[Paper Review] Heuristic and Cost-based Optimization for Diverse Provenance Tasks

Xing Niu, Raghav Kapoor+5 more|arXiv (Cornell University)|Apr 17, 2018

Scientific Computing and Data Management14 references3 citations

TL;DR

This paper presents a provenance-aware optimization framework that combines heuristic and cost-based techniques to significantly improve performance for provenance-intensive queries. By leveraging algebraic equivalences and alternative instrumentation strategies, the framework reduces execution time by several orders of magnitude across diverse provenance tasks.

ABSTRACT

A well-established technique for capturing database provenance as annotations on data is to instrument queries to propagate such annotations. However, even sophisticated query optimizers often fail to produce efficient execution plans for instrumented queries. We develop provenance-aware optimization techniques to address this problem. Specifically, we study algebraic equivalences targeted at instrumented queries and alternative ways of instrumenting queries for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization framework utilizing these optimizations. Our experiments confirm that these optimizations are highly effective, improving performance by several orders of magnitude for diverse provenance tasks.

Motivation & Objective

• Address the performance degradation caused by query instrumentation for provenance capture in database systems.
• Develop algebraic equivalences and alternative instrumentation methods to optimize provenance-aware queries.
• Design an extensible optimization framework that integrates heuristic and cost-based optimization for provenance tasks.
• Improve query execution efficiency without compromising provenance fidelity or correctness.
• Enable practical deployment of provenance systems by reducing runtime overhead through intelligent optimization

Proposed method

• Propose algebraic equivalences tailored for instrumented queries to enable optimization across provenance-aware execution plans.
• Introduce alternative instrumentation strategies that reduce annotation propagation overhead while preserving provenance semantics.
• Design a cost-based optimization framework that estimates the cost of alternative execution plans using statistics and selectivity estimates.
• Implement an adaptive optimization strategy that balances optimization time and plan quality, ensuring competitiveness within a factor of 2 of the optimal strategy.
• Use a plan space traversal algorithm with interval-based pruning to efficiently explore high-quality provenance query plans.
• Leverage provenance polynomial semantics and relational encoding to model provenance annotations as part of the query algebra

Experimental results

Research questions

• RQ1 How can algebraic equivalences be identified and applied to instrumented queries to improve provenance query performance?
• RQ2 What alternative instrumentation methods can reduce the computational cost of provenance propagation without losing fidelity?
• RQ3 How can cost-based optimization be extended to handle provenance-aware query plans effectively?
• RQ4 What is the trade-off between optimization time and plan quality in provenance query optimization?
• RQ5 Can an adaptive optimization strategy achieve near-optimal performance while limiting overhead?

Key findings

• The proposed optimization framework improves query performance by several orders of magnitude for diverse provenance tasks.
• The adaptive optimization strategy is proven to be 2-competitive, ensuring that total optimization and execution time is within a factor of two of the optimal strategy.
• Algebraic equivalences and alternative instrumentation methods significantly reduce the cost of provenance-aware query execution.
• The cost-based optimization framework effectively estimates and compares plan costs, enabling selection of high-quality execution plans.
• The plan space traversal algorithm efficiently explores promising regions of the search space using interval-based pruning and dynamic use of low/high bounds.
• Empirical evaluation confirms that the framework delivers substantial performance gains across a range of provenance workloads