Chain: operator scheduling for memory minimization in data stream systems

  • Authors:

  • Brian Babcock;Shivnath Babu;Rajeev Motwani;Mayur Datar

  • Affiliations:

  • Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA

  • Venue:
  • Proceedings of the 2003 ACM SIGMOD international conference on Management of data
  • Year:
  • 2003

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Abstract

In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams --- adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the run-time system memory usage. We then present Chain scheduling, an operator scheduling strategy for data stream systems that is near-optimal in minimizing run-time memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams, and multiple queries of the above types. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling, compare it with competing scheduling strategies, and validate our analytical conclusions.