Name
Abstract | ||
|---|---|---|
Running analytics computation inside a database engine through the use of UDFs (User Defined Functions) has been investigated, but not yet become a scalable approach due to several technical limitations. One limitation lies in the lack of generality for UDFs to express complex applications and to compose them with relational operators in SQL queries. Another limitation lies in the lack of systematic support for a UDF to cache relations initially for efficient computation in multi-calls. Further, having UDF execution interacted efficiently with query processing requires detailed system programming, which is often beyond the expertise of most application developers. To solve these problems, we extend the UDF technology in both semantic and system dimensions. We generalize UDF to support scalar, tuple as well as relation input and output, allow UDFs to be defined on the entire content of relations and allow the moderate-sized input relations to be cached in initially to avoid repeated retrieval. With such extension the generalized UDFs can be composed with other relational operators and thus integrated into queries naturally. Furthermore, based on the notion of invocation patterns, we provide focused system support for efficiently interacting UDF execution with query processing. We have taken the open-sourced PostgreSQL engine and a commercial and proprietary parallel database engine as our prototyping vehicles; we illustrated the performance, modeling power and usability of the proposed approach with the experimental results on both platforms. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14246-8_70 | WAIM |
Keywords | Field | DocType |
generalized udf,proprietary parallel database engine,database engine,open-sourced postgresql engine,system dimension,relational operator,udf technology,query processing,udf execution,generalized udfs,detailed system programming,application development | Data mining,System programming,Programming language,Parallel database,Computer science,Database engine,Artificial intelligence,Analytics,SQL,Tuple,User-defined function,Machine learning,Scalability | Conference |
Volume | ISSN | ISBN |
6184 | 0302-9743 | 3-642-14245-1 |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meichun Hsu | 1 | 3437 | 778.34 |
| Qiming Chen | 2 | 2010 | 233.16 |
| Ren Wu | 3 | 92 | 17.28 |
| Bin Zhang | 4 | 165 | 33.92 |
| Hans Zeller | 5 | 1 | 0.35 |