1.
dbisINSTITUT FÜR INFORMATIK
HUMBOLDT−UNIVERSITÄT ZU ERLINB
Feeding a Squirrel in Time—Windows in Flink
Apache Flink Meetup Munich
Matthias J. Sax
mjsax@{informatik.hu-berlin.de|apache.org}
@MatthiasJSax
Humboldt-Universit¨at zu Berlin
Department of Computer Science
November 11st
2015

2.
–MatthiasJ.Sax–WindowsinApacheFlink
1/21
About Me
Ph. D. student in CS, DBIS Group, HU Berlin
involved in Stratosphere research project
working on data stream processing and optimization
Aeolus: build on top of Apache Storm
(https://github.com/mjsax/aeolus)
Committer at Apache Flink

3.
–MatthiasJ.Sax–WindowsinApacheFlink
2/21
Stream Processing
Processing data in motion:
external sources create data constantly
data is pushed to the system
need to keep up with incoming data rate
usage of ingestion buffers (e. g., Apache Kafka)
handle data peaks
back pressure, dynamic scaling (or even load-shedding)
low processing latency (milliseconds)
no micro-batching

4.
–MatthiasJ.Sax–WindowsinApacheFlink
3/21
Other Systems
Apache Storm
widely used in industry
different processing guarantees
no guarantee
at-least-once
exactly-once (not for external writes)
no ordering guarantees
no type system
dynamic scaling (to some extent)
some high-level abstractions using Trident
windows, state, exactly-once-processing

5.
–MatthiasJ.Sax–WindowsinApacheFlink
4/21
Other Systems (cont.)
Apache Samza
similar to Storm
at-least-once processing
active state handling

6.
–MatthiasJ.Sax–WindowsinApacheFlink
5/21
Other Systems (cont.)
Google Dataflow
similar API to Flink offering very rich semantics
windows, triggers
can deal with late arriving data
dynamic scaling
only available as service in the cloud

7.
–MatthiasJ.Sax–WindowsinApacheFlink
6/21
Other Systems (cont.)
Apache Spark (Streaming)
micro-batching (no real streaming)
limited semantics
exactly-once processing
state management
no sub-second latency

8.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812

9.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812

10.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812
sum
42

11.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812
sum
42
1012812

12.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812
sum
42
1012812 10

13.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812
sum
42
1012812 10
0

14.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812
sum
42
1012812 10
0
sum
10

15.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812
sum
42
1012812 10
0
sum
10
12
10
sum
22

16.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812
sum
42
1012812 10
0
sum
10
12
10
sum
22
8
22
sum
30

17.
–MatthiasJ.Sax–WindowsinApacheFlink
7/21
Batch vs. Stream Processing
1012812 1012812
sum
42
1012812 10
0
sum
10
12
10
sum
22
8
22
sum
30
12
30
sum
42

18.
–MatthiasJ.Sax–WindowsinApacheFlink
8/21
Batch vs. Stream Processing (cont.)
DataSet API
DataSet <Tuple2 <String ,Integer >> input
= ...
DataSet <Tuple2 <String ,Integer >> result
= input.groupBy (0). sum (1);

19.
–MatthiasJ.Sax–WindowsinApacheFlink
8/21
Batch vs. Stream Processing (cont.)
DataSet API
DataSet <Tuple2 <String ,Integer >> input
= ...
DataSet <Tuple2 <String ,Integer >> result
= input.groupBy (0). sum (1);
DataStream API
DataStream <Tuple2 <String ,Integer >> input
= ...
DataStream <Tuple2 <String ,Integer >> result
= input.keyBy (0). sum (1);

20.
–MatthiasJ.Sax–WindowsinApacheFlink
9/21
Count Based Windows
1012812

21.
–MatthiasJ.Sax–WindowsinApacheFlink
9/21
Count Based Windows
1012812 1012

22.
–MatthiasJ.Sax–WindowsinApacheFlink
9/21
Count Based Windows
1012812 1012
sum
22

23.
–MatthiasJ.Sax–WindowsinApacheFlink
9/21
Count Based Windows
1012812 1012
sum
22
812
sum
20

24.
–MatthiasJ.Sax–WindowsinApacheFlink
9/21
Count Based Windows
1012812 1012
sum
22
812
sum
20
1012812

25.
–MatthiasJ.Sax–WindowsinApacheFlink
9/21
Count Based Windows
1012812 1012
sum
22
812
sum
20
1012812 1012
sum
22
128
sum
20
812
sum
20

26.
–MatthiasJ.Sax–WindowsinApacheFlink
10/21
Count Based Windows
Count Based Window (tumbling)
DataStream <Tuple2 <String ,Integer >> input
= ...
DataStream <Tuple2 <String ,Integer >> result
= input.keyBy (0). countWindow (2). sum (1);

27.
–MatthiasJ.Sax–WindowsinApacheFlink
10/21
Count Based Windows
Count Based Window (tumbling)
DataStream <Tuple2 <String ,Integer >> input
= ...
DataStream <Tuple2 <String ,Integer >> result
= input.keyBy (0). countWindow (2). sum (1);
Count Based Window (overlapping)
DataStream <Tuple2 <String ,Integer >> input
= ...
DataStream <Tuple2 <String ,Integer >> result
= input.keyBy (0). countWindow (2 ,1). sum (1);

28.
–MatthiasJ.Sax–WindowsinApacheFlink
10/21
Count Based Windows
Count Based Window (tumbling)
DataStream <Tuple2 <String ,Integer >> input
= ...
DataStream <Tuple2 <String ,Integer >> result
= input.keyBy (0). countWindow (2). sum (1);
Count Based Window (overlapping)
DataStream <Tuple2 <String ,Integer >> input
= ...
DataStream <Tuple2 <String ,Integer >> result
= input.keyBy (0). countWindow (2 ,1). sum (1);
Caution: count-windows applies to each sub-stream

29.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
abcd
Ext. prod. 1
1234
Ext. prod. 2

30.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
abcd
Ext. prod. 1
1234
Ext. prod. 2
time: 5 4 3 2 1

31.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
abcd
Ext. prod. 1
1234
Ext. prod. 2
time: 5 4 3 2 1
Source Operator

32.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
abcd
Ext. prod. 1
1234
Ext. prod. 2
time: 5 4 3 2 1
Source Operator

33.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
bcd
Ext. prod. 1
234
Ext. prod. 2
time: 5 4 3 2 1
Source Operator
a1
1.52

34.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
bcd
Ext. prod. 1
234
Ext. prod. 2
time: 5 4 3 2 1
Source Operator
a1
1.52

35.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
cd
Ext. prod. 1
34
Ext. prod. 2
time: 5 4 3 2 1
Source Operator
a1
1.52
2b
2.53.5

36.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
cd
Ext. prod. 1
34
Ext. prod. 2
time: 5 4 3 2 1
Source Operator
a1
1.52
2b
2.53.5

37.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
d
Ext. prod. 1
4
Ext. prod. 2
time: 5 4 3 2 1
Source Operator
a1
1.52
2b
2.53.5
c3
4.55

38.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
d
Ext. prod. 1
4
Ext. prod. 2
time: 5 4 3 2 1
Source Operator
a1
1.52
2b
2.53.5
c3
4.55

39.
–MatthiasJ.Sax–WindowsinApacheFlink
11/21
The Nature of Data Streams
Ext. prod. 1
Ext. prod. 2
time: 5 4 3 2 1
Source Operator
a1
1.52
2b
2.53.5
c3
4.55
d4
66.5

40.
–MatthiasJ.Sax–WindowsinApacheFlink
12/21
The Notion of Time
Event Time = Processing Time

41.
–MatthiasJ.Sax–WindowsinApacheFlink
12/21
The Notion of Time
Event Time = Processing Time
Event Time
ProcessingTime
Google Cloud Dataflow and Flink, William Vambenepe, Flink Forward 2015.

42.
–MatthiasJ.Sax–WindowsinApacheFlink
12/21
The Notion of Time
Event Time = Processing Time
Event Time
ProcessingTime
Skew
Google Cloud Dataflow and Flink, William Vambenepe, Flink Forward 2015.

43.
–MatthiasJ.Sax–WindowsinApacheFlink
13/21
Watermarks

44.
–MatthiasJ.Sax–WindowsinApacheFlink
13/21
Watermarks
1
3

45.
–MatthiasJ.Sax–WindowsinApacheFlink
13/21
Watermarks
1
3
2
4

46.
–MatthiasJ.Sax–WindowsinApacheFlink
13/21
Watermarks
1
3
2
4
1234

47.
–MatthiasJ.Sax–WindowsinApacheFlink
13/21
Watermarks
1
3
2
4
1234
wrong processing order!
2
5
8
7
1
4
7
9

48.
–MatthiasJ.Sax–WindowsinApacheFlink
13/21
Watermarks
1
3
2
4
2
5
8
7
1
4
7
9
wm=3
wm=4

49.
–MatthiasJ.Sax–WindowsinApacheFlink
13/21
Watermarks
4
5
8
7
4
7
9
wm=3
wm=4
11223

50.
–MatthiasJ.Sax–WindowsinApacheFlink
13/21
Watermarks
4
5
8
7
4
7
9
wm=3
wm=4
11223
wm=3

51.
–MatthiasJ.Sax–WindowsinApacheFlink
14/21
Streaming Tradeoffs
Processing Time
no late data / no skew
windows are simple to build
low latency
inherently non-deterministic

52.
–MatthiasJ.Sax–WindowsinApacheFlink
14/21
Streaming Tradeoffs
Processing Time
no late data / no skew
windows are simple to build
low latency
inherently non-deterministic
Event Time (external)
late data / skew
out-of-order data (windowing more difficult)
simpler to reason about semantics (deterministic)
increased latency

53.
–MatthiasJ.Sax–WindowsinApacheFlink
14/21
Streaming Tradeoffs
Processing Time
no late data / no skew
windows are simple to build
low latency
inherently non-deterministic
Event Time (external)
late data / skew
out-of-order data (windowing more difficult)
simpler to reason about semantics (deterministic)
increased latency
Event Time (ingestion)
no late data / no skew
no out-of-order
simplified watermarking

54.
–MatthiasJ.Sax–WindowsinApacheFlink
15/21
Time Based Windows
Timestamp Example
StreamExecutionEnviroment env = ...

55.
–MatthiasJ.Sax–WindowsinApacheFlink
15/21
Time Based Windows
Timestamp Example
StreamExecutionEnviroment env = ...
// alternatives : ProcessingTime / IngestionTime
env. setStreamTimeCharacteristic (
TimeCharacteristic .EventTime );

56.
–MatthiasJ.Sax–WindowsinApacheFlink
15/21
Time Based Windows
Timestamp Example
StreamExecutionEnviroment env = ...
// alternatives : ProcessingTime / IngestionTime
env. setStreamTimeCharacteristic (
TimeCharacteristic .EventTime );
DataStream <Tuple > input = ...
input. assignTimestamps (

57.
–MatthiasJ.Sax–WindowsinApacheFlink
15/21
Time Based Windows
Timestamp Example
StreamExecutionEnviroment env = ...
// alternatives : ProcessingTime / IngestionTime
env. setStreamTimeCharacteristic (
TimeCharacteristic .EventTime );
DataStream <Tuple > input = ...
input. assignTimestamps (new TimestampExtractor <Tuple > {

58.
–MatthiasJ.Sax–WindowsinApacheFlink
15/21
Time Based Windows
Timestamp Example
StreamExecutionEnviroment env = ...
// alternatives : ProcessingTime / IngestionTime
env. setStreamTimeCharacteristic (
TimeCharacteristic .EventTime );
DataStream <Tuple > input = ...
input. assignTimestamps (new TimestampExtractor <Tuple > {
public long extractTimestamp (Tuple element ,
long currentTimestamp ) {
return /* extract from element */;
}

59.
–MatthiasJ.Sax–WindowsinApacheFlink
15/21
Time Based Windows
Timestamp Example
StreamExecutionEnviroment env = ...
// alternatives : ProcessingTime / IngestionTime
env. setStreamTimeCharacteristic (
TimeCharacteristic .EventTime );
DataStream <Tuple > input = ...
input. assignTimestamps (new TimestampExtractor <Tuple > {
public long extractTimestamp (Tuple element ,
long currentTimestamp ) {
return /* extract from element */;
}
public long extractWatermark (Tuple element ,
long currentTimestamp ) {
return /* extract from element */;
}

60.
–MatthiasJ.Sax–WindowsinApacheFlink
15/21
Time Based Windows
Timestamp Example
StreamExecutionEnviroment env = ...
// alternatives : ProcessingTime / IngestionTime
env. setStreamTimeCharacteristic (
TimeCharacteristic .EventTime );
DataStream <Tuple > input = ...
input. assignTimestamps (new TimestampExtractor <Tuple > {
public long extractTimestamp (Tuple element ,
long currentTimestamp ) {
return /* extract from element */;
}
public long extractWatermark (Tuple element ,
long currentTimestamp ) {
return /* extract from element */;
}
public long getCurrentWatermark () {
return Long.MIN_VALUE;
}
});

61.
–MatthiasJ.Sax–WindowsinApacheFlink
16/21
Time Based Windows (cont.)
Sliding Time Window Example
DataStream <...> input = ...
input.keyBy (...)
// size = 5s; slide = 1s
.timeWindow(Time.of(5, TimeUnit.SECONDS),
Time.of(1, TimeUnit.SECONDS ))
.reduce (...);

62.
–MatthiasJ.Sax–WindowsinApacheFlink
16/21
Time Based Windows (cont.)
Sliding Time Window Example
DataStream <...> input = ...
input.keyBy (...)
// size = 5s; slide = 1s
.timeWindow(Time.of(5, TimeUnit.SECONDS),
Time.of(1, TimeUnit.SECONDS ))
.reduce (...);
General Window Example
DataStream <...> input = ...
input.keyBy (...)
.window (...)
.apply(new WindowsFunction <... >() {
// ...
});

63.
–MatthiasJ.Sax–WindowsinApacheFlink
17/21
Advanced Windowing Concepts
global windows (non-parallelized)

64.
–MatthiasJ.Sax–WindowsinApacheFlink
17/21
Advanced Windowing Concepts
global windows (non-parallelized)
Triggers:
closes a window (i. e., fires)
processing time
watermark
count
delta
... (with different discarding strategies)

65.
–MatthiasJ.Sax–WindowsinApacheFlink
17/21
Advanced Windowing Concepts
global windows (non-parallelized)
Triggers:
closes a window (i. e., fires)
processing time
watermark
count
delta
... (with different discarding strategies)
Evict:
removes tuple from window before function gets applied
time, count, delta

66.
–MatthiasJ.Sax–WindowsinApacheFlink
17/21
Advanced Windowing Concepts
global windows (non-parallelized)
Triggers:
closes a window (i. e., fires)
processing time
watermark
count
delta
... (with different discarding strategies)
Evict:
removes tuple from window before function gets applied
time, count, delta
mix different windows/triggers/evictors

67.
–MatthiasJ.Sax–WindowsinApacheFlink
18/21
Stateful Stream Processing
Flink can handle arbitrary user state:
state is store reliably
distributed snapshots algorithm
Example
public class CounterSum
implements RichReduceFunction <Long > {
private OperatorState <Long > counter;
public void open( Configuration config) {
counter = getRuntimeContext ()
. getOperatorState ("myCnt", Long.class , 0L);
}
public Long reduce(Long v1 , Long v2) throws Exception {
counter.update(counter.value () + 1);
return v1 + v2;
}
}

68.
–MatthiasJ.Sax–WindowsinApacheFlink
19/21
Summary
The time-problem:
processing time vs. event time vs. ingestion time
time skew, out-of-order tuples, watermarks

69.
–MatthiasJ.Sax–WindowsinApacheFlink
19/21
Summary
The time-problem:
processing time vs. event time vs. ingestion time
time skew, out-of-order tuples, watermarks
The window-question:
count-based vs. time-based
tumbling vs. overlapping
intermediate triggers
advanced windows (mix of above)

70.
–MatthiasJ.Sax–WindowsinApacheFlink
20/21
Summary (cont.)
Flink provides a rich API (Java/Scala) to express different
semantics
state handling for arbitrary UDF code
fault-tolerance with exactly-once guarantees
exaclty-once sink available

71.
–MatthiasJ.Sax–WindowsinApacheFlink
20/21
Summary (cont.)
Flink provides a rich API (Java/Scala) to express different
semantics
state handling for arbitrary UDF code
fault-tolerance with exactly-once guarantees
exaclty-once sink available
What else?
Python API is coming (right now DataSet only)
Google Dataflow on Flink
Storm on Flink
Apache SAMOA on Flink

72.
dbisINSTITUT FÜR INFORMATIK
HUMBOLDT−UNIVERSITÄT ZU ERLINB
Feeding a Squirrel in Time—Windows in Flink
Apache Flink Meetup Munich
Thanks!