Memgraph VS gqlalchemy

Memgraph | Staff C++ Database Engineer | REMOTE (Central/Western Europe, LatAm, or North America) https://memgraph.com/

Memgraph is a Seed stage, open source graph database vendor. Graph DBs are a great solution for GenAI, logistics, cybersecurity and fintech so we are looking to grow aggressively this year.

We're looking for a staff-level engineer to set technical direction, mentor junior team members, and solve some very difficult problems.

Either DM me (the hiring manager) or apply here: https://join.com/companies/memgraph/10684850-staff-software-...

It's not possible to escape tradeoffs. To deal with tradeoffs, focus is important. API to tradeoffs is also important.

I bet somebody will raise a similar question in a few years time when the list under https://db-engines.com/en/ranking/graph+dbms will be bigger.

DISCLAIMER: Coming from https://github.com/memgraph/memgraph

Albeit the significant engineering endeavor, the larger-than-memory architecture is a super valuable asset to Memgraph users since it allows them to store large amounts of data cheaply on disk without sacrificing the performance of in-memory computation. We are actively working on resolving issues introduced with the new storage mode, so feel free to ask, open an issue, or pull a request. We will be more than happy to help. Until next time 🫡

NoSQL databases are non-relational databases with flexible schema designed for high performance at a massive scale. Unlike traditional relational databases, which use tables and predefined schemas, NoSQL databases use a variety of data models. There are 4 main types of NoSQL databases - document, graph, key-value, and column-oriented databases. NoSQL databases generally are well-suited for unstructured data, large-scale applications, and agile development processes. The most popular examples of NoSQL databases are MongoDB (document), Memgraph (graph), Redis (key-value store) and Apache HBase (column-oriented).

Whether it's about identifying similar user profiles in a social network, detecting similar patterns in a communication network, or classifying nodes in a semantic network, cosine similarity contributes valuable insights. Combined with a powerful graph database system, such as Memgraph, it gives a better understanding of complex networks. Memgraph is an open-source in-memory graph database built to handle real-time use cases at an enterprise scale. Memgraph supports strongly-consistent ACID transactions and uses the standardized Cypher query language for structuring, manipulating, and exploring data.

It should be noted this article is on the blog of a project which advertises itself as open source, under a BSL license that puts limitations on distribution and use.

At the moment, benchgraph is a project under Memgraph repository (previously Mgbench). It consists of Python scripts and a C++ client. Python scripts are used to manage the benchmark execution by preparing the workload, configurations, and so on, while the C++ client actually executes the benchmark.

These five steps will result in something similar to this simplified version of demo.py example:

Take a look at this blog post about choosing the optimal index. It focuses on Memgraph graph database but it offers a theoretical background that is not vendor related.

In this tutorial, we will utilize betweenness centrality for identifying essential proteins. For this task, we are using Memgraph, a graph analytics platform, which can perform complex graph analysis on all sorts of networks. Even though we will use betweenness centrality, other graph algorithms can also be applied to the protein-protein interaction network, such as other centrality measures or the PageRank algorithm.

As already mentioned, link prediction refers to the task of predicting missing links or links that are likely to occur in the future. In this tutorial, we will make use the of MAGE spell called node2vec. Also, we will use Memgraph to store data, and gqlalchemy to connect from a Python application. The dataset will be similar to the one used in this paper: Graph Embedding Techniques, Applications, and Performance: A Survey.

For any other service provider, it is possible to implement your custom importer class, here's how. Don't forget that GQLAlchemy is an open source project, so you can submit your extended functionality on our GitHub repository.

If you think there is something crucial that is missing or are even willing the try out your expertise in Python and graphs, check out our GitHub repository and feel free to contribute.

gqlalchemy – a Python driver and object graph mapper (OGM)

There is a broad number of drivers in many different programming languages available for both solutions. While Memgraph only maintains a few in-house drivers that it develops and supports (C, C++, Python, Rust), most Neo4j drivers can also be used with Memgraph. This is due to the fact that both solutions use the Bolt protocol, labeled property graph model and Cypher query language.

Memgraph natively has several methods of data import - import from files, MySQL or PostgreSQL, and data streams. Memgraph is also highly extendable, and with the help of its Python client, GQLAlchemy, you can import data from almost anywhere.

Source code for GQLAlchemy is available at GitHub repo.

I think building your graph database/structure can be quite an engineering and time-consuming challenge, as you mentioned, which I would personally avoid. I believe there are some solutions out there that may help you. There is one open source solution for the requirements and concerns you are mentioning. It checks out most of the things you need, functionality, efficiency, and custom low-level optimizations and it is not bulky as the Neo4j Java backend. In essence, we have built Memgraph an in-memory graph database written in C++. The distinctive key feature of DB is that all the data is stored in RAM for fast queries. There is some cool stuff with ML for graphs. Take a look at this blog post about node embedding and recommendation engines, it is native integration with Python and uses PyTorch. There is also the MAGE library for graph algorithms and ML, it is also open-sourced, which is great news for customization and expansions. I share your thoughts on OpenCypher, as being an issue. Memgraph has an object graph mapper (similar to ORM), called GQLAlchemy, and is in Python. There is also a learning curve, but not a different new skill as Cypher. The good thing is allowed various features for graphs manipulation via Python. There are also some other solutions such TigerGraph, Nebula, etc. But I am not very familiar with them. Feel free to explore. I hope this helps! 😁

Using gqlalchemy we are trying to connect to Memgraph, just like we have done before in our backend.

As expected, Flask is there, so it will be installed in our container. Next, we have pymgclient, Memgraph database adapter for Python language on top of which gqlalchemy is built. We will connect to the database with gqlalchemy and it will assist us in writing and running queries on Memgraph.