Spark内存管理及缓存策略研究

Research on Memory Management and Cache Replacement Policies in Spark

Spark系统是基于Map-Reduce模型的大数据处理框架。Spark能够充分利用集群的内存,从而加快数据的处理速度。Spark按照功能把内存分成不同的区域:Shuffle Memory和Storage Memory,Unroll Memory,不同的区域有不同的使用特点。首先,测试并分析了Shuffle Memory和Storage Memory的使用特点。RDD是Spark系统最重要的抽象,能够缓存在集群的内存中;在内存不足时,需要淘汰部分RDD分区。接着,提出了一种新的RDD分布式权值缓存策略,通过RDD分区的存储时间、大小、使用次数等来分析RDD分区的权值,并根据RDD的分布式特征对需要淘汰的RDD分区进行选择。最后,测试和分析了多种缓存策略的性能。

Spark is a big data processing framework based on Map-Reduce.Spark can make full use of cluster memory,thus accelerating data processing.Spark divides memory into Shuffle Memory,Storage Memory and Unroll Memory according to their functions.These different memory zones have different characteristics.The features of Shuffle Memory and Storage Memory were tested and analyzed.RDD（Resilient Distributed Datasets）is the most important abstract in spark,which can cache in cluster memory.When the cluster memory is insufficient,Spark must select some RDD partitions to discard to make room for the new ones.A new cache replacement policies called DWRP（Distributed Weight Replacement Policy）was proposed.DWRP can compute the weight of every RDD partition based on the time of store in memory,size and frequency of use,and then select possible RDD partition to discard based on distribution features.The performance of different cache replacement policies was tested and analyzed at last.