Apache Kafka  Obligatory bold claim:  Kafka Rocks! After thispresentation you will love it  Edward Capriolo @ m6d.com
Facts and historyWritten in Java/scalaDeveloped by Linked-inRecently accepted as Apache project
Overall designDistributed messages queuing systemDesigned for throughput not complicated acknowledgment   stuff like JMS. ...
Key ConceptsMessage : Datum to sendTopic : named place to send messagesBroker: Kafka ServerPartition: A logical division o...
Design
Demo 1 Producer Consumer Shell
Startup the kafka/Broker componentscd /home/edward/kafka/kafka/bin/zookeeper-server-start.sh config/zookeeper.properties &...
Demo 2: Produce with no consumer then reconnect
Stop all consumers produce messagesStop consumer1Produce messagesStart consumer1Producing and consuming are not coupledRem...
Demo 3 multiple consumer groups
Start two consumers each with one consumer groupbin/kafka-consumer-shell.sh --partitions 1 --props   config/consumer-g1.pr...
Demo 4: Multiple clients on a consumer groupbin/kafka-consumer-shell.sh --partitions 1 --props   config/consumer-g2.proper...
Processing kafka in a distributed fashionEach topic has N partitionsWe need 1-N consumers to process these messagesCould w...
Write handle messagepackage com.jointhegrid.ironcount;import kafka.message.Message;public interface MessageHandler { publi...
Real Time Count of city and statepublic void handleMessage(Message m) {   String [] parts = getMessage(m).split("t");   lo...
Aggregate To Cassandra
Wrap upDistributed designDesigned for throughput not in memory per message seman-   ticsSurvives fail overs of consumers a...
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Apache Kafka Demo

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Information and demonstration of Apache Kafka in action. Originally presented at m6d Lunch and Learn series.

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Apache Kafka Demo

  1. 1. Apache Kafka Obligatory bold claim: Kafka Rocks! After thispresentation you will love it Edward Capriolo @ m6d.com
  2. 2. Facts and historyWritten in Java/scalaDeveloped by Linked-inRecently accepted as Apache project
  3. 3. Overall designDistributed messages queuing systemDesigned for throughput not complicated acknowledgment stuff like JMS. Again... Kafka != JMSSimple producer consumer APIAlways persists messages to disk Linear writes and reading Dont worry still fast (sendfile support)Messages are retained for specified time period (then pruned)Clients state stores position in log files Easy to replay messages Easy to deliver messages to multiple consumer groups
  4. 4. Key ConceptsMessage : Datum to sendTopic : named place to send messagesBroker: Kafka ServerPartition: A logical division of a topicProducer: An API to send messagesConsumer: An API to receive messagesConsumer Group: a group of clients that will receive only one copy of a message from a topic (more later)
  5. 5. Design
  6. 6. Demo 1 Producer Consumer Shell
  7. 7. Startup the kafka/Broker componentscd /home/edward/kafka/kafka/bin/zookeeper-server-start.sh config/zookeeper.properties &bin/kafka-server-start.sh config/server.propertiesbin/kafka-producer-shell.sh --server kafka://localhost:9092 --- topic topic1bin/kafka-consumer-shell.sh --partitions 1 --props config/consumer.properties --topic topic1
  8. 8. Demo 2: Produce with no consumer then reconnect
  9. 9. Stop all consumers produce messagesStop consumer1Produce messagesStart consumer1Producing and consuming are not coupledRemember all messages are always persisted
  10. 10. Demo 3 multiple consumer groups
  11. 11. Start two consumers each with one consumer groupbin/kafka-consumer-shell.sh --partitions 1 --props config/consumer-g1.properties --topic topic1bin/kafka-consumer-shell.sh --partitions 1 --props config/consumer-g2.properties --topic topic1#consumer group idgroupid=test-consumer-group2Each message sent to topic gets delivered once to each group
  12. 12. Demo 4: Multiple clients on a consumer groupbin/kafka-consumer-shell.sh --partitions 1 --props config/consumer-g2.properties --topic topic1 (in a new shell)Each consumer group gets 1 copy of messagemessages are balanced across consumers in the group Aka if consumer group has 3 consumers each get roughly 1/3rd of the messages (with random partitioning )
  13. 13. Processing kafka in a distributed fashionEach topic has N partitionsWe need 1-N consumers to process these messagesCould write a class that implements Kafka Consumer interface and run this on N NodesEnter IronCount (something I wrote)
  14. 14. Write handle messagepackage com.jointhegrid.ironcount;import kafka.message.Message;public interface MessageHandler { public void setWorkload(Workload w); public void handleMessage(Message m) ; public void stop(); public void setWorkerThread(WorkerThread wt) ;}
  15. 15. Real Time Count of city and statepublic void handleMessage(Message m) { String [] parts = getMessage(m).split("t"); long time_in_ms=Long.parseLong( parts[1]); String city = parts[9]; String state = parts[10]; GregorianCalendar gc = new GregorianCalendar(); gc.setTimeInMillis(time_in_ms); Mutator<String> mut = HFactory.createMutator(keyspace, StringSerializer.get()); HCounterColumn<String> hc = HFactory.createCounter- Column(state+","+city, 1L); mut.addCounter( bucketByMinute.format(new Date()), w.properties.get("rts.cf"), hc); mut.execute(); }
  16. 16. Aggregate To Cassandra
  17. 17. Wrap upDistributed designDesigned for throughput not in memory per message seman- ticsSurvives fail overs of consumers and brokersKafka partition is similar to a map/reduce partitionPartial aggregations possible because the key of a message routes it to same consumer
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