will not create znodes with those ACLs
Perform a second rolling restart of brokers, this time setting the configuration parameter zookeeper.set.acl to true, which enables the use of secure ACLs when creating znodes
Execute the ZkSecurityMigrator tool. To execute the tool, there is this script: ./bin/zookeeper-security-migration.sh with zookeeper.acl set to secure. This tool traverses the corresponding sub-trees changing the ACLs of the znodes

It is also possible to turn off authentication in a secure cluster. To do it, follow these steps:

Perform a rolling restart of brokers setting the JAAS login file, which enables brokers to authenticate, but setting zookeeper.set.acl to false. At the end of the rolling restart, brokers stop creating znodes with secure ACLs, but are still able to authenticate and manipulate all znodes
Execute the ZkSecurityMigrator tool. To execute the tool, run this script ./bin/zookeeper-security-migration.sh with zookeeper.acl set to unsecure. This tool traverses the corresponding sub-trees changing the ACLs of the znodes
Perform a second rolling restart of brokers, this time omitting the system property that sets the JAAS login file

Here is an example of how to run the migration tool:

    ./bin/zookeeper-security-migration --zookeeper.acl=secure --zookeeper.connection=localhost:2181

Run this to see the full list of parameters:

    ./bin/zookeeper-security-migration --help

Migrating the ZooKeeper ensemble

It is also necessary to enable authentication on the ZooKeeper ensemble. To do it, we need to perform a rolling restart of the server and set a few properties. Please refer to the ZooKeeper documentation for more detail:

8 - Kafka Connect

8.1 - Overview

Overview

Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. It makes it simple to quickly define connectors that move large collections of data into and out of Kafka. Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. An export job can deliver data from Kafka topics into secondary storage and query systems or into batch systems for offline analysis. Kafka Connect features include:

A common framework for Kafka connectors - Kafka Connect standardizes integration of other data systems with Kafka, simplifying connector development, deployment, and management
Distributed and standalone modes - scale up to a large, centrally managed service supporting an entire organization or scale down to development, testing, and small production deployments
REST interface - submit and manage connectors to your Kafka Connect cluster via an easy to use REST API
Automatic offset management - with just a little information from connectors, Kafka Connect can manage the offset commit process automatically so connector developers do not need to worry about this error prone part of connector development
Distributed and scalable by default - Kafka Connect builds on the existing group management protocol. More workers can be added to scale up a Kafka Connect cluster.
Streaming/batch integration - leveraging Kafka’s existing capabilities, Kafka Connect is an ideal solution for bridging streaming and batch data systems

8.2 - User Guide

User Guide

The quickstart provides a brief example of how to run a standalone version of Kafka Connect. This section describes how to configure, run, and manage Kafka Connect in more detail.

Running Kafka Connect

Kafka Connect currently supports two modes of execution: standalone (single process) and distributed. In standalone mode all work is performed in a single process. This configuration is simpler to setup and get started with and may be useful in situations where only one worker makes sense (e.g. collecting log files), but it does not benefit from some of the features of Kafka Connect such as fault tolerance. You can start a standalone process with the following command:

    > bin/connect-standalone.sh config/connect-standalone.properties connector1.properties [connector2.properties ...]

The first parameter is the configuration for the worker. This includes settings such as the Kafka connection parameters, serialization format, and how frequently to commit offsets. The provided example should work well with a local cluster running with the default configuration provided by config/server.properties. It will require tweaking to use with a different configuration or production deployment. All workers (both standalone and distributed) require a few configs:

bootstrap.servers - List of Kafka servers used to bootstrap connections to Kafka
key.converter - Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the keys in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.
value.converter - Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the values in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.

The important configuration options specific to standalone mode are:

offset.storage.file.filename - File to store offset data in

The remaining parameters are connector configuration files. You may include as many as you want, but all will execute within the same process (on different threads). Distributed mode handles automatic balancing of work, allows you to scale up (or down) dynamically, and offers fault tolerance both in the active tasks and for configuration and offset commit data. Execution is very similar to standalone mode:

    > bin/connect-distributed.sh config/connect-distributed.properties

The difference is in the class which is started and the configuration parameters which change how the Kafka Connect process decides where to store configurations, how to assign work, and where to store offsets and task statues. In the distributed mode, Kafka Connect stores the offsets, configs and task statuses in Kafka topics. It is recommended to manually create the topics for offset, configs and statuses in order to achieve the desired the number of partitions and replication factors. If the topics are not yet created when starting Kafka Connect, the topics will be auto created with default number of partitions and replication factor, which may not be best suited for its usage. In particular, the following configuration parameters, in addition to the common settings mentioned above, are critical to set before starting your cluster:

group.id (default connect-cluster) - unique name for the cluster, used in forming the Connect cluster group; note that this must not conflict with consumer group IDs
config.storage.topic (default connect-configs) - topic to use for storing connector and task configurations; note that this should be a single partition, highly replicated, compacted topic. You may need to manually create the topic to ensure the correct configuration as auto created topics may have multiple partitions or be automatically configured for deletion rather than compaction
offset.storage.topic (default connect-offsets) - topic to use for storing offsets; this topic should have many partitions, be replicated, and be configured for compaction
status.storage.topic (default connect-status) - topic to use for storing statuses; this topic can have multiple partitions, and should be replicated and configured for compaction

Note that in distributed mode the connector configurations are not passed on the command line. Instead, use the REST API described below to create, modify, and destroy connectors.

Configuring Connectors

Connector configurations are simple key-value mappings. For standalone mode these are defined in a properties file and passed to the Connect process on the command line. In distributed mode, they will be included in the JSON payload for the request that creates (or modifies) the connector. Most configurations are connector dependent, so they can’t be outlined here. However, there are a few common options:

name - Unique name for the connector. Attempting to register again with the same name will fail.
connector.class - The Java class for the connector
tasks.max - The maximum number of tasks that should be created for this connector. The connector may create fewer tasks if it cannot achieve this level of parallelism.
key.converter - (optional) Override the default key converter set by the worker.
value.converter - (optional) Override the default value converter set by the worker.

The connector.class config supports several formats: the full name or alias of the class for this connector. If the connector is org.apache.kafka.connect.file.FileStreamSinkConnector, you can either specify this full name or use FileStreamSink or FileStreamSinkConnector to make the configuration a bit shorter. Sink connectors also have one additional option to control their input:

topics - A list of topics to use as input for this connector

For any other options, you should consult the documentation for the connector.

REST API

Since Kafka Connect is intended to be run as a service, it also provides a REST API for managing connectors. By default, this service runs on port 8083. The following are the currently supported endpoints:

GET /connectors - return a list of active connectors
POST /connectors - create a new connector; the request body should be a JSON object containing a string name field and an object config field with the connector configuration parameters
GET /connectors/{name} - get information about a specific connector
GET /connectors/{name}/config - get the configuration parameters for a specific connector
PUT /connectors/{name}/config - update the configuration parameters for a specific connector
GET /connectors/{name}/status - get current status of the connector, including if it is running, failed, paused, etc., which worker it is assigned to, error information if it has failed, and the state of all its tasks
GET /connectors/{name}/tasks - get a list of tasks currently running for a connector
GET /connectors/{name}/tasks/{taskid}/status - get current status of the task, including if it is running, failed, paused, etc., which worker it is assigned to, and error information if it has failed
PUT /connectors/{name}/pause - pause the connector and its tasks, which stops message processing until the connector is resumed
PUT /connectors/{name}/resume - resume a paused connector (or do nothing if the connector is not paused)
POST /connectors/{name}/restart - restart a connector (typically because it has failed)
POST /connectors/{name}/tasks/{taskId}/restart - restart an individual task (typically because it has failed)
DELETE /connectors/{name} - delete a connector, halting all tasks and deleting its configuration

Kafka Connect also provides a REST API for getting information about connector plugins:

GET /connector-plugins- return a list of connector plugins installed in the Kafka Connect cluster. Note that the API only checks for connectors on the worker that handles the request, which means you may see inconsistent results, especially during a rolling upgrade if you add new connector jars
PUT /connector-plugins/{connector-type}/config/validate - validate the provided configuration values against the configuration definition. This API performs per config validation, returns suggested values and error messages during validation.

8.3 - Connector Development Guide

Connector Development Guide

This guide describes how developers can write new connectors for Kafka Connect to move data between Kafka and other systems. It briefly reviews a few key concepts and then describes how to create a simple connector.

Core Concepts and APIs

Connectors and Tasks

To copy data between Kafka and another system, users create a Connector for the system they want to pull data from or push data to. Connectors come in two flavors: SourceConnectors import data from another system (e.g. JDBCSourceConnector would import a relational database into Kafka) and SinkConnectors export data (e.g. HDFSSinkConnector would export the contents of a Kafka topic to an HDFS file). Connectors do not perform any data copying themselves: their configuration describes the data to be copied, and the Connector is responsible for breaking that job into a set of Tasks that can be distributed to workers. These Tasks also come in two corresponding flavors: SourceTask and SinkTask. With an assignment in hand, each Task must copy its subset of the data to or from Kafka. In Kafka Connect, it should always be possible to frame these assignments as a set of input and output streams consisting of records with consistent schemas. Sometimes this mapping is obvious: each file in a set of log files can be considered a stream with each parsed line forming a record using the same schema and offsets stored as byte offsets in the file. In other cases it may require more effort to map to this model: a JDBC connector can map each table to a stream, but the offset is less clear. One possible mapping uses a timestamp column to generate queries incrementally returning new data, and the last queried timestamp can be used as the offset.

Streams and Records

Each stream should be a sequence of key-value records. Both the keys and values can have complex structure – many primitive types are provided, but arrays, objects, and nested data structures can be represented as well. The runtime data format does not assume any particular serialization format; this conversion is handled internally by the framework. In addition to the key and value, records (both those generated by sources and those delivered to sinks) have associated stream IDs and offsets. These are used by the framework to periodically commit the offsets of data that have been processed so that in the event of failures, processing can resume from the last committed offsets, avoiding unnecessary reprocessing and duplication of events.

Dynamic Connectors

Not all jobs are static, so Connector implementations are also responsible for monitoring the external system for any changes that might require reconfiguration. For example, in the JDBCSourceConnector example, the Connector might assign a set of tables to each Task. When a new table is created, it must discover this so it can assign the new table to one of the Tasks by updating its configuration. When it notices a change that requires reconfiguration (or a change in the number of Tasks), it notifies the framework and the framework updates any corresponding Tasks.

Developing a Simple Connector

Developing a connector only requires implementing two interfaces, the Connector and Task. A simple example is included with the source code for Kafka in the file package. This connector is meant for use in standalone mode and has implementations of a SourceConnector/SourceTask to read each line of a file and emit it as a record and a SinkConnector/SinkTask that writes each record to a file. The rest of this section will walk through some code to demonstrate the key steps in creating a connector, but developers should also refer to the full example source code as many details are omitted for brevity.

Connector Example

We’ll cover the SourceConnector as a simple example. SinkConnector implementations are very similar. Start by creating the class that inherits from SourceConnector and add a couple of fields that will store parsed configuration information (the filename to read from and the topic to send data to):

    public class FileStreamSourceConnector extends SourceConnector {
        private String filename;
        private String topic;

The easiest method to fill in is getTaskClass(), which defines the class that should be instantiated in worker processes to actually read the data:

    @Override
    public Class<? extends Task> getTaskClass() {
        return FileStreamSourceTask.class;
    }

We will define the FileStreamSourceTask class below. Next, we add some standard lifecycle methods, start() and stop():

    @Override
    public void start(Map<String, String> props) {
        // The complete version includes error handling as well.
        filename = props.get(FILE_CONFIG);
        topic = props.get(TOPIC_CONFIG);
    }

    @Override
    public void stop() {
        // Nothing to do since no background monitoring is required.
    }

Finally, the real core of the implementation is in taskConfigs(). In this case we are only handling a single file, so even though we may be permitted to generate more tasks as per the maxTasks argument, we return a list with only one entry:

    @Override
    public List<Map<String, String>> taskConfigs(int maxTasks) {
        ArrayList<Map<String, String>> configs = new ArrayList<>();
        // Only one input stream makes sense.
        Map<String, String> config = new HashMap<>();
        if (filename != null)
            config.put(FILE_CONFIG, filename);
        config.put(TOPIC_CONFIG, topic);
        configs.add(config);
        return configs;
    }

Although not used in the example, SourceTask also provides two APIs to commit offsets in the source system: commit and commitRecord. The APIs are provided for source systems which have an acknowledgement mechanism for messages. Overriding these methods allows the source connector to acknowledge messages in the source system, either in bulk or individually, once they have been written to Kafka. The commit API stores the offsets in the source system, up to the offsets that have been returned by poll. The implementation of this API should block until the commit is complete. The commitRecord API saves the offset in the source system for each SourceRecord after it is written to Kafka. As Kafka Connect will record offsets automatically, SourceTasks are not required to implement them. In cases where a connector does need to acknowledge messages in the source system, only one of the APIs is typically required. Even with multiple tasks, this method implementation is usually pretty simple. It just has to determine the number of input tasks, which may require contacting the remote service it is pulling data from, and then divvy them up. Because some patterns for splitting work among tasks are so common, some utilities are provided in ConnectorUtils to simplify these cases. Note that this simple example does not include dynamic input. See the discussion in the next section for how to trigger updates to task configs.

Task Example - Source Task

Next we’ll describe the implementation of the corresponding SourceTask. The implementation is short, but too long to cover completely in this guide. We’ll use pseudo-code to describe most of the implementation, but you can refer to the source code for the full example. Just as with the connector, we need to create a class inheriting from the appropriate base Task class. It also has some standard lifecycle methods:

    public class FileStreamSourceTask extends SourceTask {
        String filename;
        InputStream stream;
        String topic;

        @Override
        public void start(Map<String, String> props) {
            filename = props.get(FileStreamSourceConnector.FILE_CONFIG);
            stream = openOrThrowError(filename);
            topic = props.get(FileStreamSourceConnector.TOPIC_CONFIG);
        }

        @Override
        public synchronized void stop() {
            stream.close();
        }

These are slightly simplified versions, but show that that these methods should be relatively simple and the only work they should perform is allocating or freeing resources. There are two points to note about this implementation. First, the start() method does not yet handle resuming from a previous offset, which will be addressed in a later section. Second, the stop() method is synchronized. This will be necessary because SourceTasks are given a dedicated thread which they can block indefinitely, so they need to be stopped with a call from a different thread in the Worker. Next, we implement the main functionality of the task, the poll() method which gets events from the input system and returns a List<SourceRecord>:

    @Override
    public List<SourceRecord> poll() throws InterruptedException {
        try {
            ArrayList<SourceRecord> records = new ArrayList<>();
            while (streamValid(stream) && records.isEmpty()) {
                LineAndOffset line = readToNextLine(stream);
                if (line != null) {
                    Map<String, Object> sourcePartition = Collections.singletonMap("filename", filename);
                    Map<String, Object> sourceOffset = Collections.singletonMap("position", streamOffset);
                    records.add(new SourceRecord(sourcePartition, sourceOffset, topic, Schema.STRING_SCHEMA, line));
                } else {
                    Thread.sleep(1);
                }
            }
            return records;
        } catch (IOException e) {
            // Underlying stream was killed, probably as a result of calling stop. Allow to return
            // null, and driving thread will handle any shutdown if necessary.
        }
        return null;
    }

Again, we’ve omitted some details, but we can see the important steps: the poll() method is going to be called repeatedly, and for each call it will loop trying to read records from the file. For each line it reads, it also tracks the file offset. It uses this information to create an output SourceRecord with four pieces of information: the source partition (there is only one, the single file being read), source offset (byte offset in the file), output topic name, and output value (the line, and we include a schema indicating this value will always be a string). Other variants of the SourceRecord constructor can also include a specific output partition and a key. Note that this implementation uses the normal Java InputStream interface and may sleep if data is not available. This is acceptable because Kafka Connect provides each task with a dedicated thread. While task implementations have to conform to the basic poll() interface, they have a lot of flexibility in how they are implemented. In this case, an NIO-based implementation would be more efficient, but this simple approach works, is quick to implement, and is compatible with older versions of Java.

Sink Tasks

The previous section described how to implement a simple SourceTask. Unlike SourceConnector and SinkConnector, SourceTask and SinkTask have very different interfaces because SourceTask uses a pull interface and SinkTask uses a push interface. Both share the common lifecycle methods, but the SinkTask interface is quite different:

    public abstract class SinkTask implements Task {
        public void initialize(SinkTaskContext context) {
            this.context = context;
        }

        public abstract void put(Collection<SinkRecord> records);
        
        public abstract void flush(Map<TopicPartition, Long> offsets);

The SinkTask documentation contains full details, but this interface is nearly as simple as the SourceTask. The put() method should contain most of the implementation, accepting sets of SinkRecords, performing any required translation, and storing them in the destination system. This method does not need to ensure the data has been fully written to the destination system before returning. In fact, in many cases internal buffering will be useful so an entire batch of records can be sent at once, reducing the overhead of inserting events into the downstream data store. The SinkRecords contain essentially the same information as SourceRecords: Kafka topic, partition, offset and the event key and value. The flush() method is used during the offset commit process, which allows tasks to recover from failures and resume from a safe point such that no events will be missed. The method should push any outstanding data to the destination system and then block until the write has been acknowledged. The offsets parameter can often be ignored, but is useful in some cases where implementations want to store offset information in the destination store to provide exactly-once delivery. For example, an HDFS connector could do this and use atomic move operations to make sure the flush() operation atomically commits the data and offsets to a final location in HDFS.

Resuming from Previous Offsets

The SourceTask implementation included a stream ID (the input filename) and offset (position in the file) with each record. The framework uses this to commit offsets periodically so that in the case of a failure, the task can recover and minimize the number of events that are reprocessed and possibly duplicated (or to resume from the most recent offset if Kafka Connect was stopped gracefully, e.g. in standalone mode or due to a job reconfiguration). This commit process is completely automated by the framework, but only the connector knows how to seek back to the right position in the input stream to resume from that location. To correctly resume upon startup, the task can use the SourceContext passed into its initialize() method to access the offset data. In initialize(), we would add a bit more code to read the offset (if it exists) and seek to that position:

        stream = new FileInputStream(filename);
        Map<String, Object> offset = context.offsetStorageReader().offset(Collections.singletonMap(FILENAME_FIELD, filename));
        if (offset != null) {
            Long lastRecordedOffset = (Long) offset.get("position");
            if (lastRecordedOffset != null)
                seekToOffset(stream, lastRecordedOffset);
        }

Of course, you might need to read many keys for each of the input streams. The OffsetStorageReader interface also allows you to issue bulk reads to efficiently load all offsets, then apply them by seeking each input stream to the appropriate position.

Dynamic Input/Output Streams

Kafka Connect is intended to define bulk data copying jobs, such as copying an entire database rather than creating many jobs to copy each table individually. One consequence of this design is that the set of input or output streams for a connector can vary over time. Source connectors need to monitor the source system for changes, e.g. table additions/deletions in a database. When they pick up changes, they should notify the framework via the ConnectorContext object that reconfiguration is necessary. For example, in a SourceConnector:

        if (inputsChanged())
            this.context.requestTaskReconfiguration();

The framework will promptly request new configuration information and update the tasks, allowing them to gracefully commit their progress before reconfiguring them. Note that in the SourceConnector this monitoring is currently left up to the connector implementation. If an extra thread is required to perform this monitoring, the connector must allocate it itself. Ideally this code for monitoring changes would be isolated to the Connector and tasks would not need to worry about them. However, changes can also affect tasks, most commonly when one of their input streams is destroyed in the input system, e.g. if a table is dropped from a database. If the Task encounters the issue before the Connector, which will be common if the Connector needs to poll for changes, the Task will need to handle the subsequent error. Thankfully, this can usually be handled simply by catching and handling the appropriate exception. SinkConnectors usually only have to handle the addition of streams, which may translate to new entries in their outputs (e.g., a new database table). The framework manages any changes to the Kafka input, such as when the set of input topics changes because of a regex subscription. SinkTasks should expect new input streams, which may require creating new resources in the downstream system, such as a new table in a database. The trickiest situation to handle in these cases may be conflicts between multiple SinkTasks seeing a new input stream for the first time and simultaneously trying to create the new resource. SinkConnectors, on the other hand, will generally require no special code for handling a dynamic set of streams.

Connect Configuration Validation

Kafka Connect allows you to validate connector configurations before submitting a connector to be executed and can provide feedback about errors and recommended values. To take advantage of this, connector developers need to provide an implementation of config() to expose the configuration definition to the framework. The following code in FileStreamSourceConnector defines the configuration and exposes it to the framework.

        private static final ConfigDef CONFIG_DEF = new ConfigDef()
            .define(FILE_CONFIG, Type.STRING, Importance.HIGH, "Source filename.")
            .define(TOPIC_CONFIG, Type.STRING, Importance.HIGH, "The topic to publish data to");

        public ConfigDef config() {
            return CONFIG_DEF;
        }

ConfigDef class is used for specifying the set of expected configurations. For each configuration, you can specify the name, the type, the default value, the documentation, the group information, the order in the group, the width of the configuration value and the name suitable for display in the UI. Plus, you can provide special validation logic used for single configuration validation by overriding the Validator class. Moreover, as there may be dependencies between configurations, for example, the valid values and visibility of a configuration may change according to the values of other configurations. To handle this, ConfigDef allows you to specify the dependents of a configuration and to provide an implementation of Recommender to get valid values and set visibility of a configuration given the current configuration values. Also, the validate() method in Connector provides a default validation implementation which returns a list of allowed configurations together with configuration errors and recommended values for each configuration. However, it does not use the recommended values for configuration validation. You may provide an override of the default implementation for customized configuration validation, which may use the recommended values.

Working with Schemas

The FileStream connectors are good examples because they are simple, but they also have trivially structured data – each line is just a string. Almost all practical connectors will need schemas with more complex data formats. To create more complex data, you’ll need to work with the Kafka Connect data API. Most structured records will need to interact with two classes in addition to primitive types: Schema and Struct. The API documentation provides a complete reference, but here is a simple example creating a Schema and Struct:

    Schema schema = SchemaBuilder.struct().name(NAME)
        .field("name", Schema.STRING_SCHEMA)
        .field("age", Schema.INT_SCHEMA)
        .field("admin", new SchemaBuilder.boolean().defaultValue(false).build())
        .build();

    Struct struct = new Struct(schema)
        .put("name", "Barbara Liskov")
        .put("age", 75);

If you are implementing a source connector, you’ll need to decide when and how to create schemas. Where possible, you should avoid recomputing them as much as possible. For example, if your connector is guaranteed to have a fixed schema, create it statically and reuse a single instance. However, many connectors will have dynamic schemas. One simple example of this is a database connector. Considering even just a single table, the schema will not be predefined for the entire connector (as it varies from table to table). But it also may not be fixed for a single table over the lifetime of the connector since the user may execute an ALTER TABLE command. The connector must be able to detect these changes and react appropriately. Sink connectors are usually simpler because they are consuming data and therefore do not need to create schemas. However, they should take just as much care to validate that the schemas they receive have the expected format. When the schema does not match – usually indicating the upstream producer is generating invalid data that cannot be correctly translated to the destination system – sink connectors should throw an exception to indicate this error to the system.

Kafka Connect Administration

Kafka Connect’s REST layer provides a set of APIs to enable administration of the cluster. This includes APIs to view the configuration of connectors and the status of their tasks, as well as to alter their current behavior (e.g. changing configuration and restarting tasks).

When a connector is first submitted to the cluster, the workers rebalance the full set of connectors in the cluster and their tasks so that each worker has approximately the same amount of work. This same rebalancing procedure is also used when connectors increase or decrease the number of tasks they require, or when a connector’s configuration is changed. You can use the REST API to view the current status of a connector and its tasks, including the id of the worker to which each was assigned. For example, querying the status of a file source (using GET /connectors/file-source/status) might produce output like the following:

    {
    "name": "file-source",
    "connector": {
        "state": "RUNNING",
        "worker_id": "192.168.1.208:8083"
    },
    "tasks": [
        {
        "id": 0,
        "state": "RUNNING",
        "worker_id": "192.168.1.209:8083"
        }
    ]
    }

Connectors and their tasks publish status updates to a shared topic (configured with status.storage.topic) which all workers in the cluster monitor. Because the workers consume this topic asynchronously, there is typically a (short) delay before a state change is visible through the status API. The following states are possible for a connector or one of its tasks:

UNASSIGNED: The connector/task has not yet been assigned to a worker.
RUNNING: The connector/task is running.
PAUSED: The connector/task has been administratively paused.
FAILED: The connector/task has failed (usually by raising an exception, which is reported in the status output).

In most cases, connector and task states will match, though they may be different for short periods of time when changes are occurring or if tasks have failed. For example, when a connector is first started, there may be a noticeable delay before the connector and its tasks have all transitioned to the RUNNING state. States will also diverge when tasks fail since Connect does not automatically restart failed tasks. To restart a connector/task manually, you can use the restart APIs listed above. Note that if you try to restart a task while a rebalance is taking place, Connect will return a 409 (Conflict) status code. You can retry after the rebalance completes, but it might not be necessary since rebalances effectively restart all the connectors and tasks in the cluster.

It’s sometimes useful to temporarily stop the message processing of a connector. For example, if the remote system is undergoing maintenance, it would be preferable for source connectors to stop polling it for new data instead of filling logs with exception spam. For this use case, Connect offers a pause/resume API. While a source connector is paused, Connect will stop polling it for additional records. While a sink connector is paused, Connect will stop pushing new messages to it. The pause state is persistent, so even if you restart the cluster, the connector will not begin message processing again until the task has been resumed. Note that there may be a delay before all of a connector’s tasks have transitioned to the PAUSED state since it may take time for them to finish whatever processing they were in the middle of when being paused. Additionally, failed tasks will not transition to the PAUSED state until they have been restarted.

9 - Kafka Streams

9.1 - Streams Developer Guide