Abstract
This paper analyzes the performance of three systems for in-memory data management: Memcached, Redis and the Resilient Distributed Datasets (RDD) implemented by Spark. By performing a thorough performance analysis of both analytics operations and fine-grained object operations such as set/get, we show that neither system handles efficiently both types of workloads. For Memcached and Redis the CPU and I/O performance of the TCP stack are the bottlenecks -- even when serving in-memory objects within a single server node. RDD does not support efficient get operation for random objects, due to a large startup cost of the get job. Our analysis reveals a set of features that a system must support in order to achieve efficient in-memory data management.
- Aredis java redis client. http://aredis.sourceforge.net/.Google Scholar
- Memcached. http://memcached.org.Google Scholar
- Redis. http://redis.io.Google Scholar
- Spymemcached memcached client. https://code.google.com/p/spymemcached/.Google Scholar
- Stanford large network dataset collection. https://snap.stanford.edu/data/.Google Scholar
- K. Lim, D. Meisner, A. G. Saidi, P. Ranganathan, and T. F. Wenisch. Thin Servers with Smart Pipes: Designing SoC Accelerators for Memcached. In ISCA, 2013. Google ScholarDigital Library
- M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker, and I. Stoica. Resilient Distributed Datasets: A Fault-tolerant Abstraction for In-memory Cluster Computing. In NSDI, 2012. Google ScholarDigital Library
Index Terms
- Efficient in-memory data management: an analysis
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