You must be wondering what GQLAlchemy is? In short, GQLAlchemy is a fully open source Python library developed to support writing and running queries on Memgraph and other Cypher & Bolt compatible graph databases. As an Object Graph Mapper (OGM), the main goal of GQLAlchemy is to simplify the creation of graph applications for Python developers.
Wondering what’s under the hood? Then head over to the GitHub repository don’t hesitate to give a star! ☆ If you want to try GQLAlchemy right now, head over to the Quick start and learn how to use it.
The rest of the article will explain in more detail what an OGM and GQLAlchemy is.
Object Graph Mapper or OGM provides a developer-friendly workflow that allows object-oriented notation when communicating with a graph database.
Instead of writing Cypher queries, you can write object-oriented code that OGM will automatically translate into Cypher queries.
GQLAlchemy is a fully open-source Python library developed to assist in writing and running queries on Memgraph and other Cypher & Bolt-compatible graph databases.
As a Python developer, you will be able to:
Release lead software engineer Mislav wrote more about GQLAlchemy in the launch announcement 🚀
OK, so everything so far caught your attention and you are wondering how an OGM works and how it can simplify your workflow? Then check out the GQLAlchemy 1.1 demo that we published on our YouTube channel.
Here are the highlights of the demo:👇
Our demo moderators, Katarina, Ivan and Mislav, have also written three tutorials on using GQLAlchemy. Here are their links and short descriptions:
In this tutorial, you will learn how to create a small part of the Twitch analytics app using GQLAlchemy and how it greatly simplifies the creation of graph-based apps.
Learn how to create and manage Memgraph data streams and database triggers using only Python and GQLAlchemy, without learning Cypher query language.
Since Memgraph is a graph database that stores data only in memory, the GQLAlchemy library provides an on-disk storage solution for large properties not used in graph algorithms. Read how it works in the article.
To try out GQLAlchemy, follow the GitHub installation instructions. Drop us a pull request if you feel inspired and want to help us enhance GQLAlchemy further.
Our growing community of Python graph developers is here to help unlock a whole new world of graph-based applications. Engage in meaningful and valuable conversations with other Memgraph developers and the Memgraph team. We are all here with the same goal - building world-class graph applications.
If you’d like to take Memgraph for a spin, you can download it for free.