Flink Forward 2015

1. dbisINSTITUT FÜR INFORMATIK
HUMBOLDT−UNIVERSITÄT ZU ERLINB
A Tale of Squirrels and Storms
Flink Forward 2015
Matthias J. Sax
mjsax@{informatik.hu-berlin.de|apache.org}
@MatthiasJSax
Humboldt-Universit¨at zu Berlin
Department of Computer Science
October 13st
2015

2. –MatthiasJ.Sax–SquirrelsandStorms
1/22
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. Flink and Storm
vs.

4. Flink and Storm
Flinkvs.Storm

5. –MatthiasJ.Sax–SquirrelsandStorms
3/22
Similarities of Flink and Storm

6. –MatthiasJ.Sax–SquirrelsandStorms
3/22
Similarities of Flink and Storm
true stream processing engines (no micro-batching)

7. –MatthiasJ.Sax–SquirrelsandStorms
3/22
Similarities of Flink and Storm
true stream processing engines (no micro-batching)
low latencies ( 100ms)

8. –MatthiasJ.Sax–SquirrelsandStorms
3/22
Similarities of Flink and Storm
true stream processing engines (no micro-batching)
low latencies ( 100ms)
executing data flow programs

9. –MatthiasJ.Sax–SquirrelsandStorms
3/22
Similarities of Flink and Storm
true stream processing engines (no micro-batching)
low latencies ( 100ms)
executing data flow programs
parallel and distributed

10. –MatthiasJ.Sax–SquirrelsandStorms
3/22
Similarities of Flink and Storm
true stream processing engines (no micro-batching)
low latencies ( 100ms)
executing data flow programs
parallel and distributed
fault-tolerant

11. –MatthiasJ.Sax–SquirrelsandStorms
3/22
Similarities of Flink and Storm
true stream processing engines (no micro-batching)
low latencies ( 100ms)
executing data flow programs
parallel and distributed
fault-tolerant
cloud or cluster environment

12. –MatthiasJ.Sax–SquirrelsandStorms
3/22
Similarities of Flink and Storm
true stream processing engines (no micro-batching)
low latencies ( 100ms)
executing data flow programs
parallel and distributed
fault-tolerant
cloud or cluster environment
Trident:
similar Java API
exactly-once processing

13. –MatthiasJ.Sax–SquirrelsandStorms
4/22
Flink vs. Storm
Advantages of Storm:
super low latency (< 10ms)
very robust:
stateless JVM for easy restart on failure
Zookeeper manages cluster state
isolation of topology
dynamic scaling (to some extent)
multi-language protocol (for experts only)
distributed RPC

14. –MatthiasJ.Sax–SquirrelsandStorms
5/22
Flink vs. Storm
Advantages of Flink:1
richer API
Java and Scala
type safe programs
system is aware of multiple input streams
ordered stream processing
system and user timestamps
count/time and customized windows
stateful processing
light weight fault-tolerance
Chandy-Lamport distributed snapshots
1
http:
//data-artisans.com/real-time-stream-processing-the-next-step-for-apache-flink/

15. –MatthiasJ.Sax–SquirrelsandStorms
6/22
Flink vs. Storm
Advantages of Flink (cont.):
provides exactly-once sinks
native flow control (back pressure)2
higher throughput (> x 100)3
no lambda or kappa architecture necessary
native support for iterations (cyclic data flows)
managed memory
2
http://data-artisans.com/how-flink-handles-backpressure/
3
http://data-artisans.com/
high-throughput-low-latency-and-exactly-once-stream-processing-with-apache-flink/

16. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm

17. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm
Nimbus

18. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm
Nimbus
Client

19. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm
Nimbus
Client
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor

20. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm
Nimbus
Client
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
Zookeeper
Zookeeper
Zookeeper

21. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm
Nimbus
Client
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
Zookeeper
Zookeeper
Zookeeper

22. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm
Nimbus
Client
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
Zookeeper
Zookeeper
Zookeeper
Worker
Worker
Worker
Worker
Worker
Worker

23. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm
Nimbus
Client
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
Zookeeper
Zookeeper
Zookeeper
Worker
Worker
Worker
Worker
Worker
Worker

24. –MatthiasJ.Sax–SquirrelsandStorms
7/22
System Architecture: Storm
Nimbus
Client
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
Zookeeper
Zookeeper
Zookeeper
Worker
Worker
Worker
Worker
Worker
Worker

25. –MatthiasJ.Sax–SquirrelsandStorms
8/22
System Architecture: Flink

26. –MatthiasJ.Sax–SquirrelsandStorms
8/22
System Architecture: Flink
JobManager

27. –MatthiasJ.Sax–SquirrelsandStorms
8/22
System Architecture: Flink
JobManager
WebClientCLI Shell

28. –MatthiasJ.Sax–SquirrelsandStorms
8/22
System Architecture: Flink
JobManager
WebClientCLI Shell
TaskManager
TaskManager
TaskManager
TaskManager
TaskManager

29. –MatthiasJ.Sax–SquirrelsandStorms
8/22
System Architecture: Flink
JobManager
WebClientCLI Shell
TaskManager
TaskManager
TaskManager
TaskManager
TaskManager

30. –MatthiasJ.Sax–SquirrelsandStorms
8/22
System Architecture: Flink
JobManager
WebClientCLI Shell
TaskManager
TaskManager
TaskManager
TaskManager
TaskManager

31. –MatthiasJ.Sax–SquirrelsandStorms
8/22
System Architecture: Flink
JobManager
WebClientCLI Shell
TaskManager
TaskManager
TaskManager
TaskManager
TaskManager
JobManager

32. –MatthiasJ.Sax–SquirrelsandStorms
9/22
Topology Deployment: Storm
per default: round-robin scheduling
high overhead due to intra JVM and/or network
communication
localOfShuffle connection pattern poorly exploited
isolation of topologies
custom scheduler possible (for experts only)

33. –MatthiasJ.Sax–SquirrelsandStorms
9/22
Topology Deployment: Storm
per default: round-robin scheduling
high overhead due to intra JVM and/or network
communication
localOfShuffle connection pattern poorly exploited
isolation of topologies
custom scheduler possible (for experts only)

34. –MatthiasJ.Sax–SquirrelsandStorms
9/22
Topology Deployment: Storm
per default: round-robin scheduling
high overhead due to intra JVM and/or network
communication
localOfShuffle connection pattern poorly exploited
isolation of topologies
custom scheduler possible (for experts only)
Src

35. –MatthiasJ.Sax–SquirrelsandStorms
9/22
Topology Deployment: Storm
per default: round-robin scheduling
high overhead due to intra JVM and/or network
communication
localOfShuffle connection pattern poorly exploited
isolation of topologies
custom scheduler possible (for experts only)
Src T1 T2

36. –MatthiasJ.Sax–SquirrelsandStorms
9/22
Topology Deployment: Storm
per default: round-robin scheduling
high overhead due to intra JVM and/or network
communication
localOfShuffle connection pattern poorly exploited
isolation of topologies
custom scheduler possible (for experts only)
Src T1 T2 F1
F2

37. –MatthiasJ.Sax–SquirrelsandStorms
9/22
Topology Deployment: Storm
per default: round-robin scheduling
high overhead due to intra JVM and/or network
communication
localOfShuffle connection pattern poorly exploited
isolation of topologies
custom scheduler possible (for experts only)
Src T1 T2 F1
F2 C1 C2

38. –MatthiasJ.Sax–SquirrelsandStorms
9/22
Topology Deployment: Storm
per default: round-robin scheduling
high overhead due to intra JVM and/or network
communication
localOfShuffle connection pattern poorly exploited
isolation of topologies
custom scheduler possible (for experts only)
Src T1 T2 F1
F2 C1 C2 Sk

39. –MatthiasJ.Sax–SquirrelsandStorms
10/22
Topology Deployment: Flink
deploys whole pipeline to each TaskManager
local-forward is default
operator chaining

40. –MatthiasJ.Sax–SquirrelsandStorms
10/22
Topology Deployment: Flink
deploys whole pipeline to each TaskManager
local-forward is default
operator chaining

41. –MatthiasJ.Sax–SquirrelsandStorms
10/22
Topology Deployment: Flink
deploys whole pipeline to each TaskManager
local-forward is default
operator chaining
Src

42. –MatthiasJ.Sax–SquirrelsandStorms
10/22
Topology Deployment: Flink
deploys whole pipeline to each TaskManager
local-forward is default
operator chaining
Src
T1 T2

43. –MatthiasJ.Sax–SquirrelsandStorms
10/22
Topology Deployment: Flink
deploys whole pipeline to each TaskManager
local-forward is default
operator chaining
Src
T1 T2
F1 F2

44. –MatthiasJ.Sax–SquirrelsandStorms
10/22
Topology Deployment: Flink
deploys whole pipeline to each TaskManager
local-forward is default
operator chaining
Src
T1 T2
F1 F2
C1 C2

45. –MatthiasJ.Sax–SquirrelsandStorms
10/22
Topology Deployment: Flink
deploys whole pipeline to each TaskManager
local-forward is default
operator chaining
Src
T1 T2
F1 F2
C1 C2
Sk

46. –MatthiasJ.Sax–SquirrelsandStorms
10/22
Topology Deployment: Flink
deploys whole pipeline to each TaskManager
local-forward is default
operator chaining
Src
T1 T2
F1 F2
C1 C2
Sk

47. Storm Compatibility

48. –MatthiasJ.Sax–SquirrelsandStorms
12/22
Storm Compatibility
Allows to4
execute Storm topologies in Flink
embed Spouts/Bolts in Flink streaming programs
4
https://ci.apache.org/projects/flink/flink-docs-master/apis/storm_compatibility.html

49. –MatthiasJ.Sax–SquirrelsandStorms
12/22
Storm Compatibility
Allows to4
execute Storm topologies in Flink
embed Spouts/Bolts in Flink streaming programs
Runtime
Distributed Streaming Dataflow
DataSet API
Batch Processing
Streaming API
Stream Processing
Local
JVM, Embedded
Cluster
Standalone, YARN
Cloud
GCE, EC2
FlinkML
MachineLearning
Gelly
GraphAPI&Library
TableAPI
Batch
HadoopM/R
Comptibility
TableAPI
Streaming
Storm
Compatibility
4
https://ci.apache.org/projects/flink/flink-docs-master/apis/storm_compatibility.html

50. –MatthiasJ.Sax–SquirrelsandStorms
13/22
Storm Compatibility: API
Execute whole topologies:
FlinkTopologyBuilder
FlinkSubmitter
FlinkClient
FlinkLocalCluster

51. –MatthiasJ.Sax–SquirrelsandStorms
13/22
Storm Compatibility: API
Execute whole topologies:
FlinkTopologyBuilder
FlinkSubmitter
FlinkClient
FlinkLocalCluster
Embedded mode:
SpoutWrapper
BoltWrapper

52. –MatthiasJ.Sax–SquirrelsandStorms
13/22
Storm Compatibility: API
Execute whole topologies:
FlinkTopologyBuilder
FlinkSubmitter
FlinkClient
FlinkLocalCluster
Embedded mode:
SpoutWrapper
BoltWrapper
Additionally:
FiniteSpout interface

53. –MatthiasJ.Sax–SquirrelsandStorms
14/22
Storm Compatibility: Internals
Wrappers for Operators and Collectors

54. –MatthiasJ.Sax–SquirrelsandStorms
14/22
Storm Compatibility: Internals
Wrappers for Operators and Collectors
Bolt

55. –MatthiasJ.Sax–SquirrelsandStorms
14/22
Storm Compatibility: Internals
Wrappers for Operators and Collectors
Bolt
BoltWrapper

56. –MatthiasJ.Sax–SquirrelsandStorms
14/22
Storm Compatibility: Internals
Wrappers for Operators and Collectors
Bolt
BoltWrapper
Flink Collector

57. –MatthiasJ.Sax–SquirrelsandStorms
14/22
Storm Compatibility: Internals
Wrappers for Operators and Collectors
redirecting method calls
run() ⇒ nextTuple()
processElement() ⇒ execute()
emit() ⇒ collect()
Bolt
BoltWrapper
Flink Collector

58. –MatthiasJ.Sax–SquirrelsandStorms
14/22
Storm Compatibility: Internals
Wrappers for Operators and Collectors
redirecting method calls
run() ⇒ nextTuple()
processElement() ⇒ execute()
emit() ⇒ collect()
Bolt
BoltWrapper
Flink Collector
execute()
processElement()
emit()
collect()

59. –MatthiasJ.Sax–SquirrelsandStorms
14/22
Storm Compatibility: Internals
Wrappers for Operators and Collectors
redirecting method calls
run() ⇒ nextTuple()
processElement() ⇒ execute()
emit() ⇒ collect()
translating data types
TupleX, POJO ⇔ Tuple/Values
primitive types for single attribute input/output
Bolt
BoltWrapper
Flink Collector
execute()
processElement()
emit()
collect()

60. –MatthiasJ.Sax–SquirrelsandStorms
15/22
WordCount on Storm
public void main(String [] args) {

61. –MatthiasJ.Sax–SquirrelsandStorms
15/22
WordCount on Storm
public void main(String [] args) {
TopologyBuilder builder
= new TopologyBuilder ();

62. –MatthiasJ.Sax–SquirrelsandStorms
15/22
WordCount on Storm
public void main(String [] args) {
TopologyBuilder builder
= new TopologyBuilder ();
builder.setSpout("source",
new FileSpout("/tmp/hamlet.txt"));

63. –MatthiasJ.Sax–SquirrelsandStorms
15/22
WordCount on Storm
public void main(String [] args) {
TopologyBuilder builder
= new TopologyBuilder ();
builder.setSpout("source",
new FileSpout("/tmp/hamlet.txt"));
builder.setBolt("tokenizer", new BoltTokenizer ())
. shuffleGrouping ("source");

64. –MatthiasJ.Sax–SquirrelsandStorms
15/22
WordCount on Storm
public void main(String [] args) {
TopologyBuilder builder
= new TopologyBuilder ();
builder.setSpout("source",
new FileSpout("/tmp/hamlet.txt"));
builder.setBolt("tokenizer", new BoltTokenizer ())
. shuffleGrouping ("source");
builder.setBolt("counter", new BoltCounter ())
. fieldsGrouping("tokenizer",
new Fields("word"));

65. –MatthiasJ.Sax–SquirrelsandStorms
15/22
WordCount on Storm
public void main(String [] args) {
TopologyBuilder builder
= new TopologyBuilder ();
builder.setSpout("source",
new FileSpout("/tmp/hamlet.txt"));
builder.setBolt("tokenizer", new BoltTokenizer ())
. shuffleGrouping ("source");
builder.setBolt("counter", new BoltCounter ())
. fieldsGrouping("tokenizer",
new Fields("word"));
builder.setBolt("sink",
new BoltFileSink("/tmp/count.txt"))
. shuffleGrouping ("counter");

66. –MatthiasJ.Sax–SquirrelsandStorms
15/22
WordCount on Storm
public void main(String [] args) {
TopologyBuilder builder
= new TopologyBuilder ();
builder.setSpout("source",
new FileSpout("/tmp/hamlet.txt"));
builder.setBolt("tokenizer", new BoltTokenizer ())
. shuffleGrouping ("source");
builder.setBolt("counter", new BoltCounter ())
. fieldsGrouping("tokenizer",
new Fields("word"));
builder.setBolt("sink",
new BoltFileSink("/tmp/count.txt"))
. shuffleGrouping ("counter");
Config conf = new Config ();
StormSubmitter. submitTopology("WordCount", conf ,
builder.createTopology ());
}

67. –MatthiasJ.Sax–SquirrelsandStorms
16/22
WordCount on Flink
public void main(String [] args) {
FlinkTopologyBuilder builder
= new FlinkTopologyBuilder ();
builder.setSpout("source",
new FileSpout("/tmp/hamlet.txt"));
builder.setBolt("tokenizer", new BoltTokenizer ())
. shuffleGrouping ("source");
builder.setBolt("counter", new BoltCounter ())
. fieldsGrouping("tokenizer",
new Fields("word"));
builder.setBolt("sink",
new BoltFileSink("/tmp/count.txt"))
. shuffleGrouping ("counter");
Config conf = new Config ();
FlinkSubmitter. submitTopology("WordCount", conf ,
builder.createTopology ());
}

68. –MatthiasJ.Sax–SquirrelsandStorms
17/22
Storm on Flink
run Storm topology on Flink:
changing two lines of code
sufficient

69. –MatthiasJ.Sax–SquirrelsandStorms
18/22
WordCount: Embedded Spout
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();

70. –MatthiasJ.Sax–SquirrelsandStorms
18/22
WordCount: Embedded Spout
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <Tuple1 <String >> source
= env.addSource(

71. –MatthiasJ.Sax–SquirrelsandStorms
18/22
WordCount: Embedded Spout
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <Tuple1 <String >> source
= env.addSource(
new SpoutWrapper <Tuple1 <String >>(

72. –MatthiasJ.Sax–SquirrelsandStorms
18/22
WordCount: Embedded Spout
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <Tuple1 <String >> source
= env.addSource(
new SpoutWrapper <Tuple1 <String >>(
new FileSpout("/tmp/hamlet.txt")),

73. –MatthiasJ.Sax–SquirrelsandStorms
18/22
WordCount: Embedded Spout
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <Tuple1 <String >> source
= env.addSource(
new SpoutWrapper <Tuple1 <String >>(
new FileSpout("/tmp/hamlet.txt")),
TypeExtractor.getForObject(
new Tuple1 <String >("")));

74. –MatthiasJ.Sax–SquirrelsandStorms
18/22
WordCount: Embedded Spout
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <Tuple1 <String >> source
= env.addSource(
new SpoutWrapper <Tuple1 <String >>(
new FileSpout("/tmp/hamlet.txt")),
TypeExtractor.getForObject(
new Tuple1 <String >("")));
// do further processing on source
source.flatMap(new Tokenizer ())
// out -> Tuple2 <String ,Integer >
.keyBy (0). sum (1). writeAsText("/tmp/count.txt");
env.execute("WordCount");
}

75. –MatthiasJ.Sax–SquirrelsandStorms
19/22
WordCount: Embedded Bolt
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <String > text
= env.readTextFile("/tmp/hamlet.txt");

76. –MatthiasJ.Sax–SquirrelsandStorms
19/22
WordCount: Embedded Bolt
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <String > text
= env.readTextFile("/tmp/hamlet.txt");
DataStream <Tuple2 <String ,Integer >> tokens
= text.transform(

77. –MatthiasJ.Sax–SquirrelsandStorms
19/22
WordCount: Embedded Bolt
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <String > text
= env.readTextFile("/tmp/hamlet.txt");
DataStream <Tuple2 <String ,Integer >> tokens
= text.transform(
"tokenizer",
new BoltWrapper <String ,
Tuple2 <String ,Integer >>(

78. –MatthiasJ.Sax–SquirrelsandStorms
19/22
WordCount: Embedded Bolt
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <String > text
= env.readTextFile("/tmp/hamlet.txt");
DataStream <Tuple2 <String ,Integer >> tokens
= text.transform(
"tokenizer",
new BoltWrapper <String ,
Tuple2 <String ,Integer >>(
new BoltTokenizer ()));

79. –MatthiasJ.Sax–SquirrelsandStorms
19/22
WordCount: Embedded Bolt
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <String > text
= env.readTextFile("/tmp/hamlet.txt");
DataStream <Tuple2 <String ,Integer >> tokens
= text.transform(
"tokenizer",
TypeExtractor.getForObject
new Tuple2 <String ,Integer >("", 0),
new BoltWrapper <String ,
Tuple2 <String ,Integer >>(
new BoltTokenizer ()));

80. –MatthiasJ.Sax–SquirrelsandStorms
19/22
WordCount: Embedded Bolt
public void main(String [] args) {
StreamExecutionEnvironment env
= StreamExecutionEnvironment
. getExecutionEnvironment ();
DataStream <String > text
= env.readTextFile("/tmp/hamlet.txt");
DataStream <Tuple2 <String ,Integer >> tokens
= text.transform(
"tokenizer",
TypeExtractor.getForObject
new Tuple2 <String ,Integer >("", 0),
new BoltWrapper <String ,
Tuple2 <String ,Integer >>(
new BoltTokenizer ()));
// do further processing on tokens
tokens.keyBy (0). sum (1). writeAsText("/tmp/count.txt");
env.execute("WordCount");
}

81. –MatthiasJ.Sax–SquirrelsandStorms
20/22
Embedded Compatibility Mode
Re-use code within Flink streaming program:
Spouts as Flink sources
Bolts as Flink operators

82. –MatthiasJ.Sax–SquirrelsandStorms
20/22
Embedded Compatibility Mode
Re-use code within Flink streaming program:
Spouts as Flink sources
Bolts as Flink operators
Pros:
mix-and-match of Storm and Flink operators
configure Spouts/Bolts (Map/Config)
spliting Spout/Bolt output streams
type-safe embedding
also raw types, ie, String instead of Tuple1 String
convert infinite Spouts to finite sources
FinitSpout interfacee

83. –MatthiasJ.Sax–SquirrelsandStorms
20/22
Embedded Compatibility Mode
Re-use code within Flink streaming program:
Spouts as Flink sources
Bolts as Flink operators
Pros:
mix-and-match of Storm and Flink operators
configure Spouts/Bolts (Map/Config)
spliting Spout/Bolt output streams
type-safe embedding
also raw types, ie, String instead of Tuple1 String
convert infinite Spouts to finite sources
FinitSpout interfacee
Cons: Currently, quite some boilderplate code necessary :/

84. –MatthiasJ.Sax–SquirrelsandStorms
21/22
Outlook: Storm Compatibility
Current status:
available in master branch
based on Storm 0.9.4
will be part of Flink 0.10.0

85. –MatthiasJ.Sax–SquirrelsandStorms
21/22
Outlook: Storm Compatibility
Current status:
available in master branch
based on Storm 0.9.4
will be part of Flink 0.10.0
Work in progress:
Hooks
Metrics

86. –MatthiasJ.Sax–SquirrelsandStorms
21/22
Outlook: Storm Compatibility
Current status:
available in master branch
based on Storm 0.9.4
will be part of Flink 0.10.0
Work in progress:
Hooks
Metrics
Next steps:
enable fault-tolerance
introduce FlinkTridentTopology
improve embedded mode (StormEnvironment)

87. dbisINSTITUT FÜR INFORMATIK
HUMBOLDT−UNIVERSITÄT ZU ERLINB
A Tale of Squirrels and Storms
Flink Forward 2015
Thanks!