A Framework For Inferring Properties of User-Defined Functions.
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| Title: | A Framework For Inferring Properties of User-Defined Functions. |
|---|---|
| Authors: | Liu, Xinyu1 liuxy@gatech.edu, Arulraj, Joy1 arulraj@gatech.edu, Orso, Alessandro1 orso@cc.gatech.edu |
| Source: | ICSE: International Conference on Software Engineering. 2024, p1-11. 11p. |
| Subjects: | Computer users, Computer software, Neural circuitry, Cosine function, Clustering algorithms |
| Abstract: | User-defined functions (UDFs) are widely used to enhance the capabilities of DBMSs. However, using UDFs comes with a significant performance penalty because DBMSs treat UDFs as black boxes, which hinders their ability to optimize queries that invoke such UDFs. To mitigate this problem, in this paper we present LAMBDA, a technique and framework for improving DBMSs' performance in the presence of UDFs. The core idea of LAMBDA is to statically infer properties of UDFs that facilitate UDF processing. Taking one such property as an example, if DBMSs know that a UDF is pure, that is it returns the same result given the same arguments, they can leverage a cache to avoid repetitive UDF invocations that have the same call arguments. We reframe the problem of analyzing UDF properties as a data flow problem. We tackle the data flow problem by building LAMBDA on top of an extensible abstract interpretation framework and developing an analysis model that is tailored for UDFs. Currently, LAMBDA supports inferring four properties from UDFs that are widely used across DBMSs. We evaluate LAMBDA on a benchmark that is derived from production query workloads and UDFs. Our evaluation results show that (1) LAMBDA conservatively and efficiently infers the considered UDF properties, and (2) inferring such properties improves UDF performance, with a time reduction ranging from 10% to 99%. In addition, when applied to 20 production UDFs, LAMBDA caught five instances in which developers provided incorrect UDF property annotations. We qualitatively compare LAMBDA against Froid, a state-of-the-art framework for improving UDF performance, and explain how LAMBDA can optimize UDFs that are not supported by Froid. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: A Framework For Inferring Properties of User-Defined Functions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Xinyu%22">Liu, Xinyu</searchLink><relatesTo>1</relatesTo><i> liuxy@gatech.edu</i><br /><searchLink fieldCode="AR" term="%22Arulraj%2C+Joy%22">Arulraj, Joy</searchLink><relatesTo>1</relatesTo><i> arulraj@gatech.edu</i><br /><searchLink fieldCode="AR" term="%22Orso%2C+Alessandro%22">Orso, Alessandro</searchLink><relatesTo>1</relatesTo><i> orso@cc.gatech.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2024, p1-11. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+users%22">Computer users</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+circuitry%22">Neural circuitry</searchLink><br /><searchLink fieldCode="DE" term="%22Cosine+function%22">Cosine function</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: User-defined functions (UDFs) are widely used to enhance the capabilities of DBMSs. However, using UDFs comes with a significant performance penalty because DBMSs treat UDFs as black boxes, which hinders their ability to optimize queries that invoke such UDFs. To mitigate this problem, in this paper we present LAMBDA, a technique and framework for improving DBMSs' performance in the presence of UDFs. The core idea of LAMBDA is to statically infer properties of UDFs that facilitate UDF processing. Taking one such property as an example, if DBMSs know that a UDF is pure, that is it returns the same result given the same arguments, they can leverage a cache to avoid repetitive UDF invocations that have the same call arguments. We reframe the problem of analyzing UDF properties as a data flow problem. We tackle the data flow problem by building LAMBDA on top of an extensible abstract interpretation framework and developing an analysis model that is tailored for UDFs. Currently, LAMBDA supports inferring four properties from UDFs that are widely used across DBMSs. We evaluate LAMBDA on a benchmark that is derived from production query workloads and UDFs. Our evaluation results show that (1) LAMBDA conservatively and efficiently infers the considered UDF properties, and (2) inferring such properties improves UDF performance, with a time reduction ranging from 10% to 99%. In addition, when applied to 20 production UDFs, LAMBDA caught five instances in which developers provided incorrect UDF property annotations. We qualitatively compare LAMBDA against Froid, a state-of-the-art framework for improving UDF performance, and explain how LAMBDA can optimize UDFs that are not supported by Froid. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1145/3597503.3639147 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Computer users Type: general – SubjectFull: Computer software Type: general – SubjectFull: Neural circuitry Type: general – SubjectFull: Cosine function Type: general – SubjectFull: Clustering algorithms Type: general Titles: – TitleFull: A Framework For Inferring Properties of User-Defined Functions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Xinyu – PersonEntity: Name: NameFull: Arulraj, Joy – PersonEntity: Name: NameFull: Orso, Alessandro IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2024 Type: published Y: 2024 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
| ResultId | 1 |
