We present HRDBMS, a novel distributed shared-nothing database system developed with the goal of improving scalability of MPP databases based on a principled combination of techniques from MPP and Big Data systems with novel communication and work-distribution techniques. HRDBMS runs on a custom distributed and asynchronous execution engine that features highly parallelized operator implementations. The system features a cost-based optimization framework, user-defined data partitioning, locality-aware query execution, a non-blocking and hierarchical shuffle, and data skipping based on caching predicate matches. Our experimental comparison with Hive, Spark SQL, and Greenplum confirms that HRDBMS’s scalability is on par with Hive and Spark SQL (up to 96 nodes) while its per-node performance can compete with MPP databases (Greenplum).
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Building learned federated query optimizers
The goal of this thesis is to introduce a new design for building federated query optimizers, based on machine learning. We propose a modular and flexible architecture, allowing a federated query optimizer to integrate with any database system that supports SQL, with close-to-zero engineering effort. By observing the performance of the external systems, our optimizer learns and builds cost models on-the-fly, enabling federated query optimization with negligible communication with the external systems. To demonstrate the potential of this research plan, we present a prototype of our federated query optimizer built on top of Spark SQL. Our implementation effectively accelerates federated queries, achieving up to 7.5x better query execution times compared to the vanilla implementation of Spark SQL.
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- Award ID(s):
- 1910830
- NSF-PAR ID:
- 10377801
- Date Published:
- Journal Name:
- CEUR workshop proceedings
- Volume:
- Vol-3186
- ISSN:
- 1613-0073
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
