Column-Oriented Storage Techniques for MapReduce

TL;DR

This paper explores how column-oriented storage techniques can significantly enhance Hadoop's MapReduce performance, achieving up to 100x speedups by optimizing data formats and handling complex data types efficiently.

Contribution

It introduces a Hadoop-compatible column-oriented storage format with a skip list and lazy deserialization, leading to substantial performance improvements over traditional text-based storage.

Findings

Binary storage formats improve performance 3x over text files

Column-oriented formats speed up MapReduce jobs by up to 10x

Skip list and lazy deserialization techniques add 1.5x performance boost

Abstract

Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from over three decades of research on parallel DBMSs. However, translating these techniques to a MapReduce implementation such as Hadoop presents unique challenges that can lead to new design choices. This paper describes how column-oriented storage techniques can be incorporated in Hadoop in a way that preserves its popular programming APIs. We show that simply using binary storage formats in Hadoop can provide a 3x performance boost over the naive use of text files. We then introduce a column-oriented storage format that is compatible with the replication and scheduling constraints of Hadoop and show that it can speed up MapReduce jobs on real workloads by an order of magnitude. We also show that dealing with complex column types such as arrays, maps, and nested records, which are common in MapReduce jobs, can incur significant CPU overhead. Finally, we introduce a novel skip list column format and lazy record construction strategy that avoids deserializing unwanted records to provide an additional 1.5x performance boost. Experiments on a real intranet crawl are used to show that our column-oriented storage techniques can improve the performance of the map phase in Hadoop by as much as two orders of magnitude.