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![Stream Processing in Kafka
● KStream
KStream<byte[], String> textLines = builder
.stream("textlines-topic", Consumed.with(Serdes.ByteArray(), Serdes.String()))
.mapValues(String::toUpperCase));
KTable<String, Long> wordCounts = textLines
.flatMapValues(textLine -> Arrays.asList(textLine.split("W+")))
.groupBy((key, word) -> word)
.count();
● KTable](https://image.slidesharecdn.com/chtijug20180626-180629142620/85/Chti-jug-2018-06-26-18-320.jpg)
More Related Content
Chti jug - 2018-06-26
- 1. Chti JUG Florent Ramière TechnicalAccount Manager florent@confluent.io @framiere
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- 4. About Confluent andApache Kafka™ 70% of active Kafka Committers Founded September 2014 Technology developed while at LinkedIn Founded by the creators of Apache Kafka Cheryl Dalrymple CFO Luanne Dauber CMO Simon Hayes Head of Corporate & Business Development Jay Kreps CEO Todd Barnett VP WW Sales Neha Narkhede CTO, VP Engineering Sarah Sproehnle VP Customer Success
- 5. Why a StreamingPlatform? All your data Real-time Fault tolerant Secure
- 6. Confluent Platform: EnterpriseStreaming based on Apache Kafka Database Changes Log Events loT Data Web Events … CRM Data Warehouse Database Hadoop Data Integration … Monitoring Analytics Custom Apps Transformations Real-time Applications … Apache Open Source Confluent Open Source Confluent Enterprise Confluent Platform Apache Kafka® Core | Connect API | Streams API Data Compatibility Schema Registry Confluent Platform Monitoring & Administration Confluent Control Center | Security Operations Replicator | Auto Data Balancing | JMS Client | JMS Connectors Development and Connectivity Clients | Connectors | REST Proxy | CLI Apache Open Source Confluent Open Source Confluent Enterprise SQL Stream Processing KSQL (Streams API)
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- 10. No Panic, it'sa walktrough! https://github.com/framiere/a-kafka-story/tree/master/step6
- 11. Trade-Offs • subscribe() • poll() •send() • flush() Consumer, Producer Flexibility Simplicity
- 12. Trade-Offs • subscribe() • poll() •send() • flush() Consumer, Producer • filter() • join() • aggregate() Kafka Streams Flexibility Simplicity
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- 17. Things Kafka StreamsDoes Runs everywhere Clustering done for you Exactly-once processing Event-time processing Integrated database Joins, windowing, aggregation S/M/L/XL/XXL/XXXL sizes
- 18. Stream Processing inKafka ● KStream KStream<byte[], String> textLines = builder .stream("textlines-topic", Consumed.with(Serdes.ByteArray(), Serdes.String())) .mapValues(String::toUpperCase)); KTable<String, Long> wordCounts = textLines .flatMapValues(textLine -> Arrays.asList(textLine.split("W+"))) .groupBy((key, word) -> word) .count(); ● KTable
- 19. Trade-Offs • subscribe() • poll() •send() • flush() Consumer, Producer • filter() • join() • aggregate() Kafka Streams • Copy In • Copy Out • SMT Kafka Connect Flexibility Simplicity
- 20. Apache Kafka™ ConnectAPI – Streaming Data Capture 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 Part of Apache Kafka project Integrated within Confluent Platform’s Control Center Flexible Integrated Reliable Compatible Connect any source to any target system
- 21. Single Message Transforms •Masksensitive information •Add identifiers •Tag events •Lineage/provenance •Remove unnecessary columns •Route high priority events to faster data stores •Direct events to different Elasticsearch indexes •Cast data types to match destination •Remove unnecessary columns Modify events before storing in Kafka: Modify events going out of Kafka:
- 22. But…Easy to Implement /** *Single message transformation for Kafka Connect record types. * * Connectors can be configured with transformations to make lightweight * message-at-a-time modifications. */ public interface Transformation<R extends ConnectRecord<R>> extends Configurable, Closeable { /** * Apply transformation to the {@code record} and return another record object. * * The implementation must be thread-safe. */ R apply(R record); /** Configuration specification for this transformation. **/ ConfigDef config(); /** Signal that this transformation instance will no longer will be used. **/ @Override void close(); }
- 23. Trade-Offs • subscribe() • poll() •send() • flush() Consumer, Producer • filter() • join() • aggregate() Kafka Streams • Select…from… • Join…where… • Group by.. KSQL Flexibility Simplicity
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- 25. KSQL for DataExploration SELECT status, bytes FROM clickstream WHERE user_agent = 'Mozilla/5.0 (compatible; MSIE 6.0)'; An easy way to inspect data in a running cluster
- 26. KSQL for StreamingETL • Kafka is popular for data pipelines. • KSQL enables easy transformations of data within the pipe. • Transforming data while moving from Kafka to another system. 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';
- 27. KSQL for AnomalyDetection CREATE TABLE possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 SECONDS) GROUP BY card_number HAVING count(*) > 3; Identifying patterns or anomalies in real-time data, surfaced in milliseconds
- 28. Once again KSQL implementsfor you the Kafka Stream application you would have implemented if you had • ... the time • ... the experience • ... the KSQL as a spec • ... the willingness to do boring code
- 29. KSQL is reallyKafka Stream ? ... yes! ./confluent start ./jmc& echo '{"something":"value"}' | ./kafka-console-producer --broker-list localhost:9092 --topic temp ./kafka-console-consumer --bootstrap-server localhost:9092 --topic temp --from-beginning {"something":"value"} ./ksql ksql>SET 'auto.offset.reset' = 'earliest'; ksql>CREATE STREAM TEMP (something varchar) WITH ( kafka_topic='temp',value_format='JSON'); ksql>SELECT * FROM TEMP; 1526371655810 | null | value
- 30. Where is KSQLnot such a great fit? BI reports (Tableau etc.) • No indexes • No JDBC (most BI tools are not good with continuous results!) Ad-hoc queries • Limited span of time usually retained in Kafka • No indexes
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- 33. Demo ... lessfun https://bit.ly/2KqPZYo
- 34. Demo ... lessfun https://bit.ly/2L5l2dj
- 35. Demo ... lessfun https://github.com/framiere/a-kafka-story/tree/master/step19 Change Data Capture Docker Producer Consumer Kafka Stream KSQL Event sourcing Influxdb Grafana S3 docker run --rm -it --name dcv -v $(pwd):/input pmsipilot/docker-compose-viz render --horizontal --output-format image --force docker-compose.yml
- 36. Demo ... lessfun ... without Confluent Control Center links
- 37. Confluent 4.2 -Nested Types SELECT userid, address.city FROM users WHERE address.state = 'CA' https://github.com/confluentinc/ksql/pull/1114
- 38. Confluent 4.2 -Remaining joins SELECT orderid, shipmentid FROM orders INNER JOIN shipments ON order.id = shipmentid;
- 39. Where to gofrom here ● KSQL project page ○ https://www.confluent.io/product/ksql ● Confluent blog ○ http://blog.confluent.io/ ● Blog Formule1 game ○ https://www.confluent.io/blog/taking-ksql-spin-using-real-time-device-data/ ● KSQL github repo ○ https://github.com/confluentinc/ksql ● CP-Demo ○ https://github.com/confluentinc/cp-demo ● A-Kafka-Story ○ https://github.com/framiere/a-kafka-story ● Un tour de l'environement Kafka ○ https://www.youtube.com/watch?v=BBo-rqmhpDM ● KSQL Recipies ○ https://github.com/bluemonk3y/ksql-recipe-fraudulent-txns/
- 40. Confluent Download –4.1 – Kafka 1.1 and KSQL GA
- 41. KSQL Capacity Planning– Sizing https://docs.confluent.io/current/ksql/docs/capacity-planning.html
- 42. Resources - ConfluentEnterprise Reference Architecture https://www.confluent.io/whitepaper/confluent-enterprise-reference- architecture/
- 43. Resources – CommunitySlack and Mailing List https://slackpass.io/confluentcommunity https://groups.google.com/forum/#!forum/confluent-platform
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