Chapter 4. About Kafka Connect Kafka Connect is an integration toolkit for streaming data between Kafka brokers and other systems. The other system is typically an external data source or target, such as a database. Kafka Connect uses a plugin architecture to provide the implementation artifacts for connectors. Plugins allow connections to other systems and provide additional configuration to manipulate data. Plugins include connectors and other components, such as data converters and transforms. A connector operates with a specific type of external system. Each connector defines a schema for its configuration. You supply the configuration to Kafka Connect to create a connector instance within Kafka Connect. Connector instances then define a set of tasks for moving data between systems. AMQ Streams operates Kafka Connect in distributed mode , distributing data streaming tasks across one or more worker pods. A Kafka Connect cluster comprises a group of worker pods. Each connector is instantiated on a single worker. Each connector comprises one or more tasks that are distributed across the group of workers. Distribution across workers permits highly scalable pipelines. Workers convert data from one format into another format that's suitable for the source or target system. Depending on the configuration of the connector instance, workers might also apply transforms (also known as Single Message Transforms, or SMTs). Transforms adjust messages, such as filtering certain data, before they are converted. Kafka Connect has some built-in transforms, but other transformations can be provided by plugins if necessary. 4.1. How Kafka Connect streams data Kafka Connect uses connector instances to integrate with other systems to stream data. Kafka Connect loads existing connector instances on start up and distributes data streaming tasks and connector configuration across worker pods. Workers run the tasks for the connector instances. Each worker runs as a separate pod to make the Kafka Connect cluster more fault tolerant. If there are more tasks than workers, workers are assigned multiple tasks. If a worker fails, its tasks are automatically assigned to active workers in the Kafka Connect cluster. The main Kafka Connect components used in streaming data are as follows: Connectors to create tasks Tasks to move data Workers to run tasks Transforms to manipulate data Converters to convert data 4.1.1. Connectors Connectors can be one of the following type: Source connectors that push data into Kafka Sink connectors that extract data out of Kafka Plugins provide the implementation for Kafka Connect to run connector instances. Connector instances create the tasks required to transfer data in and out of Kafka. The Kafka Connect runtime orchestrates the tasks to split the work required between the worker pods. MirrorMaker 2 also uses the Kafka Connect framework. In this case, the external data system is another Kafka cluster. Specialized connectors for MirrorMaker 2 manage data replication between source and target Kafka clusters. Note In addition to the MirrorMaker 2 connectors, Kafka provides two connectors as examples: FileStreamSourceConnector streams data from a file on the worker's filesystem to Kafka, reading the input file and sending each line to a given Kafka topic. FileStreamSinkConnector streams data from Kafka to the worker's filesystem, reading messages from a Kafka topic and writing a line for each in an output file. The following source connector diagram shows the process flow for a source connector that streams records from an external data system. A Kafka Connect cluster might operate source and sink connectors at the same time. Workers are running in distributed mode in the cluster. Workers can run one or more tasks for more than one connector instance. Source connector streaming data to Kafka A plugin provides the implementation artifacts for the source connector A single worker initiates the source connector instance The source connector creates the tasks to stream data Tasks run in parallel to poll the external data system and return records Transforms adjust the records, such as filtering or relabelling them Converters put the records into a format suitable for Kafka The source connector is managed using KafkaConnectors or the Kafka Connect API The following sink connector diagram shows the process flow when streaming data from Kafka to an external data system. Sink connector streaming data from Kafka A plugin provides the implementation artifacts for the sink connector A single worker initiates the sink connector instance The sink connector creates the tasks to stream data Tasks run in parallel to poll Kafka and return records Converters put the records into a format suitable for the external data system Transforms adjust the records, such as filtering or relabelling them The sink connector is managed using KafkaConnectors or the Kafka Connect API 4.1.2. Tasks Data transfer orchestrated by the Kafka Connect runtime is split into tasks that run in parallel. A task is started using the configuration supplied by a connector instance. Kafka Connect distributes the task configurations to workers, which instantiate and execute tasks. A source connector task polls the external data system and returns a list of records that a worker sends to the Kafka brokers. A sink connector task receives Kafka records from a worker for writing to the external data system. For sink connectors, the number of tasks created relates to the number of partitions being consumed. For source connectors, how the source data is partitioned is defined by the connector. You can control the maximum number of tasks that can run in parallel by setting tasksMax in the connector configuration. The connector might create fewer tasks than the maximum setting. For example, the connector might create fewer tasks if it's not possible to split the source data into that many partitions. Note In the context of Kafka Connect, a partition can mean a topic partition or a shard of data in an external system. 4.1.3. Workers Workers employ the connector configuration deployed to the Kafka Connect cluster. The configuration is stored in an internal Kafka topic used by Kafka Connect. Workers also run connectors and their tasks. A Kafka Connect cluster contains a group of workers with the same group.id . The ID identifies the cluster within Kafka. The ID is assigned in the worker configuration through the KafkaConnect resource. Worker configuration also specifies the names of internal Kafka Connect topics. The topics store connector configuration, offset, and status information. The group ID and names of these topics must also be unique to the Kafka Connect cluster. Workers are assigned one or more connector instances and tasks. The distributed approach to deploying Kafka Connect is fault tolerant and scalable. If a worker pod fails, the tasks it was running are reassigned to active workers. You can add to a group of worker pods through configuration of the replicas property in the KafkaConnect resource. 4.1.4. Transforms Kafka Connect translates and transforms external data. Single-message transforms change messages into a format suitable for the target destination. For example, a transform might insert or rename a field. Transforms can also filter and route data. Plugins contain the implementation required for workers to perform one or more transformations. Source connectors apply transforms before converting data into a format supported by Kafka. Sink connectors apply transforms after converting data into a format suitable for an external data system. A transform comprises a set of Java class files packaged in a JAR file for inclusion in a connector plugin. Kafka Connect provides a set of standard transforms, but you can also create your own. 4.1.5. Converters When a worker receives data, it converts the data into an appropriate format using a converter. You specify converters for workers in the worker config in the KafkaConnect resource. Kafka Connect can convert data to and from formats supported by Kafka, such as JSON or Avro. It also supports schemas for structuring data. If you are not converting data into a structured format, you don't need to enable schemas. Note You can also specify converters for specific connectors to override the general Kafka Connect worker configuration that applies to all workers. Additional resources Apache Kafka documentation Kafka Connect configuration of workers Synchronizing data between Kafka clusters using MirrorMaker 2

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