Cache-based Multi-query Optimization for Data-intensive Scalable Computing Frameworks

TL;DR

This paper presents a cache-based multi-query optimization technique for large-scale distributed systems that reduces redundant processing by sharing common subexpressions among queries, leading to improved efficiency.

Contribution

It introduces a novel method combining in-memory caching with multi-query optimization, formulated as a cost-based problem to enhance data-intensive framework performance.

Findings

Significant resource savings on TPC-DS workloads

Effective sharing of common subexpressions reduces computation

Prototype shows notable performance improvements

Abstract

In modern large-scale distributed systems, analytics jobs submitted by various users often share similar work, for example scanning and processing the same subset of data. Instead of optimizing jobs independently, which may result in redundant and wasteful processing, multi-query optimization techniques can be employed to save a considerable amount of cluster resources. In this work, we introduce a novel method combining in-memory cache primitives and multi-query optimization, to improve the efficiency of data-intensive, scalable computing frameworks. By careful selection and exploitation of common (sub)expressions, while satisfying memory constraints, our method transforms a batch of queries into a new, more efficient one which avoids unnecessary recomputations. To find feasible and efficient execution plans, our method uses a cost-based optimization formulation akin to the multiple-choice knapsack problem. Extensive experiments on a prototype implementation of our system show significant benefits of worksharing for both TPC-DS workloads and detailed micro-benchmarks.