Memgraph supports most of the commonly used constructs of the language. The reference guide contains the details of implemented features. Additionally, not yet supported features of the language are listed.
Cypher is a declarative graph query language that allows for expressive and efficient data querying in a property graph. It aims to be intuitive and easy to learn while providing a powerful interface for working with graph-based data.
Memgraph can be configured by editing the Memgraph configuration file or by including another configuration file.
An index stores additional information on certain types of data, so that retrieving said data becomes more efficient.
Memgraph comes with custom-built algorithms that are implemented using C++: Filtering variable-length paths, Breadth-first search, and Weighted shortest path.
Memgraph supports extending the query language with user-written procedures. These procedures are grouped into modules, which can then be loaded either on startup or later on.
Memgraph includes a set of Python query modules based on the NetworkX library of algorithms. You can find more information about all the available algorithms in the
Memgraph TensorFlow op wraps the high-performance Memgraph client for use with TensorFlow, allowing natural data transfer between Memgraph and TensorFlow at any point of the model.
Memgraph comes with the option of granting, denying, or revoking a certain set of privileges to users or groups of users.
Memgraph supports authentication and (optional) authorization using a custom-built external auth module. To learn more visit:
Memgraph supports all query audit logging. When enabled, the audit log contains records of all queries executed on the database.