Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. It makes it simple to quickly define <i>connectors</i> 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:
<ul>
<li><b>A common framework for Kafka connectors</b> - Kafka Connect standardizes integration of other data systems with Kafka, simplifying connector development, deployment, and management</li>
<li><b>Distributed and standalone modes</b> - scale up to a large, centrally managed service supporting an entire organization or scale down to development, testing, and small production deployments</li>
<li><b>REST interface</b> - submit and manage connectors to your Kafka Connect cluster via an easy to use REST API</li>
<li><b>Automatic offset management</b> - 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</li>
<li><b>Distributed and scalable by default</b> - Kafka Connect builds on the existing </li>
<li><b>Streaming/batch integration</b> - leveraging Kafka's existing capabilities, Kafka Connect is an ideal solution for bridging streaming and batch data systems</li>
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.
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:
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 <code>config/server.properties</code>. 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:
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:
<ul>
<li><code>group.id</code> (default <code>connect-cluster</code>) - unique name for the cluster, used in forming the Connect cluster group; note that this <b>must not conflict</b> with consumer group IDs</li>
<li><code>config.storage.topic</code> (default <code>connect-configs</code>) - topic to use for storing connector and task configurations; note that this should be a single partition, highly replicated topic</li>
<li><code>offset.storage.topic</code> (default <code>connect-offsets</code>) - topic to use for ; this topic should have many partitions and be replicated</li>
</ul>
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.
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:
<ul>
<li><code>name</code> - Unique name for the connector. Attempting to register again with the same name will fail.</li>
<li><code>connector.class</code> - The Java class for the connector</li>
<li><code>tasks.max</code> - 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.</li>
</ul>
Sink connectors also have one additional option to control their input:
<ul>
<li><code>topics</code> - A list of topics to use as input for this connector</li>
</ul>
For any other options, you should consult the documentation for the connector.
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:
<ul>
<li><code>GET /connectors</code> - return a list of active connectors</li>
<li><code>POST /connectors</code> - create a new connector; the request body should be a JSON object containing a string <code>name</code> field and a object <code>config</code> field with the connector configuration parameters</li>
<li><code>GET /connectors/{name}</code> - get information about a specific connector</li>
<li><code>GET /connectors/{name}/config</code> - get the configuration parameters for a specific connector</li>
<li><code>PUT /connectors/{name}/config</code> - update the configuration parameters for a specific connector</li>
<li><code>GET /connectors/{name}/tasks</code> - get a list of tasks currently running for a connector</li>
<li><code>DELETE /connectors/{name}</code> - delete a connector, halting all tasks and deleting its configuration</li>
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.
To copy data between Kafka and another system, users create a <code>Connector</code> for the system they want to pull data from or push data to. Connectors come in two flavors: <code>SourceConnectors</code> import data from another system (e.g. <code>JDBCSourceConnector</code> would import a relational database into Kafka) and <code>SinkConnectors</code> export data (e.g. <code>HDFSSinkConnector</code> would export the contents of a Kafka topic to an HDFS file).
<code>Connectors</code> do not perform any data copying themselves: their configuration describes the data to be copied, and the <code>Connector</code> is responsible for breaking that job into a set of <code>Tasks</code> that can be distributed to workers. These <code>Tasks</code> also come in two corresponding flavors: <code>SourceTask</code>and <code>SinkTask</code>.
With an assignment in hand, each <code>Task</code> 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.
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.
Not all jobs are static, so <code>Connector</code> implementations are also responsible for monitoring the external system for any changes that might require reconfiguration. For example, in the <code>JDBCSourceConnector</code> example, the <code>Connector</code> might assign a set of tables to each <code>Task</code>. When a new table is created, it must discover this so it can assign the new table to one of the <code>Tasks</code> by updating its configuration. When it notices a change that requires reconfiguration (or a change in the number of <code>Tasks</code>), it notifies the framework and the framework updates any corresponding <code>Tasks</code>.
Developing a connector only requires implementing two interfaces, the <code>Connector</code> and <code>Task</code>. A simple example is included with the source code for Kafka in the <code>file</code> package. This connector is meant for use in standalone mode and has implementations of a <code>SourceConnector</code>/<code>SourceTask</code> to read each line of a file and emit it as a record and a <code>SinkConnector</code>/<code>SinkTask</code> 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.
We'll cover the <code>SourceConnector</code> as a simple example. <code>SinkConnector</code> implementations are very similar. Start by creating the class that inherits from <code>SourceConnector</code> and add a couple of fields that will store parsed configuration information (the filename to read from and the topic to send data to):
<pre>
public class FileStreamSourceConnector extends SourceConnector {
private String filename;
private String topic;
</pre>
The easiest method to fill in is <code>getTaskClass()</code>, which defines the class that should be instantiated in worker processes to actually read the data:
<pre>
@Override
public Class<? extends Task> getTaskClass() {
return FileStreamSourceTask.class;
}
</pre>
We will define the <code>FileStreamSourceTask</code> class below. Next, we add some standard lifecycle methods, <code>start()</code> and <code>stop()</code>:
<pre>
@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.
}
</pre>
Finally, the real core of the implementation is in <code>getTaskConfigs()</code>. In this case we're only
handling a single file, so even though we may be permitted to generate more tasks as per the
<code>maxTasks</code> argument, we return a list with only one entry:
<pre>
@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;
}
</pre>
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 <code>ConnectorUtils</code> 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.
Next we'll describe the implementation of the corresponding <code>SourceTask</code>. 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 <code>Task</code> class. It also has some standard lifecycle methods:
<pre>
public class FileStreamSourceTask extends SourceTask<Object, Object> {
String filename;
InputStream stream;
String topic;
public void start(Map<String, String> props) {
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 <code>start()</code> method does not yet handle resuming from a previous offset, which will be addressed in a later section. Second, the <code>stop()</code> method is synchronized. This will be necessary because <code>SourceTasks</code> 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 <code>poll()</code> method which gets events from the input system and returns a <code>List<SourceRecord></code>:
<pre>
@Override
public List<SourceRecord> poll() throws InterruptedException {
try {
ArrayList<SourceRecord> records = new ArrayList<>();
while (streamValid(stream) && records.isEmpty()) {
Again, we've omitted some details, but we can see the important steps: the <code>poll()</code> 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 <code>SourceRecord</code> 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 <code>SourceRecord</code> constructor can also include a specific output partition and a key.
Note that this implementation uses the normal Java <code>InputStream</code>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 <code>poll()</code>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.
The previous section described how to implement a simple <code>SourceTask</code>. Unlike <code>SourceConnector</code>and <code>SinkConnector</code>, <code>SourceTask</code>and <code>SinkTask</code>have very different interfaces because <code>SourceTask</code>uses a pull interface and <code>SinkTask</code>uses a push interface. Both share the common lifecycle methods, but the <code>SinkTask</code>interface is quite different:
<pre>
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);
</pre>
The <code>SinkTask</code> documentation contains full details, but this interface is nearly as simple as the the <code>SourceTask</code>. The <code>put()</code>method should contain most of the implementation, accepting sets of <code>SinkRecords</code>, 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 <code>SinkRecords</code>contain essentially the same information as <code>SourceRecords</code>: Kafka topic, partition, offset and the event key and value.
The <code>flush()</code>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 <code>offsets</code>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 <code>flush()</code>operation atomically commits the data and offsets to a final location in HDFS.
The <code>SourceTask</code>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 <code>SourceContext</code>passed into its <code>initialize()</code>method to access the offset data. In <code>initialize()</code>, we would add a bit more code to read the offset (if it exists) and seek to that position:
Long lastRecordedOffset = (Long) offset.get("position");
if (lastRecordedOffset != null)
seekToOffset(stream, lastRecordedOffset);
}
</pre>
Of course, you might need to read many keys for each of the input streams. The <code>OffsetStorageReader</code> 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.
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 <code>ConnectorContext</code>object that reconfiguration is necessary. For example, in a <code>SourceConnector</code>:
<pre>
if (inputsChanged())
this.context.requestTaskReconfiguration();
</pre>
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 <code>SourceConnector</code>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 <code>Connector</code>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 <code>Task</code>encounters the issue before the <code>Connector</code>, which will be common if the <code>Connector</code>needs to poll for changes, the <code>Task</code>will need to handle the subsequent error. Thankfully, this can usually be handled simply by catching and handling the appropriate exception.
<code>SinkConnectors</code> 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. <code>SinkTasks</code>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 <code>SinkTasks</code>seeing a new input stream for the first time and simultaneoulsy trying to create the new resource. <code>SinkConnectors</code>, on the other hand, will generally require no special code for handling a dynamic set of streams.
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 <code>data</code> API. Most structured records will need to interact with two classes in addition to primitive types: <code>Schema</code> and <code>Struct</code>.
The API documentation provides a complete reference, but here is a simple example creating a <code>Schema</code>and <code>Struct</code>:
<pre>
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();
</pre>
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 <code>ALTER TABLE</code>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.
