Hybrid Materialization in a Disk-Based Column-Store

TL;DR

This paper introduces a hybrid materialization strategy for disk-based column-stores that combines positions and values, enabling more flexible query plans and nearly doubling performance compared to existing methods.

Contribution

It proposes a novel hybrid materialization model that manipulates both positions and values simultaneously, enhancing flexibility and performance in distributed column-stores.

Findings

Hybrid materialization is nearly twice as fast as existing strategies.

Supports a new class of flexible query plans.

Demonstrated effectiveness on TPC-H benchmark scenarios.

Abstract

In column-oriented query processing, a materialization strategy determines when lightweight positions (row IDs) are translated into tuples. It is an important part of column-store architecture, since it defines the class of supported query plans, and, therefore, impacts the overall system performance. In this paper we continue investigating materialization strategies for a distributed disk-based column-store. We start with demonstrating cases when existing approaches impose fundamental limitations on the resulting system performance. Then, in order to address them, we propose a new hybrid materialization model. The main feature of hybrid materialization is the ability to manipulate both positions and values at the same time. This way, query engine can flexibly combine advantages of all the existing strategies and support a new class of query plans. Moreover, hybrid materialization allows the query engine to flexibly customize the materialization policy of individual attributes. We describe our vision of how hybrid materialization can be implemented in a columnar system. As an example, we use PosDB~ -- a distributed, disk-based column-store. We present necessary data structures, the internals of a hybrid operator, and describe the algebra of such operators. Based on this implementation, we evaluate performance of late, ultra-late, and hybrid materialization strategies in several scenarios based on TPC-H queries. Our experiments demonstrate that hybrid materialization is almost two times faster than its counterparts, while providing a more flexible query model.