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Partitioning functions for stateful data parallelism in stream processing
2013
The VLDB journal
In this paper we study partitioning functions for stream processing systems that employ stateful data parallelism to improve application throughput. In particular, we develop partitioning functions that are effective under workloads where the domain of the partitioning key is large and its value distribution is skewed. We define various desirable properties for partitioning functions, ranging from balance properties such as memory, processing, and communication balance, structural properties
doi:10.1007/s00778-013-0335-9
fatcat:6dr55bgrwngmtoxcucmvqjb3d4