Evaluating window joins over unbounded streams

Jaewoo Kang, Jeffrey F. Naughton, Stratis D. Viglas

Research output: Contribution to conferencePaperpeer-review

244 Citations (Scopus)


We investigate algorithms for evaluating sliding window joins over pairs of unbounded streams. We introduce a unit-time-basis cost model to analyze the expected performance of these algorithms. Using the cost model, we propose strategies for maximizing the efficiency of processing joins in three scenarios. First, we consider the case where one stream is much faster than the other. We show that asymmetric combinations of join algorithms, (e.g., hash join on one input, nested-loops join on the other) can outperform symmetric joint algorithm implementations. Second, we investigate the case where system resources are insufficient to keep up with the input streams. We show that we can maximize the number of join result tuples produced in this case by properly allocating computing resources across the two input streams. Finally, we investigate strategies for maximizing the number of result tuples produced when memory is limited, and show that proper memory allocation across the two input streams can result in significantly lower resource usage and/or more result tuples produced.

Original languageEnglish
Number of pages12
Publication statusPublished - 2003
Externally publishedYes
EventNineteenth International Conference on Data Ingineering - Bangalore, India
Duration: 2003 Mar 52003 Mar 8


OtherNineteenth International Conference on Data Ingineering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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