Using Fuzzy Matching of Queries to optimize Database workloads

Directed Acyclic Graphs (DAGs) are commonly used in Databases and Big Data computational engines like Apache Spark for representing the execution plan of queries. We refer to such graphs as Query Directed Acyclic Graphs (QDAGs). This paper uses similarity hashing to arrive at a fingerprint such that...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-07
Main Authors: Singh, Sweta, Kulkarni, Vaibhav, Briggs, Mario, Mahajan, Deepak, Farchi, Eitan
Format: Article
Language:English
Subjects:
Fingerprints
Graph theory
Graphical representations
Graphs
Queries
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Singh, Sweta
Kulkarni, Vaibhav
Briggs, Mario
Mahajan, Deepak
Farchi, Eitan
description Directed Acyclic Graphs (DAGs) are commonly used in Databases and Big Data computational engines like Apache Spark for representing the execution plan of queries. We refer to such graphs as Query Directed Acyclic Graphs (QDAGs). This paper uses similarity hashing to arrive at a fingerprint such that the fingerprint embodies the compute requirements of the query for QDAGs. The fingerprint, thus obtained, can be used to predict the runtime behaviour of a query based on queries executed in the past having similar QDAGs. We discuss two approaches to arrive at a fingerprint, their pros and cons and how aspects of both approaches can be combined to improve the predictions. Using a hybrid approach, we demonstrate that we are able to predict runtime behaviour of a QDAG with more than 80% accuracy.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2689953949</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2689953949</sourcerecordid><originalsourceid>FETCH-proquest_journals_26899539493</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mSwCy3OzEtXcCutqqpU8E0sSc4AcfPTFAJLU4syU4sVSvIV8gtKMnMzq1IVXBJLEpMSi1MVyvOLsnPyE1OKeRhY0xJzilN5oTQ3g7Kba4izh25BUX5haWpxSXxWfmlRHlAq3sjMwtLS1NjSxNKYOFUAvmo4jg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2689953949</pqid></control><display><type>article</type><title>Using Fuzzy Matching of Queries to optimize Database workloads</title><source>Publicly Available Content (ProQuest)</source><creator>Singh, Sweta ; Kulkarni, Vaibhav ; Briggs, Mario ; Mahajan, Deepak ; Farchi, Eitan</creator><creatorcontrib>Singh, Sweta ; Kulkarni, Vaibhav ; Briggs, Mario ; Mahajan, Deepak ; Farchi, Eitan</creatorcontrib><description>Directed Acyclic Graphs (DAGs) are commonly used in Databases and Big Data computational engines like Apache Spark for representing the execution plan of queries. We refer to such graphs as Query Directed Acyclic Graphs (QDAGs). This paper uses similarity hashing to arrive at a fingerprint such that the fingerprint embodies the compute requirements of the query for QDAGs. The fingerprint, thus obtained, can be used to predict the runtime behaviour of a query based on queries executed in the past having similar QDAGs. We discuss two approaches to arrive at a fingerprint, their pros and cons and how aspects of both approaches can be combined to improve the predictions. Using a hybrid approach, we demonstrate that we are able to predict runtime behaviour of a QDAG with more than 80% accuracy.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Fingerprints ; Graph theory ; Graphical representations ; Graphs ; Queries</subject><ispartof>arXiv.org, 2022-07</ispartof><rights>2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2689953949?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25744,37003,44581</link.rule.ids></links><search><creatorcontrib>Singh, Sweta</creatorcontrib><creatorcontrib>Kulkarni, Vaibhav</creatorcontrib><creatorcontrib>Briggs, Mario</creatorcontrib><creatorcontrib>Mahajan, Deepak</creatorcontrib><creatorcontrib>Farchi, Eitan</creatorcontrib><title>Using Fuzzy Matching of Queries to optimize Database workloads</title><title>arXiv.org</title><description>Directed Acyclic Graphs (DAGs) are commonly used in Databases and Big Data computational engines like Apache Spark for representing the execution plan of queries. We refer to such graphs as Query Directed Acyclic Graphs (QDAGs). This paper uses similarity hashing to arrive at a fingerprint such that the fingerprint embodies the compute requirements of the query for QDAGs. The fingerprint, thus obtained, can be used to predict the runtime behaviour of a query based on queries executed in the past having similar QDAGs. We discuss two approaches to arrive at a fingerprint, their pros and cons and how aspects of both approaches can be combined to improve the predictions. Using a hybrid approach, we demonstrate that we are able to predict runtime behaviour of a QDAG with more than 80% accuracy.</description><subject>Fingerprints</subject><subject>Graph theory</subject><subject>Graphical representations</subject><subject>Graphs</subject><subject>Queries</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mSwCy3OzEtXcCutqqpU8E0sSc4AcfPTFAJLU4syU4sVSvIV8gtKMnMzq1IVXBJLEpMSi1MVyvOLsnPyE1OKeRhY0xJzilN5oTQ3g7Kba4izh25BUX5haWpxSXxWfmlRHlAq3sjMwtLS1NjSxNKYOFUAvmo4jg</recordid><startdate>20220714</startdate><enddate>20220714</enddate><creator>Singh, Sweta</creator><creator>Kulkarni, Vaibhav</creator><creator>Briggs, Mario</creator><creator>Mahajan, Deepak</creator><creator>Farchi, Eitan</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20220714</creationdate><title>Using Fuzzy Matching of Queries to optimize Database workloads</title><author>Singh, Sweta ; Kulkarni, Vaibhav ; Briggs, Mario ; Mahajan, Deepak ; Farchi, Eitan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_26899539493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Fingerprints</topic><topic>Graph theory</topic><topic>Graphical representations</topic><topic>Graphs</topic><topic>Queries</topic><toplevel>online_resources</toplevel><creatorcontrib>Singh, Sweta</creatorcontrib><creatorcontrib>Kulkarni, Vaibhav</creatorcontrib><creatorcontrib>Briggs, Mario</creatorcontrib><creatorcontrib>Mahajan, Deepak</creatorcontrib><creatorcontrib>Farchi, Eitan</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Singh, Sweta</au><au>Kulkarni, Vaibhav</au><au>Briggs, Mario</au><au>Mahajan, Deepak</au><au>Farchi, Eitan</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Using Fuzzy Matching of Queries to optimize Database workloads</atitle><jtitle>arXiv.org</jtitle><date>2022-07-14</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>Directed Acyclic Graphs (DAGs) are commonly used in Databases and Big Data computational engines like Apache Spark for representing the execution plan of queries. We refer to such graphs as Query Directed Acyclic Graphs (QDAGs). This paper uses similarity hashing to arrive at a fingerprint such that the fingerprint embodies the compute requirements of the query for QDAGs. The fingerprint, thus obtained, can be used to predict the runtime behaviour of a query based on queries executed in the past having similar QDAGs. We discuss two approaches to arrive at a fingerprint, their pros and cons and how aspects of both approaches can be combined to improve the predictions. Using a hybrid approach, we demonstrate that we are able to predict runtime behaviour of a QDAG with more than 80% accuracy.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2022-07
issn 2331-8422
language eng
recordid cdi_proquest_journals_2689953949
source Publicly Available Content (ProQuest)
subjects Fingerprints
Graph theory
Graphical representations
Graphs
Queries
title Using Fuzzy Matching of Queries to optimize Database workloads
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T23%3A09%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Using%20Fuzzy%20Matching%20of%20Queries%20to%20optimize%20Database%20workloads&rft.jtitle=arXiv.org&rft.au=Singh,%20Sweta&rft.date=2022-07-14&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2689953949%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_26899539493%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2689953949&rft_id=info:pmid/&rfr_iscdi=true