The document discusses Apache Flink, a stream processing framework that efficiently handles unbounded data with low latency and high throughput, guaranteeing exactly-once processing. It outlines various features, including its datastream and dataset APIs, as well as the capabilities for batch and stream processing. The document also provides insights into deployment options, fault tolerance, and the architecture behind Flink's processing mechanisms.

1. Ufuk Celebi
uce@apache.org
HUG London
October 15, 2015
Streaming Data Flow with
Apache Flink

2. Recent History
April ‘14 December ‘14
v0.5 v0.6 v0.7
April ‘15
Project
Incubation
Top Level
Project
v0.8 v0.9
Currently moving towards 0.10 and 1.0 release.

3. What is Flink?
Streaming
Topologies
Stream
Time
Window
Count
Low Latency
Long Batch Pipelines
Resource Utilization
1.2
1.4
1.5
1.2
0.8
0.9
1.0
0.8
Rating Matrix User Matrix Item Matrix
1.5
1.7
1.2
0.6
1.0
1.1
0.8
0.4
W X Y ZW X Y Z
A
B
C
D
4.0
4.5
5.0
3.5
2.0
3.5
4.0
2.0
1.0
= X
User
Machine Learning
Iterative Algorithms
Graph Analysis
53
1 2
4
0.5
0.2 0.9
0.3
0.1
0.4
0.7
Mutable State

4. Overview
Deployment
Local (Single JVM) · Cluster (Standalone, YARN)
DataStream API
Unbounded Data
DataSet API
Bounded Data
Runtime
Distributed Streaming Data Flow
Libraries
Machine Learning · Graph Processing · SQL-like API

5. Stream Processing
Real world data is unbounded and is pushed to
systems.
BatchStreaming

6. Stream Platform Architecture
Server
Logs
Trxn
Logs
Sensor
Logs
Downstream
Systems
Flink
– Analyze and correlate streams
– Create derived streams
Kafka
– Gather and backup streams
– Offer streams

7. Cornerstones of Flink
Low Latency for fast results.
High Throughput to handle many events per second.
Exactly-once guarantees for correct results.
Intuitive APIs for productivity.

8. DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment()
DataStream<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataStream Windowed WordCount
DataStream<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.keyBy(0) // [word, [1, 1, …]] for 10 seconds
.timeWindow(Time.of(10, TimeUnit.SECONDS))
.sum(1); // sum per word per 10 second window
counts.print();
env.execute();

9. DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment()
DataStream<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataStream Windowed WordCount
DataStream<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.keyBy(0) // [word, [1, 1, …]] for 10 seconds
.timeWindow(Time.of(10, TimeUnit.SECONDS))
.sum(1); // sum per word per 10 second window
counts.print();
env.execute();

10. DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment()
DataStream<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataStream Windowed WordCount
DataStream<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.keyBy(0) // [word, [1, 1, …]] for 10 seconds
.timeWindow(Time.of(10, TimeUnit.SECONDS))
.sum(1); // sum per word per 10 second window
counts.print();
env.execute();

11. DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment()
DataStream<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataStream Windowed WordCount
DataStream<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.keyBy(0) // [word, [1, 1, …]] for 10 seconds
.timeWindow(Time.of(10, TimeUnit.SECONDS))
.sum(1); // sum per word per 10 second window
counts.print();
env.execute();

12. DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment()
DataStream<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataStream Windowed WordCount
DataStream<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.keyBy(0) // [word, [1, 1, …]] for 10 seconds
.timeWindow(Time.of(10, TimeUnit.SECONDS))
.sum(1); // sum per word per 10 second window
counts.print();
env.execute();

13. DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment()
DataStream<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataStream Windowed WordCount
DataStream<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.keyBy(0) // [word, [1, 1, …]] for 10 seconds
.timeWindow(Time.of(10, TimeUnit.SECONDS))
.sum(1); // sum per word per 10 second window
counts.print();
env.execute();

14. DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment()
DataStream<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataStream Windowed WordCount
DataStream<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.keyBy(0) // [word, [1, 1, …]] for 10 seconds
.timeWindow(Time.of(10, TimeUnit.SECONDS))
.sum(1); // sum per word per 10 second window
counts.print();
env.execute();

15. DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment()
DataStream<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataStream Windowed WordCount
DataStream<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.keyBy(0) // [word, [1, 1, …]] for 10 seconds
.timeWindow(Time.of(10, TimeUnit.SECONDS))
.sum(1); // sum per word per 10 second window
counts.print();
env.execute();

16. Pipelining
s1 t1 w1
s2 t2 w2
Source Tokenizer Window Count
Complete Pipeline Online Concurrently.

17. Pipelining
s1 t1 w1
s2 t2 w2
Source Tokenizer Window Count
Complete Pipeline Online Concurrently.
Chained tasks

18. Pipelining
s1
s2 t2 w2
t1 w1
Source Tokenizer Window Count
Complete Pipeline Online Concurrently.
Chained tasks Pipelined Shuffle

20. Streaming Fault Tolerance
At Least Once
• Ensure that all operators see all events.
Exactly Once
• Ensure that all operators see all events.
• Do not perform duplicates updates to operator state.

21. Streaming Fault Tolerance
At Least Once
• Ensure that all operators see all events.
Exactly Once
• Ensure that all operators see all events.
• Do not perform duplicates updates to operator state.
Flink guarantees exactly once processing.

22. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

23. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

24. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

25. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

26. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

27. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

28. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

29. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

30. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

31. Distributed Snaphots
Barriers flow through the topology in line with data.
Flink guarantees exactly once processing.
Part of
snapshot

32. Distributed Snaphots
Flink guarantees exactly once processing.
JobManager
Master
State Backend
Ceckpoint Data
Source 1: State 1:
Source 2: State 2:
Source 3: Sink 1:
Source 4: Sink 2:
Offset: 6791
Offset: 7252
Offset: 5589
Offset: 6843

33. Distributed Snaphots
Flink guarantees exactly once processing.
JobManager
Master
State Backend
Ceckpoint Data
Source 1: State 1:
Source 2: State 2:
Source 3: Sink 1:
Source 4: Sink 2:
Offset: 6791
Offset: 7252
Offset: 5589
Offset: 6843
Start Checkpoint
Message

34. Distributed Snaphots
Flink guarantees exactly once processing.
JobManager
Master
State Backend
Ceckpoint Data
Source 1: 6791 State 1:
Source 2: 7252 State 2:
Source 3: 5589 Sink 1:
Source 4: 6843 Sink 2:
Emit Barriers
Acknowledge with
Position

35. Distributed Snaphots
Flink guarantees exactly once processing.
JobManager
Master
State Backend
Ceckpoint Data
Source 1: 6791 State 1:
Source 2: 7252 State 2:
Source 3: 5589 Sink 1:
Source 4: 6843 Sink 2:
Received barrier
at each input

36. Distributed Snaphots
Flink guarantees exactly once processing.
JobManager
Master
State Backend
Ceckpoint Data
Source 1: 6791 State 1:
Source 2: 7252 State 2:
Source 3: 5589 Sink 1:
Source 4: 6843 Sink 2:
s1 Write Snapshot
of its state
Received barrier
at each input

37. Distributed Snaphots
Flink guarantees exactly once processing.
JobManager
Master
State Backend
Ceckpoint Data
Source 1: 6791 State 1: PTR1
Source 2: 7252 State 2: PTR2
Source 3: 5589 Sink 1:
Source 4: 6843 Sink 2:
s1
Acknowledge with
pointer to state
s2

38. Distributed Snaphots
Flink guarantees exactly once processing.
JobManager
Master
State Backend
Ceckpoint Data
Source 1: 6791 State 1: PTR1
Source 2: 7252 State 2: PTR2
Source 3: 5589 Sink 1: ACK
Source 4: 6843 Sink 2: ACK
s1 s2
Acknowledge Checkpoint
Received barrier
at each input

39. Distributed Snaphots
Flink guarantees exactly once processing.
JobManager
Master
State Backend
Ceckpoint Data
Source 1: 6791 State 1: PTR1
Source 2: 7252 State 2: PTR2
Source 3: 5589 Sink 1: ACK
Source 4: 6843 Sink 2: ACK
s1 s2

40. Operator State
User-defined state
• Flink’s transformations are long running operators
• Feel free to keep objects around
• Hooks to include into system’s checkpoint
Windowed streams
• Time, count, and data-driven windows
• Managed by the system

41. Batch on Streaming
DataStream API
Unbounded Data
DataSet API
Bounded Data
Runtime
Distributed Streaming Data Flow
Libraries
Machine Learning · Graph Processing · SQL-like API

42. Batch on Streaming
Run a bounded stream (data set) on
a stream processor.
Bounded
data set
Unbounded
data stream

43. Batch on Streaming
Stream Windows
Pipelined
Data Exchange
Global View
Pipelined or Blocking
Data Exchange
Infinite Streams Finite Streams
Run a bounded stream (data set) on
a stream processor.

44. Batch Pipelines

45. Batch Pipelines
Data exchange
is mostly streamed

46. Batch Pipelines
Data exchange
is mostly streamed
Some operators block
(e.g. sort, hash table)

47. DataSet API
ExecutionEnvironment env = ExecutionEnvironment
.getExecutionEnvironment()
DataSet<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataSet WordCount
DataSet<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.groupBy(0) // [word, [1, 1, …]]
.sum(1); // sum per word for all occurrences
counts.print();

48. DataStream API
ExecutionEnvironment env = ExecutionEnvironment
.getExecutionEnvironment()
DataSet<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataSet WordCount
DataSet<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.groupBy(0) // [word, [1, 1, …]]
.sum(1); // sum per word for all occurrences
counts.print();

49. DataStream API
ExecutionEnvironment env = ExecutionEnvironment
.getExecutionEnvironment()
DataSet<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataSet WordCount
DataSet<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.groupBy(0) // [word, [1, 1, …]]
.sum(1); // sum per word for all occurrences
counts.print();

50. DataStream API
ExecutionEnvironment env = ExecutionEnvironment
.getExecutionEnvironment()
DataSet<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataSet WordCount
DataSet<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.groupBy(0) // [word, [1, 1, …]]
.sum(1); // sum per word for all occurrences
counts.print();

51. DataStream API
ExecutionEnvironment env = ExecutionEnvironment
.getExecutionEnvironment()
DataSet<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataSet WordCount
DataSet<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.groupBy(0) // [word, [1, 1, …]]
.sum(1); // sum per word for all occurrences
counts.print();

52. DataStream API
ExecutionEnvironment env = ExecutionEnvironment
.getExecutionEnvironment()
DataSet<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataSet WordCount
DataSet<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.groupBy(0) // [word, [1, 1, …]]
.sum(1); // sum per word for all occurrences
counts.print();

53. DataStream API
ExecutionEnvironment env = ExecutionEnvironment
.getExecutionEnvironment()
DataSet<String> data = env.fromElements(
"O Romeo, Romeo! wherefore art thou Romeo?”, ...);
// DataSet WordCount
DataSet<Tuple2<String, Integer>> counts = data
.flatMap(new SplitByWhitespace()) // (word, 1)
.groupBy(0) // [word, [1, 1, …]]
.sum(1); // sum per word for all occurrences
counts.print();

54. Batch-specific optimizations
Managed memory
• On- and off-heap memory
• Internal operators (e.g. join or sort) with out-of-core
support
• Serialization stack for user-types
Cost-based optimizer
• Program adapts to changing data size

55. Getting Started
Project Page: http://flink.apache.org

56. Getting Started
Project Page: http://flink.apache.org
Quickstarts: Java & Scala API

57. Getting Started
Project Page: http://flink.apache.org
Docs: Programming Guides

58. Getting Started
Project Page: http://flink.apache.org
Get Involved: Mailing Lists, Stack Overflow, IRC, …

59. Blogs
http://flink.apache.org/blog
http://data-artisans.com/blog
Twitter
@ApacheFlink
Mailing lists
(news|user|dev)@flink.apache.org
Apache Flink