1. 1
Stream Processing
in the Cloud
Rafał Leszko (@RafalLeszko)
Cloud Software Engineer at Hazelcast

2. Hands Up

3. Hands Up
Raise your hand if…
● ...you know what Stream Processing is?

4. Hands Up
Raise your hand if…
● ...you know what Stream Processing is?
● ...you have ever used Stream Processing?

5. Hands Up
Raise your hand if…
● ...you know what Stream Processing is?
● ...you have ever used Stream Processing?
● ...you have ever used Hazelcast Jet?

6. Agenda
● Part 1: Stream Processing Basics
○ What is Stream Processing and Hazelcast Jet?
○ Example: Word Count
● Part 2: Jet Under the Hood
○ How does it work?
○ Infinite Streams
○ Example: Twitter Cryptocurrency Analysis
● Part 3: Jet in the Cloud
○ Cloud (Kubernetes) integration
○ Example: Stock Trade Aggregator
● Part 4: Jet Features & Use Cases
○ Why would I need it?
○ Example: Web Crawler

7. Part 1: Stream Processing Basics

8. What is Hazelcast?

9. What is Hazelcast?
Products:

10. What is Hazelcast?
Products:

11. What is Hazelcast Jet?

12. What is Hazelcast Jet?
DAG - Direct Acyclic Graph

13. What is Hazelcast Jet?

14. What is Hazelcast Jet?

15. Example 1: Word Count
Problem:
Count the number of occurrences of each word in the given text.
Sample Input:
Lorem ipsum dolor, dolor.
Sample Output:
lorem=1
ipsum=1
dolor=2

16. Example 1: Word Count
Pure Java
Pattern delimiter = Pattern.compile("W+");
return lines.entrySet().stream()
.map(e -> e.getValue().toLowerCase())
.flatMap(t -> Arrays.stream(delimiter.split(t)))
.filter(word -> !word.isEmpty())
.collect(
groupingBy(
identity(),
counting()));

17. Example 1: Word Count

18. Example 1: Word Count

19. Example 1: Word Count

20. Example 1: Word Count

21. Example 1: Word Count

22. Example 1: Word Count
Hazelcast Jet
Pattern delimiter = Pattern.compile("W+");
Pipeline pipeline = Pipeline.create();
pipeline.drawFrom(Sources.<Long, String>map(LINES))
.map(e -> e.getValue().toLowerCase())
.flatMap(t -> traverseArray(delimiter.split(t)))
.filter(word -> !word.isEmpty())
.groupingKey(wholeItem())
.aggregate(counting())
.drainTo(Sinks.map(COUNTS));
return pipeline;

23. Example 1: Word Count
Pure Java
Pattern delimiter = Pattern.compile("W+");
return lines.entrySet().stream()
.map(e -> e.getValue().toLowerCase())
.flatMap(t -> Arrays.stream(delimiter.split(t)))
.filter(word -> !word.isEmpty())
.collect(
groupingBy(
identity(),
counting()));

24. Example 1: Word Count
Hazelcast Jet
Pattern delimiter = Pattern.compile("W+");
Pipeline pipeline = Pipeline.create();
pipeline.drawFrom(Sources.<Long, String>map(LINES))
.map(e -> e.getValue().toLowerCase())
.flatMap(t -> traverseArray(delimiter.split(t)))
.filter(word -> !word.isEmpty())
.groupingKey(wholeItem())
.aggregate(counting())
.drainTo(Sinks.map(COUNTS));
return pipeline;

25. Example 1: Word Count

26. Example 1: Word Count

27. Example 1: Word Count
Hazelcast Jet
Pattern delimiter = Pattern.compile("W+");
Pipeline pipeline = Pipeline.create();
pipeline.drawFrom(Sources.<Long, String>map(LINES))
.map(e -> e.getValue().toLowerCase())
.flatMap(t -> traverseArray(delimiter.split(t)))
.filter(word -> !word.isEmpty())
.groupingKey(wholeItem())
.aggregate(counting())
.drainTo(Sinks.map(COUNTS));
return pipeline;

28. Example 1: Word Count
Demo:
https://github.com/hazelcast/hazelcast-jet-code-samples

29. Part 2: Jet Under the Hood

30. How does it work?

31. How does it work?

32. How does it work?

33. How does it work?
Under the Hood:
● Generate DAG representation from Pipeline
● Serialize DAG
● Send DAG to every Node
● Deserialize DAG
● Executes DAG on each Node

35. Infinite Streams

36. Infinite Streams
Examples:
● Currency Exchange Rates
● Tweets from Twitter
● Events in some Event-Based system
● ...

37. Windowing
pipeline.drawFrom(...)
.withNativeTimestamps(0)
.window(sliding(30_000, 10_000))

38. Example 2: Twitter Cryptocurrency Analysis
Problem:
Present in real-time the sentiments about cryptocurrencies
Input:
Tweets are streamed from Twitter and categorized by coin type
(BTC, ETC, XRP, etc)
Output:
Tweets sentiments (last 30 sec, last minute, last 5 minutes)

39. Example 2: Twitter Cryptocurrency Analysis
Demo:
https://jet.hazelcast.org/demos/

40. Part 3: Jet in the Cloud

41. Jet in the Cloud: discovery plugins

42. Jet in the Cloud: discovery plugins

43. Jet in the Cloud: discovery plugins

44. Jet in the Cloud: discovery plugins

45. Jet in the Cloud: discovery plugins

46. Jet in the Cloud: discovery plugins

47. Jet in the Cloud: deploying on k8

48. Jet in the Cloud: deploying on k8
$ helm install stable/hazelcast-jet

49. Jet in the Cloud: deploying on k8
$ kubectl scale <name> --replicas=6

50. Example 3: Stock Trade Aggregator
Problem:
Present in real-time the aggregated trade price of stocks
Input:
Stock trades with name and price
Output:
Sum of prices per stock name

51. Example 3: Stock Trade Aggregator
Demo:
https://github.com/hazelcast/hazelcast-jet-code-samples/t
ree/master/integration/kubernetes

52. Part 4: Jet Features & Use Cases

53. Jet Features
Categories of Features
● Easy to Use
● Performance

54. Jet Features: Performance

55. Jet Features: Performance

56. Jet Features: other features

57. Why would I need it?
● Big Data Projects

58. Why would I need it?
● Big Data Projects
● Speed up Everything

59. Why would I need it?
● Big Data Projects
● Speed up Everything

60. Example 4: Web Crawler
Problem:
Parse all blog posts from the webpage
Input:
URL of Blog Trips
Output:
All the content from the Blog

61. Example 4: Web Crawler
Demo:
https://github.com/leszko/geodump

62. Thank You!