An adaptive query execution system for data integration

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Abstract
Query processing in data integration occurs over network- bound, autonomous data sources. This requires extensions to traditional optimization and execution techniques for three reasons: there is an absence of quality statistics about the data, data transfer rates are unpredictable and bursty, and slow or unavailable data sources can often be replaced by overlapping or mirrored sources. This paper presents the Tukwila data integration system, designed to support adap- tivity at its core using a two-pronged approach. Interleaved planning and execution with partial optimization allows Tuk- wila to quickly recover from decisions based on inaccurate estimates. During execution, Tukwila uses adaptive query operators such as the double pipelined hash join, which pro- duces answers quickly, and the dynamic collector, which ro- bustly and efficiently computes unions across overlapping data sources. We demonstrate that the Tukwila architecture extends previous innovations in adaptive execution (such as query scrambling, mid-execution re-optimization, and choose nodes), and we present experimental evidence that our tech- niques result in behavior desirable for a data integration system.

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