This talk will be about the reasons behind the new stream processing model, how it compare to the old batch model, what are their pros and cons, and a list of existing technologies implementing stream processing with their most prominent characteristics. It will contain details of one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map, beginning with an overview of all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform,showing a working demo implementation of it.

1. @nicolas_frankel
A gentle introduction to Stream
Processing
Nicolas Fränkel

2. @nicolas_frankel
Me, myself and I
 Former developer, team lead, architect,
blah-blah
 Developer Advocate
 Lots of crazy creative ideas

3. @nicolas_frankel
Hazelcast
HAZELCAST IMDG is an operational,
in-memory, distributed computing
platform that manages data using
in-memory storage and performs
execution for breakthrough
and scale.
HAZELCAST JET is the ultra
fast, application embeddable,
3rd generation stream
processing engine for low
latency batch and stream
processing.

4. @nicolas_frankel
Schedule
 Why streaming?
 Streaming approaches
 Hazelcast Jet
 Open Data
 General Transit Feed Specification
 The demo!
 Q&A

5. @nicolas_frankel
In a time before our time…
Data was neatly stored in SQL databases

6. @nicolas_frankel
The need for Extract Transform Load
 Analytics
• Supermarket sales in the last hour?
 Reporting
• Banking account annual closing

7. @nicolas_frankel
What SQL really means
 Constraints
 Joints
 Normal forms

8. @nicolas_frankel
Writes vs. reads
 Normalized vs. denormalized
 Correct vs. fast

9. @nicolas_frankel
The need for ETL
 Different actors
 With different needs
 Using the same database?

10. @nicolas_frankel
The batch model
1. Extract
2. Transform
3. Load

11. @nicolas_frankel
Batches are everywhere!

12. @nicolas_frankel
Properties of batches
 Scheduled at regular intervals
• Daily
• Weekly
• Monthly
• Yearly
 Run in a specific amount of time

13. @nicolas_frankel
Oops
 When the execution time overlaps the
next execution schedule
 When the space taken by the data
exceeds the storage capacity
 When the batch fails mid-execution
 etc.

14. @nicolas_frankel
Big data!
 Parallelize everything
• Map - Reduce
• Hadoop
 NoSQL
• Schema on Read vs. Schema on Write

15. @nicolas_frankel
Or chunking?
 Keep a cursor
• And only manage “chunks” of data
 What about new data coming in?

16. @nicolas_frankel
Event-Driven Programming
“Programming paradigm in which the flow of
the program is determined by events such
as user actions (mouse clicks, key presses),
sensor outputs, or messages from other
programs or threads”
-- Wikipedia

17. @nicolas_frankel
Event Sourcing
“Event sourcing persists the state of a business entity
such an Order or a Customer as a sequence of state-
changing events. Whenever the state of a business
entity changes, a new event is appended to the list of
events. Since saving an event is a single operation, it is
inherently atomic. The application reconstructs an entity’s
current state by replaying the events.”
-- https://microservices.io/patterns/data/event-sourcing.html

18. @nicolas_frankel
Database internals
 Ordered append-only log
• e.g. MySQL binlog

19. @nicolas_frankel
Make everything event-based!

20. @nicolas_frankel
Benefits
 Memory-friendly
 Easily processed
 Pull vs. push
• Very close to real-time
• Keeps derived data in-sync

21. @nicolas_frankel
From finite datasets to infinite

22. @nicolas_frankel
Streaming is smart ETL
Processing
Ingest
In-Memory
Operational
Storage
Combine
Join, Enrich,
Group, Aggregate
Stream
Windowing,
Event-Time
Processing
Compute
Distributed and
Parallel
Computation
Transform
Filter, Clean,
Convert
Publish
In-Memory,
Subscriber
Notifications
Notify if response
time is 10% over 24
hour average, second
by second

23. @nicolas_frankel
Use Case: Analytics and Decision Making
 Real-time dashboards
• Decision making
• Recommendations
 Stats (gaming, infrastructure monitoring)
 Prediction - often based on algorithmic
prediction
• Push stream through ML model
 Complex Event Processing

24. @nicolas_frankel
Persistent event-storage systems
 Kafka
 Pulsar

25. @nicolas_frankel
Kafka
 Distributed
 On-disk storage
 Messages sent and read from a topic
• Publish-subscribe
• Queue
 Consumer can keep track of the offset

26. @nicolas_frankel
In-memory stream processing engines
 Apache Flink
 Amazon Kinesis
 IBM Streams
 Hazelcast Jet
 Apache Beam
• Abstraction over some of the above
 …

27. @nicolas_frankel
Hazelcast Jet
 Apache 2 Open Source
 Single JAR
 Leverages Hazelcast IMDG
 Unified batch/streaming API
 (Hazelcast Jet Enterprise)

28. @nicolas_frankel
Hazelcast Jet

29. @nicolas_frankel
Concept: Pipeline
 Declaration (code) that defines and links
sources, transforms, and sinks
 Platform-specific SDK (Pipeline API in
Jet)
 Client submits pipeline to the Stream
Processing Engine (SPE)

30. @nicolas_frankel
Concept: Job
 Running instance of pipeline in SPE
 SPE executes the pipeline
 Code execution
 Data routing
 Flow control
 Parallel and distributed execution

31. @nicolas_frankel
Imperative model
final String text = "...";
final Map<String, Long> counts = new HashMap<>();
for (String word : text.split("W+")) {
Long count = counts.get(word);
counts.put(count == null ? 1L : count + 1);
}

32. @nicolas_frankel
Declarative model
Map<String, Long> counts = lines.stream()
.map(String::toLowerCase)
.flatMap(
line -> Arrays.stream(line.split("W+"))
)
.filter(word -> !word.isEmpty())
.collect(Collectors.groupingBy(
word -> word, Collectors.counting())
);

33. @nicolas_frankel
What Distributed Means to Hazelcast
 Multiple nodes
 Scalable storage and performance
 Elasticity
 Data stored, partitioned and replicated
 No single point of failure

34. @nicolas_frankel
Distributed Parallel Processing
Pipeline p = Pipeline.create();
p.drawFrom(Sources.<Long, String>map(BOOK_LINES))
.flatMap(line -> traverseArray(line.getValue().split("W+")))
.filter(word -> !word.isEmpty())
.groupingKey(wholeItem())
.aggregate(counting())
.drainTo(Sinks.map(COUNTS));
Data
Sink
Data
Source
from aggrmap filter to
Translate declarative code to a Directed Acyclic Graph

35. @nicolas_frankel
Node 1
Distributed Parallel Processing
read cmb
map
+
filter
acc sink
read cmb
map
+
filter
acc
Node 2
read cmb
map
+
filter
acc
sinkread cmb
map
+
filter
acc
Data
Source
Data
Sink
sink
sink

36. @nicolas_frankel
Open Data
« Open data is the idea that some data
should be freely available to everyone to
use and republish as they wish, without
restrictions from copyright, patents or
other mechanisms of control. »
--https://en.wikipedia.org/wiki/Open_data

37. @nicolas_frankel
Some Open Data initiatives
 France:
• https://www.data.gouv.fr/fr/
 Switzerland:
• https://opendata.swiss/en/
 European Union:
• https://data.europa.eu/euodp/en/data/

38. @nicolas_frankel
Challenges
1. Access
2. Format
3. Standard
4. Data correctness

39. @nicolas_frankel
Access
 Download a file
 Access it interactively through a web-
service

40. @nicolas_frankel
Format
In general, Open Data means Open
Format
 PDF
 CSV
 XML
 JSON
 etc.

41. @nicolas_frankel
Standard
 Let’s pretend the format is XML
• Which grammar is used?
 A shared standard is required
• Congruent to a domain

42. @nicolas_frankel
Data correctness
"32.TA.66-43","16:20:00","16:20:00","8504304"
"32.TA.66-44","24:53:00","24:53:00","8500100"
"32.TA.66-44","25:00:00","25:00:00","8500162"
"32.TA.66-44","25:02:00","25:02:00","8500170"
"32.TA.66-45","23:32:00","23:32:00","8500170"

43. @nicolas_frankel
General Transit Feed Specification
”The General Transit Feed Specification (GTFS) […] defines a
common format for public transportation schedules and
associated geographic information. GTFS feeds let public
transit agencies publish their transit data and developers write
applications that consume that data in an interoperable way.”

44. @nicolas_frankel
GTFS static model
Filename Required Defines
agency.txt Required Transit agencies with service represented in this dataset.
stops.txt Required
Stops where vehicles pick up or drop off riders. Also defines stations and station
entrances.
routes.txt Required Transit routes. A route is a group of trips that are displayed to riders as a single service.
trips.txt Required
Trips for each route. A trip is a sequence of two or more stops that occur during a
specific time period.
stop_times.txt Required Times that a vehicle arrives at and departs from stops for each trip.
calendar.txt Conditionally required
Service dates specified using a weekly schedule with start and end dates. This file is
required unless all dates of service are defined in calendar_dates.txt.
calendar_dates.txt Conditionally required
Exceptions for the services defined in the calendar.txt. If calendar.txt is omitted, then
calendar_dates.txt is required and must contain all dates of service.
fare_attributes.txt Optional Fare information for a transit agency's routes.

45. @nicolas_frankel
GTFS static model
Filename Required Defines
fare_rules.txt Optional Rules to apply fares for itineraries.
shapes.txt Optional Rules for mapping vehicle travel paths, sometimes referred to as route alignments.
frequencies.txt Optional
Headway (time between trips) for headway-based service or a compressed representation of fixed-schedule
service.
transfers.txt Optional Rules for making connections at transfer points between routes.
pathways.txt Optional Pathways linking together locations within stations.
levels.txt Optional Levels within stations.
feed_info.txt Optional Dataset metadata, including publisher, version, and expiration information.
translations.txt Optional Translated information of a transit agency.
attributions.txt Optional Specifies the attributions that are applied to the dataset.

46. @nicolas_frankel
GTFS dynamic model

47. @nicolas_frankel
Use-case: Swiss Public Transport
 Open Data
 GTFS static available as downloadable
.txt files
 GTFS dynamic available as a REST
endpoint

48. @nicolas_frankel
The available… … data model
Where’s the position?!

49. @nicolas_frankel
The dynamic data pipeline
1. Source: web service
2. Split into trip updates
3. Enrich with trip data
4. Enrich with stop times data
5. Transform hours into timestamp
6. Enrich with location data
7. Sink: Hazelcast IMDG

50. @nicolas_frankel
Architecture overview

51. @nicolas_frankel

52. @nicolas_frankel
Recap
 Streaming has a lot of benefits
 Leverage Open Data
 It’s the Wild West out there
• No standards
• Real-world data sucks!
 But you can get cool stuff done

53. @nicolas_frankel
Thanks a lot!
 https://blog.frankel.ch/
 @nicolas_frankel
 https://jet-start.sh/
 https://bit.ly/gtransportfs
 https://bit.ly/jet-train