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.