So You Want to Write a Connector?
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The document outlines key aspects of writing and managing Kafka connectors, focusing on producer and consumer applications, connector configuration, handling failures, and scalability. It details the structure and functions of source and sink tasks, data types, and metrics to capture, as well as resources for installation and configuration. Additionally, it highlights recent enhancements in Kafka Connect and available community resources for further learning.
So You Want to Write a Connector?
- 1. So You Want to Write a Connector Randall Hauch @rhauch
- 6. PRODUCER CONSUMER Producer Application Consumer Application • Where to restart ? • How to scale and parallelize ? • What metrics to capture ? • How to handle failure & retries ? • How to properly use the producer / consumer API ?
- 7. PRODUCER CONSUMER KAFKA CONNECT KAFKA CONNECT
- 8. PRODUCER CONSUMER Source Connector ConverterSMTs KAFKA CONNECT KAFKA CONNECT
- 9. PRODUCER CONSUMER Sink ConnectorSMTsSource Connector ConverterSMTs Converter KAFKA CONNECT KAFKA CONNECT
- 10. PRODUCER CONSUMER Sink ConnectorSMTsSource Connector ConverterSMTs Converter KAFKA CONNECT KAFKA CONNECT • Offset management • Elastic scalability • Parallelization • Task distribution • Metrics • Failure & retries • Configuration management • REST API • Schemas & data types
- 11. Discover connectors, SMTs, and converters
- 12. Discover connectors, SMTs, and converters
- 13. Discover connectors, SMTs, and converters Descriptions, licensing, support, and more
- 14. Discover connectors, SMTs, and converters Descriptions, licensing, support, and more
- 15. Discover connectors, SMTs, and converters Simple installs Descriptions, licensing, support, and more
- 16. Discover connectors, SMTs, and converters Simple installs Descriptions, licensing, support, and more
- 17. PRODUCER CONSUMER Sink ConnectorSMTsSource Connector ConverterSMTs Converter KAFKA CONNECT KAFKA CONNECT
- 18. KAFKA CONNECT Starting and Running
- 19. RESTAPIRESTAPIRESTAPI KAFKA CONNECT Starting and Running
- 22. RESTAPIRESTAPIRESTAPI KAFKA CONNECT Starting and Running
- 23. RESTAPIRESTAPIRESTAPI KAFKA CONNECT Starting and Running C deploy
- 24. RESTAPIRESTAPIRESTAPI KAFKA CONNECT Connector Starting and Running C deploy
- 25. RESTAPIRESTAPIRESTAPI KAFKA CONNECT Connector Starting and Running C start(…) deploy
- 36. RESTAPI Connector RESTAPI RESTAPI C Task Task Task Task poll(…) poll(…) poll(…)poll(…) TTTT
- 37. RESTAPI Connector RESTAPI RESTAPI C Task Task Task Task Other Task(s) Other Task(s) Other Task(s) poll(…) poll(…) poll(…)poll(…) Other Connector(s) TTTT
- 38. RESTAPI Connector RESTAPI RESTAPI C TTTT TTTT Task Task Task Task Other Task(s) Other Task(s) Other Task(s) Other Connector(s)
- 39. RESTAPI Connector RESTAPI RESTAPI C TTTT TTTT Task Task Task Task Other Task(s) Other Task(s) Other Task(s) Other Connector(s)
- 40. RESTAPI Connector RESTAPI C TTTT TTTT Task Task Task Other Task(s) Other Task(s) Other Connector(s)
- 41. RESTAPI Connector RESTAPI C TTTT TTTT Task Task Task Other Task(s) Recovery from Failures Other Task(s) Other Connector(s)
- 42. RESTAPI Connector RESTAPI C TTTT TTTT Task Task Task Other Task(s) Recover from Failures Other Task(s) Other Connector(s)
- 43. RESTAPI Connector RESTAPI C TTTT TTTT Task Task Task Task Other Task(s) Other Task(s) Other Connector(s)
- 44. RESTAPI Connector RESTAPI C TTTT TTTT Task Task Task Task Other Task(s) Other Connector(s) Other Task(s)
- 45. RESTAPI Connector RESTAPI C TTTT TTTT Task Task Task Task Stopping Other Task(s) Other Connector(s) Other Task(s)
- 46. RESTAPI Connector RESTAPI C TTTT TTTT Task Task Task Task Stopping stop() stop() stop()stop() Other Task(s) Other Connector(s) Other Task(s)
- 51. class SinkConnector extends Connector {} class SourceConnector extends Connector {} class Connector { String version(); ConfigDef config(); Class<? extends Task> taskClass(); void start(Map<String, String> props); List<Map<String, String>> taskConfigs(int maxTasks); void stop(); ... }
- 52. void start(Map<String, String> props); • The user-specified connector configuration • Optionally talk to external system to get information about tasks • Determine the task configuration • Optionally start thread(s) to monitor external system, and if needed ask for task reconfiguration
- 53. List<Map<String, String>> taskConfigs(int maxTasks); • Tell Connect the configurations for each of the tasks
- 54. abstract class SourceTask implements Task { protected SourceTaskContext context; void start(Map<String, String> props); List<SourceRecord> poll() throws InterruptedException; void stop(); ... }
- 55. void start(Map<String, String> props); • This task’s configuration that was created by your connector • Read previously committed offsets to know where in the external system to start reading • Create any resources it might need - Connections to external system - Buffers, queues, threads, etc.
- 56. • Called frequently • Get the next batch of records that Connect should write to Kafka - block until there are “enough” records to return - return null if no records right now • For systems that push data - use separate thread to receive and process the data and enqueue - poll then dequeues and returns records List<SourceRecord> poll() throws InterruptedException;
- 57. topic : String partition : Integer keySchema : Schema key : Object valueSchema : Schema value : Object timestamp : Long headers : Headers sourcePartition : Map sourceOffset : Map • Topic where the record is to be written • Optional partition # for the topic • Optional key and the schema that describes it • Optional value and the schema that describes it • Optional headers • Optional timestamp • Source “partition” and “offset" - Describes where this record originated - Defined by connector, used only by connector - Connect captures last partition+offset that it writes, periodically committed to connect-offsets topic - When task starts up, it reads these to know where it should start - TIP: reuse the same partition Map instance • Serialized via Converters SourceRecord
- 58. name : String version : String doc : String type : Type parameters : Map fields : List<Field> name : String schema : Schema index : int Field Schema • Name (required), version, and documentation • Type of schema: primitive, Map, Array, Struct • Whether the value described by the schema is optional • Optional metadata parameters • Information about the structure: - For Struct schemas, the fields - For Map schemas, the key schema and value schema - For Array schemas, the element schema • Name of field that this schema describes • Schema for field value • Index of field within the Struct
- 59. abstract class SinkTask implements Task { protected SingTaskContext context; void start(Map<String, String> props); void open(Collection<TopicPartition> partitions); void put(Collection<SinkRecord> records); Map<TopicPartition, OffsetAndMetadata> preCommit( Map<TopicPartition, OffsetAndMetadata> currentOffsets ); void close(Collection<TopicPartition> partitions); void stop(); ... }
- 60. void start(Map<String, String> props); • This task’s configuration that was created by your connector - includes topics and topics.regex property • Start the task and create any resources it might need
- 61. void open(Collection<TopicPartition> partitions); • Optionally pre-allocate writers and resources • Consumer will by default start based on its own committed offsets • Or use context.offsets(...) to set the desired starting point
- 62. • Called frequently with next batch of records returned from the consumer • Size of batch depends on - availability of records in our assigned topic partitions, and - consumer settings in worker, including: consumer.fetch.min.bytes and consumer.fetch.max.bytes consumer.max.poll.interval.ms and consumer.max.poll.records • Either write directly to external system or buffer them and write when there are “enough” to send void put(Collection<SinkRecord> records);
- 63. • Topic, partition, and offset from where the record was consumed • Optional key and the schema that describes it • Optional value and the schema that describes it • Timestamp and whether its create/append/none • Headers - ordered list of name-value pairs - utility to convert value to desired type, if possible • Deserialized via Converters topic : String partition : Integer keySchema : Schema key : Object valueSchema : Schema value : Object timestamp : Long timestampType : enum offset : long headers : Headers SinkRecord
- 64. • The topic partitions that are no longer associated with the task • Close any writers / resources for these topic partitions void close(Collection<TopicPartition> partitions);
- 65. Sink Tasks and Exactly Once Semantics (EOS) • Must track the offsets of records that were written to external system - typically involves atomically storing offsets in external system - for convenience can optionally still have consumer commit those offsets • When a task restarts and is assigned topic partitions, it - looks in the external system for the next offset for each topic partition - set in the task context the exact offsets where to start consuming
- 66. • Called periodically based upon worker’s offset.flush.interval.ms - defaults to 60 seconds • Supplied the current offsets for consumed records as of the last call to put(…) • Return the offsets that the consumer should actually commit - should reflect what was actually written so far to the external system for EOS - or return null if consumer should not commit offsets Map<TopicPartition, OffsetAndMetadata> preCommit( Map<TopicPartition, OffsetAndMetadata> currentOffsets );
- 67. Considerations
- 68. Configuration • Only way to control a connector • Make as simple as possible, but no simpler • Use validators and recommenders in ConfigDef • Strive for backward compatibility
- 69. Message Ordering • Each Kafka topic partition is totally ordered • Design connectors around this notion
- 70. Scaling • Use tasks to parallelize work - each task runs in its own thread - you can start other threads, but always clean them up properly • Sink connectors - typically use # of tasks specified by user • Source connectors - only one task (or several) may make sense
- 71. Testing • Design for testability • Unit tests and integration tests in your build - look for reusable integration tests (PR5516) using embedded ZK & Kafka • Continuous integration for regression tests • System tests use real ZK, Kafka, Connect, and external systems • Performance and soak tests
- 72. Be Resilient • What are the failure modes? • Can you retry (forever) rather than fail? - consider RetriableException
- 73. Logging • Don’t overuse ERROR or WARN • Use INFO level so users know what’s happening under normal operation and when misbehaving • Use DEBUG so users can diagnose why it’s behaving strangely • Use TRACE for you
- 74. Security • Use PASSWORD configuration type in ConfigDef • Communicate securely with external system • Don’t log records (except maybe at TRACE)
- 75. Package Names • Don’t use org.apache.kafka.* packages • These are reserved for use by the Apache Kafka project
- 77. /usr/share/java /opt/java/my-plugin kafka-connect-jdbc kafka-connect-s3 kafka-connect-hdfs ... debezium-mysql ... Connect’s Plugin Path • Added in AK 0.11.0 • Isolates connectors JARs • Set in Connect worker configuration on each machine • Value is comma-separated list of the directories that contain connector directories
- 78. /usr/share/java /opt/java/my-plugin kafka-connect-jdbc kafka-connect-s3 kafka-connect-hdfs ... debezium-mysql ... These should be on plugin.pathConnect’s Plugin Path • Added in AK 0.11.0 • Isolates connectors JARs • Set in Connect worker configuration on each machine • Value is comma-separated list of the directories that contain connector directories
- 79. Confluent Hub Client • Included in Confluent Platform (or can be installed separately) • Sets plugin.path correctly • Install a connector plugin, transform plugin, or converter plugin: confluent-hub install <owner>/<connector>:<version|latest> • Example: confluent-hub install debezium/debezium-connector-mysql:latest • Help confluent-hub help confluent-hub install <owner>/<connector>:<version> confluent-hub install debezium/debezium-connector-mysql:latest confluent-hub help
- 80. https://docs.confluent.io/current/connect/managing/confluent-hub/component-archive.html <plugin> <groupId>io.confluent</groupId> <version>0.11.1</version> <artifactId>kafka-connect-maven-plugin</artifactId> <executions> <execution> <goals> <goal>kafka-connect</goal> </goals> <configuration> <title>My Super Connector</title> <documentationUrl>...</documentationUrl> <description>...</description> ... <tags> <tag>database</tag> ... </tags> </configuration> </execution> </executions> </plugin> Packaging for Confluent Hub • Packaging Plugin for Maven • Defines metadata for Confluent Hub • Assembles an archive for installation • includes correct JARs, images, docs, … • Ready for you to submit
- 82. Recent Improvements AK 1.1 • Expose record headers (KIP-145) • Bug fixes and minor improvements AK 2.0 • Error handling (KIP-298) • Externalized secrets (KIP-297) • Connect REST plugin (KIP-285) • Bug fixes and minor improvements
- 83. Planned Improvements • Bug fixes and minor improvements • Logging improvements (KAFKA-3816) • Integration test framework (pr 5516) • Incremental cooperative rebalancing (wiki) • Idempotent source connectors (KIP-318) • Create topics for source connectors (KIP-158) • Exactly once source connectors • And many more
- 84. 1. You can write connectors! 2. Connect does most of the work for you 3. Connectors focus on - using the external system - mapping external data to records on topics and partitions Key Takeaways
- 85. Resources Apache Kafka Resources - kafka.apache.org (docs, mailing lists, etc.) Confluent Resources - hub.confluent.io - docs.confluent.io - blog.confluent.io - Slack community @ confluent.io/contact