Kafka Connect

• 1: Overview
• 2: User Guide
• 3: Connector Development Guide
• 4: Administration

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

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.json …]

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.
• plugin.path (default empty) - a list of paths that contain Connect plugins (connectors, converters, transformations). Before running quick starts, users must add the absolute path that contains the example FileStreamSourceConnector and FileStreamSinkConnector packaged in connect-file-"version".jar, because these connectors are not included by default to the CLASSPATH or the plugin.path of the Connect worker (see plugin.path property for examples).

The important configuration options specific to standalone mode are:

• offset.storage.file.filename - File to store source connector offsets

The parameters that are configured here are intended for producers and consumers used by Kafka Connect to access the configuration, offset and status topics. For configuration of the producers used by Kafka source tasks and the consumers used by Kafka sink tasks, the same parameters can be used but need to be prefixed with producer. and consumer. respectively. The only Kafka client parameter that is inherited without a prefix from the worker configuration is bootstrap.servers, which in most cases will be sufficient, since the same cluster is often used for all purposes. A notable exception is a secured cluster, which requires extra parameters to allow connections. These parameters will need to be set up to three times in the worker configuration, once for management access, once for Kafka sources and once for Kafka sinks.

Starting with 2.3.0, client configuration overrides can be configured individually per connector by using the prefixes producer.override. and consumer.override. for Kafka sources or Kafka sinks respectively. These overrides are included with the rest of the connector’s configuration properties.

The remaining parameters are connector configuration files. Each file may either be a Java Properties file or a JSON file containing an object with the same structure as the request body of either the POST /connectors endpoint or the PUT /connectors/{name}/config endpoint (see the OpenAPI documentation). You may include as many as you want, but all will execute within the same process (on different threads). You can also choose not to specify any connector configuration files on the command line, and instead use the REST API to create connectors at runtime after your standalone worker starts.

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. In both standalone and distributed mode, they are included in the JSON payload for the REST request that creates (or modifies) the connector. In standalone mode these can also be defined in a properties file and passed to the Connect process on the command line.

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 a few additional options to control their input. Each sink connector must set one of the following:

• topics - A comma-separated list of topics to use as input for this connector
• topics.regex - A Java regular expression of topics to use as input for this connector

For any other options, you should consult the documentation for the connector.

Transformations

Connectors can be configured with transformations to make lightweight message-at-a-time modifications. They can be convenient for data massaging and event routing.

A transformation chain can be specified in the connector configuration.

• transforms - List of aliases for the transformation, specifying the order in which the transformations will be applied.
• transforms.$alias.type - Fully qualified class name for the transformation.
• transforms.$alias.$transformationSpecificConfig Configuration properties for the transformation

For example, lets take the built-in file source connector and use a transformation to add a static field.

Throughout the example we’ll use schemaless JSON data format. To use schemaless format, we changed the following two lines in connect-standalone.properties from true to false:

key.converter.schemas.enable
value.converter.schemas.enable

The file source connector reads each line as a String. We will wrap each line in a Map and then add a second field to identify the origin of the event. To do this, we use two transformations:

• HoistField to place the input line inside a Map
• InsertField to add the static field. In this example we’ll indicate that the record came from a file connector

After adding the transformations, connect-file-source.properties file looks as following:

name=local-file-source
connector.class=FileStreamSource
tasks.max=1
file=test.txt
topic=connect-test
transforms=MakeMap, InsertSource
transforms.MakeMap.type=org.apache.kafka.connect.transforms.HoistField$Value
transforms.MakeMap.field=line
transforms.InsertSource.type=org.apache.kafka.connect.transforms.InsertField$Value
transforms.InsertSource.static.field=data_source
transforms.InsertSource.static.value=test-file-source

All the lines starting with transforms were added for the transformations. You can see the two transformations we created: “InsertSource” and “MakeMap” are aliases that we chose to give the transformations. The transformation types are based on the list of built-in transformations you can see below. Each transformation type has additional configuration: HoistField requires a configuration called “field”, which is the name of the field in the map that will include the original String from the file. InsertField transformation lets us specify the field name and the value that we are adding.

When we ran the file source connector on my sample file without the transformations, and then read them using kafka-console-consumer.sh, the results were:

"foo"
"bar"
"hello world"

We then create a new file connector, this time after adding the transformations to the configuration file. This time, the results will be:

{"line":"foo","data_source":"test-file-source"}
{"line":"bar","data_source":"test-file-source"}
{"line":"hello world","data_source":"test-file-source"}

You can see that the lines we’ve read are now part of a JSON map, and there is an extra field with the static value we specified. This is just one example of what you can do with transformations.

Included transformations

Several widely-applicable data and routing transformations are included with Kafka Connect:

• Cast - Cast fields or the entire key or value to a specific type
• DropHeaders - Remove headers by name
• ExtractField - Extract a specific field from Struct and Map and include only this field in results
• Filter - Removes messages from all further processing. This is used with a predicate to selectively filter certain messages
• Flatten - Flatten a nested data structure
• HeaderFrom - Copy or move fields in the key or value to the record headers
• HoistField - Wrap the entire event as a single field inside a Struct or a Map
• InsertField - Add a field using either static data or record metadata
• InsertHeader - Add a header using static data
• MaskField - Replace field with valid null value for the type (0, empty string, etc) or custom replacement (non-empty string or numeric value only)
• RegexRouter - modify the topic of a record based on original topic, replacement string and a regular expression
• ReplaceField - Filter or rename fields
• SetSchemaMetadata - modify the schema name or version
• TimestampConverter - Convert timestamps between different formats
• TimestampRouter - Modify the topic of a record based on original topic and timestamp. Useful when using a sink that needs to write to different tables or indexes based on timestamps
• ValueToKey - Replace the record key with a new key formed from a subset of fields in the record value

Details on how to configure each transformation are listed below:

Predicates

Transformations can be configured with predicates so that the transformation is applied only to messages which satisfy some condition. In particular, when combined with the Filter transformation predicates can be used to selectively filter out certain messages.

Predicates are specified in the connector configuration.

• predicates - Set of aliases for the predicates to be applied to some of the transformations.
• predicates.$alias.type - Fully qualified class name for the predicate.
• predicates.$alias.$predicateSpecificConfig - Configuration properties for the predicate.

All transformations have the implicit config properties predicate and negate. A predicular predicate is associated with a transformation by setting the transformation’s predicate config to the predicate’s alias. The predicate’s value can be reversed using the negate configuration property.

For example, suppose you have a source connector which produces messages to many different topics and you want to:

• filter out the messages in the ‘foo’ topic entirely
• apply the ExtractField transformation with the field name ‘other_field’ to records in all topics except the topic ‘bar’

To do this we need first to filter out the records destined for the topic ‘foo’. The Filter transformation removes records from further processing, and can use the TopicNameMatches predicate to apply the transformation only to records in topics which match a certain regular expression. TopicNameMatches’s only configuration property is pattern which is a Java regular expression for matching against the topic name. The configuration would look like this:

transforms=Filter
transforms.Filter.type=org.apache.kafka.connect.transforms.Filter
transforms.Filter.predicate=IsFoo

predicates=IsFoo
predicates.IsFoo.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.IsFoo.pattern=foo

Next we need to apply ExtractField only when the topic name of the record is not ‘bar’. We can’t just use TopicNameMatches directly, because that would apply the transformation to matching topic names, not topic names which do not match. The transformation’s implicit negate config properties allows us to invert the set of records which a predicate matches. Adding the configuration for this to the previous example we arrive at:

transforms=Filter,Extract
transforms.Filter.type=org.apache.kafka.connect.transforms.Filter
transforms.Filter.predicate=IsFoo

transforms.Extract.type=org.apache.kafka.connect.transforms.ExtractField$Key
transforms.Extract.field=other_field
transforms.Extract.predicate=IsBar
transforms.Extract.negate=true

predicates=IsFoo,IsBar
predicates.IsFoo.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.IsFoo.pattern=foo

predicates.IsBar.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.IsBar.pattern=bar

Kafka Connect includes the following predicates:

• TopicNameMatches - matches records in a topic with a name matching a particular Java regular expression.
• HasHeaderKey - matches records which have a header with the given key.
• RecordIsTombstone - matches tombstone records, that is records with a null value.

Details on how to configure each predicate are listed below:

REST API

Since Kafka Connect is intended to be run as a service, it also provides a REST API for managing connectors. This REST API is available in both standalone and distributed mode. The REST API server can be configured using the listeners configuration option. This field should contain a list of listeners in the following format: protocol://host:port,protocol2://host2:port2. Currently supported protocols are http and https. For example:

listeners=http://localhost:8080,https://localhost:8443

By default, if no listeners are specified, the REST server runs on port 8083 using the HTTP protocol. When using HTTPS, the configuration has to include the SSL configuration. By default, it will use the ssl.* settings. In case it is needed to use different configuration for the REST API than for connecting to Kafka brokers, the fields can be prefixed with listeners.https. When using the prefix, only the prefixed options will be used and the ssl.* options without the prefix will be ignored. Following fields can be used to configure HTTPS for the REST API:

• ssl.keystore.location
• ssl.keystore.password
• ssl.keystore.type
• ssl.key.password
• ssl.truststore.location
• ssl.truststore.password
• ssl.truststore.type
• ssl.enabled.protocols
• ssl.provider
• ssl.protocol
• ssl.cipher.suites
• ssl.keymanager.algorithm
• ssl.secure.random.implementation
• ssl.trustmanager.algorithm
• ssl.endpoint.identification.algorithm
• ssl.client.auth

The REST API is used not only by users to monitor / manage Kafka Connect. In distributed mode, it is also used for the Kafka Connect cross-cluster communication. Some requests received on the follower nodes REST API will be forwarded to the leader node REST API. In case the URI under which is given host reachable is different from the URI which it listens on, the configuration options rest.advertised.host.name, rest.advertised.port and rest.advertised.listener can be used to change the URI which will be used by the follower nodes to connect with the leader. When using both HTTP and HTTPS listeners, the rest.advertised.listener option can be also used to define which listener will be used for the cross-cluster communication. When using HTTPS for communication between nodes, the same ssl.* or listeners.https options will be used to configure the HTTPS client.

The following are the currently supported REST API 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. The JSON object may also optionally contain a string initial_state field which can take the following values - STOPPED, PAUSED or RUNNING (the default value)
• 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
• PATCH /connectors/{name}/config - patch the configuration parameters for a specific connector, where null values in the JSON body indicates removing of the key from the final configuration
• 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 along with their configurations
• 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. Any resources claimed by its tasks are left allocated, which allows the connector to begin processing data quickly once it is resumed.
• PUT /connectors/{name}/stop - stop the connector and shut down its tasks, deallocating any resources claimed by its tasks. This is more efficient from a resource usage standpoint than pausing the connector, but can cause it to take longer to begin processing data once resumed. Note that the offsets for a connector can be only modified via the offsets management endpoints if it is in the stopped state
• PUT /connectors/{name}/resume - resume a paused or stopped connector (or do nothing if the connector is not paused or stopped)
• POST /connectors/{name}/restart?includeTasks=<true|false>&onlyFailed=<true|false> - restart a connector and its tasks instances.
  • the “includeTasks” parameter specifies whether to restart the connector instance and task instances (“includeTasks=true”) or just the connector instance (“includeTasks=false”), with the default (“false”) preserving the same behavior as earlier versions.
  • the “onlyFailed” parameter specifies whether to restart just the instances with a FAILED status (“onlyFailed=true”) or all instances (“onlyFailed=false”), with the default (“false”) preserving the same behavior as earlier versions.
• 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
• GET /connectors/{name}/topics - get the set of topics that a specific connector is using since the connector was created or since a request to reset its set of active topics was issued
• PUT /connectors/{name}/topics/reset - send a request to empty the set of active topics of a connector
• Offsets management endpoints (see KIP-875 for more details):

  • GET /connectors/{name}/offsets - get the current offsets for a connector

  • DELETE /connectors/{name}/offsets - reset the offsets for a connector. The connector must exist and must be in the stopped state (see PUT /connectors/{name}/stop)

  • PATCH /connectors/{name}/offsets - alter the offsets for a connector. The connector must exist and must be in the stopped state (see PUT /connectors/{name}/stop). The request body should be a JSON object containing a JSON array offsets field, similar to the response body of the GET /connectors/{name}/offsets endpoint. An example request body for the FileStreamSourceConnector:

          {
    "offsets": [
      {
        "partition": {
          "filename": "test.txt"
        },
        "offset": {
          "position": 30
        }
      }
    ]
    

    }

An example request body for the FileStreamSinkConnector:

            {
      "offsets": [
        {
          "partition": {
            "kafka_topic": "test",
            "kafka_partition": 0
          },
          "offset": {
            "kafka_offset": 5
          }
        },
        {
          "partition": {
            "kafka_topic": "test",
            "kafka_partition": 1
          },
          "offset": null
        }
      ]
    }

The “offset” field may be null to reset the offset for a specific partition (applicable to both source and sink connectors). Note that the request body format depends on the connector implementation in the case of source connectors, whereas there is a common format across all sink connectors.

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
• GET /connector-plugins/{plugin-type}/config - get the configuration definition for the specified plugin.
• 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.

The following is a supported REST request at the top-level (root) endpoint:

• GET /- return basic information about the Kafka Connect cluster such as the version of the Connect worker that serves the REST request (including git commit ID of the source code) and the Kafka cluster ID that is connected to.

The admin.listeners configuration can be used to configure admin REST APIs on Kafka Connect’s REST API server. Similar to the listeners configuration, this field should contain a list of listeners in the following format: protocol://host:port,protocol2://host2:port2. Currently supported protocols are http and https. For example:

admin.listeners=http://localhost:8080,https://localhost:8443

By default, if admin.listeners is not configured, the admin REST APIs will be available on the regular listeners.

The following are the currently supported admin REST API endpoints:

• GET /admin/loggers - list the current loggers that have their levels explicitly set and their log levels
• GET /admin/loggers/{name} - get the log level for the specified logger
• PUT /admin/loggers/{name} - set the log level for the specified logger

See KIP-495 for more details about the admin logger REST APIs.

For the complete specification of the Kafka Connect REST API, see the OpenAPI documentation

Error Reporting in Connect

Kafka Connect provides error reporting to handle errors encountered along various stages of processing. By default, any error encountered during conversion or within transformations will cause the connector to fail. Each connector configuration can also enable tolerating such errors by skipping them, optionally writing each error and the details of the failed operation and problematic record (with various levels of detail) to the Connect application log. These mechanisms also capture errors when a sink connector is processing the messages consumed from its Kafka topics, and all of the errors can be written to a configurable “dead letter queue” (DLQ) Kafka topic.

To report errors within a connector’s converter, transforms, or within the sink connector itself to the log, set errors.log.enable=true in the connector configuration to log details of each error and problem record’s topic, partition, and offset. For additional debugging purposes, set errors.log.include.messages=true to also log the problem record key, value, and headers to the log (note this may log sensitive information).

To report errors within a connector’s converter, transforms, or within the sink connector itself to a dead letter queue topic, set errors.deadletterqueue.topic.name, and optionally errors.deadletterqueue.context.headers.enable=true.

By default connectors exhibit “fail fast” behavior immediately upon an error or exception. This is equivalent to adding the following configuration properties with their defaults to a connector configuration:

# disable retries on failure
errors.retry.timeout=0

# do not log the error and their contexts
errors.log.enable=false

# do not record errors in a dead letter queue topic
errors.deadletterqueue.topic.name=

# Fail on first error
errors.tolerance=none

These and other related connector configuration properties can be changed to provide different behavior. For example, the following configuration properties can be added to a connector configuration to setup error handling with multiple retries, logging to the application logs and the my-connector-errors Kafka topic, and tolerating all errors by reporting them rather than failing the connector task:

# retry for at most 10 minutes times waiting up to 30 seconds between consecutive failures
errors.retry.timeout=600000
errors.retry.delay.max.ms=30000

# log error context along with application logs, but do not include configs and messages
errors.log.enable=true
errors.log.include.messages=false

# produce error context into the Kafka topic
errors.deadletterqueue.topic.name=my-connector-errors

# Tolerate all errors.
errors.tolerance=all

Exactly-once support

Kafka Connect is capable of providing exactly-once semantics for sink connectors (as of version 0.11.0) and source connectors (as of version 3.3.0). Please note that support for exactly-once semantics is highly dependent on the type of connector you run. Even if you set all the correct worker properties in the configuration for each node in a cluster, if a connector is not designed to, or cannot take advantage of the capabilities of the Kafka Connect framework, exactly-once may not be possible.

Sink connectors

If a sink connector supports exactly-once semantics, to enable exactly-once at the Connect worker level, you must ensure its consumer group is configured to ignore records in aborted transactions. You can do this by setting the worker property consumer.isolation.level to read_committed or, if running a version of Kafka Connect that supports it, using a connector client config override policy that allows the consumer.override.isolation.level property to be set to read_committed in individual connector configs. There are no additional ACL requirements.

Source connectors

If a source connector supports exactly-once semantics, you must configure your Connect cluster to enable framework-level support for exactly-once source connectors. Additional ACLs may be necessary if running against a secured Kafka cluster. Note that exactly-once support for source connectors is currently only available in distributed mode; standalone Connect workers cannot provide exactly-once semantics.

Worker configuration

For new Connect clusters, set the exactly.once.source.support property to enabled in the worker config for each node in the cluster. For existing clusters, two rolling upgrades are necessary. During the first upgrade, the exactly.once.source.support property should be set to preparing, and during the second, it should be set to enabled.

ACL requirements

With exactly-once source support enabled, or with exactly.once.source.support set to preparing, the principal for each Connect worker will require the following ACLs:

And with exactly-once source enabled (but not if exactly.once.source.support is set to preparing), the principal for each individual connector will require the following ACLs:

Plugin Discovery

Plugin discovery is the name for the strategy which the Connect worker uses to find plugin classes and make them accessible to configure and run in connectors. This is controlled by the plugin.discovery worker configuration, and has a significant impact on worker startup time. service_load is the fastest strategy, but care should be taken to verify that plugins are compatible before setting this configuration to service_load.

Prior to version 3.6, this strategy was not configurable, and behaved like the only_scan mode which is compatible with all plugins. For version 3.6 and later, this mode defaults to hybrid_warn which is also compatible with all plugins, but logs a warning for plugins which are incompatible with service_load. The hybrid_fail strategy stops the worker with an error if a plugin incompatible with service_load is detected, asserting that all plugins are compatible. Finally, the service_load strategy disables the slow legacy scanning mechanism used in all other modes, and instead uses the faster ServiceLoader mechanism. Plugins which are incompatible with that mechanism may be unusable.

Verifying Plugin Compatibility

To verify if all of your plugins are compatible with service_load, first ensure that you are using version 3.6 or later of Kafka Connect. You can then perform one of the following checks:

• Start your worker with the default hybrid_warnstrategy, and WARN logs enabled for the org.apache.kafka.connect package. At least one WARN log message mentioning the plugin.discovery configuration should be printed. This log message will explicitly say that all plugins are compatible, or list the incompatible plugins.
• Start your worker in a test environment with hybrid_fail. If all plugins are compatible, startup will succeed. If at least one plugin is not compatible the worker will fail to start up, and all incompatible plugins will be listed in the exception.

If the verification step succeeds, then your current set of installed plugins is compatible, and it should be safe to change the plugin.discovery configuration to service_load. If the verification fails, you cannot use service_load strategy and should take note of the list of incompatible plugins. All plugins must be addressed before using the service_load strategy. It is recommended to perform this verification after installing or changing plugin versions, and the verification can be done automatically in a Continuous Integration environment.

Operators: Artifact Migration

As an operator of Connect, if you discover incompatible plugins, there are multiple ways to resolve the incompatibility. They are listed below from most to least preferable.

1. Check the latest release from your plugin provider, and if it is compatible, upgrade.
2. Contact your plugin provider and request that they migrate the plugin to be compatible, following the source migration instructions, and then upgrade to the compatible version.
3. Migrate the plugin artifacts yourself using the included migration script.

The migration script is located in bin/connect-plugin-path.sh and bin\windows\connect-plugin-path.bat of your Kafka installation. The script can migrate incompatible plugin artifacts already installed on your Connect worker’s plugin.path by adding or modifying JAR or resource files. This is not suitable for environments using code-signing, as this can change artifacts such that they will fail signature verification. View the built-in help with --help.

To perform a migration, first use the list subcommand to get an overview of the plugins available to the script. You must tell the script where to find plugins, which can be done with the repeatable --worker-config, --plugin-path, and --plugin-location arguments. The script will ignore plugins on the classpath, so any custom plugins on your classpath should be moved to the plugin path in order to be used with this migration script, or migrated manually. Be sure to compare the output of list with the worker startup warning or error message to ensure that all of your affected plugins are found by the script.

Once you see that all incompatible plugins are included in the listing, you can proceed to dry-run the migration with sync-manifests --dry-run. This will perform all parts of the migration, except for writing the results of the migration to disk. Note that the sync-manifests command requires all specified paths to be writable, and may alter the contents of the directories. Make a backup of your plugins in the specified paths, or copy them to a writable directory.

Ensure that you have a backup of your plugins and the dry-run succeeds before removing the --dry-run flag and actually running the migration. If the migration fails without the --dry-run flag, then the partially migrated artifacts should be discarded. The migration is idempotent, so running it multiple times and on already-migrated plugins is safe. After the script finishes, you should verify the migration is complete. The migration script is suitable for use in a Continuous Integration environment for automatic migration.

Developers: Source Migration

To make plugins compatible with service_load, it is necessary to add ServiceLoader manifests to your source code, which should then be packaged in the release artifact. Manifests are resource files in META-INF/services/ named after their superclass type, and contain a list of fully-qualified subclass names, one on each line.

In order for a plugin to be compatible, it must appear as a line in a manifest corresponding to the plugin superclass it extends. If a single plugin implements multiple plugin interfaces, then it should appear in a manifest for each interface it implements. If you have no classes for a certain type of plugin, you do not need to include a manifest file for that type. If you have classes which should not be visible as plugins, they should be marked abstract. The following types are expected to have manifests:

• org.apache.kafka.connect.sink.SinkConnector
• org.apache.kafka.connect.source.SourceConnector
• org.apache.kafka.connect.storage.Converter
• org.apache.kafka.connect.storage.HeaderConverter
• org.apache.kafka.connect.transforms.Transformation
• org.apache.kafka.connect.transforms.predicates.Predicate
• org.apache.kafka.common.config.provider.ConfigProvider
• org.apache.kafka.connect.rest.ConnectRestExtension
• org.apache.kafka.connect.connector.policy.ConnectorClientConfigOverridePolicy

For example, if you only have one connector with the fully-qualified name com.example.MySinkConnector, then only one manifest file must be added to resources in META-INF/services/org.apache.kafka.connect.sink.SinkConnector, and the contents should be similar to the following:

# license header or comment
com.example.MySinkConnector

You should then verify that your manifests are correct by using the verification steps with a pre-release artifact. If the verification succeeds, you can then release the plugin normally, and operators can upgrade to the compatible version.

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. Pick a package and class name, these examples will use the FileStreamSourceConnector but substitute your own class name where appropriate. In order to make the plugin discoverable at runtime, add a ServiceLoader manifest to your resources in META-INF/services/org.apache.kafka.connect.source.SourceConnector with your fully-qualified class name on a single line:

com.example.FileStreamSourceConnector

Create a class that inherits from SourceConnector and add a field that will store the configuration information to be propagated to the task(s) (the topic to send data to, and optionally - the filename to read from and the maximum batch size):

package com.example;

public class FileStreamSourceConnector extends SourceConnector {
    private Map<String, String> props;

The easiest method to fill in is taskClass(), which defines the class that should be instantiated in worker processes to actually read the data:

@Override
public Class<? extends Task> taskClass() {
    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) {
    // Initialization logic and setting up of resources can take place in this method.
    // This connector doesn't need to do any of that, but we do log a helpful message to the user.

    this.props = props;
    AbstractConfig config = new AbstractConfig(CONFIG_DEF, props);
    String filename = config.getString(FILE_CONFIG);
    filename = (filename == null || filename.isEmpty()) ? "standard input" : config.getString(FILE_CONFIG);
    log.info("Starting file source connector reading from {}", filename);
}

@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) {
    // Note that the task configs could contain configs additional to or different from the connector configs if needed. For instance,
    // if different tasks have different responsibilities, or if different tasks are meant to process different subsets of the source data stream).
    ArrayList<Map<String, String>> configs = new ArrayList<>();
    // Only one input stream makes sense.
    configs.add(props);
    return configs;
}

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 {
    private String filename;
    private InputStream stream;
    private String topic;
    private int batchSize;

    @Override
    public void start(Map<String, String> props) {
        filename = props.get(FileStreamSourceConnector.FILE_CONFIG);
        stream = openOrThrowError(filename);
        topic = props.get(FileStreamSourceConnector.TOPIC_CONFIG);
        batchSize = props.get(FileStreamSourceConnector.TASK_BATCH_SIZE_CONFIG);
    }

    @Override
    public synchronized void stop() {
        stream.close();
    }
}

These are slightly simplified versions, but show 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));
                if (records.size() >= batchSize) {
                    return records;
                }
            } 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, a key, and headers.

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.

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.

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 void flush(Map<TopicPartition, OffsetAndMetadata> currentOffsets) {
    }
}

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, the event key and value, and optional headers.

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.

Errant Record Reporter

When error reporting is enabled for a connector, the connector can use an ErrantRecordReporter to report problems with individual records sent to a sink connector. The following example shows how a connector’s SinkTask subclass might obtain and use the ErrantRecordReporter, safely handling a null reporter when the DLQ is not enabled or when the connector is installed in an older Connect runtime that doesn’t have this reporter feature:

<

private ErrantRecordReporter reporter;

@Override
public void start(Map<String, String> props) {
    ...
    try {
        reporter = context.errantRecordReporter(); // may be null if DLQ not enabled
    } catch (NoSuchMethodException | NoClassDefFoundError e) {
        // Will occur in Connect runtimes earlier than 2.6
        reporter = null;
    }
}

@Override
public void put(Collection<SinkRecord> records) {
    for (SinkRecord record: records) {
        try {
            // attempt to process and send record to data sink
            process(record);
        } catch(Exception e) {
            if (reporter != null) {
                // Send errant record to error reporter
                reporter.report(record, e);
            } else {
                // There's no error reporter, so fail
                throw new ConnectException("Failed on record", e);
            }
        }
    }
}

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.

Exactly-once source connectors

Supporting exactly-once

With the passing of KIP-618, Kafka Connect supports exactly-once source connectors as of version 3.3.0. In order for a source connector to take advantage of this support, it must be able to provide meaningful source offsets for each record that it emits, and resume consumption from the external system at the exact position corresponding to any of those offsets without dropping or duplicating messages.

Defining transaction boundaries

By default, the Kafka Connect framework will create and commit a new Kafka transaction for each batch of records that a source task returns from its poll method. However, connectors can also define their own transaction boundaries, which can be enabled by users by setting the transaction.boundary property to connector in the config for the connector.

If enabled, the connector’s tasks will have access to a TransactionContext from their SourceTaskContext, which they can use to control when transactions are aborted and committed.

For example, to commit a transaction at least every ten records:

private int recordsSent;

@Override
public void start(Map<String, String> props) {
    this.recordsSent = 0;
}

@Override
public List<SourceRecord> poll() {
    List<SourceRecord> records = fetchRecords();
    boolean shouldCommit = false;
    for (SourceRecord record : records) {
        if (++this.recordsSent >= 10) {
            shouldCommit = true;
        }
    }
    if (shouldCommit) {
        this.recordsSent = 0;
        this.context.transactionContext().commitTransaction();
    }
    return records;
}

Or to commit a transaction for exactly every tenth record:

private int recordsSent;

@Override
public void start(Map<String, String> props) {
    this.recordsSent = 0;
}

@Override
public List<SourceRecord> poll() {
    List<SourceRecord> records = fetchRecords();
    for (SourceRecord record : records) {
        if (++this.recordsSent % 10 == 0) {
            this.context.transactionContext().commitTransaction(record);
        }
    }
    return records;
}

Most connectors do not need to define their own transaction boundaries. However, it may be useful if files or objects in the source system are broken up into multiple source records, but should be delivered atomically. Additionally, it may be useful if it is impossible to give each source record a unique source offset, if every record with a given offset is delivered within a single transaction.

Note that if the user has not enabled connector-defined transaction boundaries in the connector configuration, the TransactionContext returned by context.transactionContext() will be null.

Validation APIs

A few additional preflight validation APIs can be implemented by source connector developers.

Some users may require exactly-once semantics from a connector. In this case, they may set the exactly.once.support property to required in the configuration for the connector. When this happens, the Kafka Connect framework will ask the connector whether it can provide exactly-once semantics with the specified configuration. This is done by invoking the exactlyOnceSupport method on the connector.

If a connector doesn’t support exactly-once semantics, it should still implement this method to let users know for certain that it cannot provide exactly-once semantics:

@Override
public ExactlyOnceSupport exactlyOnceSupport(Map<String, String> props) {
    // This connector cannot provide exactly-once semantics under any conditions
    return ExactlyOnceSupport.UNSUPPORTED;
}

Otherwise, a connector should examine the configuration, and return ExactlyOnceSupport.SUPPORTED if it can provide exactly-once semantics:

@Override
public ExactlyOnceSupport exactlyOnceSupport(Map<String, String> props) {
    // This connector can always provide exactly-once semantics
    return ExactlyOnceSupport.SUPPORTED;
}

Additionally, if the user has configured the connector to define its own transaction boundaries, the Kafka Connect framework will ask the connector whether it can define its own transaction boundaries with the specified configuration, using the canDefineTransactionBoundaries method:

@Override
public ConnectorTransactionBoundaries canDefineTransactionBoundaries(Map<String, String> props) {
    // This connector can always define its own transaction boundaries
    return ConnectorTransactionBoundaries.SUPPORTED;
}

This method should only be implemented for connectors that can define their own transaction boundaries in some cases. If a connector is never able to define its own transaction boundaries, it does not need to implement this method.

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.

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.

static final ConfigDef CONFIG_DEF = new ConfigDef()
    .define(FILE_CONFIG, Type.STRING, null, Importance.HIGH, "Source filename. If not specified, the standard input will be used")
    .define(TOPIC_CONFIG, Type.STRING, ConfigDef.NO_DEFAULT_VALUE, new ConfigDef.NonEmptyString(), Importance.HIGH, "The topic to publish data to")
    .define(TASK_BATCH_SIZE_CONFIG, Type.INT, DEFAULT_TASK_BATCH_SIZE, Importance.LOW,
        "The maximum number of records the source task can read from the file each time it is polled");

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", SchemaBuilder.bool().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.

4 - Administration

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, a rebalance is triggered between the Connect workers in order to distribute the load that consists of the tasks of the new connector. This same rebalancing procedure is also used when connectors increase or decrease the number of tasks they require, when a connector’s configuration is changed, or when a worker is added or removed from the group as part of an intentional upgrade of the Connect cluster or due to a failure.

In versions prior to 2.3.0, the Connect workers would rebalance the full set of connectors and their tasks in the cluster as a simple way to make sure that each worker has approximately the same amount of work. This behavior can be still enabled by setting connect.protocol=eager.

Starting with 2.3.0, Kafka Connect is using by default a protocol that performs incremental cooperative rebalancing that incrementally balances the connectors and tasks across the Connect workers, affecting only tasks that are new, to be removed, or need to move from one worker to another. Other tasks are not stopped and restarted during the rebalance, as they would have been with the old protocol.

If a Connect worker leaves the group, intentionally or due to a failure, Connect waits for scheduled.rebalance.max.delay.ms before triggering a rebalance. This delay defaults to five minutes (300000ms) to tolerate failures or upgrades of workers without immediately redistributing the load of a departing worker. If this worker returns within the configured delay, it gets its previously assigned tasks in full. However, this means that the tasks will remain unassigned until the time specified by scheduled.rebalance.max.delay.ms elapses. If a worker does not return within that time limit, Connect will reassign those tasks among the remaining workers in the Connect cluster.

The new Connect protocol is enabled when all the workers that form the Connect cluster are configured with connect.protocol=compatible, which is also the default value when this property is missing. Therefore, upgrading to the new Connect protocol happens automatically when all the workers upgrade to 2.3.0. A rolling upgrade of the Connect cluster will activate incremental cooperative rebalancing when the last worker joins on version 2.3.0.

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, the GET /connectors/file-source/status request shows the status of a connector named file-source:

{
    "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.
• STOPPED: The connector has been stopped. Note that this state is not applicable to tasks because the tasks for a stopped connector are shut down and won’t be visible in the status API.
• FAILED: The connector/task has failed (usually by raising an exception, which is reported in the status output).
• RESTARTING: The connector/task is either actively restarting or is expected to restart soon

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.

Starting with 2.5.0, Kafka Connect uses the status.storage.topic to also store information related to the topics that each connector is using. Connect Workers use these per-connector topic status updates to respond to requests to the REST endpoint GET /connectors/{name}/topics by returning the set of topic names that a connector is using. A request to the REST endpoint PUT /connectors/{name}/topics/reset resets the set of active topics for a connector and allows a new set to be populated, based on the connector’s latest pattern of topic usage. Upon connector deletion, the set of the connector’s active topics is also deleted. Topic tracking is enabled by default but can be disabled by setting topic.tracking.enable=false. If you want to disallow requests to reset the active topics of connectors during runtime, set the Worker property topic.tracking.allow.reset=false.

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.

In 3.5.0, Connect introduced a stop API that completely shuts down the tasks for a connector and deallocates any resources claimed by them. This is different from pausing a connector where tasks are left idling and any resources claimed by them are left allocated (which allows the connector to begin processing data quickly once it is resumed). Stopping a connector is more efficient from a resource usage standpoint than pausing it, but can cause it to take longer to begin processing data once resumed. Note that the offsets for a connector can be only modified via the offsets management endpoints if it is in the stopped state.