Why is My Stream Processing Job Slow? with Xavier Leaute
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The goal of most streams processing jobs is to process data and deliver insights to the business – fast. Unfortunately, sometimes our streams processing jobs fall short of this goal. Or perhaps the job used to run fine, but one day it just isn’t fast enough? In this talk, we’ll dive into the challenges of analyzing performance of real-time stream processing applications. We’ll share troubleshooting suggestions and some of our favorite tools. So next time someone asks “why is this taking so long?”, you’ll know what to do.
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Why is My Stream Processing Job Slow? with Xavier Leaute
- 1. 1 Why is My Stream Processing Job Slow? Xavier Léauté, Software Engineer Gwen Shapira, Principal Data Architect
- 2. 2 Kafka 101 Distributed Scalable Fault-Tolerant Partitioned + Replicated Log Ordering guarantees Consumers advance independently Exactly-once delivery Transactional commits
- 3. What people think of Stream Monitoring 3
- 4. What our typical experience is 4
- 5. Confidential 5 Real Customer Experiences
- 6. Confidential 5 Real Customer Experiences Client Side Broken Streaming Job / App
- 7. Confidential 5 Real Customer Experiences Client Side Broken Streaming Job / App End-to-End Slow Replication
- 8. Your Kafka stream job stopped humming… now what? 6
- 9. Confidential 7 What we check Consumer Lag Partition Assignment Partition Skew Client Logs GC Log Metrics Request Latencies Commit Rates Group Rebalancing Basic Tuning Batch Sizes Commit Rate Application Profiling
- 10. 8 The Newbie - During an incident… GC Logs? Metrics? How do I get those? I’ll just change some configs and reboot everything.
- 11. 9 Consumer Lag Wait for me!
- 12. 10 Bad Capacity Allocation kafka-consumer-groups --bootstrap-server localhost:9092 --describe --group fast-data-reader TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID fast-data 1 8661694 8703404 41710 myapp-1 fast-data 3 8577975 8616490 38515 myapp-2 fast-data 0 4902354 8741872 3839518 myapp-3 fast-data 2 4922614 8621757 3699143 myapp-3
- 13. 10 Bad Capacity Allocation kafka-consumer-groups --bootstrap-server localhost:9092 --describe --group fast-data-reader TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID fast-data 1 8661694 8703404 41710 myapp-1 fast-data 3 8577975 8616490 38515 myapp-2 fast-data 0 4902354 8741872 3839518 myapp-3 fast-data 2 4922614 8621757 3699143 myapp-3
- 14. 10 Bad Capacity Allocation kafka-consumer-groups --bootstrap-server localhost:9092 --describe --group fast-data-reader TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID fast-data 1 8661694 8703404 41710 myapp-1 fast-data 3 8577975 8616490 38515 myapp-2 fast-data 0 4902354 8741872 3839518 myapp-3 fast-data 2 4922614 8621757 3699143 myapp-3
- 15. 11 Watch for Partition Skew kafka-consumer-groups --bootstrap-server localhost:9092 --describe --group fast-data-reader TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID fast-data 1 8661694 8703404 41710 myapp-1 fast-data 3 8577975 8616490 38515 myapp-2 fast-data 0 4902354 8741872 3839518 myapp-3 fast-data 2 4922614 8621757 3699143 myapp-3
- 16. 11 Watch for Partition Skew kafka-consumer-groups --bootstrap-server localhost:9092 --describe --group fast-data-reader TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID fast-data 1 8661694 8703404 41710 myapp-1 fast-data 3 8577975 8616490 38515 myapp-2 fast-data 0 4902354 8741872 3839518 myapp-3 fast-data 2 4922614 8621757 3699143 myapp-3
- 17. 12 Not all partitions are created equal Important for Keyed topics Custom partitioned topics Early warning signs some partitions lagging uneven CPU / Network usage Typical cause skewed key distribution in your data bad joins (null keys) imbalance across brokers
- 18. 13 Clients have metrics too! Start with the basics GC / CPU / Network General Slowness Consumer or Producer Side? Global Request Latencies Some partitions still lagging Per Broker metrics (bad node / network) Per Topic metrics (data / tuning) Buffer Size Offset Commit
- 19. 14 Turn up the log level The logs took too much space, so we deleted them.
- 20. 15 Time for some profiling https://github.com/jvm-profiling-tools/async-profiler https://github.com/brendangregg/FlameGraph ./profiler.sh -d 30 -f flamegraph.svg <pid> To impress your coworkers https://github.com/Netflix/flamescope
- 21. 16 Here’s where your CPU cycles went % CPU Time Stack
- 22. 16 Here’s where your CPU cycles went GC % CPU Time Stack
- 23. 16 Here’s where your CPU cycles went RocksDB % CPU Time Stack
- 24. 16 Here’s where your CPU cycles went Kafka poll() loop % CPU Time Stack
- 25. 16 Here’s where your CPU cycles went Actual Processing Time % CPU Time Stack
- 26. 17 Spark Streaming Clickstream Example (using Kafka)
- 27. 18 Spark Streaming Clickstream Example (using Kafka)
- 28. 18 Spark Streaming Clickstream Example (using Kafka) Scheduler Event Loop
- 29. 18 Spark Streaming Clickstream Example (using Kafka) Shuffle Writes Scheduler Event Loop
- 30. 18 Spark Streaming Clickstream Example (using Kafka) 30% deserialization Shuffle Writes Scheduler Event Loop
- 31. 18 Spark Streaming Clickstream Example (using Kafka) 30% deserialization Shuffle Writes Scheduler Event Loop Read from Kafka & Processing
- 32. 19 Maybe it’s your code
- 33. 20 Let’s commit, just to be safe, right? Common beginner mistake Commit only as needed keep recovery short maximize throughput Metrics to validate commit-rate commit-latency-avg MESSAGES COMMIT MESSAGES
- 35. 21 Right-size your batches Bigger Batches increase throughput improve compression
- 36. 21 Right-size your batches Bigger Batches increase throughput improve compression Small enough (<< 10MB) to keep GC low
- 37. 21 Right-size your batches Bigger Batches increase throughput improve compression Small enough (<< 10MB) to keep GC low batch.size + linger.ms
- 38. 21 Right-size your batches Bigger Batches increase throughput improve compression Small enough (<< 10MB) to keep GC low batch.size + linger.ms don’t forget!
- 39. 21 Right-size your batches Bigger Batches increase throughput improve compression Small enough (<< 10MB) to keep GC low batch.size + linger.ms Watch request-rate request-latency-avg compression-rate don’t forget!
- 40. 22 My app keeps rebalancing Symptoms low throughput high network chatter consumer logs galore no progress hanging
- 41. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6
- 42. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Hi!
- 43. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Join Group Hi!
- 44. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Join Group Hi!
- 45. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Join Group Hi!
- 46. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Join Response
- 47. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Join Response
- 48. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Sync Group
- 49. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Sync Response
- 50. 23 Kafka Consumer Group Rebalancing 101 Consumer A Consumer B Consumer C Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 New Assignment
- 51. 24 Restoring a Happy Balance Timing Issues long GC pauses (tens of seconds) infrequent calls to poll() timeouts too short? flaky network 1 bad machine affects the entire group Watch join-rate sync-rate
- 52. 25 Competent Users • Monitor Consumer Lag • Lookout for Partition Skew • Commit Offsets Sparingly • Collect Logs • Understand how to tune Batch Sizes
- 53. 26 Kafka Pros • Watch Group Partition Assignment • Monitor Client Metrics • Understand Consumer Rebalancing • Profile their applications • Distinguish Client/App/Broker problems
- 54. Replication Everything is Slow 27
- 55. 28 Famous last words… “You just consume, and produce. How hard can this be?”
- 56. 29 Famous last words… “We have a disaster in our main cluster. Can we fail over to secondary? We can’t lose more than 7 seconds of data.”
- 57. 30 Monitor Replication Lag - In messages
- 58. 31 Monitor Replication Lag - or in seconds… Screenshot of replicator streams monitoring
- 59. Confidential 32 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full Buffer
- 60. Confidential 32 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full io-ratio io-wait-ratio outgoing-byte-rate Buffer
- 61. Confidential 32 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full io-ratio io-wait-ratio outgoing-byte-rate batch-size-avg batch-size-max Buffer
- 62. Confidential 32 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full io-ratio io-wait-ratio outgoing-byte-rate batch-size-avg batch-size-max record-retry-rate record-error-rate Buffer
- 63. Confidential 32 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full io-ratio io-wait-ratio outgoing-byte-rate batch-size-avg batch-size-max record-retry-rate record-error-rate waiting-threads bufferpool-wait-time Buffer
- 64. Confidential 32 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full io-ratio io-wait-ratio outgoing-byte-rate batch-size-avg batch-size-max record-retry-rate record-error-rate waiting-threads bufferpool-wait-time io-ratio io-wait-ratio byte-consumed-rate Buffer
- 65. Confidential 32 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full io-ratio io-wait-ratio outgoing-byte-rate batch-size-avg batch-size-max record-retry-rate record-error-rate waiting-threads bufferpool-wait-time io-ratio io-wait-ratio byte-consumed-rate Buffer fetch-size-avg fetch-size-max fetch-rate
- 66. Confidential 32 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full io-ratio io-wait-ratio outgoing-byte-rate batch-size-avg batch-size-max record-retry-rate record-error-rate waiting-threads bufferpool-wait-time io-ratio io-wait-ratio byte-consumed-rate Buffer fetch-size-avg fetch-size-max fetch-rate record-max-lag
- 67. Confidential 33 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full Buffer
- 68. Confidential 33 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full network or destination kafka performance Buffer
- 69. Confidential 33 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full network or destination kafka performance increase batch.size Buffer
- 70. Confidential 33 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full network or destination kafka performance increase batch.size destination kafka issues Buffer
- 71. Confidential 33 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full network or destination kafka performance increase batch.size destination kafka issues network or origin kafka performance Buffer
- 72. Confidential 33 Simple and elegant design Origin Destination Consumer producer Buffer block when buffer is full network or destination kafka performance increase batch.size destination kafka issues network or origin kafka performance Buffer fetch.max.bytes fetch.min.bytes fetch.max.wait
- 73. 34 Network Tuning • WAN has high latency. We deal with it. • Compute buffer size to match: https://www.switch.ch/network/tools/tcp_throughput/ • send.buffer.bytes and receive.buffer.bytes on producer, consumer, brokers • OS tuning: https://wwwx.cs.unc.edu/~sparkst/howto/network_tuning.php net.core.rmem_default, net.core.rmem_max, net.core.wmem_default, net.core.wmem_max • Enable logging to check if this had any effect: log4j.logger.org.apache.kafka.common.network.Selector=DEBUG • Additional tips in our docs
- 74. 35 Competent users • Monitor consumer lag • Add processes when things are slow • Automate deployment
- 75. 36 Kafka Pros • Monitor time lag • Collect client metrics • Knows which side to blame • Know which configs to tune • Tunes the network over the WAN
- 76. Resources and Next Steps https://github.com/confluentinc/cp-demo https://www.confluent.io/download/ https://slackpass.io/confluentcommunity https://www.confluent.io/blog