Kafka Streams is a lightweight stream processing library included in Apache Kafka since version 0.10. It provides a simple yet powerful API for building stream processing applications. The API uses a domain-specific language that allows developers to define stream processing topologies where data from Kafka topics acts as input streams and can be transformed before writing the results to output topics. The library handles common stream processing tasks like state management, windowing, and fault tolerance using Kafka's distributed and fault-tolerant architecture.

1. Kafka Streams
Stream processing Made Simple with Kafka
1
Guozhang Wang
Hadoop Summit, June 28, 2016

2. 2
What is NOT Stream Processing?

3. 3
Stream Processing isn’t (necessarily)
• Transient, approximate, lossy…
• .. that you must have batch processing as safety net

4. 4

5. 5

6. 6

7. 7

8. 8
Stream Processing
• A different programming paradigm
• .. that brings computation to unbounded data
• .. with tradeoffs between latency / cost / correctness

9. 9
Why Kafka in Stream Processing?

10. 10
• Persistent Buffering
• Logical Ordering
• Highly Scalable “source-of-truth”
Kafka: Real-time Platforms

11. 11
Stream Processing with Kafka

12. 12
• Option I: Do It Yourself !
Stream Processing with Kafka

13. 13
• Option I: Do It Yourself !
Stream Processing with Kafka
while (isRunning) {
// read some messages from Kafka
inputMessages = consumer.poll();
// do some processing…
// send output messages back to Kafka
producer.send(outputMessages);
}

14. 14
• Ordering
• Partitioning &
Scalability
• Fault tolerance
DIY Stream Processing is Hard
• State Management
• Time, Window &
Out-of-order Data
• Re-processing

15. 15
• Option I: Do It Yourself !
• Option II: full-fledged stream processing system
• Storm, Spark, Flink, Samza, ..
Stream Processing with Kafka

16. 16
MapReduce Heritage?
• Config Management
• Resource Management
• Configuration
• etc..

17. 17
MapReduce Heritage?
• Config Management
• Resource Management
• Deployment
• etc..

18. 18
MapReduce Heritage?
• Config Management
• Resource Management
• Deployment
• etc..
Can I just use my own?!

19. 19
• Option I: Do It Yourself !
• Option II: full-fledged stream processing system
• Option III: lightweight stream processing library
Stream Processing with Kafka

20. Kafka Streams
• In Apache Kafka since v0.10, May 2016
• Powerful yet easy-to-use stream processing library
• Event-at-a-time, Stateful
• Windowing with out-of-order handling
• Highly scalable, distributed, fault tolerant
• and more..
20

21. 21
Anywhere, anytime
Ok. Ok. Ok. Ok.

22. 22
Anywhere, anytime
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-streams</artifactId>
<version>0.10.0.0</version>
</dependency>

23. 23
Anywhere, anytime
War File
Rsync
Puppet/Chef
YARN
M
esos
Docker
Kubernetes
Very Uncool Very Cool

24. 24
Simple is Beautiful

25. Kafka Streams DSL
25
public static void main(String[] args) {
// specify the processing topology by first reading in a stream from a topic
KStream<String, String> words = builder.stream(”topic1”);
// count the words in this stream as an aggregated table
KTable<String, Long> counts = words.countByKey(”Counts”);
// write the result table to a new topic
counts.to(”topic2”);
// create a stream processing instance and start running it
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}

26. Kafka Streams DSL
26
public static void main(String[] args) {
// specify the processing topology by first reading in a stream from a topic
KStream<String, String> words = builder.stream(”topic1”);
// count the words in this stream as an aggregated table
KTable<String, Long> counts = words.countByKey(”Counts”);
// write the result table to a new topic
counts.to(”topic2”);
// create a stream processing instance and start running it
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}

27. Kafka Streams DSL
27
public static void main(String[] args) {
// specify the processing topology by first reading in a stream from a topic
KStream<String, String> words = builder.stream(”topic1”);
// count the words in this stream as an aggregated table
KTable<String, Long> counts = words.countByKey(”Counts”);
// write the result table to a new topic
counts.to(”topic2”);
// create a stream processing instance and start running it
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}

28. Kafka Streams DSL
28
public static void main(String[] args) {
// specify the processing topology by first reading in a stream from a topic
KStream<String, String> words = builder.stream(”topic1”);
// count the words in this stream as an aggregated table
KTable<String, Long> counts = words.countByKey(”Counts”);
// write the result table to a new topic
counts.to(”topic2”);
// create a stream processing instance and start running it
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}

29. Kafka Streams DSL
29
public static void main(String[] args) {
// specify the processing topology by first reading in a stream from a topic
KStream<String, String> words = builder.stream(”topic1”);
// count the words in this stream as an aggregated table
KTable<String, Long> counts = words.countByKey(”Counts”);
// write the result table to a new topic
counts.to(”topic2”);
// create a stream processing instance and start running it
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}

30. Kafka Streams DSL
30
public static void main(String[] args) {
// specify the processing topology by first reading in a stream from a topic
KStream<String, String> words = builder.stream(”topic1”);
// count the words in this stream as an aggregated table
KTable<String, Long> counts = words.countByKey(”Counts”);
// write the result table to a new topic
counts.to(”topic2”);
// create a stream processing instance and start running it
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}

31. 31
Native Kafka Integration
Property cfg = new Properties();
cfg.put(StreamsConfig.APPLICATION_ID_CONFIG, “my-streams-app”);
cfg.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, “broker1:9092”);
cfg.put(ConsumerConfig.AUTO_OFFSET_RESET_CONIFG, “earliest”);
cfg.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG, “SASL_SSL”);
cfg.put(KafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, “registry:8081”);
StreamsConfig config = new StreamsConfig(cfg);
…
KafkaStreams streams = new KafkaStreams(builder, config);

32. 32
Property cfg = new Properties();
cfg.put(StreamsConfig.APPLICATION_ID_CONFIG, “my-streams-app”);
cfg.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, “broker1:9092”);
cfg.put(ConsumerConfig.AUTO_OFFSET_RESET_CONIFG, “earliest”);
cfg.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG, “SASL_SSL”);
cfg.put(KafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, “registry:8081”);
StreamsConfig config = new StreamsConfig(cfg);
…
KafkaStreams streams = new KafkaStreams(builder, config);
Native Kafka Integration

33. 33
API, coding
“Full stack” evaluation
Operations, debugging, …

34. 34
API, coding
“Full stack” evaluation
Operations, debugging, …
Simple is Beautiful

35. 35
Key Idea:
Outsource hard problems to Kafka!

36. Kafka Concepts: the Log
4 5 5 7 8 9 10 11 12...
Producer Write
Consumer1 Reads
(offset 7)
Consumer2 Reads
(offset 10)
Messages
3

37. Topic 1
Topic 2
Partitions
Producers
Producers
Consumers
Consumers
Brokers
Kafka Concepts: the Log

38. 38
Kafka Streams: Key Concepts

39. Stream and Records
39
Key Value Key Value Key Value Key Value
Stream
Record

40. Processor Topology
40
Stream

41. Processor Topology
41
Stream
Processor

42. Processor Topology
42
KStream<..> stream1 = builder.stream(”topic1”);
KStream<..> stream2 = builder.stream(”topic2”);
KStream<..> joined = stream1.leftJoin(stream2, ...);
KTable<..> aggregated = joined.aggregateByKey(...);
aggregated.to(”topic3”);

43. Processor Topology
43
KStream<..> stream1 = builder.stream(”topic1”);
KStream<..> stream2 = builder.stream(”topic2”);
KStream<..> joined = stream1.leftJoin(stream2, ...);
KTable<..> aggregated = joined.aggregateByKey(...);
aggregated.to(”topic3”);

44. Processor Topology
44
KStream<..> stream1 = builder.stream(”topic1”);
KStream<..> stream2 = builder.stream(”topic2”);
KStream<..> joined = stream1.leftJoin(stream2, ...);
KTable<..> aggregated = joined.aggregateByKey(...);
aggregated.to(”topic3”);

45. Processor Topology
45
KStream<..> stream1 = builder.stream(”topic1”);
KStream<..> stream2 = builder.stream(”topic2”);
KStream<..> joined = stream1.leftJoin(stream2, ...);
KTable<..> aggregated = joined.aggregateByKey(...);
aggregated.to(”topic3”);

46. Processor Topology
46
KStream<..> stream1 = builder.stream(”topic1”);
KStream<..> stream2 = builder.stream(”topic2”);
KStream<..> joined = stream1.leftJoin(stream2, ...);
KTable<..> aggregated = joined.aggregateByKey(...);
aggregated.to(”topic3”);

47. Processor Topology
47
Source Processor
Sink Processor
KStream<..> stream1 = builder.stream(
KStream<..> stream2 = builder.stream(
aggregated.to(

48. Processor Topology
48Kafka Streams Kafka

49. Kafka Topic B
Data Parallelism
49
Kafka Topic A
MyApp.1 MyApp.2
Task2Task1

50. 50
• Ordering
• Partitioning &
Scalability
• Fault tolerance
Stream Processing Hard Parts
• State Management
• Time, Window &
Out-of-order Data
• Re-processing

51. States in Stream Processing
51
• filter
• map
• join
• aggregate
Stateless
Stateful

52. 52

53. States in Stream Processing
53
KStream<..> stream1 = builder.stream(”topic1”);
KStream<..> stream2 = builder.stream(”topic2”);
KStream<..> joined = stream1.leftJoin(stream2, ...);
KTable<..> aggregated = joined.aggregateByKey(...);
aggregated.to(”topic2”);
State

54. Kafka Topic B
Task2Task1
States in Stream Processing
54
Kafka Topic A
State State

55. It’s all about Time
• Event-time (when an event is created)
• Processing-time (when an event is processed)
55

56. Event-time 1 2 3 4 5 6 7
Processing-time 1999 2002 2005 1997 1980 1983 2015
56
PHANTOMMENACE
ATTACKOFTHECLONES
REVENGEOFTHESITH
ANEWHOPE
THEEMPIRESTRIKESBACK
RETURNOFTHEJEDI
THEFORCEAWAKENS
Out-of-Order

57. Timestamp Extractor
57
public long extract(ConsumerRecord<Object, Object> record) {
return System.currentTimeMillis();
}
public long extract(ConsumerRecord<Object, Object> record) {
return record.timestamp();
}

58. Timestamp Extractor
58
public long extract(ConsumerRecord<Object, Object> record) {
return System.currentTimeMillis();
}
public long extract(ConsumerRecord<Object, Object> record) {
return record.timestamp();
}
processing-time

59. Timestamp Extractor
59
public long extract(ConsumerRecord<Object, Object> record) {
return System.currentTimeMillis();
}
public long extract(ConsumerRecord<Object, Object> record) {
return record.timestamp();
}
processing-time
event-time

60. Windowing
60
t
…

61. Windowing
61
t
…

62. Windowing
62
t
…

63. Windowing
63
t
…

64. Windowing
64
t
…

65. Windowing
65
t
…

66. Windowing
66
t
…

67. 67
• Ordering
• Partitioning &
Scalability
• Fault tolerance
Stream Processing Hard Parts
• State Management
• Time, Window &
Out-of-order Data
• Re-processing

68. Stream v.s.Table?
68
KStream<..> stream1 = builder.stream(”topic1”);
KStream<..> stream2 = builder.stream(”topic2”);
KStream<..> joined = stream1.leftJoin(stream2, ...);
KTable<..> aggregated = joined.aggregateByKey(...);
aggregated.to(”topic2”);
State

69. 69
Tables ≈ Streams

70. 70

71. 71

72. 72

73. The Stream-Table Duality
• A stream is a changelog of a table
• A table is a materialized view at time of a stream
• Example: change data capture (CDC) of databases
73

74. KStream = interprets data as record stream
~ think: “append-only”
KTable = data as changelog stream
~ continuously updated materialized view
74

75. 75
alice eggs bob lettuce alice milk
alice lnkd bob googl alice msft
KStream
KTable
User purchase history
User employment profile

76. 76
alice eggs bob lettuce alice milk
alice lnkd bob googl alice msft
KStream
KTable
User purchase history
User employment profile
time
“Alice bought eggs.”
“Alice is now at LinkedIn.”

77. 77
alice eggs bob lettuce alice milk
alice lnkd bob googl alice msft
KStream
KTable
User purchase history
User employment profile
time
“Alice bought eggs and milk.”
“Alice is now at LinkedIn
Microsoft.”

78. 78
alice 2 bob 10 alice 3
timeKStream.aggregate()
KTable.aggregate()
(key: Alice, value: 2)
(key: Alice, value: 2)

79. 79
alice 2 bob 10 alice 3
time
(key: Alice, value: 2 3)
(key: Alice, value: 2+3)
KStream.aggregate()
KTable.aggregate()

80. 80
KStream KTable
reduce()
aggregate()
…
toStream()
map()
filter()
join()
…
map()
filter()
join()
…

81. 81
KTable aggregated
KStream joined
KStream stream1KStream stream2
Updates Propagation in KTable
State

82. 82
KTable aggregated
KStream joined
KStream stream1KStream stream2
State
Updates Propagation in KTable

83. 83
KTable aggregated
KStream joined
KStream stream1KStream stream2
State
Updates Propagation in KTable

84. 84
KTable aggregated
KStream joined
KStream stream1KStream stream2
State
Updates Propagation in KTable

85. 85
• Ordering
• Partitioning &
Scalability
• Fault tolerance
Stream Processing Hard Parts
• State Management
• Time, Window &
Out-of-order Data
• Re-processing

86. 86
Remember?

87. 87
StateProcess
StateProcess
StateProcess
Kafka ChangelogFault Tolerance
Kafka
Kafka Streams
Kafka

88. 88
StateProcess
StateProcess
Protoco
l
StateProcess
Fault Tolerance
Kafka
Kafka Streams
Kafka Changelog
Kafka

89. 89
StateProcess
StateProcess
Protoco
l
StateProcess
Fault Tolerance
StateProcess
Kafka
Kafka Streams
Kafka Changelog
Kafka

90. 90

91. 91

92. 92

93. 93

94. 94
• Ordering
• Partitioning &
Scalability
• Fault tolerance
Stream Processing Hard Parts
• State Management
• Time, Window &
Out-of-order Data
• Re-processing

95. 95
• Ordering
• Partitioning &
Scalability
• Fault tolerance
Stream Processing Hard Parts
• State Management
• Time, Window &
Out-of-order Data
• Re-processing
Simple is Beautiful

96. 96
But how to get data in / out Kafka?

97. 97

98. 98

99. 99

100. 100

101. Take-aways
• Stream Processing: a new programming paradigm
101

102. Take-aways
• Stream Processing: a new programming paradigm
• Kafka Streams: stream processing made easy
102

103. Take-aways
• Stream Processing: a new programming paradigm
• Kafka Streams: stream processing made easy
103
THANKS!
Guozhang Wang | guozhang@confluent.io | @guozhangwang
Visit Confluent at the Syncsort Booth (#1303), live demos @ 29th
Download Kafka Streams: www.confluent.io/product

104. 104
We are Hiring!