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  9. Query Processing And Optimisation
  11. Arcadb: A Disaggregated Query Engine For Heterogenous Computational Environments

ArcaDB: A Disaggregated Query Engine for Heterogenous Computational Environments

Kristalys Ruiz-Rohena1, Manuel Rodriguez-Martínez2

  • 1Department of Electrical and Computer Engineering, University of Puerto Rico, Mayagüez.

Proceedings. IEEE International Conference on Cloud Computing
|November 4, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

ArcaDB is a new data analytics engine using container technology to optimize query performance. It intelligently places data processing tasks on suitable hardware, achieving up to 3.5x faster results than traditional systems.

Area of Science:

  • Computer Science
  • Data Engineering
  • High-Performance Computing

Background:

  • Modern enterprises require robust data management for large datasets.
  • Hardware accelerators like GPUs and TPUs are essential for scaling data processing.
  • Existing data analytics engines need architectural evolution to leverage new hardware trends.

Purpose of the Study:

  • To introduce ArcaDB, a disaggregated query engine designed for modern data analytics.
  • To demonstrate how ArcaDB utilizes container technology for efficient operator placement.
  • To evaluate the performance improvements offered by ArcaDB.

Main Methods:

  • Developed ArcaDB, a disaggregated query engine using Java, Python, and Docker containers.
  • Implemented a system where query plans are dispatched to worker nodes with diverse computing characteristics.
Keywords:
Cloud computingDBaaScloud acceleratorsdata analytics

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  • Annotated operators with preferred compute node types for optimized execution.

    • Main Results:

    • ArcaDB successfully places operators on compute nodes matching their performance profiles.
    • A prototype implementation was created and tested using image and scientific data.
    • Preliminary studies show ArcaDB speeds up query performance by up to 3.5x compared to shared-nothing architectures.

    Conclusions:

    • ArcaDB's disaggregated architecture and container-based operator placement enhance data analytics efficiency.
    • The engine effectively leverages heterogeneous compute nodes for improved query performance.
    • ArcaDB represents a significant advancement in data analytics engine design for modern hardware.
    query engine