The POSTGRES next generation database management system
Communications of the ACM
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Performance tradeoffs for client-server query processing
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An adaptive query execution system for data integration
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
The state of the art in distributed query processing
ACM Computing Surveys (CSUR)
Garlic: a new flavor of federated query processing for DB2
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Optimizing Queries Across Diverse Data Sources
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Efficient query processing for data integration
Efficient query processing for data integration
Adapting to source properties in processing data integration queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Distributed/Heterogeneous Query Processing in Microsoft SQL Server
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Integrating Databases with Publish/Subscribe
ICDCSW '05 Proceedings of the Fourth International Workshop on Distributed Event-Based Systems (DEBS) (ICDCSW'05) - Volume 04
Maximizing the output rate of multi-way join queries over streaming information sources
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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The evolution from relational DBMS to data integration system brings new challenges to the design and implementation of query execution engine that must be extended to support queries over multiple distributed, heterogeneous, and autonomous data sources. In this paper, we introduce our work on extending PostgreSQL to support distributed query processing. Although PostgreSQL has no built-in distributed query processor, its function mechanism provides possibilities for us to integrate data of various data sources and execute distributed queries. We point out several limitations in PostgreSQL's query engine and present corresponding query execution techniques to improve performance of distributed query processing. Our experimental results show that the techniques can significantly reduce response times when running a workload consisting of TPC-H queries.