Deep Dive into Apache Kafka
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In the last few years, Apache Kafka has been used extensively in enterprises for real-time data collecting, delivering, and processing. In this presentation, Jun Rao, Co-founder, Confluent, gives a deep dive on some of the key internals that help make Kafka popular. - Companies like LinkedIn are now sending more than 1 trillion messages per day to Kafka. Learn about the underlying design in Kafka that leads to such high throughput. - Many companies (e.g., financial institutions) are now storing mission critical data in Kafka. Learn how Kafka supports high availability and durability through its built-in replication mechanism. - One common use case of Kafka is for propagating updatable database records. Learn how a unique feature called compaction in Apache Kafka is designed to solve this kind of problem more naturally.
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Deep Dive into Apache Kafka
- 1. Deep Dive into Apache Kafka Jun Rao co-founder of Confluent
- 2. Kafka Usage
- 4. Agenda • High throughput • Reliability and durability • Compacted topic
- 5. Scaled-out Architecture 5 Kafka cluster broker 1 … producer producer producer consumer consumer broker 2 broker n topic partition
- 7. Detailed Log Representation 7 offset 0 - 10000 timestamp index offset index offset 10001 - 20000 offset 20001 - 30000 offset index offset index timestamp index timestamp index
- 8. Message Format offset message length CRC timestamp key length key content value length value content 8 bytes 4 bytes 4 bytes magic byte 1 byte attribute 1 byte 8 bytes 4 bytes varies 4 bytes varies
- 9. Batching and Compression compressed batch 1send() send() send() send() producer async flush poll()compressed batch 2 compressed batch 3 compressed batch 1 compressed batch 2 compressed batch 3 consumerbroker
- 10. Agenda • High throughput • Reliability and durability • Compacted topic
- 11. Kafka Replication • Configurable replication factor • Tolerating f – 1 failures with f replicas • Unlike quorum based replication • Automated failover
- 12. Replicas and Layout • Topic partition has replicas • Replicas spread evenly among brokers topic1-part1 logs broker 1 topic1-part2 logs broker 2 topic2-part2 topic2-part1 logs broker 3 topic1-part1 logs broker 4 topic1-part2 topic2-part2 topic1-part1 topic1-part2 topic2-part1 topic2-part2 topic2-part1
- 13. High Level Data Flow in Replication broker 1 producer leader broker 2 follower broker 3 follower 4 2 2 3 commit ack When producer receives ack Latency Durability on failures acks=0 (no ack) no network delay some data loss acks=1 (wait for leader) 1 network roundtrip a few data loss acks=all (wait for committed) 2 network roundtrips no data loss topic1-part1 topic1-part1 topic1-part1 consumer 1
- 14. Extend to Multiple Partitions Leaders are evenly spread among brokers broker 1 broker 2 topic3-part1 follower broker 3 topic3-part1 follower topic1-part1 producer leader topic1-part1 follower topic1-part1 follower broker 4 topic3-part1 leader producer topic2-part1 producer leader topic2-part1 follower topic2-part1 follower
- 15. In-sync Replicas (ISR) broker 1 producer leader broker 2 follower broker 3 follower 2 2 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 ISR last committed m2, m1 In-sync : replica reads from leader’s log end within replica.lag.time.max.ms
- 16. Follower Failure broker 1 producer leader broker 2 follower broker 3 follower 2 2 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 ISR last committed
- 17. Shrinking ISR broker 1 producer leader broker 2 follower broker 3 2 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 ISR m3 m3 m4 m4last committed m4, m3 follower
- 18. Failed Replica Coming Back broker 1 producer leader broker 2 follower broker 3 2 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 ISR m3 m3 m4 m4last committed m3 2 follower
- 19. Leader Failure broker 1 producer leader broker 2 follower broker 3 2 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 ISR m3 m3 m4 m4last committed m3 2 follower
- 20. Selecting New Leader from ISR broker 1 producer leader broker 2 leader broker 3 2 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 ISR m3 m3 m4 m4last committed m3 follower
- 21. Expanding ISR broker 1 producer leader broker 2 leader broker 3 2 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 ISR m3 m3 m4 m4 last committed m3 follower m4 m5 m5 m5
- 22. m5 Unclean Leader Election broker 1 producer leader ??? broker 2 leader broker 3 2 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 ISR m3 m3 m4 m4last committed m3 follower m4 m5
- 23. m5 Guaranteed Replicas broker 1 producer broker 2 leader broker 3 topic1-part1 topic1-part1 topic1-part1 1 m1 m1 m1 m2 m2 m2 m3 m3 m4 m4 last committed m3 m4 m5 ISR > min.insync.replicas ? m6
- 24. Mission Critical Data • Disable Unclean Leader Election • unclean.leader.election.enable = false • Set replication factor • default.replication.factor = 3 • Set minimum ISRs • min.insync.replicas = 2 • Set producer acks • acks = all 24
- 25. Failure Detection and Controller Flow broker 1 broker 2 topic3-part1 follower broker 3 topic3-part1 follower topic1-part1 controller leader topic1-part1 follower topic1-part1 follower broker 4 topic3-part1 leader topic2-part1 leader topic2-part1 follower topic2-part1 follower new leaders and ISRs
- 26. Agenda • High throughput • Reliability and durability • Compacted topic
- 27. Log Compaction: What is it?
- 28. Use Case product catalog search index item1 “new description”
- 29. Adding a New Index Instance product catalog search index item1 “new description” new search index
- 30. Using a Compacted Topic product catalog search index item1 “new description” new search index set consumer offset to 0
- 31. Log Cleaner Implementation offset 3001 - 4000 firstDirty 1. build map 2. scan and probe map firstDirty offset 2001 - 3000offset 1001 - 2000 offset 4001 - 5000 offset 5001 - 6000 offset 3001 - 5000offset 1001 - 3000 offset 5001 - 6000 key1 3500 key2 3700 key3 4200key1 1500 key last offset reject key4 2100 keep key3 4200 keep
- 32. Cleaning Configs • log.cleaner.min.cleanable.ratio (default 0.5) • dirty/total ratio when log cleaner is triggered • log.cleaner.io.max.bytes.per.second (default infinite) • Max rate cleaning can be done • Can be used for throttling
- 33. Be Careful with Deletes • Delete tombstone modeled as null message • Danger of removing a deleted key too soon • Consumer still assumes the old value with the key • log.cleaner.delete.retention.ms (default 1 day) • “Delete tombstone” removed after that time • Consumer needs to finish consuming the tombstone before that time
- 34. Summary • Apache Kafka is a streaming platform • The storage part supports • High throughput • High availability and durability • Retaining database-like data
- 35. Coming Up Next Date Title Speaker 10/27 Data Integration with Kafka Gwen Shapira 11/17 Demystifying Stream Processing Neha Narkhede 12/1 A Practical Guide To Selecting A Stream Processing Technology Michael Noll 12/15 Streaming in Practice: Putting Apache Kafka in Production Roger Hoover
Hinweis der Redaktion
- It’s widely used and in production at thousands of companies. Let’s walk through the the basics of Kafka and understand how it acts as a streaming platform.
- Now we’ve talked about these two motivations for streams---solving pipline spawl and asynchronous stream processing. It won’t surprise anyone that when I talk about this streaming platform that enables these pipelines and processing I am talking about Apache Kafka.
- New producer api. Be aware of non-java api.
- Put partitions inside topic? Maybe useful to use another picture. 3-tier
- New producer api. Be aware of non-java api.
- Leaders spread over brokers. Another picture. Animated transitioin.
- New producer api. Be aware of non-java api.
- Actually in 0.8.1
- Actually in 0.8.1