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Real-time streaming and data pipelines with Apache Kafka

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Get up and running quickly with Apache Kafka http://kafka.apache.org/

* Fast * A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients.

* Scalable * Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers

* Durable * Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact.

* Distributed by Design * Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.

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Real-time streaming and data pipelines with Apache Kafka

  1. 1. REAL-TIME STREAMING AND DATA PIPELINES WITH APACHE KAFKA
  2. 2. Real-time Streaming
  3. 3. Real-time Streaming with Data Pipelines Sync Z-Y < 20ms Y-X < 20ms Async Z-Y < 1ms Y-X < 1ms
  4. 4. Real-time Streaming with Data Pipelines Sync Y-X < 20ms Async Y-X < 1ms
  5. 5. Real-time Streaming with Data Pipelines
  6. 6. Before we get started  Samples  https://github.com/stealthly/scala-kafka  Apache Kafka 0.8.0 Source Code  https://github.com/apache/kafka  Docs  http://kafka.apache.org/documentation.html  Wiki  https://cwiki.apache.org/confluence/display/KAFKA/Index  Presentations  https://cwiki.apache.org/confluence/display/KAFKA/Kafka+papers+and+presentations Tools  https://cwiki.apache.org/confluence/display/KAFKA/Replication+tools  https://github.com/apache/kafka/tree/0.8/core/src/main/scala/kafka/tools
  7. 7. Apache Kafka  Fast - A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients.  Scalable - Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers  Durable - Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact.  Distributed by Design - Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
  8. 8. Up and running with Apache Kafka 1) Install Vagrant http://www.vagrantup.com/ 2) Install Virtual Box https://www.virtualbox.org/ 3) git clone https://github.com/stealthly/scala-kafka 4) cd scala-kafka 5) vagrant up Zookeeper will be running on 192.168.86.5 BrokerOne will be running on 192.168.86.10 All the tests in ./src/test/scala/* should pass (which is also /vagrant/src/test/scala/* in the vm) 6) ./sbt test [success] Total time: 37 s, completed Dec 19, 2013 11:21:13 AM
  9. 9. Maven <dependency> <groupId>org.apache.kafka</groupId> <artifactId>kafka_2.10</artifactId> <version>0.8.0</version> <exclusions> <!-- https://issues.apache.org/jira/browse/KAFKA-1160 --> <exclusion> <groupId>com.sun.jdmk</groupId> <artifactId>jmxtools</artifactId> </exclusion> <exclusion> <groupId>com.sun.jmx</groupId> <artifactId>jmxri</artifactId> </exclusion> </exclusions> </dependency>
  10. 10. SBT "org.apache.kafka" % "kafka_2.10" % "0.8.0“ intransitive() Or libraryDependencies ++= Seq( "org.apache.kafka" % "kafka_2.10" % "0.8.0", ) https://issues.apache.org/jira/browse/KAFKA-1160 https://github.com/apache/kafka/blob/0.8/project/Build.scala?source=c
  11. 11. Apache Kafka Producers Consumers Brokers  Topics  Brokers  Sync Producers  Async Producers (Async/Sync)=> Akka Producers  Topics  Partitions  Read from the start  Read from where last left off  Partitions  Load Balancing Producers  Load Balancing Consumer  In Sync Replicaset (ISR)  Client API
  12. 12. Producers  Topics  Brokers  Sync Producers  Async Producers  (Async/Sync)=> Akka Producers
  13. 13. Producer /** at least one of these for every partition **/ val producer = new KafkaProducer(“topic","192.168.86.10:9092") producer.sendMessage(testMessage)
  14. 14. Kafka Producer case class KafkaProducer( topic: String, brokerList: String, synchronously: Boolean = true, compress: Boolean = true, batchSize: Integer = 200, messageSendMaxRetries: Integer = 3 ){ val props = new Properties() val codec = if(compress) DefaultCompressionCodec.codec else NoCompressionCodec.codec props.put("compression.codec", codec.toString) props.put("producer.type", if(synchronously) "sync" else "async") props.put("metadata.broker.list", brokerList) props.put("batch.num.messages", batchSize.toString) props.put("message.send.max.retries", messageSendMaxRetries.toString) wrapper for core/src/main/kafka/producer/Producer.Scala val producer = new Producer[AnyRef, AnyRef](new ProducerConfig(props)) def sendMessage(message: String) = { try { producer.send(new KeyedMessage(topic,message.getBytes)) } catch { case e: Exception => e.printStackTrace System.exit(1) } } }
  15. 15. Docs http://kafka.apache.org/documentation.html#producerconfigs Producer Config Source https://github.com/apache/kafka/blob/0.8/core/src/main/scala/kafka/ producer/ProducerConfig.scala?source=c
  16. 16. class KafkaAkkaProducer extends Actor with Logging { private val producerCreated = new AtomicBoolean(false) var producer: KafkaProducer = null def init(topic: String, zklist: String) = { if (!producerCreated.getAndSet(true)) { producer = new KafkaProducer(topic,zklist) } Akka Producer def receive = { case t: (String, String) ⇒ { init(t._1, t._2) } case msg: String ⇒ { producer.sendString(msg) } } }
  17. 17. Consumers Topics Partitions Read from the start Read from where last left off
  18. 18. class KafkaConsumer( topic: String, groupId: String, zookeeperConnect: String, readFromStartOfStream: Boolean = true ) extends Logging { val props = new Properties() props.put("group.id", groupId) props.put("zookeeper.connect", zookeeperConnect) props.put("auto.offset.reset", if(readFromStartOfStream) "smallest" else "largest") val config = new ConsumerConfig(props) val connector = Consumer.create(config) val filterSpec = new Whitelist(topic) val stream = connector.createMessageStreamsByFilter(filterSpec, 1, new DefaultDecoder(), new DefaultDecoder()).get(0) def read(write: (Array[Byte])=>Unit) = { for(messageAndTopic <- stream) { write(messageAndTopic.message) } } def close() { connector.shutdown() } }
  19. 19. Source Sample https://github.com/apache/kafka/blob/0.8/core/src/main/sca la/kafka/tools/SimpleConsumerShell.scala?source=c Low Level Consumer
  20. 20. val topic = “publisher” val consumer = new KafkaConsumer(topic ,”loop”,"192.168.86.5:2181") val count = 2 val pdc = system.actorOf(Props[KafkaAkkaProducer].withRouter(RoundRobinRouter(count)), "router") 1 to countforeach { i =>( pdc ! (topic,"192.168.86.10:9092")) } def exec(binaryObject: Array[Byte]) = { val message = new String(binaryObject) pdc ! message } pdc ! “go, go gadget stream 1” pdc ! “go, go gadget stream 2” consumer.read(exec)
  21. 21. Brokers  Partitions  Load Balancing Producers  Load Balancing Consumer  In Sync Replicaset (ISR)  Client API
  22. 22. Partitions make up a Topic
  23. 23. Load Balancing Producer By Partition
  24. 24. Load Balancing Consumer By Partition
  25. 25. Replication – In Sync Replicas
  26. 26. Client API o Developers Guide https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol o Non-JVM Clients https://cwiki.apache.org/confluence/display/KAFKA/Clients o Python o Pure Python implementation with full protocol support. Consumer and Producer implementations included, GZIP and Snappy compression supported. oC o High performance C library with full protocol support o C++ o Native C++ library with protocol support for Metadata, Produce, Fetch, and Offset. o Go (aka golang) o Pure Go implementation with full protocol support. Consumer and Producer implementations included, GZIP and Snappy compression supported. o Ruby o Pure Ruby, Consumer and Producer implementations included, GZIP and Snappy compression supported. Ruby 1.9.3 and up (CI runs MRI 2. o Clojure o https://github.com/pingles/clj-kafka/ 0.8 branch
  27. 27. Questions? /*********************************** Joe Stein Founder, Principal Consultant Big Data Open Source Security LLC http://www.stealth.ly Twitter: @allthingshadoop ************************************/

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