05. – 10. August 2012, Dagstuhl-Seminar 12321

Robust Query Processing


Goetz Graefe (HP Labs – Madison, US)
Wey Guy (Redmond, US)
Glenn Paulley (Conestoga College – Kitchener, CA)


Harumi Anne Kuno (HP Labs – Palo Alto, US)

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In early August 2012 researchers from both academia and industry assembled in Dagstuhl at the 2012 Dagstuhl Workshop on Robust Query Processing, Workshop 12321. An earlier Workshop---Dagstuhl Workshop 10381---held in September 2010 had supplied an opportunity to look at issues of Robust Query Processing but had failed to make significant progress in exploring the topic to any significant depth. In 2012, 12321 Workshop participants looked afresh at some of the issues surrounding Robust Query Processing with greater success and with the strong possibility of future publications in the area that would advance the state-of-the-art in query processing technology.

Background and related research

A considerable amount of query processing research over the past 20 years has focused on improving relational database system optimization and execution techniques for complex queries and complex, ever-changing workloads. Complex queries provide optimization challenges because selectivity and cardinality estimation errors multiply, and so there is a large body of work on improving cardinality estimation techniques and doing so in an autonomic fashion: from capturing histogram information at run time, to mitigating the effects of correlation on the independence assumption , to utilizing constraints to bound estimation error, to permitting various query rewritings to simplify the original statement. Studies of the feasibility of query re-optimization, or deferring optimization to execution time, have until recently largely been based on the premise that the need for such techniques is due either to recovering from estimation errors at optimization time in the former case, or avoiding the problem entirely by performing all optimization on-the-fly, such as with Eddies rather than in a staged, "waterfall" kind of paradigm.

More recent work on adaptive query processing has considered techniques to handle the interaction of query workloads, coupled with the realization that changes to environmental conditions can significantly impact a query's chosen execution plan. These environmental conditions include:

  • changes to the amount of memory available (buffer pool, heap memory);
  • changes to I/O bandwidth due to concurrent disk activity;
  • locking and other waits caused by concurrency control mechanisms;
  • detected flaws in the currently executing plan;
  • number of available CPU cores;
  • changes to the server's multiprogramming level;
  • changes to physical access paths, such as the availability of indexes, which could be created on the fly;
  • congestion with the telecommunications network;
  • contents of the server's buffer pool;
  • inter-query interaction (contention on the server's transaction log, 'hot' rows, and so on.

Dagstuhl-Seminar Series


  • Databases / Information Retrieval
  • Data Structures / Algorithms / Complexity
  • Optimization / Scheduling
  • Autonomic Computing


  • Robust query processing
  • Adaptive query optimization
  • Query execution
  • Indexing
  • Workload management
  • Reliability
  • Application availability


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