Important Warning

Update July 2016: This Tech Preview documentation from March 2016 is outdated and deprecated. Please use the latest Confluent Platform documentation instead.

Developer 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 Kafka Connect 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 instantiate Kafka Connectors for the systems 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).

Implementations of the Connector class do not perform data copying themselves: their configuration describes the set of data to be copied, and the Connector is responsible for breaking that job into a set of Tasks that can be distributed to Kafka Connect workers. These Tasks also come in two corresponding flavors: SourceTask and SinkTask.

With an assignment of data to be copied in hand, each Task must copy its subset of the data to or from Kafka. The data that a connector copies must be represented as a partitioned stream, similar to the model of a Kafka topic, where each partition is an ordered sequence of records with offsets. Each task is assigned a subset of the partitions to process. Sometimes this mapping is clear: each file in a set of log files can be considered a partition, each line within a file is a record, and offsets are simply the position in the file. In other cases it may require a bit more effort to map to this model: a JDBC connector can map each table to a partition, but the offset is less clear. One possible mapping uses a timestamp column to generate queries to incrementally return new data, and the last queried timestamp can be used as the offset.

../_images/connector-model.png

Example of a source connector which has created two tasks, which copy data from input partitions and write records to Kafka.

Partitions and Records

Each partition is an ordered sequence of key-value records. Both the keys and values can have complex structures, represented by the data structures in the org.apache.kafka.data package. Many primitive types are supported, but arrays, structs, and nested data structures are also supported. For most types, standard Java types like java.lang.Integer, java.lang.Map, and java.lang.Collection can be used directly. For structured records, the Struct class should be used.

../_images/data-model-stream-partition-record.png

A partitioned stream: the data model that connectors must map all source and sink systems to. Each record contains keys and values (with schemas), a partition ID, and offsets within that partition.

In order to track the structure and compatibility of records in partitions, Schemas may be included with each record. Because schemas are commonly generated on the fly, based on the data source, a SchemaBuilder class is included which makes constructing schemas very easy.

This runtime data format does not assume any particular serialization format; this conversion is handled by Converters implementations, which convert between org.apache.kafka.data runtime format and serialized data represented as byte[]. Connector developers should not have to worry about the details of this conversion.

In addition to the key and value, records have partition IDs and offsets. These are used by the framework to periodically commit the offsets of data that have been processed. In the event of a failure, processing can resume from the last committed offsets, avoiding unnecessary reprocessing and duplication of events.

Dynamic Connectors

Not all connectors have a static set of partitions, 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 of connectors that read and write lines from and to files is included in the source code for Kafka Connect in the org.apache.kafka.connect.file package. The classes SourceConnector/SourceTask implement a source connector that reads lines from files and SinkConnector/SinkTask implement a sink connector that writes each record to a file.

The rest of this section will walk through some code to demonstrate the key steps in creating a connector, but developers should also refer to the full example source code as many details are omitted for brevity.

Connector Example

We’ll cover the SourceConnector as a simple example. SinkConnector implementations are very similar. Start by creating the class that inherits from SourceConnector and add a couple of fields that will store parsed configuration information (the filename to read from and the topic to send data to):

public class FileStreamSourceConnector extends SourceConnector {
  private String filename;
  private String topic;

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

@Override
public Class<? extends Task> getTaskClass() {
  return FileStreamSourceTask.class;
}

We will define the FileStreamSourceTask class below. Next, we add some standard lifecycle methods, start() and stop():

@Override
public void start(Map<String, String> props) {
  // The complete version includes error handling as well.
  filename = props.get(FILE_CONFIG);
  topic = props.get(TOPIC_CONFIG);
}

@Override
public void stop() {
  // Nothing to do since no background monitoring is required
}

Finally, the real core of the implementation is in getTaskConfigs(). In this case we’re 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>> getTaskConfigs(int maxTasks) {
  ArrayList<Map<String, String>> configs = new ArrayList<>();
  // Only one input partition makes sense.
  Map<String, String> config = new HashMap<>();
  if (filename != null)
    config.put(FILE_CONFIG, filename);
  config.put(TOPIC_CONFIG, topic);
  configs.add(config);
  return configs;
}

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 class is small, but too long to cover completely in this guide. We’ll use helper methods which we won’t provide the details of 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;

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

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

These are slightly simplified versions, but show that that these methods should be relatively simple and the only work they 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 that gets records 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 sourcePartition = Collections.singletonMap("filename", filename);
        Map sourceOffset = Collections.singletonMap("position", streamOffset);
        records.add(new SourceRecord(sourcePartition, sourceOffset, topic, Schema.STRING_SCHEMA, line));
      } else {
        Thread.sleep(1);
      }
    }
    return records;
  } catch (IOException e) {
    // Underlying stream was killed, probably as a result of calling stop. Allow to return
    // null, and driving thread will handle any shutdown if necessary.
  }
  return null;
}

Again, we’ve omitted some details, but we can see the important steps: the poll() method is going to be called repeatedly, and for each call it will loop trying to read records from the file. For each line it reads, it also tracks the file offset. It uses this information to create an output SourceRecord with four pieces of information: the source partition (there is only one, the single file being read), source offset (position in the file), output topic name, and output value (the line, including a schema indicating this value will always be a string). Other variants of the SourceRecord constructor can also include a specific output partition and a key.

Note that this implementation uses the normal Java InputStream interface and may sleep if data is not available. This is acceptable because Kafka Connect provides each task with a dedicated thread. While task implementations have to conform to the basic poll() interface, they have a lot of flexibility in how they are implemented. In this case, an NIO-based implementation would be more efficient, but this simple approach works, is quick to implement, and is compatible with older versions of Java.

Sink Tasks

The previous section described how to implement a simple SourceTask. Unlike SourceConnector and SinkConnector, SourceTask and SinkTask have very different interfaces because SourceTask uses a pull interface and SinkTask uses a push interface. Both share the common lifecycle methods, but the SinkTask interface is quite different:

public abstract class SinkTask implements Task {
  ... [ lifecycle methods omitted ] ...

  public void initialize(SinkTaskContext context) { ... }

  public abstract void put(Collection<SinkRecord> records);
  public abstract void flush(Map<TopicPartition, Long> offsets);

  public void onPartitionsAssigned(Collection<TopicPartition> partitions) {}
  public void onPartitionsRevoked(Collection<TopicPartition> partitions) {}
}

The SinkTask documentation contains full details, but this interface is nearly as simple as the 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 process does not need to ensure the data has been fully written to the destination system before returning. In fact, in many cases some internal buffering will be useful so an entire batch of records can be sent at once (much like Kafka’s producer), reducing the overhead of inserting events into the downstream data store. The SinkRecords contain essentially the same information as SourceRecords: Kafka topic, partition, and offset and the event key and value.

The flush() method is used during the offset commit process, which allows tasks to recover from failures and resume from a safe point such that no events will be missed. The method should push any outstanding data to the destination system and then block until the write has been acknowledged. The offsets parameter can often be ignored, but is useful in some cases where implementations want to store offset information in the destination store to provide exactly-once delivery. For example, an HDFS connector could do this and use atomic move operations to make sure the flush() operation atomically commits the data and offsets to a final location in HDFS.

Finally, there are two additional methods, onPartitionsAssigned and onPartitionsRevoked, which may optionally be overridden. These are tied to the underlying rebalance callbacks of the KafkaConsumer that is driving the SinkTask. In most cases connectors will not need to override these methods. However, they are useful in cases where exactly-once delivery is possible by managing offsets in the destination system instead of relying on Kafka Connect’s offset commit management.

Resuming from Previous Offsets

The SourceTask implementation included a partition 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, rebalancing of work, etc). 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 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);
}

There are two important points to note about this implementation. First, offsets for this connector are just Longs, a primitive type. However, more complex structures including Maps and Lists can be used as offsets too. Second, the data returned is “schema-free”. This is necessary because it cannot be guaranteed that the underlying Converter that serializes offsets can track schemas. This makes reliable parsing of offsets a bit more challenging for the connector developer, but makes the choice of serialization formats much more flexible.

Of course, you also might need to read many keys for each of the input partitions – only a simple connector like this one will have one input partition. The OffsetStorageReader interface also allows you to issue bulk reads to efficiently load all offsets, then apply them by seeking each input partition to the appropriate position.

Dynamic Input/Output Partitions

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 partitions 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 partitions 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 partitions, which may translate to new entries in their outputs. The Kafka Connect 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 partitions, 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 partition 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 partitions.

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 connectors will need schemas with more complex data formats.

To create more complex data, you’ll need to work with the org.apache.kafka.connect.data API. Most structured records will need to interact with two classes in addition to primitive types: Schema and Struct.

The API documentation provides a complete reference, but here is a simple example creating a Schema and Struct:

Schema schema = SchemaBuilder.struct().name(NAME)
                    .field("name", Schema.STRING_SCHEMA)
                    .field("age", Schema.INT_SCHEMA)
                    .field("admin", new SchemaBuilder.boolean().defaultValue(false).build())
                    .build();

Struct struct = new Struct(schema)
                    .put("name", "Barbara Liskov")
                    .put("age", 75)
                    .build();

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 if 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 example of this is a database connector. Considering even just a single table, the schema will 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 by creating an updated Schema.

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 Kafka Connect framework.

When using the AvroConverter included with the Confluent Platform, schemas are registered under the hood with the Confluent Schema Registry, so any new schemas must satisfy the compatibility requirements for the destination topic.

Testing

Testing connectors can be challenging because Kafka Connect connectors interact with two systems that may be difficult to mock – Kafka and the system the connector is connecting to. It can be tempting to write “unit tests” that are really integration tests. It’s better to very specifically test the functionality of the Connector and Task classes independently while mocking the external services.

Once you have sufficiently unit tested them, we recommend adding separate integration tests to verify end-to-end functionality.