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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
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.
Steffen Zeuch / DFKI GmbH / 08/27/2019 3/14
Is ingestion at memory-speed possible?
Network is not the bottleneck in the future.
Steffen Zeuch / DFKI GmbH / 08/27/2019 4/14
What is possible?
No SPE is yet ready for processing at memory speed.
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.
Steffen Zeuch / DFKI GmbH / 08/27/2019 6/14
How do SPEs transfer data?
Queues are the major bottleneck for scale-up processing.
Steffen Zeuch / DFKI GmbH / 08/27/2019 7/14
How do SPEs parallelize a query?
For scale-up, there are alternatives to partitioning.
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.
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
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.
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.
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.
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
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

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Analyzing Efficient Stream Processing on Modern Hardware

  • 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