Automatic Optimization for MapReduce Programs

TL;DR

This paper introduces Manimal, an automatic system that analyzes MapReduce programs to identify optimization opportunities, significantly improving performance without requiring programmer intervention.

Contribution

Manimal automatically detects data operation semantics in MapReduce code and applies optimizations, bridging the gap between database query optimization and MapReduce.

Findings

Achieves up to 1,121% speedup on existing MapReduce programs.

Successfully detects various data operations for optimization.

Requires no programmer hints or modifications.

Abstract

The MapReduce distributed programming framework has become popular, despite evidence that current implementations are inefficient, requiring far more hardware than a traditional relational databases to complete similar tasks. MapReduce jobs are amenable to many traditional database query optimizations (B+Trees for selections, column-store- style techniques for projections, etc), but existing systems do not apply them, substantially because free-form user code obscures the true data operation being performed. For example, a selection in SQL is easily detected, but a selection in a MapReduce program is embedded in Java code along with lots of other program logic. We could ask the programmer to provide explicit hints about the program's data semantics, but one of MapReduce's attractions is precisely that it does not ask the user for such information. This paper covers Manimal, which automatically analyzes MapReduce programs and applies appropriate data- aware optimizations, thereby requiring no additional help at all from the programmer. We show that Manimal successfully detects optimization opportunities across a range of data operations, and that it yields speedups of up to 1,121% on previously-written MapReduce programs.