Kafka Summit SF 2017 - Kafka Connect Best Practices – Advice from the Field
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Presented at Kafka Summit SF by Randall Hauch
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Kafka Summit SF 2017 - Kafka Connect Best Practices – Advice from the Field
- 1. 1 Kafka Connect Best Practices: Advice from the Field Randall Hauch, Software Engineer @rhauch
- 2. 2 Agenda 1. Kafka Connect basics 2. Choosing and using connectors 3. Planning Kafka Connect deployments
- 3. 3 Agenda 1. Kafka Connect basics 2. Choosing and using connectors 3. Planning Kafka Connect deployments
- 16. 16 Kafka Connect: Connectors Connectors are reusable components that know how to talk to specific sources and sinks.
- 17. 17 Kafka Connect: Converters Convert between the source and sink record objects and the binary format used to persist them in Kafka. JSON, Avro, and others
- 18. 18 Kafka Connect: Single Message Transforms (SMTs) Modify the structure of keys and values, topic, and partition of source and sink record objects.
- 19. 19 Kafka Connect: Single Message Transforms (SMTs) Modify the structure of keys and values, topics, and partition of source and sink record objects.
- 20. 20 Kafka Connect: Plugins Connectors, transforms, and converters are pluggable components.
- 21. 21 Kafka Connect: Classpath Isolation A plugin is a directory containing the JARs for a connector, transform, and/or converters. JAR files, sample configs, etc.
- 22. 22 Kafka Connect: Classpath Isolation my-plugins (include on the plugin.path ) JAR files, sample configs, etc. kafka-connect-foo-connector The plugin.path worker configuration property lists the directories that contain plugins
- 23. 23 Kafka Connect: Classpath Isolation my-plugins (include on the plugin.path ) kafka-connect-foo-connector kafka-connect-bar-connector Workers isolate the JARs for each connector, transform, and converter to prevent conflicts.
- 24. 24 Kafka Connect: Offsets Kafka Connect automatically and periodically commits the progress of connectors. Connectors restart at last committed position.
- 25. 25 Kafka Connect: Delivery Guarantees Kafka Connect processes each record once under normal operations. But things can go wrong, so it can only guarantee at-least-once delivery. Sink connectors can achieve exactly once when they store offsets in the sink system.
- 26. 26 Agenda 1. Kafka Connect basics 2. Choosing and using connectors 3. Planning Kafka Connect deployments
- 28. 28 Source Connectors JDBC source connector works with lots of DBMSes. Access data in tables, views, or custom queries. Incremental mode requires creation and/or modification columns. Detects soft deletes only, not removed rows.
- 29. 29 Source Connectors Change Data Capture (CDC) connectors monitor system for all changes, including deleted rows. CDC connectors often require non-standard and source-specific APIs. Typically detect changes only in physical tables only.
- 31. 31 Sink Connectors How are topics and partitions mapped to the external system? Some sink connectors are more flexible than others.
- 32. 32 Sink Connectors Is the sink connector at least once or exactly once? Confluent’s HDFS and S3 are exactly once. But many at-least-once sink connectors are idempotent.
- 33. 33 Choosing and using connectors Use a playground that is easy to clean up and restart. Confluent CLI is perfect for this!
- 34. 34 Confluent Command Line Interface (CLI) Utility to easily operate Kafka-related services on your local machine. $ confluent start $ confluent current $ confluent help $ confluent logs $ confluent top $ confluent status $ confluent status connectors $ confluent config <connector_name> $ confluent load <connector_name> $ confluent status <connector_name> $ confluent unload <connector_name> $ confluent stop connect $ confluent stop $ confluent destroy Uses configuration files in `etc/kafka` and `etc/schema-registry` Stores data in temporary directory: $CONFLUENT_CURRENT or `confluent current` Zookeeper, Kafka, Schema Registry, Kafka REST, and Connect Distributed
- 35. 35 Confluent Command Line Interface (CLI) Demo
- 36. 36 Agenda 1. Kafka Connect basics 2. Choosing and using connectors 3. Planning Kafka Connect deployments
- 37. 37 Planning Kafka Connect deployments Understand the schemas of your records. Confluent Schema Registry and Avro Converters make schema evolution possible. Producers and consumers can adapt to new schemas at different times. Enforce forward and/or backward compatibility.
- 38. 38 Planning Kafka Connect deployments Install connectors on all workers in the cluster, not just one of the workers.
- 39. 39 Planning Kafka Connect deployments Kafka Connect is a simple Java application, so it can be deployed on many systems, including Kubernetes, Mesos, EC2, etc.
- 40. 40 Planning Kafka Connect deployments Don’t overload your workers. If they are too busy, they may skip heartbeats and drop out of the cluster, causing a rebalance. (improvement: KAFKA-5741)
- 41. 41 Planning Kafka Connect deployments Tune the producers and consumers. • producers and consumers • offset commit intervals • poll intervals, batch sizes, # of tasks
- 42. 42 Planning Kafka Connect deployments Minimize rebalances. If you need more isolation and control, use separate worker clusters.
- 43. 43 Summary
- 44. 44 Summary • Use Kafka Connect for sharing and moving data • Choose the right connectors • Learn how to use the connectors • Plan your deployments
- 45. 45 Thank You!