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For example, a pattern MATCH (p:Person)-[:LIVES_IN]->(c:City) will generate a two-column output table. The first column named p will contain references to nodes with a label Person . The second column named c will contain references to nodes with a label City , denoting the city where the person lives.
Cypher is a declarative graph query language that allows for expressive and efficient data querying in a property graph. [1]Cypher was largely an invention of Andrés Taylor while working for Neo4j, Inc. (formerly Neo Technology) in 2011. [2]
GraphQL is a data query and manipulation language that allows specifying what data is to be retrieved ("declarative data fetching") or modified. A GraphQL server can process a client query using data from separate sources and present the results in a unified graph. [2] The language is not tied to any specific database or storage engine.
Gremlin is a graph traversal language and virtual machine developed by Apache TinkerPop of the Apache Software Foundation.Gremlin works for both OLTP-based graph databases as well as OLAP-based graph processors.
GraphQL: an open-source data query and manipulation language for APIs. Dgraph implements modified GraphQL language called DQL (formerly GraphQL+-) Gremlin: a graph programming language that is a part of Apache TinkerPop open-source project [49] SPARQL: a query language for RDF databases that can retrieve and manipulate data stored in RDF format
Query by Example (QBE) is a database query language for relational databases. It was devised by Moshé M. Zloof at IBM Research during the mid-1970s, in parallel to the development of SQL . [ 1 ] It is the first graphical query language, using visual tables where the user would enter commands, example elements and conditions.
Both the C99 and C++11 standards require at least one argument, but since C++20 this limitation has been lifted through the __VA_OPT__ functional macro. The __VA_OPT__ macro is replaced by its argument when arguments are present, and omitted otherwise. Common compilers also permit passing zero arguments before this addition, however.
The magic sets algorithm takes a Datalog program and a query, and produces a more efficient program that computes the same answer to the query while still using bottom-up evaluation. [23] A variant of the magic sets algorithm has been shown to produce programs that, when evaluated using semi-naïve evaluation, are as efficient as top-down ...