Neo4j vs Memgraph - How to Choose a Graph Database?
In the face of the intensifying race between graph and relational databases, graph databases are gaining traction across a variety of applications and industry sectors. From fraud detection and supply chain optimization to machine learning and artificial intelligence, graphs enable developers to build and scale a new generation of applications centered around network analysis.
In this article, we will compare two leading graph databases, Memgraph and Neo4j graph database, to help you choose the best platform for your needs. Although comparisons usually focus on performance benchmarks, there are many other crucial factors to consider when choosing a database for your business. With that in mind, we'll start by looking at some of the differentiating elements between Memgraph and Neo4j graph databases.
Neo4j Graph Database
Neo4j is an ACID-compliant transactional native graph storage and database. It is a disk-based system mainly implemented in Java and has been publicly available since 2007. Fairly enough, the Neo4j graph database is the most widely utilized graph technology out there - one of the pioneers in the space.
Memgraph
Memgraph is an open-source graph database built and battle-tested for real-time streaming that is also compatible with Neo4j. This transactional native graph database is powered by high-performance and ACID compliance. Memgraph is engineered from the ground-up leveraging an in-memory first, durable, and redundant architecture. Its C/C++ implementation delivers the unique capability of supporting both transactional and analytical workloads. You can download its community edition, get a quote for the enterprise version or start your free trial for the Memgraph cloud service with just a few clicks.
Read more:
- Memgraph vs. Neo4j: Benchmark Performance Comparison
- Memgraph Key Advantages over Neo4j
- Memgraph Benchmark Results
Differences Between Neo4j and Memgraph Graph Databases
| Feature | Neo4j | Memgraph |
|---|---|---|
| Initial release | 2007 | 2017 |
| License | AGPLv3 / Commercial | BSL / Apache 2 / Commercial |
| Written in | Java | C++ |
| Data model | Labeled Property Graph | Labeled Property Graph |
| Data storage | On-disk | In memory |
| Source code | GitHub | GitHub |
| Hosted Cloud service | Neo4j Aura | Memgraph Cloud |
Both solutions have been around for some time now. While Neo4j has a longer track record, Memgraph has the benefit of being implemented in C++. This makes Memgraph a more optimized and more performant in-memory storage engine compared to Neo4j, which implements a traditional on-disk storage solution.
On-disk vs in-memory storage
Even though there are many differences between on-disk and in-memory, the choice primarily depends on your use case and your requirements. The on-disk storage method is a default choice if you are storing big data or a large number of objects that don't need to be retrieved very often, for example, if you need a system of record and a general-purpose graph storage solution. If that is the case, Neo4j is the way to go.
On the other side, Memgraph has implemented a complete in-memory solution that focuses on stream data processing and real-time computations that need to be executed in the shortest possible timeframe. So, if you have large graphs that require frequent analysis and you need real-time responses and answers without performance-related issues, then Memgraph will do an amazing job.
Technical Features
| Feature | Neo4j | Memgraph |
|---|---|---|
| ACID transactions | Yes | Yes |
| Replication | Yes | Yes |
| Query language | Cypher | Cypher |
| Drivers & clients | .Net, Clojure, Elixir, Go, Groovy, Haskell, Java JavaScript, Perl, PHP, Python, Ruby, Scala | .Net, C, C++, Go, Haskell, Java, JavaScript, PHP, Python, Ruby, Scala |
| Triggers | Yes | Yes |
| Concurrency | Yes | Yes |
| Durability | Yes | Yes |
| Bolt protocol support | Yes | Yes |
| Backups | Yes | Yes |
| Streaming platform integrations | Apache Kafka, Redpanda | Apache Kafka, Redpanda, Apache Pulsar |
| Query execution Plans | Yes | Yes |
| Authentication and Authorization | Yes | Yes |
| Data encryption in transit | Yes | Yes |
| Data science library | GDS | MAGE |
| Custom procedures | Java | Python, C, C++, Rust [1] |
[1] You can write the procedures in any programming language which can work with C and can be compiled to the ELF shared library format.
There are of course many more features that Neo4j and Memgraph implement, but we will be focusing on the ones that are necessary for stream data processing.
Drivers and Clients
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.
Streaming Platform Integrations
Memgraph includes connectors out of the box for Apache Kafka and Apache Pulsar with a few more on the way in future releases. Neo4j also offers a Kafka Connect plugin that brings streaming support to the whole ecosystem. Memgraph has also been tested with Redpanda, a high-performance Kafka alternative.
Neo4j GDS & Memgraph MAGE
Neo4j GDS or Graph Data Science is a library that provides efficiently implemented, parallel versions of common graph algorithms for Neo4j, exposed as Cypher procedures. It contains many of the most popular graph algorithms out there and you can use it to perform complex graph analysis tasks.
Memgraph MAGE or Memgraph Advanced Graph Extensions is an open-source library for running graph algorithms exposed as Cypher procedures. It focuses on real-time analysis of nodes and relationships and implements a few online algorithms like PageRank, community detection, and node2vec. These algorithms are suited for streaming data that needs to be processed incrementally whenever new nodes or relationships are created, or existing ones are updated.
Custom Procedures in Neo4j and Memgraph
Neo4j and Cypher query language can be extended with User Defined Procedures and Functions. Neo4j itself provides and utilizes custom procedures. Many of the monitoring, introspection, and security features available through the Neo4j-Browser are implemented using these custom procedures. However, given that Neo4j is implemented in Java, the custom procedures and functions also depend on the native Java API.
Memgraph is mainly focused on the Python ecosystem and community. While the core engine is implemented in C++ to ensure the best resource utilization and performance, custom procedures (called query modules in Memgraph) can be implemented in multiple programming languages and, most importantly, in Python as well. These procedures can contain graph algorithms, utility tools, custom APIs, etc. You can call them from the Cypher query language like you would any other query and combine them with other features such as streams or triggers.
What is the best graph database for your use case?
As both graph databases offer a broad range of features, your decision will mostly depend on your specific use case. If performance and cost are not crucial factors, then Neo4j will work for you. However, if you are dealing with real-time data and need a faster and a more optimized alternative, Memgraph won't leave you disappointed.
As already mentioned, Neo4j is the pioneer among graph databases and graph technologies in general. It is good for Java-oriented developers and for static data storage that doesn't rely on frequent write operations. On the other side, Memgraph focuses on stream processing, real-time graph analytics and caters more to Python, C++, and Rust developers. If you need to run complex graph algorithms and traversals often and expect the results in the shortest amount of time, Memgraph is an effective and choice.
Conclusion
In conclusion, choosing between Neo4j and Memgraph graph databases comes down to understanding your specific needs and use case. While Neo4j is a well-established and widely-used graph database with a larger community and a range of features, Memgraph uses its in-memory architecture to offer faster performance and supports unique capabilities, including real-time graph analytics.
Ultimately, it's important to carefully consider factors such as scalability, ease of use, cost, and integration with other tools and technologies before making a decision. By taking the time to evaluate your requirements and conduct thorough research on other graph databases too, you can make an informed choice and leverage the power of graph computing to drive innovation into your business or project.
To try out Memgraph, feel free to request a Memgraph demo (no installation or registration required). It would also be helpful to explore our guides, samples, and references on Memgraph Docs, join our growing community on Discord, and share your projects with us for greater awareness.
