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When can we trust progress estimators for SQL queries?
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Proceedings of the 2005 ACM SIGMOD international conference on Management of data table of contents
Baltimore, Maryland
SESSION: Research papers: estimation and approximation table of contents
Pages: 575 - 586  
Year of Publication: 2005
ISBN:1-59593-060-4
Authors
Surajit Chaudhuri  Microsoft Research
Raghav Kaushik  Microsoft Research
Ravishankar Ramamurthy  Microsoft Research
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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ABSTRACT

The problem of estimating progress for long-running queries has recently been introduced. We analyze the characteristics of the progress estimation problem, from the perspective of providing robust, worst-case guarantees. Our first result is that in the worst case, no progress estimation algorithm can yield anything even moderately better than the trivial guarantee that identifies the progress as lying between 0% and 100%. In such cases, we introduce an estimator that can optimally bound the error. However, we show that in many "good" scenarios, it is possible to design effective progress estimators with small error bounds. We then demonstrate empirically that these "good" scenarios are common in practice and discuss possible ways of combining the estimators.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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N. Bruno and S. Chaudhuri. Statistics on query expressions. In SIGMOD, 2002.
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S. Chaudhuri and V. Narasayya. The sky server database. http://skyserver.sdss.org.
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The TPCH Benchmark. http://www.tpc.org.
 
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Program for TPC-D Data Generation with Skew. ftp://ftp.research.microsoft.com/users/viveknar/tpcdskew.

Collaborative Colleagues:
Surajit Chaudhuri: colleagues
Raghav Kaushik: colleagues
Ravishankar Ramamurthy: colleagues