Parallel Evaluation of Multi-Semi-Joins

TL;DR

This paper introduces algorithms for parallel evaluation of complex semi-join queries in MapReduce, optimizing total computation time while maintaining low query response times, and demonstrates their effectiveness through experiments.

Contribution

It proposes a novel multi-semi-join MapReduce operator and greedy algorithms for optimizing parallel query plans for SGF queries, including disjunction and negation.

Findings

Parallel query plans outperform sequential plans in total time.

Optimizations significantly improve scalability and efficiency.

Experimental results show advantages over Pig and Hive.

Abstract

While services such as Amazon AWS make computing power abundantly available, adding more computing nodes can incur high costs in, for instance, pay-as-you-go plans while not always significantly improving the net running time (aka wall-clock time) of queries. In this work, we provide algorithms for parallel evaluation of SGF queries in MapReduce that optimize total time, while retaining low net time. Not only can SGF queries specify all semi-join reducers, but also more expressive queries involving disjunction and negation. Since SGF queries can be seen as Boolean combinations of (potentially nested) semi-joins, we introduce a novel multi-semi-join (MSJ) MapReduce operator that enables the evaluation of a set of semi-joins in one job. We use this operator to obtain parallel query plans for SGF queries that outvalue sequential plans w.r.t. net time and provide additional optimizations aimed at minimizing total time without severely affecting net time. Even though the latter optimizations are NP-hard, we present effective greedy algorithms. Our experiments, conducted using our own implementation Gumbo on top of Hadoop, confirm the usefulness of parallel query plans, and the effectiveness and scalability of our optimizations, all with a significant improvement over Pig and Hive.