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You could also send an email to the <a href="https://meta.wikimedia.org/wiki/Special:MyLanguage/Stewards" class="extiw" title="m:Special:MyLanguage/Stewards">stewards</a> <a href="https://meta.wikimedia.org/wiki/Special:MyLanguage/VRT" class="extiw" title="m:Special:MyLanguage/VRT">VRT</a> queue at <kbd>stewards@wikimedia.org</kbd> including all above details. </p> </div> <p>Other useful links: <a href="https://meta.wikimedia.org/wiki/Global_blocks" class="extiw" title="m:Global blocks">Global blocks</a> · <a href="/wiki/Help:I_have_been_blocked" title="Help:I have been blocked">Help:I have been blocked</a> </p> </div></li></ul><hr /> <div id="viewsourcetext">You can view and copy the source of this page:</div><textarea readonly="" accesskey="," id="wpTextbox1" cols="80" rows="25" style="" class="mw-editfont-monospace" lang="en" dir="ltr" name="wpTextbox1">==Extending existing graph query languages== The GQL project draws on multiple sources or inputs, notably existing industrial languages and a new section of the SQL standard. In preparatory discussions within WG3 surveys of the history<ref name="GQLs history">{{cite web|url=https://s3.amazonaws.com/artifacts.opencypher.org/website/materials/DM32.2/DM32.2-2018-00085R1-recent_history_of_property_graph_query_languages.pdf|title=''An overview of the recent history of Graph Query Languages''|last=Lindaaker|first=Tobias|date=May 2018|publisher=opencypher.org|access-date=October 6, 2019}}</ref> and comparative content of some of these inputs<ref name="Summary Chart">{{cite web|url=https://s3.amazonaws.com/artifacts.opencypher.org/website/materials/DM32.2/DM32.2-2018-00086r1-summary-chart-of-cypher-pgql-gcore.pdf|title=''Summary Chart of Cypher, PGQL, and G-Core''|last=Plantikow|first=Stefan|date=May 2018|publisher=opencypher.org|access-date=November 3, 2019}}</ref> were presented. GQL is a declarative language with its own distinct syntax, playing a similar role to SQL in the building of a database application. Other graph query languages have been defined which offer direct procedural features such as branching and looping (Apache Tinkerpop's [[Gremlin (query language)|Gremlin]]<ref name="Gremlin 2015">{{cite book|url=https://dl.acm.org/citation.cfm?id=2815073|title= Proceedings of the 15th Symposium on Database Programming Languages|last=Rodriguez|first=Marko A.|chapter= The Gremlin graph traversal machine and language (Invited talk)|date=2015|pages= 1–10|publisher=ACM|doi=10.1145/2815072.2815073|arxiv= 1508.03843|isbn=9781450339025|s2cid=32623848|access-date=November 10, 2019}}</ref>), and GSQL,<ref name="GSQL white paper"/> making it possible to traverse a graph iteratively to perform a class of graph algorithms, but GQL will not directly incorporate such features.<ref name="Wood Graph Query">{{cite journal|url=https://dl.acm.org/ft_gateway.cfm?id=2206879&ftid=1212198&dwn=1&CFID=106456621&CFTOKEN=92daacebb1744396-220C1ECA-F98C-4552-03541423FDE04D18|title= Query languages for graph databases|last=Wood|first=Peter T.|journal=ACM SIGMOD Record|date=25 April 2012|volume=41|issue=1|pages=50–60|publisher=ACM|doi=10.1145/2206869.2206879|s2cid=13537601|access-date=October 25, 2019}}</ref><ref name="GQL foundations">{{cite journal|url=https://dl.acm.org/citation.cfm?id=3104031|title= Foundations of Modern Query Languages for Graph Databases |last=Angles|first=Renzo|journal=ACM Computing Surveys |display-authors=etal|date=September 2017|volume=50 |issue=5 |pages=68:1–40 |publisher=ACM|doi=10.1145/3104031 |arxiv=1610.06264 |s2cid=13526884 |access-date=November 12, 2019}}</ref> However, GQL is envisaged as a specific case of a more general class of graph languages, which share a graph type system and a calling interface for procedures that process graphs. === SQL/PGQ Property Graph Query === Prior work by WG3 and SC32 mirror bodies, particularly in [[International Committee for Information Technology Standards|INCITS]] Data Management (formerly INCITS DM32), has helped to define a new planned Part 16 of the SQL Standard, which allows a read-only graph query to be called inside a SQL SELECT statement, matching a graph pattern using syntax which is very close to Cypher, PGQL and G-CORE, and returning a table of data values as the result. SQL/PGQ also contains DDL to allow SQL tables to be mapped to a graph view schema object with nodes and edges associated to sets of labels and set of data properties.<ref name="SQL Part 16 PGQ">{{cite web|title=ISO/IEC 9075-16 Information technology — Database languages SQL — Part 16: SQL Property Graph Queries (SQL/PGQ)|url=https://www.iso.org/standard/79473.html?browse=tc|access-date=January 7, 2022|publisher=ISO}}</ref><ref name="W3C Berlin SQL and GQL">{{cite web|url=https://www.w3.org/Data/events/data-ws-2019/assets/slides/KeithWHare-2.pdf|title=''SQL and GQL'', W3C Workshop on Web Standardization for Graph Data. Creating Bridges: RDF, Property Graph and SQL.|last=Hare|first=Keith|display-authors=etal|date=March 2019|publisher=W3C|access-date=October 6, 2019}}</ref><ref name="LDBC SQL/PGQ">{{cite web|url=https://ldbcouncil.org/event/twelfth-tuc-meeting/attachments/106233859/111706119.pdf|title=''Property graph extensions for the SQL standard''. LDBC 12th TUC.|last=Trigonakis|first=Vasileios|date=July 2019|publisher=LBDC|access-date=January 7, 2022}}</ref> The GQL project coordinates closely with the SQL/PGQ "project split" of (extension to) ISO 9075 SQL, and the technical working groups in the U.S. (INCITS DM32) and at the international level (SC32/WG3) have several expert contributors who work on both projects.<ref name="W3C Berlin SQL and GQL"/> The GQL project proposal mandates close alignment of SQL/PGQ and GQL, indicating that GQL will in general be a superset of SQL/PGQ. More details about the pattern matching language can be found in the paper "Graph Pattern Matching in GQL and SQL/PGQ"<ref>{{cite arXiv|last1=Deutsch|first1=Alin|last2=Francis|first2=Nadime|last3=Green|first3=Alastair|last4=Hare|first4=Keith|last5=Li|first5=Bei|last6=Libkin|first6=Leonid|last7=Lindaaker|first7=Tobias|last8=Marsault|first8=Victor|last9=Martens|first9=Wim|last10=Michels|first10=Jan|last11=Murlak|first11=Filip|display-authors=10|date=2021-12-12|title=Graph Pattern Matching in GQL and SQL/PGQ|class=cs.DB|eprint=2112.06217}}</ref> <ref>{{Cite book |last1=Deutsch |first1=Alin |last2=Francis |first2=Nadime |last3=Green |first3=Alastair |last4=Hare |first4=Keith |last5=Li |first5=Bei |last6=Libkin |first6=Leonid |last7=Lindaaker |first7=Tobias |last8=Marsault |first8=Victor |last9=Martens |first9=Wim |last10=Michels |first10=Jan |last11=Murlak |first11=Filip |last12=Plantikow |first12=Stefan |last13=Selmer |first13=Petra |last14=van Rest |first14=Oskar |last15=Voigt |first15=Hannes |title=Proceedings of the 2022 International Conference on Management of Data |chapter=Graph Pattern Matching in GQL and SQL/PGQ |date=2022-06-11 |chapter-url=https://doi.org/10.1145/3514221.3526057 |series=SIGMOD '22 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=2246–2258 |doi=10.1145/3514221.3526057 |isbn=978-1-4503-9249-5|s2cid=245124268 |url=https://www.research.ed.ac.uk/en/publications/f391e0a9-cb61-4e88-a553-2842d0a40258 }}</ref> === Cypher === Cypher<ref name="Cypher">{{cite book|url=https://dl.acm.org/citation.cfm?id=3190657|title= Proceedings of the 2018 International Conference on Management of Data|last=Francis|first=Nadime|chapter= Cypher: An Evolving Query Language for Property Graphs|date=27 May 2018|pages=1433–1445|display-authors=etal|publisher=ACM|doi=10.1145/3183713.3190657|isbn=9781450347037|s2cid=13919896|access-date=October 25, 2019}}</ref> is a language originally designed by Andrés Taylor and colleagues at Neo4j Inc., and first implemented by that company in 2011. Since 2015 it has been made available as an open source language description<ref name="Cypher 9">{{cite web|url=https://s3.amazonaws.com/artifacts.opencypher.org/openCypher9.pdf|title=''Cypher Query Language Reference (Version 9)'' |display-authors=etal|publisher=opencypher.org|access-date=November 10, 2019}}</ref> with grammar tooling, a [[Java virtual machine|JVM]] front-end that parses Cypher queries, and a Technology Compatibility Kit (TCK) of over 2000 test scenarios, using [[Cucumber (software)|Cucumber]] for implementation language portability.<ref name="Cypher Resources">{{cite web|url=http://www.opencypher.org/resources|title=''openCypher Resources''|display-authors=etal|publisher=ACM|access-date=November 10, 2019}}</ref> The TCK reflects the language description and an enhancement for temporal datatypes and functions documented in a Cypher Improvement Proposal.<ref name="Date-Time CIP">{{cite web|url=https://github.com/thobe/openCypher/blob/date-time/cip/1.accepted/CIP2015-08-06-date-time.adoc|title=''CIP2015-08-06 - Date and Time''|date=15 May 2019|publisher=opencypher.org|access-date=October 25, 2019}}</ref> Cypher allows creation, reading, updating and deleting of graph elements, and is a language that can therefore be used for analytics engines and transactional databases. ====Querying with visual path patterns==== Cypher uses compact fixed- and variable-length patterns which combine visual representations of node and relationship (edge) topologies, with label existence and property value predicates. (These patterns are usually referred to as "[[ASCII art]]" patterns, and arose originally as a way of commenting programs which used a lower-level graph API.<ref name="GQLs history"/>) By matching such a pattern against graph data elements, a query can extract references to nodes, relationships and paths of interest. Those references are emitted as a "binding table" where column names are bound to a multiset of graph elements. The name of a column becomes the name of a "binding variable", whose value is a specific graph element reference for each row of the table. For example, a pattern &nbsp;{{code|MATCH (p:Person)-[:LIVES_IN]->(c:City)}}&nbsp; will generate a two-column output table. The first column named &nbsp;{{code|p}}&nbsp; will contain references to nodes with a label &nbsp;{{code|Person}}&nbsp;. The second column named &nbsp;{{code|c}}&nbsp; will contain references to nodes with a label &nbsp;{{code|City}}&nbsp;, denoting the city where the person lives. The binding variables &nbsp;{{code|p}}&nbsp; and &nbsp;{{code|c}}&nbsp; can then be dereferenced to obtain access to property values associated with the elements referred to by a variable. The example query might be terminated with a &nbsp;{{code|RETURN}}, resulting in a complete query like this: <syntaxhighlight lang="cypher">MATCH (p:Person)-[:LIVES_IN]->(c:City) RETURN p.first_name, p.last_name, c.name, c.state</syntaxhighlight> This would result in a final four-column table listing the names of the residents of the cities stored in the graph. Pattern-based queries are able to express joins, by combining multiple patterns which use the same binding variable to express a natural join using the &nbsp;{{code|MATCH}}&nbsp; clause: <syntaxhighlight lang="cypher">MATCH (p:Person)-[:LIVES_IN]->(c:City), (p:Person)-[:NATIONAL_OF]->(EUCountry) RETURN p.first_name, p.last_name, c.name, c.state</syntaxhighlight> This query would return the residential location only of EU nationals. An outer join can be expressed by &nbsp;{{code|MATCH ... OPTIONAL MATCH}}&nbsp;: <syntaxhighlight lang="cypher">MATCH (p:Person)-[:LIVES_IN]->(c:City) OPTIONAL MATCH (p:Person)-[:NATIONAL_OF]->(ec:EUCountry) RETURN p.first_name, p.last_name, c.name, c.state, ec.name</syntaxhighlight> This query would return the city of residence of each person in the graph with residential information, and, if an EU national, which country they come from. Queries are therefore able to first project a sub-graph of the graph input into the query, and then extract the data values associated with that subgraph. Data values can also be processed by functions, including aggregation functions, leading to the projection of computed values which render the information held in the projected graph in various ways. Following the lead of G-CORE and Morpheus, GQL aims to project the sub-graphs defined by matching patterns (and graphs then computed over those sub-graphs) as new graphs to be returned by a query. Patterns of this kind have become pervasive in property graph query languages, and are the basis for the advanced pattern sub-language being defined in SQL/PGQ, which is likely to become a subset of the GQL language. Cypher also uses patterns for insertion and modification clauses (&nbsp;{{code|CREATE}}&nbsp; and &nbsp;{{code|MERGE}}&nbsp;), and proposals have been made in the GQL project for collecting node and edge patterns to describe graph types. ====Cypher 9 and Cypher 10==== The current version of Cypher (including the temporal extension) is referred to as Cypher 9. Prior to the GQL project it was planned to create a new version, Cypher 10 ['''REF HEADING BELOW'''], that would incorporate features like schema and composable graph queries and views. The first designs for Cypher 10, including graph construction and projection, were implemented in the Cypher for Apache Spark project starting in 2016.<ref name="CAPS Morpheus">{{cite web|url=https://github.com/opencypher/morpheus|title=''Morpheus brings the leading graph query language, Cypher, onto the leading distributed processing platform, Spark.''|last=Rydberg|first=Mats|display-authors=etal|date=July 2016|publisher=openCypher|access-date=November 3, 2019}}</ref> === PGQL === PGQL<ref name="PGQL Grades">{{cite book|chapter-url=https://dl.acm.org/citation.cfm?id=2960421/ |last=van Rest|first=Oskar|title=Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems |chapter=PGQL: A property graph query language |display-authors=etal|date=June 2016|pages=1–6 |publisher=ACM|doi=10.1145/2960414.2960421|isbn=978-1-4503-4780-8 |s2cid=6806901|access-date=October 25, 2019}}</ref> is a language designed and implemented by Oracle Inc., but made available as an open source specification,<ref name="PGQL spec">{{cite web|url=http://pgql-lang.org/|title=PGQL|publisher=pgql.org|access-date=October 6, 2019}}</ref> along with JVM parsing software.<ref name="PGQL parser">{{cite web|url=https://github.com/oracle/pgql-lang|title=''PGQL is an SQL-based query language for the Property Graph data model''. |last=van Rest|first=Oskar|display-authors=etal|date=September 2015|publisher=pgql.org|access-date=November 3, 2019}}</ref> PGQL combines familiar SQL SELECT syntax including SQL expressions and result ordering and aggregation with a pattern matching language very similar to that of Cypher. It allows the specification of the graph to be queried, and includes a facility for macros to capture "pattern views", or named sub-patterns. It does not support insertion or updating operations, having been designed primarily for an analytics environment, such as Oracle's PGX product. PGQL has also been implemented in Oracle Big Data Spatial and Graph, and in a research project, PGX.D/Async.<ref name="PGQL PGX.D">{{cite book|chapter-url=https://dl.acm.org/citation.cfm?doid=3078447.3078454/ |last=Roth|first=Nicholas P.|title=Proceedings of the Fifth International Workshop on Graph Data-management Experiences & Systems |chapter=PGX.D/Async: A Scalable Distributed Graph Pattern Matching Engine |display-authors=etal|date=2017|pages=1–6 |publisher=ACM|doi=10.1145/3078447.3078454|isbn=978-1-4503-5038-9 |s2cid=26283328|access-date=October 29, 2019}}</ref> === G-CORE=== G-CORE is a research language designed by a group of academic and industrial researchers and language designers which draws on features of Cypher, PGQL and [[SPARQL]].<ref name="G-CORE">{{cite book|chapter-url=https://dl.acm.org/citation.cfm?id=3190654/ |last=Angles|first=Renzo|title=Proceedings of the 2018 International Conference on Management of Data |chapter=G-CORE: A Core for Future Graph Query Languages |display-authors=etal|date=2018|pages=1421–1432 |publisher=ACM|doi=10.1145/3183713.3190654|isbn=978-1-4503-4703-7 |s2cid=4623760|url=https://ir.cwi.nl/pub/27560 |access-date=November 9, 2019}}</ref><ref name="G-CORE summary">{{cite web|url=https://dl.acm.org/citation.cfm?id=3190654/|title=''G-CORE: The LDBC Graph Query Language Proposal''. In archives of FOSDEM 2018.|last=Voigt|first=Hannes|date=February 2018|access-date=November 12, 2019}}</ref> The project was conducted under the auspices of the Linked Data Benchmark Council (LDBC), starting with the formation of a Graph Query Language task force in late 2015, with the bulk of the work of paper writing occurring in 2017. G-CORE is a composable language which is closed over graphs: graph inputs are processed to create a graph output, using graph projections and graph set operations to construct the new graph. G-CORE queries are pure functions over graphs, having no side effects, which mean that the language does not define operations which mutate (update or delete) stored data. G-CORE introduces views (named queries). It also incorporates paths as elements in a graph ("paths as first class citizens"), which can be queried independently of projected paths (which are computed at query time over node and edge elements). G-CORE has been partially implemented in open-source research projects in the LDBC GitHub organization.<ref name="G-CORE parser">{{cite web|url=https://github.com/ldbc/ldbc_gcore_parser|title=''G-CORE Grammar and Parser''|last=van Rest|first=Oskar|date=2017|publisher=LDBC|access-date=November 12, 2019}}</ref><ref name="G-CORE project">{{cite web|url=https://homepages.cwi.nl/~boncz/msc/2018-GeorgianaCiocirdel.pdf|title=''A G-CORE (Graph Query Language) Interpreter'', Master's Thesis in Parallel and Distributed Computer Systems, CWI and Vrije Universiteit Amsterdam|last=Ciocîrdel|first=Georgiana Diana|date=2018|publisher=CWI|access-date=November 12, 2019}}</ref><ref name="G-CORE interpreter">{{cite web|url=https://github.com/ldbc/gcore-spark|title=''G-CORE interpreter on Spark''|last1=Ciocîrdel|first1=Georgiana Diana|last2=Boncz|first2=Peter|date=2017|publisher=LDBC|access-date=November 12, 2019}}</ref> === GSQL === GSQL<ref name="GSQL white paper">{{cite web|url=https://info.tigergraph.com/gsql|title=''GSQL: An SQL-Inspired Graph Query Language''|last1=Wu|first1=Mingxi|last2=Deutsch|first2=Alin|access-date=November 9, 2019}}</ref> is a language designed for TigerGraph Inc.'s proprietary graph database. Since October 2018 TigerGraph language designers have been promoting and working on the GQL project. GSQL is a Turing-complete language that incorporates procedural flow control and iteration, and a facility for gathering and modifying computed values associated with a program execution for the whole graph or for elements of a graph called accumulators. These features are designed to enable iterative graph computations to be combined with data exploration and retrieval. GSQL graphs must be described by a schema of vertexes and edges, which constrains all insertions and updates. This schema therefore has the closed world property of an SQL schema, and this aspect of GSQL (also reflected in design proposals deriving from the Morpheus project<ref name="PGS">{{cite web|url=https://s3.amazonaws.com/artifacts.opencypher.org/website/materials/sql-pg-2018-0056r1-Property-Graph-Schema.pdf|title=''Property Graph Schema'', ANSI INCITS DM32.2 SQL Property Graph Extensions Ad Hoc submission ''sql-pg-2018-0056r1'', Neo4j Query Languages Standards and Research Team|last1=Voigt|first1=Hannes|last2=Selmer|first2=Petra|last3=Lindaaker|first3=Tobias|last4=Plantikow|first4=Stefan|last5=Green|first5=Alastair|last6=Furniss|first6=Peter|date=December 2018|publisher=openCypher.org|access-date=November 12, 2019}}</ref>) is proposed as an important optional feature of GQL. Vertexes and edges are named schema objects which contain data but also define an imputed type, much as SQL tables are data containers, with an associated implicit row type. GSQL graphs are then composed from these vertex and edge sets, and multiple named graphs can include the same vertex or edge set. GSQL has developed new features since its release in September 2017,<ref name="GSQL 1.0">{{cite web|url=https://doc-archive.tigergraph.com/1.0/GSQL-Language-Reference-Part-1---Defining-Graphs-and-Loading-Data.html|title=''GSQL documentation Tigergraph 1.0''.|date=2017|access-date=November 9, 2019}}</ref> most notably introducing variable-length edge pattern matching<ref name="GSQL patterns">{{cite web|url=https://docs.tigergraph.com/v/2.4/release-notes-change-log/release-notes-tigergraph-2.4|title=''Pattern Matching'', TigerGraph 2.4 Release Notes.|date=June 2019|access-date=November 9, 2019}}</ref> using a syntax related to that seen in Cypher, PGQL and SQL/PGQ, but also close in style to the fixed-length patterns offered by Microsoft SQL/Server Graph<ref name="SQLServer Graph">{{cite web|url=https://docs.microsoft.com/en-us/sql/relational-databases/graphs/sql-graph-overview?view=sql-server-ver15#query-language-extensions|title=''Query language extensions'', Graph processing with SQL Server and Azure SQL Database|display-authors=etal|date=2017|publisher=Microsoft Inc.|access-date=November 10, 2019}}</ref> GSQL also supports the concept of Multigraphs <ref name="GSQL multigraphs">{{cite web|url=https://docs-legacy.tigergraph.com/tigergraph-platform-overview/multigraph-overview|title=''Multigraphs'', TigerGraph Online Documentation|date=June 2019|access-date=January 7, 2022}}</ref> which allow subsets of a graph to have role-based access control. Multigraphs are important for enterprise-scale graphs that need fine-grain access control for different users. === Morpheus: multiple graphs and composable graph queries in Apache Spark === The opencypher Morpheus project<ref name="CAPS Morpheus"/> implements Cypher for Apache Spark users. Commencing in 2016, this project originally ran alongside three related efforts, in which Morpheus designers also took part: SQL/PGQ, G-CORE and design of Cypher extensions for querying and constructing multiple graphs.<ref name="MGCQ CIP">{{cite web|url=https://github.com/boggle/openCypher/blob/CIP2017-06-18-multiple-graphs/cip/1.accepted/CIP2017-06-18-multiple-graphs.adoc|title=''CIP2017-06-18 Querying and constructing multiple graphs''|last1=Taylor|first1=Andrés|last2=Plantikow|first2=Stefan|last3=Selmer|first3=Petra|date=2017–2018|publisher=opencypher.org|access-date=November 12, 2019}}</ref> The Morpheus project acted as a testbed for extensions to Cypher (known as "Cypher 10") in the two areas of graph DDL and query language extensions. Graph DDL features include<ref name="CAPS oCIM V">{{cite web|url=https://s3.amazonaws.com/artifacts.opencypher.org/website/ocim5/slides/ocim5+-+CAPS+%2B+GraphDDL.pdf|title=''Multiple graphs and composable queries in Cypher for Apache Spark''. openCypher Implementers Meeting V, Berlin.|last=Kiessling|first=Max|date=2019|publisher=opencypher.org|access-date=November 9, 2019}}</ref> #definition of property graph views over [[Java Database Connectivity|JDBC]]-connected SQL tables and Spark DataFrames<ref name="EXTENDS element type">{{cite web|url=https://github.com/tobias-johansson/graphddl-example-ldbc/|title=''graphddl-example-ldbc: A cypher-for-apache-spark example showing the use of SqlPropertyGraphSource and GraphDDL to provide a property graph view of a SQL dataset''.|last=Johanssen|first=Tobias|website=[[GitHub]]|display-authors=etal|date=2019|access-date=November 9, 2019}}</ref> #definition of graph schemas or types defined by assembling node type and edge type patterns, with subtyping<ref name="EXTENDS element type"/> #constraining the content of a graph by a closed or fixed schema #creating catalog entries for multiple named graphs in a hierarchically organized catalog #graph data sources to form a federated, heterogeneous catalog #creating catalog entries for named queries (views) Graph query language extensions include<ref name="CAPS oCIM V"/> #graph union #projection of graphs computed from the results of pattern matches on multiple input graphs #support for tables (Spark DataFrames) as inputs to queries ("driving tables") #views which accept named or projected graphs as parameters. These features have been proposed as inputs to the standardization of property graph query languages in the GQL project. </textarea><div class="templatesUsed"></div><p id="mw-returnto">Return to <a href="/wiki/Graph_Query_Language" title="Graph Query Language">Graph Query Language</a>.</p> <noscript><img src="https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1" alt="" width="1" height="1" style="border: none; position: absolute;"></noscript> <div class="printfooter" data-nosnippet="">Retrieved from "<a dir="ltr" href="https://en.wikipedia.org/wiki/Graph_Query_Language">https://en.wikipedia.org/wiki/Graph_Query_Language</a>"</div></div> <div id="catlinks" class="catlinks catlinks-allhidden" data-mw="interface"></div> </div> </main> </div> <div class="mw-footer-container"> <footer id="footer" class="mw-footer" > <ul id="footer-info"> </ul> <ul id="footer-places"> <li id="footer-places-privacy"><a href="https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Privacy_policy">Privacy policy</a></li> <li id="footer-places-about"><a href="/wiki/Wikipedia:About">About Wikipedia</a></li> <li id="footer-places-disclaimers"><a href="/wiki/Wikipedia:General_disclaimer">Disclaimers</a></li> <li id="footer-places-contact"><a href="//en.wikipedia.org/wiki/Wikipedia:Contact_us">Contact Wikipedia</a></li> <li id="footer-places-wm-codeofconduct"><a href="https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Universal_Code_of_Conduct">Code of Conduct</a></li> <li id="footer-places-developers"><a href="https://developer.wikimedia.org">Developers</a></li> <li id="footer-places-statslink"><a href="https://stats.wikimedia.org/#/en.wikipedia.org">Statistics</a></li> <li id="footer-places-cookiestatement"><a href="https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Cookie_statement">Cookie statement</a></li> <li id="footer-places-mobileview"><a href="//en.m.wikipedia.org/w/index.php?title=Graph_Query_Language&action=edit&section=9&mobileaction=toggle_view_mobile" class="noprint stopMobileRedirectToggle">Mobile view</a></li> </ul> <ul id="footer-icons" class="noprint"> <li id="footer-copyrightico"><a href="https://wikimediafoundation.org/" class="cdx-button cdx-button--fake-button cdx-button--size-large cdx-button--fake-button--enabled"><img src="/static/images/footer/wikimedia-button.svg" width="84" height="29" alt="Wikimedia Foundation" loading="lazy"></a></li> <li id="footer-poweredbyico"><a href="https://www.mediawiki.org/" class="cdx-button cdx-button--fake-button cdx-button--size-large cdx-button--fake-button--enabled"><img src="/w/resources/assets/poweredby_mediawiki.svg" alt="Powered by MediaWiki" width="88" height="31" loading="lazy"></a></li> </ul> </footer> </div> </div> </div> <div class="vector-settings" id="p-dock-bottom"> <ul></ul> </div><script>(RLQ=window.RLQ||[]).push(function(){mw.config.set({"wgHostname":"mw-web.codfw.main-5cd4cd96d5-bnbx9","wgBackendResponseTime":239,"wgPageParseReport":{"limitreport":{"cputime":"0.059","walltime":"0.085","ppvisitednodes":{"value":418,"limit":1000000},"postexpandincludesize":{"value":17740,"limit":2097152},"templateargumentsize":{"value":6556,"limit":2097152},"expansiondepth":{"value":9,"limit":100},"expensivefunctioncount":{"value":0,"limit":500},"unstrip-depth":{"value":0,"limit":20},"unstrip-size":{"value":469,"limit":5000000},"entityaccesscount":{"value":0,"limit":400},"timingprofile":["100.00% 62.762 1 -total"," 99.87% 62.683 2 Template:Blocked_text"," 52.00% 32.638 2 Template:Replace"," 34.13% 21.421 1 Template:Colocationwebhost"," 31.24% 19.606 1 Template:Hidden"," 15.20% 9.540 1 Template:Hidden_begin"," 11.52% 7.231 1 Template:Tlx"," 1.78% 1.120 1 Template:Hidden_end"," 1.75% 1.099 1 MediaWiki:Wikimedia-globalblocking-blockedtext-mistake"," 1.43% 0.900 1 MediaWiki:Wikimedia-globalblocking-blockedtext-mistake-email-steward"]},"scribunto":{"limitreport-timeusage":{"value":"0.010","limit":"10.000"},"limitreport-memusage":{"value":1043316,"limit":52428800}},"cachereport":{"origin":"mw-web.codfw.main-5cd4cd96d5-bnbx9","timestamp":"20241127070132","ttl":2592000,"transientcontent":false}}});});</script> </body> </html>