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GHive: A Demonstration of GPU-Accelerated Query Processing in Apache Hive

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

Pages 2417 - 2420

https://doi.org/10.1145/3514221.3520166

Published: 11 June 2022 Publication History

Abstract

As a distributed, fault-tolerant data warehouse system for large-scale data analytics, Apache Hive has been used for various applications in many organizations (e.g., Facebook, Amazon, and Huawei). Exploiting the large degrees of parallelism of GPU to improve the performance of online analytical processing (OLAP) in database system is a common practice in the industry. Meanwhile, it is a common practice to exploit the large degrees of parallelism of GPU to improve the performance of online analytical processing (OLAP) in database systems. This demo presents GHive, which enables Apache Hive to accelerate OLAP queries by jointly utilizing CPU and GPU in intelligent and efficient ways. The takeaways for SIGMOD attendees include: (1) the superior performance of GHive compared with vanilla Hive that only uses CPU; (2) intuitive visualizations of execution statistics for Hive and GHive to understand where the acceleration of GHive comes from; (3) detailed profiling of the time taken by each operator on CPU and GPU to show the advantages of GPU execution.

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Cited By

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Jain NTiwari P(2024)A Genetic Algorithm Model for Join Order Query Optimization in Hadoop-Hive FrameworkEmerging Trends in Expert Applications and Security10.1007/978-981-97-3745-1_25(289-300)Online publication date: 28-Sep-2024
https://doi.org/10.1007/978-981-97-3745-1_25
Tang B(2023)Towards Building The Next Generation Computation EngineProceedings of the ACM Turing Award Celebration Conference - China 202310.1145/3603165.3607435(129-130)Online publication date: 25-Sep-2023
https://doi.org/10.1145/3603165.3607435
Shen QYou ZYan XZhang CXu KZeng DQin JTang B(2023)QEVIS: Multi-Grained Visualization of Distributed Query ExecutionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332693030:1(153-163)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3326930

Index Terms

GHive: A Demonstration of GPU-Accelerated Query Processing in Apache Hive
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing

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Published In

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

June 2022

2597 pages

ISBN:9781450392495

DOI:10.1145/3514221

General Chair:
Zachary Ives
University of Pennsylvania (USA)
,
Program Chairs:
Angela Bonifati
Lyon 1 University (France)
,
Amr El Abbadi
University of California, Santa Barbara (USA)

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 11 June 2022

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SIGMOD

SIGMOD/PODS '22: International Conference on Management of Data

June 12 - 17, 2022

PA, Philadelphia, USA

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Jain NTiwari P(2024)A Genetic Algorithm Model for Join Order Query Optimization in Hadoop-Hive FrameworkEmerging Trends in Expert Applications and Security10.1007/978-981-97-3745-1_25(289-300)Online publication date: 28-Sep-2024
https://doi.org/10.1007/978-981-97-3745-1_25
Tang B(2023)Towards Building The Next Generation Computation EngineProceedings of the ACM Turing Award Celebration Conference - China 202310.1145/3603165.3607435(129-130)Online publication date: 25-Sep-2023
https://doi.org/10.1145/3603165.3607435
Shen QYou ZYan XZhang CXu KZeng DQin JTang B(2023)QEVIS: Multi-Grained Visualization of Distributed Query ExecutionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332693030:1(153-163)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3326930

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GHive: accelerating analytical query processing in apache hive via CPU-GPU heterogeneous computing

Accelerating a computer vision algorithm on a mobile SoC using CPU-GPU co-processing: a case study on face detection

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