Kafka Summit SF 2017 - Running Kafka as a Service at Scale
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Presented at Kafka Summit SF 2017 by Sriram Subramanian, Director, Platform & Infra Engineering, Confluent
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Kafka Summit SF 2017 - Running Kafka as a Service at Scale
- 1. 1 Running Apache Kafka as a Service at Scale Sriram Subramanian, Director of Engineering
- 2. 2 52% of Kafka users in the cloud
- 4. 4 Managing Distributed Systems is really really hard
- 5. 5 Failure is inevitable – Cloud makes it worse
- 6. 6 Need to adapt to change
- 7. 7 Observability is a dark art You can’t fix what you can’t see
- 8. 8 Observability is a dark art Observability is a dark art perfected only with time and experience!
- 9. 9 Kafka is no different
- 10. 10 Takes years of iteration = v1.0
- 11. 11 Takes years of iteration &
- 13. 13 A stitch in time saves nine test data
- 14. 14 Types of testing we do today
- 16. 16
- 17. 17 Broker 1 Broker 2 Broker N Producer Producer Producer Consumer Consumer Kafka Cluster Topic partition REPLICATIONREPLICATION Failed tests involved correlated failures
- 18. 18 m1 m3 m4 m5 m1 m2 m4 m5 Replica A Replica B 0 1 2 3 0 1 2 3 State of the logs
- 19. 19 m1 m1 Replica A Replica B 0 0 HW HW HW = High Water Mark – Tracks the committed messages L How Replication Works?
- 20. 20 m1 m1 Replica A Replica B 0 0 HW HW m2 L How Replication Works?
- 21. 21 m1 m1 Replica A Replica B 0 0 HW HW L m21 How Replication Works?
- 22. 22 m1 m1 Replica A Replica B 0 0 HW HW L m2 m2 1 How Replication Works?
- 23. 23 m1 m1 Replica A Replica B 0 0 HW HW L m2m2 11 How Replication Works?
- 24. 24 m1 m1 Replica A Replica B 0 0 HW HW L m2m2 ACK 11 How Replication Works?
- 25. 25 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 How Replication Works?
- 26. 26 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 Updated HW to 1 How Replication Works?
- 27. 27 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 How Replication Works?
- 28. 28
- 29. 29 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 Root Cause
- 30. 30 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 Root Cause
- 31. 31 m1 m1 Replica A Replica B 0 0 HW HW L m2Truncate 1 Root Cause
- 32. 32 m1 m1 Replica A Replica B 0 0 HW HW L m21 Root Cause
- 33. 33 m1 m1 Replica A Replica B 0 0 HW HW L m21 Root Cause Get m2
- 34. 34 m1 m1 Replica A Replica B 0 0 HW HW L m21 Root Cause
- 35. 35 m1 m1 Replica A Replica B 0 0 HW HW m21 Root Cause L Message M2 is Lost
- 37. 37 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 Zero Data Loss LG0 0 LEADER GENERATION MAP LG0 LG0 LG0 LG0
- 38. 38 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 Zero Data Loss LG0 0 LEADER GENERATION MAP LG0 LG0 LG0 LG0
- 39. 39 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 LG0 0 LEADER GENERATION MAP LG0 LG0 LG0 LG0 Leader Generation Request LG0 Zero Data Loss
- 40. 40 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 LG0 0 LEADER GENERATION MAP LG0 LG0 LG0 LG0 Offset = 1 Zero Data Loss
- 41. 41 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 LG0 0 LEADER GENERATION MAP LG0 LG0 LG0 LG0 Zero Data Loss
- 42. 42 m1 m1 Replica A Replica B 0 0 HW HW L m2m21 1 LG0 0 LEADER GENERATION MAP LG0 LG0 LG0 LG0 Zero Data Loss
- 43. 43 m1 m1 Replica A Replica B 0 0 HW HW m2m21 1 LG0 0 LEADER GENERATION MAP LG0 LG0 LG0 LG0 L Zero Data Loss
- 44. 44 When availability became more important than ability
- 45. 45 Broker 1 Broker 2 Broker N Producer Producer Producer Consumer Consumer Kafka Cluster Topic partition REPLICATIONREPLICATION Controller Where did my topic partitions go?
- 46. 46 ● Leader Election ● Replica Reassignment ● Create Topic ● Delete Topic ● Add Partitions ● Broker start and shutdown Responsibilities of the Controller No Controller - No Cluster
- 47. 47 ● Controller state - topic creation, topic deletion etc ● Time taken to perform an operation ● Rate at which an admin operation is performed ● Queue sizes within the controller Lack of Metrics
- 48. 48 Root Cause Partition 1 Partition 2 Partition 3 Partition 4
- 49. 49 Root Cause Upgrades! ● Synchronous per-partition zookeeper writes ● Sequential per-partition controller-to-broker requests ● Complicated concurrency semantics ● No separation of control plane from data plane
- 50. 50 How did we fix it? Partition 1,2,3,4
- 51. 51 Zero controller downtime! ● Highly available cluster ● 10x faster leader elections ● More number of topic partitions per cluster ● Faster broker shutdown and upgrades
- 52. 52 When everything is INFINITE nothing is ever ENOUGH
- 53. 53 Broker 1 Broker 2 Broker N Producer Producer Producer Consumer Consumer Kafka Cluster Topic partition REPLICATIONREPLICATION Can I get my latency?
- 54. 54 But I have bytes quota set ● Throttle byte rate per second on the broker ● Response delayed on exceeding threshold ● Avoids bad clients from consuming all the bandwidth
- 55. 55 Root Cause Byte Rate Quotas is useful but not sufficient
- 56. 56 Root Cause ● Too many small sized requests ● DDos attack from client ● Decompression on the server takes a long time ● With more consumer instances, more requests
- 57. 57 bin/kafka-configs --zookeeper localhost:2181 --alter --add-config 'request_percentage=50' --entity-name user1 --entity-type users Request Quota - percentage of time a client can spend on request handler (I/O) threads and network threads within each quota window Predictable Latency
- 58. 58 + + Apache Kafka Tools Automation
- 59. 59 Apache Kafka as a Service
- 60. 60 confluent.io/confluent-cloud How do you get on Confluent Cloud?
- 61. 61 Thank You!