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Apache Kafka - Scalable Message-Processing and more !
The document provides an overview of Apache Kafka, a distributed publish-subscribe messaging system designed for real-time processing of data streams. It covers Kafka's architecture, components like Kafka Connect and Kafka Streams, operational metrics, and its integration within big data ecosystems and enterprise architectures. The summary also highlights the importance of monitoring and the support provided by organizations such as Confluent and Oracle for Kafka implementations.
Apache Kafka - Scalable Message-Processing and more !
- 1. BASEL BERN BRUGGDÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH Apache Kafka Scalable Message Processing and more! Guido Schmutz - 24.4.2017 @gschmutz guidoschmutz.wordpress.com
- 2. Guido Schmutz Working atTrivadis for more than 20 years Oracle ACE Director for Fusion Middleware and SOA Consultant, Trainer Software Architect for Java, Oracle, SOA and Big Data / Fast Data Member of Trivadis Architecture Board Technology Manager @ Trivadis More than 30 years of software development experience Contact: guido.schmutz@trivadis.com Blog: http://guidoschmutz.wordpress.com Slideshare: http://www.slideshare.net/gschmutz Twitter: gschmutz Apache Kafka - Scalable Message Processing and more!
- 3. Agenda 1. Introduction &Motivation 2. Kafka Core 3. Kafka Connect 4. Kafka Streams 5. Kafka and "Big Data" / "Fast Data" Ecosystem 6. Kafka in Enterprise Architecture 7. Confluent Data Platform 8. Summary Apache Kafka - Scalable Message Processing and more!
- 4. Introduction & Motivation ApacheKafka - Scalable Message Processing and more!
- 5. Apache Kafka -Overview Distributed publish-subscribe messaging system Designed for processing of real time activity stream data (logs, metrics collections, social media streams, …) Initially developed at LinkedIn, now part of Apache Does not use JMS API and standards Kafka maintains feeds of messages in topics Apache Kafka - Scalable Message Processing and more!
- 6. Apache Kafka -Motivation LinkedIn’s motivation for Kafka was: • "A unified platform for handling all the real-time data feeds a large company might have." Must haves • High throughput to support high volume event feeds • Support real-time processing of these feeds to create new, derived feeds. • Support large data backlogs to handle periodic ingestion from offline systems • Support low-latency delivery to handle more traditional messaging use cases • Guarantee fault-tolerance in the presence of machine failures Apache Kafka - Scalable Message Processing and more!
- 7. Apache Kafka History ApacheKafka - Scalable Message Processing and more! Source: Confluent
- 8. Apache Kafka -Unix Analogy Apache Kafka - Scalable Message Processing and more! $ cat < in.txt | grep "kafka" | tr a-z A-Z > out.txt Kafka Connect API Kafka Connect APIKafka Streams API Kafka Core (Cluster) Source: Confluent
- 9. Kafka Core Apache Kafka- Scalable Message Processing and more!
- 10. Kafka High LevelArchitecture The who is who • Producers write data to brokers. • Consumers read data from brokers. • All this is distributed. The data • Data is stored in topics. • Topics are split into partitions, which are replicated. Kafka Cluster Consumer Consumer Consumer Producer Producer Producer Broker 1 Broker 2 Broker 3 Zookeeper Ensemble Apache Kafka - Scalable Message Processing and more!
- 11. Apache Kafka -Architecture Kafka Broker Movement Processor Movement Topic Engine-Metrics Topic 1 2 3 4 5 6 Engine Processor1 2 3 4 5 6 Truck Apache Kafka - Scalable Message Processing and more!
- 12. Apache Kafka -Architecture Kafka Broker Movement Processor Movement Topic Engine-Metrics Topic 1 2 3 4 5 6 Engine Processor Partition 0 1 2 3 4 5 6 Partition 0 1 2 3 4 5 6 Partition 1 Movement Processor Truck Apache Kafka - Scalable Message Processing and more!
- 13. Apache Kafka Kafka Broker 1 Movement Processor Truck Movement Topic P 0 Movement Processor 12 3 4 5 P 2 1 2 3 4 5 Kafka Broker 2 Movement Topic P 2 1 2 3 4 5 P 1 1 2 3 4 5 Kafka Broker 3 Movement Topic P 0 1 2 3 4 5 P 1 1 2 3 4 5 Movement Processor
- 14. Apache Kafka -Architecture • Write Ahead Log / Commit Log • Producers always append to tail • think append to file Kafka Broker Movement Topic 1 2 3 4 5 Truck 6 6 Apache Kafka - Scalable Message Processing and more!
- 15. Kafka Topics Creating atopic • Command line interface • Using AdminUtils.createTopic method • Auto-create via auto.create.topics.enable = true Modifying a topic https://kafka.apache.org/documentation.html#basic_ops_modify_topic Deleting a topic • Command Line interface $ kafka-topics.sh –zookeeper zk1:2181 --create --topic my.topic –-partitions 3 –-replication-factor 2 --config x=y Apache Kafka - Scalable Message Processing and more!
- 16. Kafka Producer Apache Kafka- Scalable Message Processing and more! private Properties kafkaProps = new Properties(); kafkaProps.put("bootstrap.servers","broker1:9092,broker2:9092"); kafkaProps.put("key.serializer", "...StringSerializer"); kafkaProps.put("value.serializer", "...StringSerializer"); producer = new KafkaProducer<String, String>(kafkaProps); ProducerRecord<String, String> record = new ProducerRecord<>(”topicName", ”Key", ”Value"); try { producer.send(record); } catch (Exception e) {}
- 17. Durability Guarantees Producer canconfigure acknowledgements Apache Kafka - Scalable Message Processing and more! Value Description Throughput Latency Durability 0 • Producer doesn’t wait for leader high low low (no guarantee) 1 (default) • Producer waits for leader • Leader sends ack when message written to log • No wait for followers medium medium medium (leader) all (-1) • Producer waits for leader • Leader sends ack when all In-Sync Replica have acknowledged low high high (ISR)
- 18. Apache Kafka -Partition offsets Offset: messages in the partitions are each assigned a unique (per partition) and sequential id called the offset • Consumers track their pointers via (offset, partition, topic) tuples Consumer Group A Consumer Group B Apache Kafka - Scalable Message Processing and more! Source: Apache Kafka
- 19. Data Retention –3 options 1. Never 2. Time based (TTL) log.retention.{ms | minutes | hours} 3. Size based log.retention.bytes 4. Log compaction based (entries with same key are removed) kafka-topics.sh --zookeeper localhost:2181 --create --topic customers --replication-factor 1 --partitions 1 --config cleanup.policy=compact Apache Kafka - Scalable Message Processing and more!
- 20. Apache Kafka –Some numbers Kafka at LinkedIn => over 1800+ broker machines / 79K+ Topics Kafka Performance at our own infrastructure => 6 brokers (VM) / 1 cluster • 445’622 messages/second • 31 MB / second • 3.0405 ms average latency between producer / consumer 1.3 Trillion messages per day 330 Terabytes in/day 1.2 Petabytes out/day Peak load for a single cluster 2 million messages/sec 4.7 Gigabits/sec inbound 15 Gigabits/sec outbound http://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million-writes-second-three-cheap-machines https://engineering.linkedin.com/kafka/running-kafka-scale Apache Kafka - Scalable Message Processing and more!
- 21. Kafka Connect Apache Kafka- Scalable Message Processing and more!
- 22. Kafka Connect Architecture ApacheKafka - Scalable Message Processing and more! Source: Confluent
- 23. Kafka Connector Hub– Certified Connectors Source: http://www.confluent.io/product/connectors Apache Kafka - Scalable Message Processing and more!
- 24. Kafka Connector Hub– Additional Connectors Source: http://www.confluent.io/product/connectors Apache Kafka - Scalable Message Processing and more!
- 25. Kafka Connect –Twitter example Apache Kafka - Scalable Message Processing and more! ./connect-standalone.sh ../demo-config/connect-simple-source-standalone.properties ../demo-config/twitter-source.properties name=twitter-source connector.class=com.eneco.trading.kafka.connect.twitter.TwitterSourceConnector tasks.max=1 topic=tweets twitter.consumerkey=<consumer-key> twitter.consumersecret=<consumer-secret> twitter.token=<token> twitter.secret=<token-secret> track.terms=bigdata bootstrap.servers=localhost:9095,localhost:9096,localhost:9097 key.converter=org.apache.kafka.connect.storage.StringConverter value.converter=org.apache.kafka.connect.storage.StringConverter ...
- 26. Kafka Streams Apache Kafka- Scalable Message Processing and more!
- 27. Kafka Streams • Designedas a simple and lightweight library in Apache Kafka • no external dependencies on systems other than Apache Kafka • Part of open source Apache Kafka, introduced in 0.10+ • Leverages Kafka as its internal messaging layer • agnostic to resource management and configuration tools • Supports fault-tolerant local state • Event-at-a-time processing (not microbatch) with millisecond latency • Windowing with out-of-order data using a Google DataFlow-like model Apache Kafka - Scalable Message Processing and more!
- 28. Streams API inthe context of Kafka Apache Kafka - Scalable Message Processing and more! Source: Confluent
- 29. Kafka and "BigData" / "Fast Data" Ecosystem Apache Kafka - Scalable Message Processing and more!
- 30. Kafka and theBig Data / Fast Data ecosystem Kafka integrates with many popular products / frameworks • Apache Spark Streaming • Apache Flink • Apache Storm • Apache NiFi • Streamsets • Apache Flume • Oracle Stream Analytics • Oracle Service Bus • Oracle GoldenGate • Spring Integration Kafka Support • …Storm built-in Kafka Spout to consume events from Kafka Apache Kafka - Scalable Message Processing and more!
- 31. Kafka in “EnterpriseArchitecture” Apache Kafka - Scalable Message Processing and more!
- 32. Hadoop Clusterd Hadoop Cluster BigData Cluster Traditional Big Data Architecture BI Tools Enterprise Data Warehouse Billing & Ordering CRM / Profile Marketing Campaigns File Import / SQL Import SQL Search Online & Mobile Apps Search NoSQL Parallel Batch Processing Distributed Filesystem • Machine Learning • Graph Algorithms • Natural Language Processing Apache Kafka - Scalable Message Processing and more!
- 33. Event Hub Event Hub Hadoop Clusterd Hadoop Cluster BigData Cluster Event Hub – handle event stream data BI Tools Enterprise Data Warehouse Location Social Click stream Sensor Data Billing & Ordering CRM / Profile Marketing Campaigns Event Hub Call Center Weather Data Mobile Apps SQL Search Online & Mobile Apps Search Data Flow NoSQL Parallel Batch Processing Distributed Filesystem • Machine Learning • Graph Algorithms • Natural Language Processing
- 34. Hadoop Clusterd Hadoop Cluster BigData Cluster Event Hub – taking Velocity into account Location Social Click stream Sensor Data Billing & Ordering CRM / Profile Marketing Campaigns Call Center Mobile Apps Batch Analytics Streaming Analytics Event Hub Event Hub Event Hub NoSQL Parallel Batch Processing Distributed Filesystem Stream Analytics NoSQL Reference / Models SQL Search Dashboard BI Tools Enterprise Data Warehouse Search Online & Mobile Apps File Import / SQL Import Weather Data Apache Kafka - Scalable Message Processing and more!
- 35. Container Hadoop Clusterd Hadoop Cluster BigData Cluster Event Hub – Asynchronous Microservice Architecture Location Social Click stream Sensor Data Billing & Ordering CRM / Profile Marketing Campaigns Call Center Mobile Apps Event Hub Event Hub Event Hub Parallel Batch ProcessingDistributed Filesystem Microservice NoSQLRDBMS SQL Search BI Tools Enterprise Data Warehouse Search Online & Mobile Apps File Import / SQL Import Weather Data Apache Kafka - Scalable Message Processing and more! { } API
- 36. Confluent Platform Apache Kafka- Scalable Message Processing and more!
- 37. Confluent Data Platform3.2 Apache Kafka - Scalable Message Processing and more! Source: Confluent
- 38. Confluent Data Platform3.2 Apache Kafka - Scalable Message Processing and more! Source: Confluent
- 39. Confluent Enterprise –Control Center Apache Kafka - Scalable Message Processing and more! Source: Confluent
- 40. Summary Apache Kafka -Scalable Message Processing and more!
- 41. Summary • Kafka canscale to millions of messages per second, and more • Easy to start in a Proof of Concept (PoC), but more to invest to setup a production environment • Monitoring is key • Vibrant community and ecosystem • Fast paced technology • Confluent provides distribution and support for Apache Kafka • Oracle Event Hub Service offers a Kafka Managed Service Apache Kafka - Scalable Message Processing and more!
- 42. Weather Data SQL Import Hadoop Clusterd HadoopCluster Hadoop Cluster Location Social Click stream Sensor Data Billing & Ordering CRM / Profile Marketing Campaigns Call Center Mobile Apps Batch Analytics Streaming Analytics Event Hub Event Hub Event Hub NoSQL Parallel Processing Distributed Filesystem Stream Analytics NoSQL Reference / Models SQL Search Dashboard BI Tools Enterprise Data Warehouse Search Online & Mobile Apps Customer Event Hub – mapping of technologies Apache Kafka - Scalable Message Processing and more!
- 43. Guido Schmutz Technology Manager guido.schmutz@trivadis.com ApacheKafka - Scalable Message Processing and more! @gschmutz guidoschmutz.wordpress.com