Query a graph database by writing objects in Python which GQLAlchemy automatically translates into Cypher queries.
Cypher works well for small projects, but the boilerplate CRUD operations can get tedious on large scales without the right OGM.
GQLAlchemy is free to use, distribute, and modify. Contribute and make linking between graph database objects and Python objects better for everyone.
GQLAlchemy was created as part of Memgraph — an open source platform for graph computation on streaming data that includes a suite of ecosystem tools like Lab for graph visualisation and MAGE, a library of graph algorithms, and more.
Define nodes and relationships from your data model as Python classes. GQLAlchemy will automatically serialize and deserialize the needed objects.
While graph databases are by design schemaless, GQLAlchemy provides you with the tools of defining and enforcing a strict schema of the graph data model.
GQLAlchemy offers the option of storing some properties on disk instead of in memory.
GQLAlchemy offers the option of implementing custom data loaders from different sources and various formats such as Parquet, CSV, ORC, etc.
Use standard Python syntax to traverse graphs by constructing queries with the GQLAlchemy query builder.
You can start, stop, connect to and monitor Memgraph instances with GQLAlchemy.
You can create and start Kafka or Pulsar streams using GQLAlchemy to ingest data from various real-time sources.
GQLAlchemy implements a simple interface for maintaining database triggers on CREATE, UPDATE and DELETE operations.
This is a tutorial where you can learn how to build a small part of the Twitch analytics app with the help of GQLAlchemy. Our OGM and graph database make building graph-based apps much easier.