Kafka Connect
Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. It makes it simple to quickly define connectors that move large collections of data into and out of Kafka. Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. An export job can deliver data from Kafka topics into secondary storage and query systems or into batch systems for offline analysis. Kafka Connect features include:
A common framework for Kafka connectors - Kafka Connect standardizes integration of other data systems with Kafka, simplifying connector development, deployment, and management
Distributed and standalone modes - scale up to a large, centrally managed service supporting an entire organization or scale down to development, testing, and small production deployments
REST interface - submit and manage connectors to your Kafka Connect cluster via an easy to use REST API
Automatic offset management - with just a little information from connectors, Kafka Connect can manage the offset commit process automatically so connector developers do not need to worry about this error prone part of connector development
Distributed and scalable by default - Kafka Connect builds on the existing group management protocol. More workers can be added to scale up a Kafka Connect cluster.
Streaming/batch integration - leveraging Kafka's existing capabilities, Kafka Connect is an ideal solution for bridging streaming and batch data systems
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 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:
The difference is in the class which is started and the configuration parameters which change how the Kafka Connect process decides where to store configurations, how to assign work, and where to store offsets and task statues. In the distributed mode, Kafka Connect stores the offsets, configs and task statuses in Kafka topics. It is recommended to manually create the topics for offset, configs and statuses in order to achieve the desired the number of partitions and replication factors. If the topics are not yet created when starting Kafka Connect, the topics will be auto created with default number of partitions and replication factor, which may not be best suited for its usage. In particular, the following configuration parameters are critical to set before starting your cluster:
group.id(defaultconnect-cluster) - unique name for the cluster, used in forming the Connect cluster group; note that this must not conflict with consumer group IDsconfig.storage.topic(defaultconnect-configs) - topic to use for storing connector and task configurations; note that this should be a single partition, highly replicated topic. You may need to manually create the topic to ensure single partition for the config topic as auto created topics may have multiple partitions.offset.storage.topic(defaultconnect-offsets) - topic to use for storing offsets; this topic should have many partitions and be replicatedstatus.storage.topic(defaultconnect-status) - topic to use for storing statuses; this topic can have multiple partitions and should be replicated
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:
name- Unique name for the connector. Attempting to register again with the same name will fail.connector.class- The Java class for the connectortasks.max- The maximum number of tasks that should be created for this connector. The connector may create fewer tasks if it cannot achieve this level of parallelism.
The connector.class config supports several formats: the full name or alias of the class for this connector. If the connector is org.apache.kafka.connect.file.FileStreamSinkConnector, you can either specify this full name or use FileStreamSink or FileStreamSinkConnector to make the configuration a bit shorter. Sink connectors also have one additional option to control their input:
topics- A list of topics to use as input for this connector
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 provides a REST API for managing connectors. By default this service runs on port 8083. The following are the currently supported endpoints:
GET /connectors- return a list of active connectorsPOST /connectors- create a new connector; the request body should be a JSON object containing a stringnamefield and a objectconfigfield with the connector configuration parametersGET /connectors/{name}- get information about a specific connectorGET /connectors/{name}/config- get the configuration parameters for a specific connectorPUT /connectors/{name}/config- update the configuration parameters for a specific connectorGET /connectors/{name}/status- get current status of the connector, including if it is running, failed, paused, etc., which worker it is assigned to, error information if it has failed, and the state of all its tasksGET /connectors/{name}/tasks- get a list of tasks currently running for a connectorGET /connectors/{name}/tasks/{taskid}/status- get current status of the task, including if it is running, failed, paused, etc., which worker it is assigned to, and error information if it has failedPUT /connectors/{name}/pause- pause the connector and its tasks, which stops message processing until the connector is resumedPUT /connectors/{name}/resume- resume a paused connector (or do nothing if the connector is not paused)POST /connectors/{name}/restart- restart a connector (typically because it has failed)POST /connectors/{name}/tasks/{taskId}/restart- restart an individual task (typically because it has failed)DELETE /connectors/{name}- delete a connector, halting all tasks and deleting its configuration
Kafka Connect also provides a REST API for getting information about connector plugins:
GET /connector-plugins- return a list of connector plugins installed in the Kafka Connect cluster. Note that the API only checks for connectors on the worker that handles the request, which means you may see inconsistent results, especially during a rolling upgrade if you add new connector jarsPUT /connector-plugins/{connector-type}/config/validate- validate the provided configuration values against the configuration definition. This API performs per config validation, returns suggested values and error messages during validation.
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 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) andSinkConnectors 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.
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 Connector implementations are also responsible for monitoring the external system for any changes that might require reconfiguration. For example, in the JDBCSourceConnectorexample, 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 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.
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):
The easiest method to fill in is getTaskClass(), which defines the class that should be instantiated in worker processes to actually read the data:
We will define the FileStreamSourceTask class below. Next, we add some standard lifecycle methods,start() and stop():
Finally, the real core of the implementation is in getTaskConfigs(). 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:
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 bypoll. 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. 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 inConnectorUtils 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 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:
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 becauseSourceTasks 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, thepoll() method which gets events from the input system and returns a List<SourceRecord>:
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 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.
The previous section described how to implement a simple SourceTask. Unlike SourceConnector andSinkConnector, 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 theSinkTask interface is quite different:
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 asSourceRecords: 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 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.
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:
Of course, you might need to read many keys for each of the input streams. The OffsetStorageReaderinterface 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 ConnectorContext object that reconfiguration is necessary. For example, in aSourceConnector:
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 Connectorneeds 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.
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.
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.
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:
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.
Kafka Connect's REST layer provides a set of APIs to enable administration of the cluster. This includes APIs to view the configuration of connectors and the status of their tasks, as well as to alter their current behavior (e.g. changing configuration and restarting tasks).
When a connector is first submitted to the cluster, the workers rebalance the full set of connectors in the cluster and their tasks so that each worker has approximately the same amount of work. This same rebalancing procedure is also used when connectors increase or decrease the number of tasks they require, or when a connector's configuration is changed. You can use the REST API to view the current status of a connector and its tasks, including the id of the worker to which each was assigned. For example, querying the status of a file source (using GET /connectors/file-source/status) might produce output like the following:
Connectors and their tasks publish status updates to a shared topic (configured withstatus.storage.topic) which all workers in the cluster monitor. Because the workers consume this topic asynchronously, there is typically a (short) delay before a state change is visible through the status API. The following states are possible for a connector or one of its tasks:
UNASSIGNED: The connector/task has not yet been assigned to a worker.
RUNNING: The connector/task is running.
PAUSED: The connector/task has been administratively paused.
FAILED: The connector/task has failed (usually by raising an exception, which is reported in the status output).
In most cases, connector and task states will match, though they may be different for short periods of time when changes are occurring or if tasks have failed. For example, when a connector is first started, there may be a noticeable delay before the connector and its tasks have all transitioned to the RUNNING state. States will also diverge when tasks fail since Connect does not automatically restart failed tasks. To restart a connector/task manually, you can use the restart APIs listed above. Note that if you try to restart a task while a rebalance is taking place, Connect will return a 409 (Conflict) status code. You can retry after the rebalance completes, but it might not be necessary since rebalances effectively restart all the connectors and tasks in the cluster.
It's sometimes useful to temporarily stop the message processing of a connector. For example, if the remote system is undergoing maintenance, it would be preferable for source connectors to stop polling it for new data instead of filling logs with exception spam. For this use case, Connect offers a pause/resume API. While a source connector is paused, Connect will stop polling it for additional records. While a sink connector is paused, Connect will stop pushing new messages to it. The pause state is persistent, so even if you restart the cluster, the connector will not begin message processing again until the task has been resumed. Note that there may be a delay before all of a connector's tasks have transitioned to the PAUSED state since it may take time for them to finish whatever processing they were in the middle of when being paused. Additionally, failed tasks will not transition to the PAUSED state until they have been restarted.
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