Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Fundamental techniques for order optimization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
New methods to color the vertices of a graph
Communications of the ACM
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Querying Multiple Features of Groups in Relational Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
WinMagic: subquery elimination using window aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient computation of multiple group by queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Avoiding sorting and grouping in processing queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A combined framework for grouping and order optimization
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Enhanced subquery optimizations in Oracle
Proceedings of the VLDB Endowment
Sort-sharing-aware query processing
The VLDB Journal — The International Journal on Very Large Data Bases
Adaptive and big data scale parallel execution in oracle
Proceedings of the VLDB Endowment
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Analytic functions represent the state-of-the-art way of performing complex data analysis within a single SQL statement. In particular, an important class of analytic functions that has been frequently used in commercial systems to support OLAP and decision support applications is the class of window functions. A window function returns for each input tuple a value derived from applying a function over a window of neighboring tuples. However, existing window function evaluation approaches are based on a naive sorting scheme. In this paper, we study the problem of optimizing the evaluation of window functions. We propose several efficient techniques, and identify optimization opportunities that allow us to optimize the evaluation of a set of window functions. We have integrated our scheme into PostgreSQL. Our comprehensive experimental study on the TPC-DS datasets as well as synthetic datasets and queries demonstrate significant speedup over existing approaches.