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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 tools listed here support emulating [1] or simulating APIs and software systems.They are also called [2] API mocking tools, service virtualization tools, over the wire test doubles and tools for stubbing and mocking HTTP(S) and other protocols. [1]
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.
The following examples of Gremlin queries and responses in a Gremlin-Groovy environment are relative to a graph representation of the MovieLens dataset. [4] The dataset includes users who rate movies. Users each have one occupation, and each movie has one or more categories associated with it. The MovieLens graph schema is detailed below.
Weak mutation testing (or weak mutation coverage) requires that only the first and second conditions are satisfied. Strong mutation testing requires that all three conditions are satisfied. Strong mutation is more powerful, since it ensures that the test suite can really catch the problems. Weak mutation is closely related to code coverage ...
A GraphML file consists of an XML file containing a graph element, within which is an unordered sequence of node and edge elements. Each node element should have a distinct id attribute, and each edge element has source and target attributes that identify the endpoints of an edge by having the same value as the id attributes of those endpoints.
The pub/sub pattern scales well for small networks with a small number of publisher and subscriber nodes and low message volume. However, as the number of nodes and messages grows, the likelihood of instabilities increases, limiting the maximum scalability of a pub/sub network. Example throughput instabilities at large scales include:
In the above example, the application might supply the values "bike" for the first parameter and "10900" for the second parameter, and then later the values "shoes" and "7400". The alternative to a prepared statement is calling SQL directly from the application source code in a way that combines code and data.