This document analyzes efficient stream processing on modern hardware. It identifies sources of inefficiency in current streaming systems and explores design changes to better utilize hardware, such as avoiding managed runtimes and queues. The authors show that an optimized scale-up solution could achieve up to two orders of magnitude performance improvement over state-of-the-art streaming systems through techniques like hardware-tailored compilation and late data merging.

1. Steffen Zeuch / DFKI GmbH / 08/27/2019 1/14
Analyzing Efficient Stream
Processing on Modern Hardware
Steffen Zeuch, Bonaventura Del Monte, Jeyhun
Karimov, Clemens Lutz, Manuel Renz, Jonas Traub,
Sebastian Breß, Tilmann Rabl, Volker Markl

2. Steffen Zeuch / DFKI GmbH / 08/27/2019 2/14
What is this paper about?
This paper is about showing the potential of
hardware-tailored code compilation and data
ingestion at memory speed for a scale-up SPE.
This paper is not about
benchmarking existing SPEs.

3. Steffen Zeuch / DFKI GmbH / 08/27/2019 3/14
Is ingestion at memory-speed possible?
Network is not the bottleneck in the future.

4. Steffen Zeuch / DFKI GmbH / 08/27/2019 4/14
What is possible?
No SPE is yet ready for processing at memory speed.

5. Steffen Zeuch / DFKI GmbH / 08/27/2019 5/14
What did we do?
• Analyze state-of-the-art streaming systems and
identify sources of inefficiency.
• Investigate data-related and processing-related
design space.
• Derive design changes for streaming systems to
exploit modern hardware more efficiently.

6. Steffen Zeuch / DFKI GmbH / 08/27/2019 6/14
How do SPEs transfer data?
Queues are the major bottleneck for scale-up processing.

7. Steffen Zeuch / DFKI GmbH / 08/27/2019 7/14
How do SPEs parallelize a query?
For scale-up, there are alternatives to partitioning.

8. Steffen Zeuch / DFKI GmbH / 08/27/2019 8/14
How do SPEs execute a query?
All systems use an interpretation based approach.
All systems, except streambox, use a managed runtime.

9. Steffen Zeuch / DFKI GmbH / 08/27/2019 9/14
What’s the scale-up performance?
Yahoo Streaming
Benchmark
Linear Road Benchmark
(partly)
New York Taxi Query
Overhead for entire framework: up to 80x
Overhead for managed runtime: up to 56x

10. Steffen Zeuch / DFKI GmbH / 08/27/2019 10/14
What’s the scale-out performance?(reported)
An optimized scale-up solution outperforms even 10 node
cluster.

11. Steffen Zeuch / DFKI GmbH / 08/27/2019 11/14
How does Flink scale out?
Add new nodes to the system does not solve the problem.

12. Steffen Zeuch / DFKI GmbH / 08/27/2019 12/14
Why are current SPEs inefficient?
Large instruction footprints, virtual function calls, and suboptimal
access patterns reduce efficiency.

13. Steffen Zeuch / DFKI GmbH / 08/27/2019 13/14
What should we do to scale-up?
• Avoid managed runtimes
• Use a compilation-based approach to
produce hardware-tailored code
• Avoid queues and use operator fusion
• Use late merge instead of partitioning
– enables producer/consumer fusing

14. Steffen Zeuch / DFKI GmbH / 08/27/2019 14/14
Summary
• We explore the data-related and processing-related
design space.
• We show that an up to two orders of magnitude
performance improvement is possible.
• We derive design changes for streaming systems to
exploit modern hardware more efficiently.
https://git.io/fjAZg