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STREAM: The Stanford Data Stream Management System

STREAM: The Stanford Data Stream Management System. Rebuttal Team Mingzhu Wei Di Yang CS525s - Fall 2006. Rebuttal Areas. Foundation Windows Joins Full Recalculation Strategy Language Issues. Foundation. Rebuttal

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STREAM: The Stanford Data Stream Management System

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  1. STREAM: The Stanford Data Stream Management System Rebuttal Team Mingzhu Wei Di Yang CS525s - Fall 2006

  2. Rebuttal Areas • Foundation • Windows • Joins • Full Recalculation Strategy • Language Issues

  3. Foundation • Rebuttal • No proof provided to guarantee correctness or completeness of query plans resulting from the combination of operators, queues, and synopses • Analysis • STREAM is based on relation database theory • Relational databases have been around for a long time • Proofs exist that demonstrate their correctness • CQL is a minor extension to SQL • More effort could have been put into providing a more formal proof of CQL

  4. Windows • Rebuttal • Stream does not provide value-based windows • For example, without a value-based window the system cannot process a query such as: • Give me the name of the students who have the top 10 exam scores, efficiently • Analysis • Feature not supported by STREAM

  5. Joins • Rebuttal • STREAM only uses the self-purge mechanism when performing a window-based join • Analysis • STREAM’s criteria for judging when a tuple (in the state of the window) has expired is determined by comparison of its timestamp with that of the new incoming tuples in the same stream • Cross-purge might be more efficient in some cases • Cross-purge = Compare timestamps across two streams

  6. Full Recalculation Strategy • Rebuttal • Stream uses a full recalculation strategy for result updating • Could be very inefficient with big window sizes • Example: • We are trying to join two windows each of size 1000 • If both windows only slide 10 at each time, recalculation for the whole result would be much more expensive, than incremental result updating • Analysis • Using an Incremental Result Update Strategy might be more efficient in some cases • Keep most of the joined result and only calculate those for newly arrived tuples

  7. Language Issues • Rebuttal • Stream does not provide the stream to stream operator • Analysis • The absence of a stream-to-stream operator is not explicitly justified in the paper • It’s absence is reasonable because STREAM operators treat all input as relations • STREAM does provide operators for converting streams to relations and for converting relations to streams

  8. Language Issues (cont) • Rebuttal • Stream uses an append-only model • It does not provide an operator for updating data value in stream • Analysis • Although not perfect, this is a common assumption in current stream processing papers

  9. Conclusion • Foundation • Windows • Joins • Full Recalculation Strategy • Language Issues

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