In dataflow architectures, each dataflow operation is typically executed on a single physical node. We are concerned with distributed data-intensive systems, in which each base (i.e., persistent) set of data has been declustered over many physical nodes to achieve load balancing. Because of large base set size, each operation is executed where the base set resides, and intermediate results are transferred between physical nodes. In such systems, each dataflow operation is typically executed on many physical nodes. Furthermore, because computations are data-dependent, we cannot know until run time which subset of the physical nodes containing a particular base set will be involved in a given dataflow operation. This uncertainty creates several problems.
We examine the problems of efficient program loading, dataflow-operation activation and termination, control of data transfer among dataflow operations, and transaction commit and abort in a distributed data-intensive system. We show how these problems are interrelated, and we present a unified set of mechanisms for efficiently solving them. For some of the problems, we present several solutions and compare them quantitatively.
