Encyclopedia of Database Systems

Living Edition
| Editors: Ling Liu, M. Tamer Özsu

Adaptive Query Processing

  • Evaggelia PitouraEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7993-3_865-2



While in traditional query processing, a query is first optimized and then executed, adaptive query processing techniques use runtime feedback to modify query processing in a way that provides better response time, more efficient CPU utilization or more useful incremental results. Adaptive query processing makes query processing more robust to optimizer mistakes, unknown statistics, and dynamically changing data, runtime and workload characteristics. The spectrum of adaptive query processing techniques is quite broad: they may span the executions of multiple queries or adapt within the execution of a single query; they may affect the query plan being executed or just the scheduling of operations within the plan.

Key Points

Conventional query processing follows an optimize-then-execute strategy: after generating alternative query plans, the query optimizer selects the most cost-efficient among them and passes it to the execution engine that directly executes it, typically with little or no runtime decision-making. As queries become more complex, this strategy faces many limitations such as missing statistics, unexpected correlations, and dynamically changing data, runtime, and workload characteristics. These problems are aggregated in the case of long-running queries over data streams as well as in the case of queries over multiple potentially heterogeneous data sources across wide-area networks. Adaptive query processing tries to address these shortcomings by using feedback during query execution to tune query processing. The goal is to increase throughput, improve response time or provide more useful incremental results.

To implement adaptivity, regular query execution is supplemented with a control system for monitoring and analyzing at run-time various parameters that affect query execution. Based on this analysis, certain decisions are made about how the system behavior should be changed. Clearly, this may introduce considerable overheads.

The complete space of adaptive query processing techniques is quite broad and varied. Adaptability may be applied to query execution of multiple queries or just a single one. It may also affect the whole query plan being executed or just the scheduling of operations within the plan. Adaptability techniques also differ on how much they interleave plan generation and execution. Some techniques interleave planning and execution just a few times, by just having the plan re-optimized at specific points, whereas other techniques interleave planning and execution to the point where they are not even clearly distinguishable.

A number of fundamental adaptability techniques include:
  • Horizontal partitioning, where different plans are used on different portions of the data. Partitioning may be explicit or implicit in the functioning of the operator.

  • Query execution by tuple routing, where query execution is treated as the process of routing tuples through operators and adaptability is achieved by changing the order in which tuples are routed.

  • Plan partitioning, where execution progresses in stages, by interleaving optimization and execution steps at a number of well-defined points during query execution.

  • Runtime binding decisions, where certain plan choices are deferred until runtime, allowing the execution engine to select among several alternative plans by potentially re-invoking the optimizer.

  • In-operator adaptive logic, where scheduling and other decisions are made part of the individual query operators, rather than the optimizer.

Many adaptability techniques rely on a symmetric hash join operator that offers a non-blocking variant of join by building hash tables on both the input relations. When an input tuple is read, it is stored inthe appropriate hash table and probed against the opposite table, thus producing incremental output. The symmetric hash join operator can process data from either input, depending on availability. It also enables additional adaptivity, since it has frequent moments of symmetry, that is, points at which the join order can be changed without compromising correctness or losing work.

The eddy operator provides an example of fine-grained run-time control by tuple routing through operators. An eddy is used as a tuple router; it monitors execution, and makes routing decisions for the tuples. Eddies achieve adaptability by simply changing the order in which the tuples are routed through the operators. The degree of adaptability achieved depends on the type of the operators. Pipelined operators, such as the symmetric hash join, offer the most freedom, whereas, blocking operators, such as the sort-merge join, are less suitable since they do not produce output before consuming the input relations in their entirety.


Recommended Reading

  1. 1.
    Avnur R, Hellerstein JM. Eddies: continuously adaptive query processing. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 261–72.Google Scholar
  2. 2.
    Babu S, Bizarro P. Adaptive query processing in the looking glass. In: Proceedings of the 2nd Biennial Conference on Innovative Data Systems Research; 2005. p. 238–49.Google Scholar
  3. 3.
    Deshpande A, Ives ZG, Raman V. Adaptive query processing. Found Trends Databases. 2007;1(1):1–140.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of IoanninaIoanninaGreece

Section editors and affiliations

  • Evaggelia Pitoura
    • 1
  1. 1.Dept. of Computer ScienceUniv. of IoanninaIoanninaGreece