Top Ten Kafka Configs for Optimum Performance
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Bill Bejeck, Software Engineer, Confluent
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Top Ten Kafka Configs for Optimum Performance
- 1. 1 Top Ten Kafka Configs Important Configurations for Optimum Kafka Performance and Robustness
- 2. 2 Brief Introduction • Worked at Confluent (Streams Team) 2 years • Apache Kafka Committer • Author Kafka Streams in Action
- 3. 3 Special Thanks • Dustin Cote • Justin Manchester • Koelli Mungee • Ryan Prigeon
- 4. 4 Agenda • Quick Intro to Kafka • Ground Rules • Broker Configs • Client Configs • Consumer • Producer
- 5. 5 Intro to Kafka Broker 1 Broker 2 Broker 3
- 6. 6 Intro to Kafka Broker 1 Broker 2 Broker 3 Topic
- 7. 7 Intro to Kafka Topic 0 1 2 3 Next record
- 8. 8 Intro to Kafka 0 1 2 3 Producer
- 9. 9 Intro to Kafka 0 1 2 3 Producer Consumer
- 10. 10 Intro to Kafka 0 1 2 3 Producer Consumer Consumer reads available records then commits the offsets
- 11. 11 Configuration Ground Rules • Kafka is the central nervous system of your Event based platform • Proper configuration is important
- 12. 12 Configuration Ground Rules Not All Configurations need to be tuned!
- 13. 13 Configuration Ground Rules • Make sure you understand the behavior and what you’re trying to achieve before changing configs • Focus on what’s essential for your needs
- 15. 15 Broker Configs • JMX metrics • UncleanLeaderElection • Retention (Topic and Broker) • Min InSync Replicas (discussed in Producer section)
- 16. 16 JMX Metrics • JMX metrics not exposed remotely by default • No insight into broker activity without them
- 17. 17 JMX Metrics • Set the environment variable JMX_PORT to enable remote JMX metrics
- 18. 18 JMX Metrics • With environment variable JMX_PORT set Kafka uses these default properties for JMX -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false
- 19. 19 JMX Metrics • To override/customize the JMX settings, i.e. provide authentication set the KAFKA_JMX_OPTS env variable
- 20. 20 JMX Metrics Viewing JConsole https://docs.oracle.com/javase/8/docs/technotes/guid es/management/jconsole.html
- 21. 21 JMX Metrics Connecting ssh -fN -D 7777 -i ~/.ssh/<key name> <instance-public-ip> jconsole -J-DsocksProxyHost=localhost -J-DsocksProxyPort=7777 -J-DsocksNonProxyHosts= service:jmx:rmi:///jndi/rmi://localhost:<JMX_PORT>/jmxrmi
- 22. 22 JMX Metrics BrokerTopicMetrics • MessagesInPerSec • BytesInPerSec • BytesOutPerSec
- 23. 23 JMX Metrics RequestMetrics • Requests per second • Produce - requests from producer to send records • FetchConsumer – requests from consumer to retrieve records • FetchFollower – requests from follower broker to keep in sync with leader for given topic-partition
- 24. 24 JMX Metrics RequestChannel • ReqeustQueueSize Special thanks to Dustin Cote for the Broker Load metrics
- 25. 25 UncleanLeaderElection Leader Broker Follower Broker Leader has records up to offset 300 Follower is only up to 150
- 26. 26 UncleanLeaderElection Leader Broker Follower Broker Leader has records up to offset 300 Follower is only up to 150
- 27. UncleanLeaderElection Allow Follower to become leader • Almost immediately available • Potential for missing records
- 28. UncleanLeaderElection Wait to bring back old leader broker • Not highly available • Downtime until old leader recovered • No potential for missing records
- 29. UncleanLeaderElection • Default setting is false • Use with caution
- 30. 30 Retention • Size • Age of records • now – largest timestamp > retention time
- 31. 31 Timestamps - CreateTime Producer sets timestamp Producer Broker Topic
- 32. 32 Timestamps -LogAppendTime Producer sets timestamp Producer Broker Topic
- 33. 33 Retention Topic A retention 1 week Topic B retention 2 days Kafka Streams Application
- 35. 35 Consumer Configurations • Max Poll Interval • Committing
- 36. 36 Max Poll Interval max.poll.interval.ms • max time between poll calls • If exceeded then consumer kicked out of group, rebalancing while (true) { ConsumerRecords<String, String> records = consumer.poll(Duration.ofSeconds(5)); for (ConsumerRecord<String, String> record : records) { //do something with recrods } }
- 37. 37 Max Poll Interval max.poll.interval.ms = 30000/30 seconds while (true) { ConsumerRecords<String, String> records = consumer.poll(Duration.ofSeconds(5)); for (ConsumerRecord<String, String> record : records) { //This loop must complete in under 30 seconds } }
- 38. 38 Max Poll Interval Processing time for fetched records on consumer takes 45 seconds while (true) { ConsumerRecords<String, String> records = consumer.poll(Duration.ofSeconds(5)); for (ConsumerRecord<String, String> record : records) { //Processing in this loop takes 45 seconds this consumer kicked out of group, and a rebalance is triggered } }
- 39. 39 Max Poll Interval max.poll.interval.ms solutions - • Increase the poll interval time – maybe • Possibly decrease max.poll.records while (true) { ConsumerRecords<String, String> records = consumer.poll(Duration.ofSeconds(5)); for (ConsumerRecord<String, String> record : records) { //This loop must complete in under 30 seconds } }
- 40. 40 Max Poll Interval max.poll.interval.ms solutions - • Decrease processing time • Take processing out of loop while (true) { ConsumerRecords<String, String> records = consumer.poll(Duration.ofSeconds(5)); for (ConsumerRecord<String, String> record : records) { //This loop must complete in under 30 seconds } }
- 41. 41 Committing Partition A Consumer By default auto commit enabled every 5 seconds
- 42. 42 Committing Partition A Consumer Only commit last record processed
- 43. 43 Committing 1 2 3 4 5 6 7 8 9 10 6 last processed offset 10 Records in batch
- 44. 44 Committing 1 2 3 4 5 6 7 8 9 10 Committing offset 11 means 7, 8, and 9 committed as well!
- 46. 46 Producer Configs • Linger • Acks • Retries and Delivery Timeouts • Keeping Records in Order
- 48. 48 Linger – Normal Traffic Producer Record Buffer
- 49. 49 Linger – Lighter Traffic Producer Record Buffer
- 50. 50 Linger Trade Offs • Wait for more records • Waiting to send a batch • Decreased number of requests to broker
- 51. 51 Linger Trade Offs • Set linger.ms to zero • Send batches faster • More requests to broker
- 52. 52 Acks • Number of acknowledgements from broker that it has persisted the message • Zero • One • All
- 53. 53 Acks Zero Producer “Fire” and forget don’t wait for any response Broker (Leader) Broker (Follower) Broker (Follower)
- 54. 54 Acks One Producer Produce records and wait for leader broker to acknowledge Broker (Leader) Broker (Follower) Broker (Follower) Got it!
- 55. 55 Acks All Producer Produce waits for leader broker to acknowledge after all followers have acknowledged Broker (Leader) Broker (Follower) Broker (Follower) Got it!
- 56. 56 Acks All Producer Produce waits for leader broker to acknowledge after all followers have acknowledged Broker (Leader) Broker (Follower) Broker (Follower) Got it! Got it!
- 57. 57 InSync Replicas • acks = “all” • Leader broker is considered a replica!
- 58. 58 InSync Replicas Producer acks = “all” min.insync.replicas = 1 Broker (Leader) Broker (Follower) Broker (Follower) Got it!
- 59. 59 InSync Replicas Producer acks = “all” min.insync.replicas = 1 possible data loss Broker (Leader) Broker (Follower) Broker (Follower) Got it!
- 60. 60 InSync Replicas Producer acks = “all” min.insync.replicas = 2 no data loss Broker (Leader) Broker (Follower) Broker (Follower) Got it!
- 61. 61 InSync Replicas Producer acks = “all” min.insync.replicas = 2 no data loss Broker (Leader) Broker (Follower) Broker (Follower) Got it! Got it!
- 62. 62 InSync Replicas Producer acks = “all” min.insync.replicas = 2 no data loss Broker (Leader) Broker (Follower) Broker (Follower) Got it!
- 63. 63 Retries Topic A Partition 0 Producer Retriable Exception
- 64. 64 Retries Topic A Partition 0 Producer Retriable Exception 0.. Max Retries
- 65. 65 Retries Topic A Partition 0 Producer Retriable Exception 0.. Max Retries or
- 66. 66 Retries Topic A Partition 0 Producer Retriable Exception Use delivery.timeout.ms for limit of time from sending to receiving acknowledgement see KIP-91 for details
- 67. 67 Keeping Records in Order Topic A Partition 0 Producer Retriable Exception Batch 1
- 68. 68 Keeping Records in Order Topic A Partition 0 Producer Retriable Exception Batch 1 Batch 2
- 69. 69 Keeping Records in Order Topic A Partition 0 Producer Retriable Exception Batch 1 Batch 2 Batch 1
- 70. 70 Keeping Records in Order Topic A Partition 0 Producer Retriable Exception Batch 1 Batch 2 Batch 1 Batch 2 processed before Batch 1 !
- 71. 71 Keeping Records in Order Topic A Partition 0 Producer Retriable Exception Batch 1 Batch 2 Batch 1 max.in.flight.requests.per.connection = 1
- 72. 72 Keeping Records in Order Topic A Partition 0 Producer Retriable Exception Batch 1 Batch 1 max.in.flight.requests.per.connection = 1 Batch 2
- 73. 73 Summary • Configuration is important to the health of a Kafka Cluster/ • Need to know which knobs to turn • Measure the before and after • Try it in development first
- 74. 74 Summary • Kafka The Definitive Guide - http://shop.oreilly.com/product/0636920044123.do • Apache Kafka docs - https://kafka.apache.org/documentation/
- 75. NOMINATE YOURSELF OR A PEER AT CONFLUENT.IO/NOMINATE
- 76. KS19Meetup. CONFLUENT COMMUNITY DISCOUNT CODE 25% OFF* *Standard Priced Conference pass
- 77. 77 Thanks! Stay in Touch! • https://slackpass.io/confluentcommunity • https://www.confluent.io/blog/ • Twitter @bbejeck • We are hiring! https://www.confluent.io/careers/
Editor's Notes
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- This is me I’ve worked at Confluent for 1.5 years on Streams team I authored the book Kafka Streams in Action Now let’s get started! First let’s go over what we are going to cover today
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Image you have a Kafka topic and you need to do a group-by count on it, without Kafka Streams you'd need to do some manual processing to acheive this. Here is the main method and setting up the consumer and producer and subscribing to two topics This is more boiler plate work that needs to be done
- Image you have a Kafka topic and you need to do a group-by count on it, without Kafka Streams you'd need to do some manual processing to acheive this. Here is the main method and setting up the consumer and producer and subscribing to two topics This is more boiler plate work that needs to be done
- Image you have a Kafka topic and you need to do a group-by count on it, without Kafka Streams you'd need to do some manual processing to acheive this. Here is the main method and setting up the consumer and producer and subscribing to two topics This is more boiler plate work that needs to be done
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Don’t want to blindly increase the max.poll.interval, this setting is piggy backed to other settings on the broker
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Toplogy is a collection of procesing nodes in a graph A sub-topology is a collection of processing nodes connected by common input topic Relationship between tasks threads and state stores Next let's take a look a life before Kafka Streams so we can get a sense of what Kafka Streams is.
- Thanks for your time Stay in touch and use these resources to participate in the community We have a book signing for Kafka Streams in Action at the Confluent Booth at 4:45 PM today stop By and pick up a signed copy and check out what’s going on at Confluent
- Thanks for your time Stay in touch and use these resources to participate in the community We have a book signing for Kafka Streams in Action at the Confluent Booth at 4:45 PM today stop By and pick up a signed copy and check out what’s going on at Confluent
- Thanks for your time Stay in touch and use these resources to participate in the community We have a book signing for Kafka Streams in Action at the Confluent Booth at 4:45 PM today stop By and pick up a signed copy and check out what’s going on at Confluent