Multiple query optimization in SQL-on-Hadoop systems
US10572478B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 1, 2014 |
| Grant date | Feb 25, 2020 |
| Priority date | — |
| Expiry date | Sep 3, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2209/5017
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
To reduce the overall computation time of a batch of queries, multiple query optimization in SQL-on-Hadoop systems groups multiple MapReduce jobs converted from queries into a single one, thus avoiding redundant computations by taking sharing opportunities of data scan, map function and map output. SQL-on-Hadoop converts a query into a DAG of MapReduce jobs and each map function is a part of query plan composed of a sequence of relational operators. As each map function is a part of query plan which is usually complex and heavy, disclosed method creates a cost model to simulate the computation time which takes both I/O cost for reading/writing input file and intermediate data and CPU cost for the computation of map function into consideration. A heuristic algorithm is disclosed to find near-optimal integrated query plan for each group based on an observation that each query plan is locally optimal.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.