Optimizations and Heuristics to improve Compression in Columnar Database Systems

TL;DR

This paper introduces two novel optimizations for compression techniques in in-memory columnar databases, enhancing efficiency and proposing heuristics for selecting optimal encoding schemes to handle large-scale data more effectively.

Contribution

The paper presents two new compression optimizations—Block Size Optimized Cluster Encoding and Block Size Optimized Indirect Encoding—that outperform existing methods, along with heuristics for selecting the best compression scheme.

Findings

Optimized compression techniques improve data size reduction.

Heuristics effectively select the best encoding scheme.

Enhanced performance in large-scale data processing.

Abstract

In-memory columnar databases have become mainstream over the last decade and have vastly improved the fast processing of large volumes of data through multi-core parallelism and in-memory compression thereby eliminating the usual bottlenecks associated with disk-based databases. For scenarios, where the data volume grows into terabytes and petabytes, keeping all the data in memory is exorbitantly expensive. Hence, the data is compressed efficiently using different algorithms to exploit the multi-core parallelization technologies for query processing. Several compression methods are studied for compressing the column array, post Dictionary Encoding. In this paper, we will present two novel optimizations in compression techniques - Block Size Optimized Cluster Encoding and Block Size Optimized Indirect Encoding - which perform better than their predecessors. In the end, we also propose heuristics to choose the best encoding amongst common compression schemes.