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In this paper we present a study of how Java programs dispose of objects. Unlike prior work on object demographics and lifetime patterns, our goal is to precisely characterize the actions that cause objects to become unreachable. We use a recently-developed tracing tool, called Elephant Tracks, which can localize object deaths within a specific method and tell us the proximal cause. Our analysis centers around garbage clusters: groups of connected objects that become unreachable at precisely the same time due to a single program action. We classify these clusters using traditional features, such as size, allocation site, and lifetime, and using new ones, such as death site and cause of death. We then use this knowledge about garbage clusters in a new GC algorithm: the Cluster Aware Garbage Collection (CAGC) algorithm. We present results for a set of standard benchmarks including SPECJVM98, SPECjbb, and DaCapo. We identify several patterns that could inform the design of new collectors or tuning of existing systems. Most garbage clusters are small, suggesting that these programs almost always dispose of large structures in a piecemeal fashion. In addition, most clusters die in one of only a dozen or so places in the program. Furthermore, these death sites are much more stable and predictable than object lifetimes. Finally we show that the CAGC algorithm can in certain cases improve GC performance.
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An Experimental Evaluation of Garbage Collectors on Big Data Applications
Popular big data frameworks, ranging from Hadoop MapReduce to Spark, rely on garbage-collected languages, such as Java and Scala. Big data applications are especially sensitive to the effectiveness of garbage collection (i.e., GC), because they usually process a large volume of data objects that lead to heavy GC overhead. Lacking in-depth understanding of GC performance has impeded performance improvement in big data applications. In this paper, we conduct the first comprehensive evaluation on three popular garbage collectors, i.e., Parallel, CMS, and G1, using four representative Spark applications. By thoroughly investigating the correlation between these big data applications’ memory usage patterns and the collectors’ GC patterns, we obtain many findings about GC inefficiencies. We further propose empirical guidelines for application developers, and insightful optimization strategies for designing big-data-friendly garbage collectors.
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- PAR ID:
- 10159018
- Date Published:
- Journal Name:
- The 45th International Conference on Very Large Data Bases (VLDB'19)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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