Slides from my OSCON 2016 talk. Not super useful without the accompanying video and code. I'll post that when it's available.

1. Robust
Stream Processing
with
Apache Flink
Jamie Grier
@jamiegrier
jamie@data-artisans.com

2. Who am I?
• Director of Applications Engineering at data
Artisans
• Previously working on streaming computation at
Twitter, Gnip and Boulder Imaging
• Involved in various kinds of stream processing for
about a decade
• High-speed video, social media streaming, general
frameworks for stream processing

3. Overview
• What is Apache Flink?
• What is Stateful Stream Processing?
• Windowed computation over streams
• Robust Time Handling (Event Time vs Processing Time)
• Robust Failure Handling
• Robust Planned Downtime Handling
• Robust Reprocessing

4. What is
Apache Flink?
s an open source platform for distributed stream and batch da

5. What is
Apache Flink?

6. Stream Processing
Your
Code
Data Stream Data Stream

7. Stateful
Stream Processing
Your
Code
Data Stream Data Stream
State

8. More Complex
Example
Kafka
Files
Rabb
itMQ
Filter
Map
Join /
Sum
Influx
DB
C*

9. Distributed and Parallel
Deployment
Kafka
Files
Rabb
it
MQ
Filter
Pars
e
Join /
Sum
Influx
DB
C*

10. Robust Stream Processing
with Apache Flink

11. Code Example!

12. Windowing

13. Processing Time
vs
Event Time

14. Windowing in Processing
Time
0 1 2 34 56 7 8 9 0 1 2 3 4 5 6 7 8 9
Processing Time
Event Time

15. Windowing in Event
Time
0 1 2 34 56 7 8 9 0 1 2 3 4 5 6 7 8 9
Event Time

16. Processing Time = Errors!

17. Event Time = Accuracy

18. Failure Handling

19. Downtime Handling

20. Data Reprocessing

21. We’re Hiring!
http://data-artisans.com/careers

22. Flink Forward 2016, Berlin
Submission deadline: June 30, 2016
Early bird deadline: July 15, 2016
www.flink-forward.org

23. Questions?

24. Thanks!