Stream Processing Engines (SPEs) execute long-running queries on unbounded data streams. They follow an interpretation-based processing model and do not perform runtime optimizations. This limits the utilization of modern hardware and neglects changing data characteristics at runtime. In this paper, we present Grizzly, a novel adaptive query compilation-based SPE, to enable highly efficient query execution. We extend query compilation and task-based parallelization for the unique requirements of stream processing and apply adaptive compilation to enable runtime reoptimizations. The combination of light-weight statistic gathering with just-in-time compilation enables Grizzly to adjust to changing data-characteristics dynamically at runtime. Our experiments show that Grizzly outperforms state-of-the-art SPEs by up to an order of magnitude in throughput.

1. Grizzly: Efficient Stream Processing
Through Adaptive Query Compilation
Philipp M. Grulich¹, Sebastian Breß², Steffen Zeuch¹², Jonas Traub¹,
Janis von Bleichert¹, Zongxiong Chen², Tilmann Rabl³, Volker Markl¹²
Technische Universität Berlin¹, DFKI GmbH², HPI & Universität Potsdam³
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2. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Limitations of state-of-the-art SPEs
Current SPEs use hardware resources inefficiently [Zeuch et al., Zhang et al.]
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3. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Limitations of state-of-the-art SPEs
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1. Interpretation-based processing model causes poor cache utilization.
Current SPEs use hardware resources inefficiently [Zeuch et al., Zhang et al.]

4. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Limitations of state-of-the-art SPEs
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1. Interpretation-based processing model causes poor cache utilization.
2. Upfront-Partitioning causes high overhead on single nodes.
Current SPEs use hardware resources inefficiently [Zeuch et al., Zhang et al.]

5. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Limitations of state-of-the-art SPEs
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1. Interpretation-based processing model causes poor cache utilization.
2. Upfront-Partitioning causes high overhead on single nodes.
3. SPEs do not react to changing data-characteristics at runtime.
Current SPEs use hardware resources inefficiently [Zeuch et al., Zhang et al.]
Data Stream

6. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Limitations of state-of-the-art SPEs
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1. Interpretation-based processing model causes poor cache utilization.
2. Upfront-Partitioning causes high overhead on single nodes.
3. SPEs do not react to changing data-characteristics.
An SPE should be hardware- and data-conscious.
Current SPEs use hardware resources inefficiently [Zeuch et al., Zhang et al.]

7. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Our Proposal
Grizzly: Efficient Stream Processing Through
Adaptive Query Compilation
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8. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Grizzly’s Core Principles
Order Preserving
Task-based Parallelization
Continuous Adaptive
Optimizations
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Query Compilation for
Stream Processing

9. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Grizzly’s Core Principles
Query Compilation for
Stream Processing
● Fuses operators to
compact code blocks.
● Support unique stream
processing operators.
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Order Preserving
Task-based Parallelization
Continuous Adaptive
Optimizations

10. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Query Compilation
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From(user_purchases )
.filter(origin=’Germany’)
.keyBy(userid)
.windowBy(TumblingWindow(days(7)), Max(price).as(max_price))
.filter(max_price > 42)

11. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Grizzly’s Core Principles
Query Compilation for
Stream Processing
● Fuses operators to
compact code blocks.
● How to support
combination of
window assignment,
function, and trigger?
Order Preserving
Task-based Parallelization
● Concurrent execution
on a global state.
● Supporting order
requirement of stream
processing.
● Exploiting
NUMA-configuration.
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12. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Task-based Parallelization
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● Input stream is processed in small batches (sized to network buffer).
● Pipelines are executed concurrently on a shared state.

13. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Task-based Parallelization
Lock-Free Window Processing
● Allows threads to process
windows concurrently.
● Lightweight coordination for
window triggering.
NUMA-awareness
● Pre-aggregate window results on
locally to minimize inter-NUMA
node communication.
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14. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Grizzly’s Core Principles
Order Preserving
Task-based Parallelization
Continuous Adaptive
Optimizations
● Feedback loop between
code-generation and
query execution.
● Lightweight monitoring
at runtime.
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Query Compilation for
Stream Processing

15. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Adaptive Re-Optimization
Generic Execution:
● Without data-dependent optimizations.
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Instrumentalized Execution:
● Injects profiling code to collect statistics.
(predicate selectivity, value distribution)
Specialized Execution:
● Specialize operator implementation
(predication, fixed hash-tables)

16. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Adaptive Optimization
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Deoptimization:
● Migrates from optimized to less optimized execution.
● Caused by violated assumptions or changed data characteristics.

17. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Evaluation
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18. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Grizzly outperforms state-of-the-art SPEs by up-to 10x.
Evaluation: System Comparison
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19. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Code generation is beneficial for a wide range of workloads.
Evaluation: Workloads
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20. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Evaluation: Adaptive Optimizations
Adaptive optimizations are crucial to reach peak performance.
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21. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Summary
www.nebula.stream
@NebulaStream
Grizzly:
● Query compilation for stream processing.
● Task-based parallelization while taking ordering
requirements into account.
● Adaptive optimization to reach to changing data
characteristics.
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22. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
Query Compilation
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23. Sigmod 2020, Grizzly: Efficient Stream Processing Through Adaptive Query Compilation, Grulich et al.
System Architecture
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