research-article

Grizzly: Efficient Stream Processing Through Adaptive Query Compilation

Authors:

Philipp M. Grulich,

Breß Sebastian,

Janis von Bleichert,

Zongxiong Chen,

Volker MarklAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 2487 - 2503

https://doi.org/10.1145/3318464.3389739

Published: 31 May 2020 Publication History

Abstract

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 re-optimizations. 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.

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Cited By

Justen DRitter DFraser CLamb ALee ABodner THaddad MZeuch SMarkl VBoehm M(2024)POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least ResistanceProceedings of the VLDB Endowment10.14778/3648160.364817517:6(1350-1363)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648175
Grulich PLepping ANugroho DPandey VDel Monte BZeuch SMarkl V(2024)Query Compilation Without RegretsProceedings of the ACM on Management of Data10.1145/36549682:3(1-28)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654968
Kozar ADel Monte BZeuch SMarkl V(2024)Fault Tolerance Placement in the Internet of ThingsProceedings of the ACM on Management of Data10.1145/36549412:3(1-29)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654941
Show More Cited By

Index Terms

Grizzly: Efficient Stream Processing Through Adaptive Query Compilation
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Data streams
    2. Database management system engines
      1. DBMS engine architectures
      2. Stream management

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cover image ACM Conferences

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

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Published: 31 May 2020

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Cited By

Justen DRitter DFraser CLamb ALee ABodner THaddad MZeuch SMarkl VBoehm M(2024)POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least ResistanceProceedings of the VLDB Endowment10.14778/3648160.364817517:6(1350-1363)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648175
Grulich PLepping ANugroho DPandey VDel Monte BZeuch SMarkl V(2024)Query Compilation Without RegretsProceedings of the ACM on Management of Data10.1145/36549682:3(1-28)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654968
Kozar ADel Monte BZeuch SMarkl V(2024)Fault Tolerance Placement in the Internet of ThingsProceedings of the ACM on Management of Data10.1145/36549412:3(1-29)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654941
Chaudhry HRossi M(2024)Optimising Queries for Pattern Detection Over Large Scale Temporally Evolving GraphsIEEE Access10.1109/ACCESS.2024.341735212(86790-86808)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3417352
Lepping APham HMons LRueb BGrulich PChaudhary AZeuch SMarkl V(2023)Showcasing Data Management Challenges for Future IoT Applications with NebulaStreamProceedings of the VLDB Endowment10.14778/3611540.361158816:12(3930-3933)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.14778/3611540.3611588
Böther MBenson LKlimovic ARabl T(2023)Analyzing Vectorized Hash Tables across CPU ArchitecturesProceedings of the VLDB Endowment10.14778/3611479.361148516:11(2755-2768)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611485
Zheng ZPan ZWang DZhu KZhao WGuo TQiu XSun MBai JZhang FDu XZhai JLin W(2023)BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler ApproachProceedings of the ACM on Management of Data10.1145/36173271:3(1-29)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3617327
Schubert NGrulich PZeuch SMarkl V(2023)Exploiting Access Pattern Characteristics for Join ReorderingProceedings of the 19th International Workshop on Data Management on New Hardware10.1145/3592980.3595304(10-18)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3592980.3595304
Fathollahzadeh SBoehm M(2023)GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by ExampleProceedings of the ACM on Management of Data10.1145/35892651:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589265
Stolz TKoren ITirpitz LGeisler S(2023)GALOIS: A Hybrid and Platform-Agnostic Stream Processing ArchitectureProceedings of the International Workshop on Big Data in Emergent Distributed Environments10.1145/3579142.3594287(1-6)Online publication date: 18-Jun-2023
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