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Devoxx university - Kafka de haut en bas
- 1. #DevoxxFR Kafka … de haut en bas ! University Florent Ramière @framiere Jean-Louis Boudart @jlboudart Nicolas Romanetti @nromanetti 1
- 2. 2 Massive volumes of new data generated every day Mobile Cloud Microservices Internet of Things Machine Learning Distributed across apps, devices, datacenters, clouds Structured, unstructured polymorphic What
- 3. 3 Problem ?
- 4. 4 Silos explained by Data Gravity concept As data accumulates (builds mass) there is a greater likelihood that additional services and applications will be attracted to this data. This is the same effect gravity has on objects around a planet. As the mass or density increases, so does the strength of gravitational pull.
- 5. 5 With
- 6. 6 How
- 7. 7 Store & ETL Process Publish & Subscribe In short
- 8. 8 From a simple idea
- 9. 9 From a simple idea
- 10. 10 with great properties ! • Scalability • Retention • Durability • Replication • Security • Resiliency • Throughput • Ordering • Exactly Once Semantic • Transaction • Idempotency • Immutability • …
- 11. 11 11 Producer
- 12. 12 Anatomy of a Message
- 13. 13
- 14. 14 Producing to Kafka - No Key Time Messages will be produced in a round robin fashion
- 15. 15 Producing to Kafka - With Key Time A B C D hash(key) % numPartitions = N
- 16. 16 Partition Leadership and Replication Broker 1 Topic1 partition1 Broker 2 Broker 3 Broker 4 Topic1 partition1 Topic1 partition1 Leader Follower Topic1 partition2 Topic1 partition2 Topic1 partition2 Topic1 partition3 Topic1 partition4 Topic1 partition3 Topic1 partition3 Topic1 partition4 Topic1 partition4
- 17. 17 Partition Leadership and Replication - node failure Broker 1 Topic1 partition1 Broker 2 Broker 3 Broker 4 Topic1 partition1 Topic1 partition1 Leader Follower Topic1 partition2 Topic1 partition2 Topic1 partition2 Topic1 partition3 Topic1 partition4 Topic1 partition3 Topic1 partition3 Topic1 partition4 Topic1 partition4
- 18. 18 Producer Guarantees P Broker 1 Broker 2 Broker 3 Topic1 partition1 Leader Follower Topic1 partition1 Topic1 partition1 Producer Properties acks=0
- 19. 19 Producer Guarantees P Broker 1 Broker 2 Broker 3 Topic1 partition1 Leader Follower Topic1 partition1 Topic1 partition1 ack Producer Properties acks=1
- 20. 20 Producer Guarantees P Broker 1 Broker 2 Broker 3 Topic1 partition1 Leader Follower Topic1 partition1 Topic1 partition1 Producer Properties acks=all min.insync.replica=2 First copy returns ack ack
- 21. 21 21 Consumer
- 22. 22 Consuming From Kafka - Single Consumer C
- 23. 23 Consuming From Kafka - Grouped Consumers CC C1 CC C2
- 24. 24 Consuming From Kafka - Grouped Consumers C C C C
- 25. 25 Consuming From Kafka - Grouped Consumers 0 1 2 3
- 26. 26 Consuming From Kafka - Grouped Consumers 0 1 2 3
- 27. 27 Consuming From Kafka - Grouped Consumers 0, 3 1 2 3
- 28. 28 Compacted Topics – Keep only the most recent value for a key
- 29. 29 29 Destroy all the magic!
- 32. 32 Anatomy of a Producer Request on a Broker
- 33. 33 Anatomy of a Fetch Request on a Broker
- 34. 34 34 Not so fast !
- 35. 35 Set up secure Kafka & build your first app Understand streaming Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streaming Infrastructure & apps across LOBs Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streaming Self-service on shared Kafka Infrastructure & applications across LOBs Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streamingUnderstand streaming Pre-streamingValue Stream Everything 05Break Silos 04 03 Go To Production 02 Learn Kafka 01 Investment & Time Solve A Critical Need Maturity model
- 36. 36 Set up secure Kafka & build your first app Understand streaming Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streaming Infrastructure & apps across LOBs Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streaming Self-service on shared Kafka Infrastructure & applications across LOBs Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streamingUnderstand streaming Pre-streamingValue Stream Everything 05Break Silos 04 03 Go To Production 02 Learn Kafka 01 Solve A Critical Need Maturity model
- 37. 37 Set up secure Kafka & build your first app Understand streaming Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streaming Infrastructure & apps across LOBs Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streaming Self-service on shared Kafka Infrastructure & applications across LOBs Monitor & manage a mission-critical solution Set up secure Kafka & build your first app Understand streamingUnderstand streaming Pre-streamingValue Stream Everything 05Break Silos 04 03 Go To Production 02 Learn Kafka 01 Solve A Critical Need Maturity model
- 39. 39 39 This is a full platform
- 40. 40 … spawned a full platform Apache Kafka® Core | Connect API | Streams API Stream Processing & Compatibility KSQL | Schema Registry Operations Replicator | Auto Data Balancer | Connectors | MQTT Proxy | Operator Database Changes Log Events IoT Data Web Events other events Hadoop Database Data Warehouse CRM other DATA INTEGRATION Transformations Custom Apps Analytics Monitoring other REAL-TIME APPLICATIONS OPEN SOURCE FEATURES COMMERCIAL FEATURES Datacenter Public Cloud Confluent Cloud CONFLUENT PLATFORM Administration & Monitoring Control Center | Security Connectivity Clients | Connectors | REST Proxy CONFLUENT FULLY-MANAGEDCUSTOMER SELF-MANAGED
- 41. 41 41 ETL
- 42. 42 I
- 44. 44
- 45. 45
- 46. 46
- 47. 47
- 48. 48
- 49. 49
- 51. 51
- 52. 52 T1
- 53. 53 T1,T2,T3
- 61. 61 Apache Kafka Connect API: Import and Export Data In & Out of Kafka JDBC Mongo MySQL Elastic Cassandra HDFS Kafka Connect API Kafka Pipeline Connector Connector Connector Connector Connector Connector Sources Sinks Fault tolerant Manage hundreds of data sources and sinks Preserves data schema Integrated within Confluent Control Center
- 62. 62 Connectors: Connect Kafka Easily with Data Sources and Sinks Databases Datastore/File Store Analytics Applications / Other
- 63. 63 Kafka Connect API, Part of the Apache Kafka™ Project Connect any source to any target system Integrated • 100% compatible with Kafka v0.9 and higher • Integrated with Confluent’s Schema Registry • Easy to manage with Confluent Control Center Flexible • 40+ open source connectors available • Easy to develop additional connectors • Flexible support for data types and formats Compatible • Maintains critical metadata • Preserves schema information • Supports schema evolution Reliable • Automated failover • Exactly-once guarantees • Balances workload between nodes
- 64. 64 Confluent Hub - The Kafka App Store
- 66. 66 Clients: Communicate with Kafka in a Broad Variety of Languages Apache Kafka Confluent Platform Community Supported Proxy http/REST stdin/stdout Confluent Platform Clients developed and fully supported by Confluent
- 67. 67 REST Proxy: Talking to Non-native Kafka Apps and Outside the Firewall REST Proxy Non-Java Applications Native Kafka Java Applications Schema Registry REST / HTTP Simplifies administrative actions Simplifies message creation and consumption Provides a RESTful interface to a Kafka cluster
- 68. 68 68 Processing
- 69. 69 Stream Processing by Analogy Kafka Cluster Connect API Stream Processing Connect API $ cat < in.txt | grep "ksql" | tr a-z A-Z > out.txt
- 70. 70 • subscribe() • poll() • send() • flush() Consumer, Producer Flexibility Simplicity Trade offs
- 72. 72 • subscribe() • poll() • send() • flush() Consumer, Producer • mapValues() • filter() • punctuate() Kafka Streams Flexibility Simplicity Trade offs
- 75. Consumer Group Protocol Power! App Streams API App Streams API App Streams API Same app, many instances
- 78. 78 Things Kafka Streams Does Runs everywhere Clustering done for you Exactly-once processing Event-time processing Integrated database Joins, windowing, aggregation S/M/L/XL/XXL/XXXL sizes
- 79. 79 • subscribe() • poll() • send() • flush() Consumer, Producer • mapValues() • filter() • punctuate() Kafka Streams Flexibility Simplicity Trade offs
- 80. 80 80 Kafka Streams Time time time
- 81. 81 Time! Time! Time! Time! Time! Time! Time! Time!
- 82. 82 Windowing in Kafka Streams
- 86. 86 Event Time Processing Event-time ”The point in time when an event or data record occurred, i.e. was originally created "by the source". Achieving event-time semantics typically requires embedding timestamps in the data records at the time a data record is being produced.” Processing-time ”The point in time when the event or data record happens to be processed by the stream processing application, i.e. when the record is being consumed. The processing-time may be milliseconds, hours, or days etc. later than the original event- time.” Ingestion-time “The point in time when an event or data record is stored in a topic partition by a Kafka broker.”
- 87. 87 87 Kafka Streams Exactly once semantic
- 88. 88 Delivery Guarantee At most once “Messages may be lost but are never redelivered.” At least once “Messages are never lost but may be redelivered.“ Exactly once “Each message is delivered once and only once.“
- 90. 90 Failure Scenario : Duplicate Writes
- 91. 91 Failure Scenario : Duplicate Processing
- 92. 92 Producer Guarantees - without exactly once guarantees P Broker 1 Broker 2 Broker 3 Topic1 partition1 Leader Follower Topic1 partition1 Topic1 partition1 Producer Properties acks=all min.insync.replica=2 {key: 1234 data: abcd} - offset 3345 Failed ack Successful write
- 93. 93 Producer Guarantees - without exactly once guarantees P Broker 1 Broker 2 Broker 3 Topic1 partition1 Leader Follower Topic1 partition1 Topic1 partition1 Producer Properties acks=all min.insync.replica=2 {key: 1234, data: abcd} - offset 3345 {key: 1234, data: abcd} - offset 3346 retry ack dupe!
- 94. 94 Producer Guarantees - with exactly once guarantees P Broker 1 Broker 2 Broker 3 Topic1 partition1 Leader Follower Topic1 partition1 Topic1 partition1 Producer Properties enable.idempotence=true max.inflight.requests.per.connection=1 acks = “all” retries > 0 (preferably MAX_INT) (pid, seq) [payload] (100, 1) {key: 1234, data: abcd} - offset 3345 (100, 1) {key: 1234, data: abcd} - rejected, ack re-sent (100, 2) {key: 5678, data: efgh} - offset 3346 retry ack no dupe!
- 95. 95 Exactly once Idempotent Producer Transactions Isolation Level • Read committed • Read uncommitted 95
- 97. 97 Exactly once made simple with Kafka Streams
- 99. 99 Interactive Queries App Streams API kTable = aStream .groupByKey() .reduce(reducer,materialize) From our App, how to query the state store? State Store Kafka Cluster
- 100. 100 Interactive Queries App Streams API store = kafkaStreams .store(name, types) value = store.get(key) From our App, how to query the state store? - Get the store « by name & types» - Then the value « by key » READ ONLY (Streams DSL) Kafka Cluster
- 101. 101 Interactive Queries App Streams API store = kafkaStreams .store(name, types) value = store.get(key) You can serve that value to your client Front End key Kafka Cluster
- 102. 102 Interactive Queries App Streams API store = kafkaStreams .store(name, types) value = store.get(key) We add App nodes to make it scale Which App to call to get the value ? Front End App Streams API App Streams API ? ? ? key Kafka Cluster
- 103. 103 Interactive Queries App Streams API store = kafkaStreams .store(name, types) value = store.get(key) We add App nodes to make it scale Which App to call to get the value ? è Any node è We shift the problem to the App Front End App Streams API App Streams API key Kafka Cluster
- 104. 104 Interactive Queries App Streams API metadata = kafkaStreams .metadataForKey(name,key) host = metadata.host() port = metadata.port() How does the App locate the value? - Thanks to the metadata exchanged with the coordinator - Some simple configuration is required Front End App Streams API App Streams API key Kafka Cluster Metadata
- 105. 105 Interactive Queries App Streams API metadata = kafkaStreams .metadataForKey(name,key) host = metadata.host() port = metadata.port() Once the data is located, the App forwards the call to the target node Front End App Streams API App Streams API key Kafka Cluster Metadata
- 106. 106 Interactive Queries App Streams API metadata = kafkaStreams .metadataForKey(name,key) host = metadata.host() port = metadata.port() Beware! The state store can be queried only in « RUNNING » state è Not during a rebalance è May impact your SLAs if you expose the data to your customers Front End App Streams API App Streams API key App Streams API Kafka Cluster
- 107. 107 Interactive Queries App Streams API metadata = kafkaStreams .metadataForKey(name,key) host = metadata.host() port = metadata.port() Solution ? Second App cluster, but: - More resources... - 1 more hop Front End key Kafka Cluster App Streams API App Streams API App Streams API App Streams API App (b) Streams API App (a) Streams API
- 108. 108 • subscribe() • poll() • send() • flush() Consumer, Producer • mapValues() • filter() • punctuate() Kafka Streams • Select…from… • Join…where… • Group by.. KSQL Flexibility Simplicity Trade offs
- 109. 109 KSQL for Data Exploration SELECT status, bytes FROM clickstream WHERE user_agent = 'Mozilla/5.0 (compatible; MSIE 6.0)';
- 110. 110 KSQL for Streaming ETL Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 id 1 id 2 id 3Business
- 111. 111 KSQL for Streaming ETL Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 id 1 id 2 id 3 Fact X Fact Y Fact Z
- 112. 112 KSQL for Streaming ETL Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 id 1 id 2 id 3 Fact X Fact Y Fact Z Fact A Fact B Fact C Fact D Fact E Fact K Fact L Fact M Fact N Id X
- 113. 113 KSQL for Streaming ETL CREATE STREAM vip_actions AS SELECT userid, page, action FROM clickstream c LEFT JOIN users u ON c.userid = u.user_id WHERE u.level = 'Platinum';
- 114. 114 Nested Types SELECT eventid, address.city FROM users WHERE address.state = 'CA';
- 115. 115 User Defined Functions (UDF) SELECT eventid, anomaly(sensorinput) FROM sensor @Udf(description = "apply analytic model to sensor input") public String anomaly(String sensorinput){ return your_logic; }
- 116. 116 KSQL for Anomaly Detection CREATE TABLE possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 SECONDS) GROUP BY card_number HAVING count(*) > 3;
- 117. 117
- 118. 118 Plenty of KSQL Recipies https://www.confluent.io/stream-processing-cookbook/
- 119. 119 Plenty of KSQL Recipies https://www.confluent.io/stream-processing-cookbook/
- 120. 120 Plenty of KSQL Recipies https://www.confluent.io/stream-processing-cookbook/
- 121. 121 KSQL: Enable Stream Processing using SQL-like Semantics Example Use Cases • Streaming ETL • Anomaly detection • Event monitoring Leverage Kafka Streams API without any coding required KSQL server Engine (runs queries) REST API CLIClients Confluent Control Center GUI Kafka Cluster Use any programming language Connect via CLI or Control Center user interface
- 122. 122 KSQL is really Kafka Stream ? ... yes!
- 123. 123 • subscribe() • poll() • send() • flush() Consumer, Producer • mapValues() • filter() • punctuate() Kafka Streams • Select…from… • Join…where… • Group by.. KSQL Flexibility Simplicity Trade offs
- 124. 124 Lowering the Bar to Enter the World of Streaming Kafka User Population CodingSophistication Core Java developers Core developers who don’t use Java/Scala Data engineers, architects, DevOps/SRE BI analysts streams
- 125. 125 125 Schema
- 126. 126 The Challenge of Data Compatibility at Scale : implicit à explicit ! App 1 App 2 App 3 Many sources without a policy causes mayhem in a centralized data pipeline Ensuring downstream systems can use the data is key to an operational stream pipeline Example: Date formats Even within a single application, different formats can be presented Incompatibly formatted message
- 127. 127 Schema Registry: Make Data Backwards Compatible and Future-Proof ● Define the expected fields for each Kafka topic ● Automatically handle schema changes (e.g. new fields) ● Prevent backwards incompatible changes ● Support multi-data center environments Elastic Cassandra HDFS Example Consumers Serializer App 1 Serializer App 2 ! Kafka Topic! Schema Registry
- 128. 128 128 Deployment
- 129. 129 Which one do you prefer ? • Zip • Yum/apt • Ansible • Docker • DC/OS • Helm-charts • Confluent Operator • ... Cloud!
- 130. 130 130 Tools
- 132. 132 132 Monitoring
- 133. 133 System Health Are all brokers and topics available? How much data is being processed? What can be tuned to improve performance? End-to-End SLA Monitoring Does Kafka process all events <15 seconds? Is the 8am report missing data? Are there duplicate events?
- 135. 135 Confluent Control Center– Cluster Health & Administration Cluster health dashboard • Monitor the health of your Kafka clusters and get alerts if any problems occur • Measure system load, performance, and operations • View aggregate statistics or drill down by broker or topic Cluster administration • Monitor topic configurations
- 136. 136 View consumer-partition lag across topics for a consumer group Alert on max consumer group lag across all topics Consumer Lag Monitoring 136
- 137. 137 137 Resources
- 139. 139 Optimizing Your Apache Kafka® Deployment https://www.confluent.io/white-paper/optimizing-your-apache-kafka-deployment/
- 140. 140 Resources - Confluent Enterprise Reference Architecture https://www.confluent.io/whitepaper/confluent-enterprise-reference-architecture/
- 141. 141 141 Community
- 142. 142 Resources – Community Slack and Mailing List https://slackpass.io/confluentcommunity https://groups.google.com/forum/#!forum/confluent-platform
- 143. 143 Confluent Blog
- 144. 144 Confluent Platform Demo : cp-demo https://github.com/confluentinc/cp-demo With security inside!
- 145. 145 Examples Examples Examples ! https://github.com/confluentinc/examples
- 148. 148 148 Take Away
- 149. 149 Kafka Provides a Central Nervous System for the Modern Digital Enterprise Enabling companies to respond accurately and in real time to business events
- 150. 150 150 Jeudi: Neil Avery KAFKA - THE ASYNCHRONOUS MICROSERVICES RUNTIME FOR STATE, SCALE AND PERFORMANCE Vendredi 14:30 - 15:15 - Florent & Loulou APACHE KAFKA : PATTERNS / ANTI-PATTERNS Vendredi: 15:30 – 17:30 - Florent, Nicolas & Loulou APACHE KAFKA - LES MAINS DEDANS