Data stream partitioning re-optimization based on runtime dependency mining

In distributed data stream processing, a program made of multiple queries can be parallelized by partitioning input streams according to the values of specific attributes, or partitioning keys. Applying different partitioning keys to different queries requires re-partitioning intermediary streams, causing extra communication and reduced throughput. Re-partitionings can be avoided by detecting dependencies between the partitioning keys applicable to each query. Existing partitioning optimization methods analyze query syntax at compile-time to detect inter-key dependencies and avoid re-partitionings. This paper extends those compile-time methods by adding a runtime re-optimization step based on the mining of temporal approximate dependencies (TADs) between partitioning keys. A TAD is defined in this paper as a type of dependency that can be approximately valid over a moving time window. Our evaluation, based on a simulation of the Linear Road Benchmark, showed a 94.5% reduction of the extra communication cost.