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Multi-Cluster and Failover for Apache Kafka - Kafka Summit SF 17

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Discuss the options available for multi data-center availability and failover.

Veröffentlicht in: Software

Multi-Cluster and Failover for Apache Kafka - Kafka Summit SF 17

  1. 1. 1 One Data Center is Not Enough Scale and Availability of Apache Kafka in Multiple Data Centers @gwenshap
  2. 2. 2
  3. 3. 3 Bad Things • Kafka cluster failure • Major storage / network outage • Entire DC is demolished • Floods and Earthquakes
  4. 4. 4 Disaster Recovery Plan: “When in trouble or in doubt run in circles, scream and shout”
  5. 5. 5 Disaster Recovery Plan: When This Happens Do That Kafka cluster failure Failover to a second cluster in same data center Major storage / network Outage Failover to a second cluster in another “zone” in same building Entire data-center is demolished Single Kafka cluster running in multiple near-by data-centers / buildings. Flood and Earthquakes Failover to a second cluster in another region
  6. 6. 6 There is no such thing as a free lunch Anyone who tells you differently is selling something
  7. 7. 7 Reality: The same event will not appear in two DCs at the exact same time.
  8. 8. 8 Things to ask: • What are the guarantees in an event of unplanned failover? • What are the guarantees in an event of planned failover? • What is the process for failing back? • How many data-centers are required? • How does the solution impact my production performance? • What are the bandwidth requirements between the data-centers?
  9. 9. 9 Every solution needs to balance these trade offs Kafka takes DIY approach
  10. 10. 11 Stretch Cluster The easy way • Take 3 nearby data centers. • Single digit ms latency is good • Install at least 1 Zookeeper in each • Install at least one Kafka broker in each • Configure each DC as a “rack” • Configure acks=all, min.isr=2 • Enjoy
  11. 11. 12 Diagram!
  12. 12. 13 Pros • Easy to set up • Failover is “business as usual” • Sync replication – only method to guarantee no loss of data. Cons • Need 3 data centers nearby • Cluster failure is still a disaster • Higher latency, lower throughput compared to “normal” cluster • Traffic between DCs can be bottleneck • Costly infrastructure
  13. 13. 14 Want sync replication but only two data centers?
  14. 14. 15 Solution I hesistate because… 2 ZK nodes in each DC and “observer” somewhere else. Did anyone do this before? 3 ZK nodes in each DC and manually reconfigure quorum for failover • You may lose ZK updates during failover • Requires manual intervention2 separate ZK cluster + replication Solutions I can’t recommend:
  15. 15. 16 Most companies don’t do stretch. Because: • Only 2 data centers • Data centers are far • One cluster isn’t safe enough • Not into “high latency”
  16. 16. 17 So you want to run 2 Kafka clusters And replicate events between them?
  17. 17. 18 Basic async replication
  18. 18. 19 Replication Lag
  19. 19. 20 Demo #1 Monitoring Replication Lag
  20. 20. 21 Active-Active or Active-Passive? • Active-Active is efficient you use both DCs • Active-Active is easier because both clusters are equivalent • Active-Passive has lower network traffic • Active-Passive requires less monitoring
  21. 21. 22 Active-Active Setup
  22. 22. 23 Disaster Strikes
  23. 23. 24 Desired Post-Disaster State
  24. 24. 25 Only one question left: What does it consume next?
  25. 25. 26 Kafka consumers normally use offsets
  26. 26. 27 In an ideal world…
  27. 27. 28 Unfortunately, this is not that simple 1. There is no guarantee that offsets are identical in the two data centers. Event with offset 26 in NYC can be offset 6 or offset 30 in ATL. 2. Replication of each topic and partition is independent. So.. 1. Offset metadata may arrive ahead of events themselves 2. Offset metadata may arrive late Nothing prevents you from replicating offsets topic and using it. Just be realistic about the guarantees.
  28. 28. 29 If accuracy is no big-deal… 1. If duplicates are cool – start from the beginning. Use Cases: • Writing to a DB • Anything idempotent • Sending emails or alerts to people inside the company 2. If lost events are cool – jump to the latest event. Use Cases: • Clickstream analytics • Log analytics • “Big data” and analytics use-cases
  29. 29. 30 Personal Favorite – Time-based Failover • Offsets are not identical, but… 3pm is 3pm (within clock drift) • Relies on new features: • Timestamps in events! • Time-based indexes! • Force consumer to timestamps tool!
  30. 30. 31 How we do it? 1. Detect Kafka in NYC is down. Check the time of the incident. • Even better: Use an interceptor to track timestamps of events as they are consumed. Now you know “last consumed time-stamp” 2. Run Consumer Groups tool in ATL and set the offsets for “following-orders” consumer to time of incident (or “last consumed time”) 3. Start the ”following-orders” consumer in ATL 4. Have a beer. You just aced your annual failover drill.
  31. 31. 32 bin/kafka-consumer-groups --bootstrap-server localhost:29092 --reset-offsets --topic NYC.orders --group following-orders --execute --to-datetime 2017-08-22T06:00:33.236
  32. 32. 33 Few practicalities • Above all – practice • Constantly monitor replication lag. High enough lag and everything is useless. • Also monitor replicator for liveness, errors, etc. • Chances are the line to the remote DC is both high latency and low throughput. Prepare to do some work to tune the producers/consumers of the replicator. • RTFM: http://docs.confluent.io/3.3.0/multi-dc/replicator-tuning.html • Replicator plays nice with containers and auto-scale. Give it a try. • Call your legal dept. You may be required to encrypt everything you replicate. • Watch different versions of this talk. We discuss more architectures and more ops concerns.
  33. 33. 34 Thank You!