8.1 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:

8.2 User Guide

The quickstart provides a brief example of how to run a standalone version of Kafka Connect. This section describes how to configure, run, and manage Kafka Connect in more detail.

Running Kafka Connect

Kafka Connect currently supports two modes of execution: standalone (single process) and distributed. In standalone mode all work is performed in a single process. This configuration is simpler to setup and get started with and may be useful in situations where only one worker makes sense (e.g. collecting log files), but it does not benefit from some of the features of Kafka Connect such as fault tolerance. You can start a standalone process with the following command: 
> bin/connect-standalone.sh config/connect-standalone.properties connector1.properties [connector2.properties ...]
The first parameter is the configuration for the worker. This includes settings such as the Kafka connection parameters, serialization format, and how frequently to commit offsets. The provided example should work well with a local cluster running with the default configuration provided by config/server.properties. It will require tweaking to use with a different configuration or production deployment. The remaining parameters are connector configuration files. You may include as many as you want, but all will execute within the same process (on different threads). Distributed mode handles automatic balancing of work, allows you to scale up (or down) dynamically, and offers fault tolerance both in the active tasks and for configuration and offset commit data. Execution is very similar to standalone mode: 
> bin/connect-distributed.sh config/connect-distributed.properties
The difference is in the class which is started and the configuration parameters which change how the Kafka Connect process decides where to store configurations, how to assign work, and where to store offsets. In particular, the following configuration parameters are critical to set before starting your cluster: Note that in distributed mode the connector configurations are not passed on the command line. Instead, use the REST API described below to create, modify, and destroy connectors.

Configuring Connectors

Connector configurations are simple key-value mappings. For standalone mode these are defined in a properties file and passed to the Connect process on the command line. In distributed mode, they will be included in the JSON payload for the request that creates (or modifies) the connector. Most configurations are connector dependent, so they can't be outlined here. However, there are a few common options: Sink connectors also have one additional option to control their input: For any other options, you should consult the documentation for the connector.

REST API

Since Kafka Connect is intended to be run as a service, it also supports a REST API for managing connectors. By default this service runs on port 8083. The following are the currently supported endpoints:

8.3 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: SourceTaskand 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 anycorresponding Tasks.

Developing a Simple Connector

Developing a connector only requires implementing two interfaces, the Connector and Task. A simple example is included with the source code for Kafka in the file package. This connector is meant for use in standalone mode and has implementations of a SourceConnector/SourceTask to read each line of a file and emit it as a record and a SinkConnector/SinkTask that writes each record to a file. The rest of this section will walk through some code to demonstrate the key steps in creating a connector, but developers should also refer to the full example source code as many details are omitted for brevity.
Connector Example
We'll cover the SourceConnector as a simple example. SinkConnector implementations are very similar. Start by creating the class that inherits from SourceConnector and add a couple of fields that will store parsed configuration information (the filename to read from and the topic to send data to): 
public class FileStreamSourceConnector extends SourceConnector {
    private String filename;
    private String topic;
The easiest method to fill in is getTaskClass(), which defines the class that should be instantiated in worker processes to actually read the data: 
@Override
public Class 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 stream makes sense.
    Map<String, String> config = new Map<>();
    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 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<Object, Object> {
    String filename;
    InputStream stream;
    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 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 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 (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 inclue a specific output partition and a key. Note that this implementation uses the normal Java InputStreaminterface and may sleep if data is not avaiable. 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 SourceConnectorand SinkConnector, SourceTaskand SinkTaskhave very different interfaces because SourceTaskuses a pull interface and SinkTaskuses a push interface. Both share the common lifecycle methods, but the SinkTaskinterface is quite different: 
public abstract class SinkTask implements Task {
public void initialize(SinkTaskContext context) { ... }

public abstract void put(Collection<SinkRecord> records);

public abstract void flush(Map<TopicPartition, Long> offsets);
The SinkTask documentation contains full details, but this interface is nearly as simple as the 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 SinkRecordscontain essentially the same information as SourceRecords: Kafka topic, partition, offset and the event key and value. The flush()method is used during the offset commit process, which allows tasks to recover from failures and resume from a safe point such that no events will be missed. The method should push any outstanding data to the destination system and then block until the write has been acknowledged. The offsetsparameter can often be ignored, but is useful in some cases where implementations want to store offset information in the destination store to provide exactly-once delivery. For example, an HDFS connector could do this and use atomic move operations to make sure the flush()operation atomically commits the data and offsets to a final location in HDFS.
Resuming from Previous Offsets
The SourceTaskimplementation 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 SourceContextpassed into its initialize()method to access the offset data. In initialize(), we would add a bit more code to read the offset (if it exists) and seek to that position: 
    stream = new FileInputStream(filename);
    Map<String, Object> offset = context.offsetStorageReader().offset(Collections.singletonMap(FILENAME_FIELD, filename));
    if (offset != null) {
        Long lastRecordedOffset = (Long) offset.get("position");
        if (lastRecordedOffset != null)
            seekToOffset(stream, lastRecordedOffset);
    }
Of course, you might need to read many keys for each of the input streams. The OffsetStorageReader interface also allows you to issue bulk reads to efficiently load all offsets, then apply them by seeking each input stream to the appropriate position.

Dynamic Input/Output Streams

Kafka Connect is intended to define bulk data copying jobs, such as copying an entire database rather than creating many jobs to copy each table individually. One consequence of this design is that the set of input or output streams for a connector can vary over time. Source connectors need to monitor the source system for changes, e.g. table additions/deletions in a database. When they pick up changes, they should notify the framework via the ConnectorContextobject 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 SourceConnectorthis 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 Connectorand 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 Taskencounters the issue before the Connector, which will be common if the Connectorneeds to poll for changes, the Taskwill 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. SinkTasksshould 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 SinkTasksseeing a new input stream for the first time and simultaneoulsy trying to create the new resource. SinkConnectors, on the other hand, will generally require no special code for handling a dynamic set of streams.

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 Schemaand 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 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 TABLEcommand. 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.