[SF JUG] Apache Kafka — A Streaming Data Platform

When it comes time to choose a distributed messaging system, everyone knows the answer: Apache Kafka. But how about when you’re on the hook to choose a world-class, horizontally scalable stream data processing system? When you need not just publish and subscribe messaging, but also long-term storage, a flexible integration framework, and a means of deploying real-time stream processing applications at scale without having to integrate a number of different pieces of infrastructure yourself? The answer is still Apache Kafka.

In this talk, we’ll make a rapid-fire review of the breadth of Kafka as a streaming data platform. We’ll look at its internal architecture, including how it partitions messaging workloads in a fault-tolerant way. We’ll learn how it provides message durability. We’ll look at its approach to pub/sub messaging. We’ll even take a peek at how Kafka Connect provides code-free, scalable, fault-tolerant integration, and how the Streams API provides a complete framework for computation over all the streaming data in your cluster.

Transcript

1. @ Apache Kafka A Streaming Data Platform and #javapuzzlersng @gamussa @sfjava @confluentinc
2. @ @gamussa @sfjava @confluentinc
3. @ @gamussa @sfjava @confluentinc
4. @ @gamussa @sfjava @confluentinc Who am I?
5. @ @gamussa @sfjava @confluentinc Solutions Architect Who am I?
6. @ @gamussa @sfjava @confluentinc Solutions Architect Developer Advocate Who am I?
7. @ @gamussa @sfjava @confluentinc Solutions Architect Developer Advocate @gamussa in internetz Who am I?
8. @ @gamussa @sfjava @confluentinc Solutions Architect Developer Advocate @gamussa in internetz Hey you, yes, you, go follow me in twitter © Who am I?
9. @ @gamussa @sfjava @confluentinc Kafka & Confluent
10. @ @gamussa @sfjava @confluentinc We are hiring! https://www.confluent.io/careers/
11. @ @gamussa @sfjava @confluentinc
12. @ @gamussa @sfjava @confluentinc A company is build on
13. @ @gamussa @sfjava @confluentinc A company is build on DATA FLOWS but All we have is DATA STORES
14. @ @gamussa @sfjava @confluentinc
15. @ @gamussa @sfjava @confluentinc
16. @ @gamussa @sfjava @confluentinc
17. @ @gamussa @sfjava @confluentinc Kafka is a Streaming Platform The Log Connectors Connectors Producer Consumer Streaming Engine
18. @ @gamussa @sfjava @confluentinc Kafka Serving Layer (Cassandra, KV-storage etc.) Kafka Streams / KSQL Continuous Computation High Throughput Messaging API based clustering Origins in Stream Processing
19. @ @gamussa @sfjava @confluentinc authorization_attempts possible_fraud What exactly is Stream Processing?
20. @ @gamussa @sfjava @confluentinc CREATE STREAM possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3; authorization_attempts possible_fraud What exactly is Stream Processing?
21. @ @gamussa @sfjava @confluentinc CREATE STREAM possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3; authorization_attempts possible_fraud What exactly is Stream Processing?
22. @ @gamussa @sfjava @confluentinc CREATE STREAM possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3; authorization_attempts possible_fraud What exactly is Stream Processing?
23. @ @gamussa @sfjava @confluentinc CREATE STREAM possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3; authorization_attempts possible_fraud What exactly is Stream Processing?
24. @ @gamussa @sfjava @confluentinc CREATE STREAM possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3; authorization_attempts possible_fraud What exactly is Stream Processing?
25. @ @gamussa @sfjava @confluentinc CREATE STREAM possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3; authorization_attempts possible_fraud What exactly is Stream Processing?
26. @ @gamussa @sfjava @confluentinc Streaming
 is the toolset for dealing 
 with events 
 as they move!
27. @ @gamussa @sfjava @confluentinc What is a Streaming Platform? The Log Connectors Connectors Producer Consumer Streaming Engine
28. @ @gamussa @sfjava @confluentinc Kafka’s Distributed Log The Log Connectors Connectors Producer Consumer Streaming Engine
29. @ @gamussa @sfjava @confluentinc The log is a type of durable messaging system
30. @ @gamussa @sfjava @confluentinc Similar to a traditional messaging system (ActiveMQ, Rabbit etc) but with: (a) Far better scalability (b) Built in fault tolerance / HA (c) Storage The log is a type of durable messaging system
31. @ @gamussa @sfjava @confluentinc The log is a simple idea Messages are added at the end of the log Old New
32. @ @gamussa @sfjava @confluentinc Consumers have a position all of their own Sally is here George is here Fred is here Old New Scan Scan Scan
33. @ @gamussa @sfjava @confluentinc Only Sequential Access Old New Read to offset & scan
34. @ @gamussa @sfjava @confluentinc Scaling Out
35. @ @gamussa @sfjava @confluentinc Shard data to get scalability Messages are sent to different partitions Producer (1) Producer (2) Producer (3) Cluster of machines Partitions live on different machines
36. @ @gamussa @sfjava @confluentinc Replicate to get fault tolerance replicate msg msg leader Machine A Machine B
37. @ @gamussa @sfjava @confluentinc 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
38. @ @gamussa @sfjava @confluentinc Replication provides resiliency A ‘replica’ takes over on machine failure
39. @ @gamussa @sfjava @confluentinc 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
40. @ @gamussa @sfjava @confluentinc Linearly Scalable Architecture Single topic: - Many producers machines - Many consumer machines - Many Broker machines No Bottleneck!! Consumers Producers
41. @ @gamussa @sfjava @confluentinc Worldwide, localized views !33 NY London Tokyo Replicator Replicator Replicator
42. @ @gamussa @sfjava @confluentinc The Connect API The Log Connectors Connectors Producer Consumer Streaming Engine
43. @ @gamussa @sfjava @confluentinc Ingest / Egest into any data source Kafka Connect Kafka Connect
44. @ @gamussa @sfjava @confluentinc Ingest/Egest data from/to data sources Amazon S3 Elasticsearch HDFS JDBC Couchbase Cassandra Oracle SAP Vertica Blockchain JMX Kenesis MongoDB MQTT NATS Postgres Rabbit Redis Twitter DynamoDB FTP Github BigQuery Google Pub Sub RethinkDB Salesforce Solr Splunk
45. @ @gamussa @sfjava @confluentinc Kafka Streams and KSQL The Log Connectors Connectors Producer Consumer Streaming Engine
46. @ @gamussa @sfjava @confluentinc SELECT card_number, count(*) FROM authorization_attempts WINDOW (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3; Engine for Continuous Computation
47. @ @gamussa @sfjava @confluentinc But it’s just an API public static void main(String[] args) { 
 StreamsBuilder builder = new StreamsBuilder();
 builder.stream("caterpillars")
 .map(StreamsApp ::coolTransformation)
 .to("butterflies");
 
 new KafkaStreams(builder.build(), props()).start(); 
 }
48. @ @gamussa @sfjava @confluentinc Compacted Topic Join Stream Table Kafka Kafka Streams / KSQL Topic Join Streams and Tables
49. @ @gamussa @sfjava @confluentinc KAFKA Payments Orders Buffer 5 mins Emailer Windows / Retention – Handle Late Events In an asynchronous world, will the payment come first, or the order? Join by Key
50. @ @gamussa @sfjava @confluentinc Windows / Retention – Handle Late Events KAFKA Payments Orders Buffer 5 mins Emailer Join by Key KStream orders = builder.stream("Orders");
 KStream payments = builder.stream("Payments");
 
 orders.join(payments, 
 KeyValue ::new, 
 JoinWindows.of(1 * MIN))
 .peek((key, pair) -> emailer.sendMail(pair));
51. @ @gamussa @sfjava @confluentinc A KTable is just a stream with infinite retention KAFKA Emailer Orders, Payments Customers Join
52. @ @gamussa @sfjava @confluentinc A KTable is a stream with infinite retention KAFKA Emailer Orders, Payments Customers Join Materialize a table in two lines of code! KStream orders = builder.stream("Orders");
 KStream payments = builder.stream("Payments");
 KTable customers = builder.table("Customers");
 
 orders.join(payments, EmailTuple ::new, JoinWindows.of(1*MIN))
 .join(customers, (tuple, cust) -> tuple.setCust(cust))
 .peek((key, tuple) -> emailer.sendMail(tuple));
53. @ @gamussa @sfjava @confluentinc The Log Connectors Connectors Producer Consumer Streaming Engine Kafka is a complete Streaming Platform
54. @ @gamussa @sfjava @confluentinc The Log Connectors Connectors Producer Consumer Streaming Engine Kafka is a complete Streaming Platform
55. @ @gamussa @sfjava @confluentinc https://www.confluent.io/download/
56. @ @gamussa @sfjava @confluentinc We are hiring! https://www.confluent.io/careers/
57. @ @gamussa @sfjava @confluentinc One more thing…
58. @ @gamussa @sfjava @confluentinc
59. @ @gamussa @sfjava @confluentinc
60. @ @gamussa @sfjava @confluentinc
61. @ @gamussa @sfjava @confluentinc
62. @ @gamussa @sfjava @confluentinc
63. @ @gamussa @sfjava @confluentinc A Major New Paradigm
64. @ @gamussa @sfjava @confluentinc Thanks! Stay for #javapuzzlersng!!! @gamussa viktor@confluent.io We are hiring! https://www.confluent.io/careers/