Deploying Kafka Streams Applications with Docker and Kubernetes

Deploying Kafka Streams Applications with Docker and Kubernetes March, 2019 / Portland, OR @gamussa | @pjug | @ConfluentINc

2 @gamussa | @PJUG | @ConfluentINc

Raffle, yeah 🚀 Follow @gamussa 📸🖼🏋 Tag @gamussa With @pjug @confluentinc

4 Special thanks! @gwenshap @gamussa @MatthiasJSax | @PJUG | @ConfluentINc

5 Agenda Kafka Streams 101 How do Kafka Streams applications scale? Kubernetes 101 Recommendations for Kafka Streams @gamussa | @pjug | @ConfluentINc

https://gamov.dev/ks-k8s-stocks @gamussa | @PJUG | @ConfluentINc

7 Kafka Streams – 101 Your App @gamussa | @PJUG | @ConfluentINc Other Systems Kafka Connect Kafka Connect Other Systems Kafka Streams

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 | @PJUG | @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 | @PJUG | @ConfluentINc

10 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 | @PJUG | @ConfluentINc

11 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 | @PJUG | @ConfluentINc

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

How Do Kafka Streams Application Scale? @gamussa | @PJUG | @ConfluentINc

14 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 | @PJUG | @ConfluentINc

15 Scaling with State “no state” Trade Stats App Instance 1 @gamussa | @PJUG | @ConfluentINc

Scaling with State “no state” Trade Stats App Trade Stats App Instance 1 @gamussa Instance 2 | @PJUG | @ConfluentINc 16

17 Scaling with State “no state” Trade Stats App Trade Stats App Instance 1 @gamussa Trade Stats App Instance 2 | @PJUG | @ConfluentINc Instance 3

18 Scaling and FaultTolerance Two Sides of Same Coin @gamussa | @pjug | @ConfluentINc

19 Fault-Tolerance Trade Stats App Trade Stats App Instance 1 @gamussa | Instance 2 @PJUG | Trade Stats App @ConfluentINc Instance 3

20 Fault-Tolerant State State Updates Input Topic Changelog Topic Result Topic @gamussa | @PJUG | @ConfluentINc

21 Migrate State Trade Stats App Instance 1 Trade Stats App Instance 2 restore @gamussa | Changelog Topic @PJUG | @ConfluentINc

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

23 Recovery Overhead Changelog topic State size: 20 GB (per shard) Active Segment Topic size can grow larger if not compacted Segments (default size 1GB) After compaction Min Topic Size: 21 GB (per shard) Active Segment Segments (default size 1GB) @gamussa Recovery overhead about 5% | @PJUG | @ConfluentINc

24 Recovery Overhead Changelog topic State size: 100 MB (per shard) Active Segment Segments (default size 1GB) Min Topic Size: 1.1 GB Compaction Recovery overhead about 1000% Active Segment Each key is stored up to 11 times… Segment (only 100 MB) @gamussa | @PJUG | @ConfluentINc

25 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 | @pjug | @ConfluentINc

26 Kubernetes Fundamentals @gamussa | @PJUG | @ConfluentINc

27 https://twitter.com/sahrizv/status/1018184792611827712 @gamussa | @PJUG | @ConfluentINc

28 Orchestration ●Compute ●Networking ●Storage ●Service Discovery @gamussa | @PJUG | @ConfluentINc

29 Kubernetes ●Schedules and allocates resources ●Networking between Pods ●Storage ●Service Discovery @gamussa | @PJUG | @ConfluentINc

30 Refresher - Kubernetes Architecture kubectl https://thenewstack.io/kubernetes-an-overview/ @gamussa | @PJUG | @ConfluentINc

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

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

33 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 | @pjug | @ConfluentINc

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

35 Storage • Persistent Volume (PV) & Persistent Volume Claim (PVC) • PVCs consume PV @gamussa | @pjug | @ConfluentINc

36 Stateful Workloads @gamussa | @PJUG | @ConfluentINc

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

Recommendations for Kafka Streams @gamussa | @PJUG | @ConfluentINc

39 Stock Stats App Stock Stats App Stock Stats App Kafka Streams Kafka Streams Kafka Streams Instance 1 Instance 2 Instance 3 @gamussa | @PJUG | @ConfluentINc

40 WordCount App WordCount App WordCount App Kafka Streams Kafka Streams Kafka Streams Instance 1 Instance 2 Instance 3 @gamussa | @PJUG | @ConfluentINc

41 StatefulSets are new and complicated. We don’t need them. @gamussa | @PJUG | @ConfluentINc

42 Recovering state takes time. Statelful is faster. @gamussa | @PJUG | @ConfluentINc

43 But I’ll want to scale-out and back anyway. @gamussa | @PJUG | @ConfluentINc

44 @gamussa | @PJUG | @ConfluentINc

45 I don’t really trust my storage admin anyway @gamussa | @PJUG | @ConfluentINc

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

apiVersion: apps/v1 kind: “Deployment” metadata: name: “streams-stock-stats” spec: replicas: 1 template: spec: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - “streams-stock-stats” topologyKey: “kubernetes.io/hostname” containers: - name: kafka-streams-stockstat image: gamussa/kafka-streams-stockstat:latest @gamussa | @PJUG | @ConfluentINc 47

apiVersion: apps/v1 kind: “Deployment” metadata: name: “streams-stock-stats” spec: replicas: 1 template: spec: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - “streams-stock-stats” topologyKey: “kubernetes.io/hostname” containers: - name: kafka-streams-stockstat image: gamussa/kafka-streams-stockstat:latest @gamussa | @PJUG | @ConfluentINc 48

apiVersion: apps/v1 kind: “Deployment” metadata: name: “streams-stock-stats” spec: replicas: 1 template: spec: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - “streams-stock-stats” topologyKey: “kubernetes.io/hostname” containers: - name: kafka-streams-stockstat image: gamussa/kafka-streams-stockstat:latest @gamussa | @PJUG | @ConfluentINc 49

apiVersion: apps/v1 kind: StatefulSet spec: serviceName: “streams-stock-stats” replicas: 2 podManagementPolicy: “Parallel” template: spec: containers: - name: kafka-streams-stockstat image: kafka-streams-stockstat:latest volumeMounts: - name: rocksdb mountPath: /var/lib/kafka-streams volumeClaimTemplates: - metadata: name: rocksdb spec: accessModes: [ “ReadWriteOnce” ] resources: requests: storage: 1Gi @gamussa | @PJUG | @ConfluentINc 50

apiVersion: apps/v1 kind: StatefulSet spec: serviceName: “streams-stock-stats” replicas: 2 podManagementPolicy: “Parallel” template: spec: containers: - name: kafka-streams-stockstat image: kafka-streams-stockstat:latest volumeMounts: - name: rocksdb mountPath: /var/lib/kafka-streams volumeClaimTemplates: - metadata: name: rocksdb spec: accessModes: [ “ReadWriteOnce” ] resources: requests: storage: 1Gi @gamussa | @PJUG | @ConfluentINc 51

apiVersion: apps/v1 kind: StatefulSet spec: serviceName: “streams-stock-stats” replicas: 2 podManagementPolicy: “Parallel” template: spec: containers: - name: kafka-streams-stockstat image: kafka-streams-stockstat:latest volumeMounts: - name: rocksdb mountPath: /var/lib/kafka-streams volumeClaimTemplates: - metadata: name: rocksdb spec: accessModes: [ “ReadWriteOnce” ] resources: requests: storage: 1Gi @gamussa | @PJUG | @ConfluentINc 52

53 🛑 Stop! Demo time! @gamussa | @pjug | @ConfluentINc

54 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 | @pjug | @ConfluentINc

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

56 But what about Kafka itself? @gamussa | @pjug | @ConfluentINc

57 Confluent Operator Automate provisioning Scale your Kafkas and CP clusters elastically Monitor SLAs through Confluent Control Center or Prometheus Operate at scale with enterprise support from Confluent @gamussa | @PJUG | @ConfluentINc

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

59 One more thing…

https://kafka-summit.org Gamov30 @gamussa | @ @tlberglund | #DEVnexus

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

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