Streams Must Flow: Deployment fault-tolerant stream processing application with Kafka Streams, Kubernetes

Streams Must Flow: fault-tolerant stream processing apps on Kubernetes April, 2019 / New York, NY @gamussa | #raleighKafka | @ConfluentINc

2 Special thanks! @gwenshap @gamussa @MatthiasJSax | #raleighKafka | @ConfluentINc

3 Agenda Kafka Streams 101 How do Kafka Streams applications scale? Kubernetes 101 Recommendations for Kafka Streams @gamussa | #raleighKafka | @ConfluentINc

https://gamov.dev/kstreams-k8s-pr @gamussa | #raleighKafka | @ConfluentINc

5 Kafka Streams – 101 Your App @gamussa | #raleighKafka | @ConfluentINc Other Systems Kafka Connect Kafka Connect Other Systems Kafka Streams

6 Stock Trade Stats Example KStream<String, Trade> source = builder.stream(STOCK_TOPIC); KStream<Windowed<String>, TradeStats> stats = source .groupByKey() .windowedBy(TimeWindows.of(5000).advanceBy(1000)) .aggregate(TradeStats::new, (k, v, tradestats) -> tradestats.add(v), Materialized.<~>as(“trade-aggregates”) .withValueSerde(new TradeStatsSerde())) .toStream() .mapValues(TradeStats::computeAvgPrice); stats.to(STATS_OUT_TOPIC, Produced.keySerde(WindowedSerdes.timeWindowedSerdeFrom(String.class))); @gamussa | #raleighKafka | @ConfluentINc

7 Stock Trade Stats Example KStream<String, Trade> source = builder.stream(STOCK_TOPIC); KStream<Windowed<String>, TradeStats> stats = source .groupByKey() .windowedBy(TimeWindows.of(5000).advanceBy(1000)) .aggregate(TradeStats::new, (k, v, tradestats) -> tradestats.add(v), Materialized.<~>as(“trade-aggregates”) .withValueSerde(new TradeStatsSerde())) .toStream() .mapValues(TradeStats::computeAvgPrice); stats.to(STATS_OUT_TOPIC, Produced.keySerde(WindowedSerdes.timeWindowedSerdeFrom(String.class))); @gamussa | #raleighKafka | @ConfluentINc

8 Stock Trade Stats Example KStream<String, Trade> source = builder.stream(STOCK_TOPIC); KStream<Windowed<String>, TradeStats> stats = source .groupByKey() .windowedBy(TimeWindows.of(5000).advanceBy(1000)) .aggregate(TradeStats::new, (k, v, tradestats) -> tradestats.add(v), Materialized.<~>as(“trade-aggregates”) .withValueSerde(new TradeStatsSerde())) .toStream() .mapValues(TradeStats::computeAvgPrice); stats.to(STATS_OUT_TOPIC, Produced.keySerde(WindowedSerdes.timeWindowedSerdeFrom(String.class))); @gamussa | #raleighKafka | @ConfluentINc

9 Stock Trade Stats Example KStream<String, Trade> source = builder.stream(STOCK_TOPIC); KStream<Windowed<String>, TradeStats> stats = source .groupByKey() .windowedBy(TimeWindows.of(5000).advanceBy(1000)) .aggregate(TradeStats::new, (k, v, tradestats) -> tradestats.add(v), Materialized.<~>as(“trade-aggregates”) .withValueSerde(new TradeStatsSerde())) .toStream() .mapValues(TradeStats::computeAvgPrice); stats.to(STATS_OUT_TOPIC, Produced.keySerde(WindowedSerdes.timeWindowedSerdeFrom(String.class))); @gamussa | #raleighKafka | @ConfluentINc

10 Topologies builder.stream Source Node state stores source.groupByKey .windowedBy(…) .aggregate(…) Processor Node mapValues() Processor Node streams Sink Node to(…) @gamussa | #raleighKafka | @ConfluentINc Processor Topology

How Do Kafka Streams Application Scale? @gamussa | #raleighKafka | @ConfluentINc

12 Partitions, Tasks, and Consumer Groups input topic Task executes processor topology One consumer group: can be executed with 1 - 4 threads on 1 - 4 machines 4 input topic partitions => 4 tasks result topic @gamussa | #raleighKafka | @ConfluentINc

13 Scaling with State “no state” Trade Stats App Instance 1 @gamussa | #raleighKafka | @ConfluentINc

Scaling with State “no state” Trade Stats App Trade Stats App Instance 1 @gamussa Instance 2 | #raleighKafka | @ConfluentINc 14

15 Scaling with State “no state” Trade Stats App Trade Stats App Instance 1 @gamussa Instance 2 | #raleighKafka | Trade Stats App Instance 3 @ConfluentINc

16 Scaling and FaultTolerance Two Sides of Same Coin @gamussa | #raleighKafka | @ConfluentINc

17 Fault-Tolerance Trade Stats App Trade Stats App Instance 1 @gamussa | Instance 2 #raleighKafka | Trade Stats App @ConfluentINc Instance 3

18 Fault-Tolerant State State Updates Input Topic Changelog Topic Result Topic @gamussa | #raleighKafka | @ConfluentINc

19 Migrate State Trade Stats App Instance 1 Trade Stats App Instance 2 restore Changelog Topic @gamussa | #raleighKafka | @ConfluentINc

20 Recovery Time • Changelog topics are log compacted • Size of changelog topic linear in size of state • Large state implies high recovery times @gamussa | #raleighKafka | @ConfluentINc

21 Recovery Overhead • Recovery overhead is proportional to ○ segment-size / state-size • Segment-size is smaller than state-size => reduced overhead • Update changelog topic segment size accordingly ○ topic config: log.segments.bytes ○ log cleaner interval important, too @gamussa | #raleighKafka | @ConfluentINc

22 Kubernetes Fundamentals @gamussa | #raleighKafka | @ConfluentINc

23 https://twitter.com/sahrizv/status/1018184792611827712 @gamussa | #raleighKafka | @ConfluentINc

24 @gamussa | #raleighKafka | @ConfluentINc

25 Orchestration ●Compute ●Networking ●Storage ●Service Discovery @gamussa | #raleighKafka | @ConfluentINc

26 Kubernetes ●Schedules and allocates resources ●Networking between Pods ●Storage ●Service Discovery @gamussa | #raleighKafka | @ConfluentINc

27 Refresher - Kubernetes Architecture kubectl https://thenewstack.io/kubernetes-an-overview/ @gamussa | #raleighKafka | @ConfluentINc

28 Pod • Basic Unit of Deployment in Kubernetes • A collection of containers sharing: • Namespace • Network • Volumes @gamussa | #raleighKafka | @ConfluentINc

29 Storage • Persistent Volume (PV) & Persistent Volume Claim (PVC) • Both PV and PVC are ‘resources’ @gamussa | #raleighKafka | @ConfluentINc

30 Storage • Persistent Volume (PV) & Persistent Volume Claim (PVC) • PV is a piece of storage that is provisioned dynamic or static of any individual pod that uses the PV @gamussa | #raleighKafka | @ConfluentINc

31 Storage • Persistent Volume (PV) & Persistent Volume Claim (PVC) • PVC is a request for storage by a User @gamussa | #raleighKafka | @ConfluentINc

32 Storage • Persistent Volume (PV) & Persistent Volume Claim (PVC) • PVCs consume PV @gamussa | #raleighKafka | @ConfluentINc

33 Stateful Workloads @gamussa | #raleighKafka | @ConfluentINc

34 StatefulSet ● Rely on Headless Headless Service Service to provide network identity Pod-0 ● Ideal for highly available stateful Pod-1 Pod-2 Containers Containers Containers Volumes Volumes Volumes workloads @gamussa | #raleighKafka | @ConfluentINc

Recommendations for Kafka Streams @gamussa | #raleighKafka | @ConfluentINc

36 Stock Stats App Stock Stats App Stock Stats App Kafka Streams Kafka Streams Kafka Streams Instance 1 Instance 2 Instance 3 @gamussa | #raleighKafka | @ConfluentINc

37 WordCount App WordCount App WordCount App Kafka Streams Kafka Streams Kafka Streams Instance 1 Instance 2 Instance 3 @gamussa | #raleighKafka | @ConfluentINc

38 StatefulSets are new and complicated. We don’t need them. @gamussa | #raleighKafka | @ConfluentINc

39 Recovering state takes time. Statelful is faster. @gamussa | #raleighKafka | @ConfluentINc

40 But I’ll want to scale-out and back anyway. @gamussa | #raleighKafka | @ConfluentINc

41 @gamussa | #raleighKafka | @ConfluentINc

42 I don’t really trust my storage admin anyway @gamussa | #raleighKafka | @ConfluentINc

43 Recommendations ● Keep changelog shards small ● If you trust your storage: Use StatefulSets ● Use anti-affinity when possible ● Use “parallel” pod management

44 🛑 Stop! Demo time! @gamussa | #raleighKafka | @ConfluentINc

45 Summary Kafka Streams has recoverable state, that gives streams apps easy elasticity and high availability Kubernetes makes it easy to scale applications It also has StatefulSets for applications with state @gamussa | #raleighKafka | @ConfluentINc

46 Summary Now you know how to deploy Kafka Streams on Kubernetes and take advantage on all the scalability and highavailability capabilities @gamussa | #raleighKafka | @ConfluentINc

47 But what about Kafka itself? @gamussa | #raleighKafka | @ConfluentINc

48 https://www.confluent.io/resources/kafka-summit-new-york-2019/ @gamussa | #raleighKafka | @ConfluentINc

49 Confluent Operator Automate provisioning Scale your Kafkas and CP clusters elastically Monitoring with Confluent Control Center or Prometheus Operate at scale with enterprise support from Confluent @gamussa | #raleighKafka | @ConfluentINc

50 Resources and Next Steps https://cnfl.io/helm_video https://cnfl.io/cp-helm https://cnfl.io/k8s https://slackpass.io/confluentcommunity #kubernetes @gamussa | #raleighKafka | @ConfluentINc

Thanks! @gamussa viktor@confluent.io We are hiring! https://www.confluent.io/careers/ @gamussa | @ #raleighKafka | @ConfluentINc

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