Runtime Optimization of Join Location in Parallel Data Management Systems

TL;DR

This paper introduces runtime techniques for optimizing join location decisions in parallel data systems, balancing data transfer, skew, and UDF computation to enhance throughput.

Contribution

It presents an extended ski-rental based algorithm for per-key join location decisions with performance guarantees, implemented on Hadoop, Spark, and Muppet.

Findings

Significant throughput improvements over existing methods.

Effective load balancing considering CPU, network, and I/O.

Robust performance guarantees in various scenarios.

Abstract

Applications running on parallel systems often need to join a streaming relation or a stored relation with data indexed in a parallel data storage system. Some applications also compute UDFs on the joined tuples. The join can be done at the data storage nodes, corresponding to reduce side joins, or by fetching data from the storage system to compute nodes, corresponding to map side join. Both may be suboptimal: reduce side joins may cause skew, while map side joins may lead to a lot of data being transferred and replicated. In this paper, we present techniques to make runtime decisions between the two options on a per key basis, in order to improve the throughput of the join, accounting for UDF computation if any. Our techniques are based on an extended ski-rental algorithm and provide worst-case performance guarantees with respect to the optimal point in the space considered by us. Our techniques use load balancing taking into account the CPU, network and I/O costs as well as the load on compute and storage nodes. We have implemented our techniques on Hadoop, Spark and the Muppet stream processing engine. Our experiments show that our optimization techniques provide a significant improvement in throughput over existing techniques.