Lifetime-Based Memory Management for Distributed Data Processing Systems

TL;DR

This paper introduces Deca, a lifetime-based memory management framework for distributed data processing systems like Spark, significantly reducing garbage collection overhead and improving performance by analyzing data object lifetimes.

Contribution

It proposes a novel lifetime analysis approach and implements Deca on Spark to optimize memory allocation and release, enhancing scalability and efficiency.

Findings

Reduces garbage collection time by up to 99.9%

Achieves up to 22.7x speedup without data spilling

Consumes up to 46.6% less memory

Abstract

In-memory caching of intermediate data and eager combining of data in shuffle buffers have been shown to be very effective in minimizing the re-computation and I/O cost in distributed data processing systems like Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap, which may quickly saturate the garbage collector, especially when handling a large dataset, and hence would limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects, and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca, a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. An extensive experimental study using both synthetic and real datasets shows that, in comparing to Spark, Deca is able to 1) reduce the garbage collection time by up to 99.9%, 2) to achieve up to 22.7x speed up in terms of execution time in cases without data spilling and 41.6x speedup in cases with data spilling, and 3) to consume up to 46.6% less memory.