Memgraph 1.0 is now publicly available and production-ready!
Our engineering team has been hard at work for the past three years to ensure that Memgraph maintains the highest standards in terms of performance, reliability, and ease of use. With a great deal of feedback from our users and clients, Memgraph 1.0 comes with the following key features:
Optimized storage engine and reduced memory footprint – We overhauled our storage engine and rewritten it from scratch which resulted in a 1.4x to 14x reduction in RAM usage and an average 2x - 3x speedup for read and write queries.
Python Query Module – Users can now write imperative procedures and extend the existing feature set. The most performant way to implement procedures remains the Memgraph C Query Module API. However, for faster development and iterations we have released a Python Query Module API. By embedding a Python interpreter within the Memgraph process, data scientists can easily leverage libraries like NetworkX to analyze the data stored inside Memgraph.
CSV import tool – If you are already familiar with the Neo4j import tools, then using the Memgraph import tool should be easy. The CSV import tool is fully compatible with the Neo4j CSV format. If you already have a pipeline set-up for Neo4j, you should be able to easily import data by using “mg_import_csv”.
Tensorflow integration (Beta) – In another effort to make life easier for data scientists and machine learning developers, we have developed Memgraph to enable easier development and production serving of your machine learning models based on graph data by allowing you to query Memgraph directly from TensorFlow. The TensorFlow op wraps the high-performance Memgraph client for use with TensorFlow, allowing natural data transfer between Memgraph and TensorFlow.
As always, if you have any questions or feedback, please feel free to post on these forums.
Thank you for your support and happy coding!
The Memgrpah team.