7 Best Open Source Graph Databases

Sa Wang
|
Software Engineer
|
December 30, 2024
7 Best Open Source Graph Databases

Graph databases have been at the forefront of tackling the huge influx of highly interconnected data and scaling the global digital infrastructure. If we observe the growth of the graph database market, we expect it to grow by USD 7.40 billion from 2022 to 2027.

On top of that, like other niches of the software industry, open source brings compelling benefits when it comes to adopting graph databases. In this blog post, we’ll look at the seven leading open-source graph databases available in the market today. We'll also discuss how open source graph databases work and the key factors we recommend you consider when choosing one. In the end, you’ll have the means to confidently pick the perfect database that can unlock the full potential of your vast enterprise data.

What is an open source graph database?

Graph databases represent and store data using graph structures, consisting of nodes, edges, and properties to model and query relationships. Unlike traditional relational databases that rely on tables and rows, graph databases inherently reflect the interconnected nature of modern data.

At their core, graph databases consist of three primary elements:

  • Nodes: Represent main data points or entities.
  • Edges: Define relationships connecting these nodes.
  • Properties: Enrich nodes and edges with additional information.
Figure: friend of friend of a person with id “123”

For example, in a supply chain scenario, nodes could represent manufacturers, suppliers, distribution centers, and retailers. Edges define relationships like "supplies to," "manufactures," and "ships to," while properties store details such as inventory levels, shipping dates, and costs.

To manage these relationships efficiently, graph databases leverage specialized storage and indexing strategies. Vendors typically adopt one of two approaches:

  • Native graph storage engines optimized for direct node and edge manipulation.
  • Graph layers built atop existing relational or NoSQL databases, which utilize their mature ecosystems.

Graph databases also use query languages like Cypher and Gremlin to simplify working with relationships. These languages allow developers to focus on patterns rather than procedural steps. For instance, querying "find all users who interacted with at least three products in the same category in the past month" is intuitive and efficient, without requiring in-depth knowledge of the underlying implementation.

In addition to storage and querying, many graph databases support built-in graph algorithms. These algorithms enable advanced analytics directly on graph data, allowing users to derive deeper insights from their interconnected datasets. For example:

  • Shortest path algorithms: Useful for finding the quickest route between two points in a transportation network.
  • Centrality algorithms: Identify influential nodes in a social network or communication system.
  • Community detection algorithms: Uncover hidden clusters or groups within a dataset, such as customer segments in marketing analytics.

The inclusion of graph algorithms makes graph databases not just a data storage and querying solution but a comprehensive tool for understanding and analyzing complex networks.

Understanding open source graph databases

As organizations evaluate graph database solutions, many turn to open-source options for their flexibility and community-driven development. Like many open-source software, these graph databases are distributed under specific licenses that govern their usage, modification, and distribution rights. These licenses primarily fall into two categories: permissive licenses and copyleft licenses, though some projects adopt a dual licensing model to balance open-source principles with commercial interests.

Permissive licenses

Permissive licenses, such as the MIT License and the Apache License 2.0, are known for their flexibility. They allow users to modify the software and redistribute it, even as part of proprietary projects. For instance, the Apache License 2.0 includes provisions for patent protection, making it popular for projects that aim to balance open collaboration with commercial use. A company can use an open-source graph database governed by the Apache License 2.0, customize it for their use case, and integrate it into a commercial product without the obligation to release their modifications to the public.

Copyleft licenses

In contrast, copyleft licenses like the GNU General Public License (GPL) require any derivative work to be distributed under the same license. This ensures that any improvements or modifications remain open source, fostering a culture of shared innovation. However, this can be restrictive for companies that want to integrate the database into proprietary solutions. If a graph database uses the GPL, any changes a developer makes to the code must also be released under the GPL when distributed, maintaining the open-source ethos.

Dual licensing models

Some graph databases adopt a dual licensing model, where the core product is open source, but certain features or enterprise-level support are available only through a commercial license. This approach allows companies to benefit from the open-source community while monetizing advanced functionality. For example, Neo4j uses a dual licensing model, providing its Community Edition under an open-source license and its Enterprise Edition under a commercial license with extra features like advanced security and scalability tools.

Benefits of open source graph database

Open-source graph databases offer several advantages that make them an attractive choice for organizations:

  • Cost-effectiveness: With no licensing fees, they lower the barrier to entry, making them ideal for startups and budget-conscious enterprises.
  • Flexibility and customization: Users can adapt the database to their specific needs, ensuring it aligns with unique workflows or requirements.
  • Community and innovation: A global community of developers continuously improves open-source graph databases, driving innovation and providing extensive support.
  • Transparency and trust: Open access to the source code ensures that users can audit it for security, compliance, or performance issues.

Key factors to consider for open source graph databases

To choose the right open source graph database, you must carefully evaluate various factors so the database aligns with your project needs and technical requirements. Let's discuss some of the most important ones.

Graph database engine architectures

The architecture of a graph database engine plays a significant role in its performance, scalability, and versatility. Here are the primary architectural models to consider:

  • Native graph storage: Databases with native graph storage are optimized for graph-specific operations like traversal and querying. They are ideal for use cases requiring high-performance querying, such as machine learning or applications with embedded object-oriented structures.
  • Multi-model engines: Multi-model databases combine graph, document, and key-value features, offering flexibility to handle diverse data and relationships. These are particularly useful in industries like healthcare and finance, where data dynamics are complex. While multi-model engines may initially sacrifice graph traversal performance, modern implementations have significantly improved their graph processing capabilities.
  • In-memory models: In-memory graph databases store data in RAM, providing ultra-fast indexing and querying. They are well-suited for applications requiring time-sensitive analytics, such as fraud detection or stock market analysis. However, scalability can be a challenge due to the reliance on memory capacity.
  • Specialized optimizations: Some graph databases offer features like real-time graph processing enhancements, enabling high throughput in demanding scenarios. These optimizations can significantly boost application performance and developer productivity.

Optimizing for high performance and scalability

Performance and scalability are critical for applications dealing with large datasets, high query complexity, or real-time analytics. Here are key considerations:

  • Horizontal scaling: Horizontal scaling through graph partitioning and sharding distributes data across multiple servers while maintaining efficient traversals. This approach improves performance, fault tolerance, and the ability to handle growing datasets and user demands.
  • Replication strategies: Replication ensures high availability and fault tolerance by duplicating data across servers. It protects against node failures and keeps your system operational under heavy loads.
  • Query optimization: Features like lazy evaluation and parallel processing significantly improve response times for complex traversals. Choosing a database with robust query optimization capabilities is crucial for intensive data processing applications.
  • Hybrid scaling approaches: Modern graph databases often combine different scaling strategies, such as mixing horizontal and vertical scaling, to address diverse workloads and ensure optimal performance.

Ensuring data integrity and consistency

Maintaining data integrity and consistency is essential for the reliability of graph databases, particularly when dealing with complex, interconnected datasets. Graph databases achieve this through robust transaction handling and consistency models.

Transactions in graph databases adhere to the ACID principles: atomicity ensures that all changes within a transaction are either fully applied or not at all, preventing partial updates; consistency ensures that each transaction transitions the database to a valid state based on predefined rules; isolation keeps concurrent transactions from interfering with each other; and durability guarantees that once a transaction is committed, its changes are permanent even after a system failure. For example, in a financial application, transactions must debit and credit accounts accurately without leaving the database in an inconsistent state.

Consistency models also play a critical role. Strong consistency ensures that every read operation reflects the latest data, which is crucial for applications such as financial systems. Eventual consistency, on the other hand, allows replicas of the database to converge over time, making it more suitable for scenarios like social networks where immediate consistency is less critical. Causal consistency preserves the order of related operations, ensuring logical data flow.

To prevent errors, graph databases often include features like schema enforcement to validate data structures, unique constraints to avoid duplicates, and referential integrity to maintain valid relationships. These mechanisms ensure that the database remains consistent and reliable, even as it scales or handles complex operations.

By supporting transactions and consistency models, graph databases provide a solid foundation for building accurate and dependable applications across various domains.

Query language diversity

The query language acts as the primary interface between the user and the database. It determines how efficiently you can retrieve and analyze data. You have to evaluate whether the query language aligns with your use case and team’s technical skills. Fortunately, open-source graph databases offer a variety of query languages. Each possesses unique advantages and suits different operations. For example:

  • Cypher: Known for its highly readable syntax, Cypher is a declarative language that excels at expressing complex graph patterns. Its intuitive structure makes it easier to learn and use, particularly for users familiar with SQL. This is a popular choice for tasks like social network analysis and recommendation engines where relationships are central.

  • Gremlin: A more imperative language, Gremlin offers greater flexibility and control over the traversal process. Its step-by-step approach allows for fine-grained manipulation of graph data, making it well-suited for tasks requiring complex traversals and transformations, such as fraud detection or knowledge graph exploration.

  • SPARQL: Designed specifically for querying RDF data, SPARQL is widely used in semantic web applications and linked data management. Its strength lies in handling ontologies and inferencing, making it ideal for knowledge representation and reasoning tasks.

Ultimately, the best query language depends on your specific needs and the complexity of your graph data. Consider factors like the types of queries you'll be performing, the structure of your data, and the expertise of your team when making your selection."

Active community and development

A vibrant open source community is crucial for the long-term success and reliability of a graph database. The health of the project can be assessed through regular commits and updates to the codebase, which indicate ongoing maintenance and development. The size and engagement of the developer community on platforms like GitHub serves as a strong indicator of the project's sustainability. Equally important is the quality of documentation, including community-contributed guides and tutorials that help new users navigate the system effectively. Projects with strong communities typically demonstrate responsive maintenance teams who address bug reports and feature requests promptly, while maintaining active discussion forums, Stack Overflow presence, and community chat channels for user support.

Licensing and commercial support

Understanding the licensing model is essential when selecting an open source graph database for your project. The type of license (Apache, MIT, GPL, etc.) directly impacts how you can use, modify, and distribute the software. Some projects offer dual licensing models, combining open source versions with commercial options that provide additional features or support. It's crucial to evaluate whether the project offers commercial support options or enterprise editions that might become necessary as your implementation grows. Professional services and consulting availability can also be vital for complex deployments. Additionally, consider the project's governance model and how license changes might affect your long-term usage.

Extensibility and customization

Open source graph databases should offer substantial flexibility for customization to meet specific organizational needs. The ability to access and modify source code allows organizations to tailor the database to their unique requirements. A well-designed plugin architecture enables functionality extensions without modifying core code. Documentation should clearly outline the process for building custom extensions and integrating with existing systems. The most valuable open source solutions provide robust integration capabilities with other tools in your technology stack, allowing for seamless data flow and operation within your existing infrastructure.

Deployment options and independence

Open source solutions offer significant advantages in deployment flexibility. Organizations can self-host on premises or in any cloud environment, maintaining full control over their data and infrastructure. This independence from vendor lock-in provides the freedom to modify and optimize deployment configurations according to specific performance and security requirements. Organizations can control their upgrade cycles and versions, ensuring system changes align with their operational needs. Modern open source graph databases should support various deployment patterns, including containerization and orchestration, to facilitate scalable and maintainable implementations.

Total cost of ownership

While open source software is free to use, organizations must consider the complete cost picture when evaluating graph database solutions. This includes assessing the internal development resources required for maintenance and customization, as well as infrastructure costs for self-hosted deployments. Organizations need to account for training and skill development to build internal expertise. Support and consulting services, while optional, may become necessary for complex implementations or performance optimization. Some enterprise features might require additional licensing, which should be factored into the long-term budget planning.

Top 7 best open-Source graph databases of 2025

In this section, we’ll look at some of the most prominent graph databases to help you narrow down your evaluation process in choosing the best solution for your needs.

ArangoDB Logo

ArangoDB

ArangoDB functions as a multi-model database combining graph, document, and key-value data models with native JSON support through VelocyPack. It features AQL (ArangoDB Query Language) for querying across all data models and provides distributed architecture with clustering support. The database includes native graph capabilities, full-text search, and Kubernetes integration for cloud deployments. Since October 2023, it has operated under the ArangoDB Community License, which includes restrictions on commercial use and a 100GB data limit for single clusters, while an Enterprise Edition offers additional features under a commercial license.

Neo4j Logo

Neo4j

Neo4j provides native graph storage and processing optimized for managing nodes and relationships. It employs the Cypher query language for intuitive graph querying and maintains data consistency through ACID-compliant transactions. The database architecture supports both standalone and clustered deployments, offering high-performance native API access and integration with popular data science tools. Neo4j offers a Community Edition under the GPLv3 license, while its feature-rich Enterprise Edition requires a commercial license.

Dgraph Logo

Dgraph

Dgraph presents itself as a distributed graph database engineered for production environments. It features native GraphQL support, ACID transactions, and horizontal scaling capabilities for managing large datasets. The database includes built-in search and indexing capabilities and supports a JSON-based data model. Currently, Dgraph's core operates under the Apache 2.0 license, with plans to make all features available under the same license in the upcoming v25 release in early 2025.

JanusGraph

JanusGraph excels as a distributed graph database designed for handling massive-scale graphs, supporting hundreds of billions of vertices and edges. It integrates naturally with Apache TinkerPop and the Gremlin query language, while offering flexible storage backend support for systems like Apache Cassandra and HBase. The database maintains ACID transactions even in distributed environments and supports global graph analytics through Apache Spark integration. JanusGraph operates entirely under the Apache 2 license, making it fully open source.

Memgraph

Memgraph functions as a high-performance in-memory graph database optimized for real-time analytics. It supports ACID-compliant transactions and uses OpenCypher query language, maintaining compatibility with Neo4j. The database includes the MAGE (Memgraph Advanced Graph Extensions) library for graph algorithms and offers native integration with streaming platforms like Kafka. Memgraph comes in multiple editions: a free Community Edition with core features, an Enterprise Edition with additional security and scalability features, and a Cloud Edition available as a fully managed service on AWS.

OrientDB

OrientDB operates as a multi-model database supporting Document, Graph, Key-Value, and Object models with native graph capabilities. It features an SQL-like query language with graph functionality and offers schema-less, schema-full, or schema-mixed modes. The database provides ACID compliance and uses persistent pointers instead of runtime JOINs for efficient data connections. OrientDB offers both a Community Edition under the Apache 2 license and an Enterprise Edition with additional features like non-stop backup, query profiler, and distributed clustering configuration.

NebulaGraph

NebulaGraph is a distributed, open-source graph database designed to store and query large-scale, complex graph data with high performance and low latency. Its native graph storage engine supports massive vertex and edge counts while ensuring strong data consistency and horizontal scalability. NebulaGraph offers an openCypher-based query language for intuitive graph traversal and analysis, along with flexible schema options ranging from strict enforcement to a more relaxed approach. The system ensures ACID transactions and is optimized for efficient data access without reliance on runtime JOINs. NebulaGraph provides both a Community Edition under the Apache 2.0 license and an Enterprise Edition that includes advanced security features, role-based access control, and enhanced management tools.

Bonus: PuppyGraph   

PuppyGraph is the first and only graph query engine that lets you query existing relational data stores as a unified graph without ETL processes - no separate graph database needed. It seamlessly integrates with data lakes like Apache Iceberg, Apache Hudi, and Delta Lake, as well as databases including MySQL, PostgreSQL, and DuckDB, eliminating the need for a separate graph database.

Figure: Relational data sources supported by PuppyGraph

PuppyGraph's zero-ETL architecture enables direct graph querying of data warehouses and lakes, while its advanced architecture handles petabyte-scale datasets through automatic sharding. The engine achieves remarkable performance, executing complex operations like 10-hop neighbor searches in 2.26 seconds using parallel processing and vectorized evaluation.

Figure: Example architecture with PuppyGraph

Deployment is also straightforward: users simply download the free Docker image, connect PuppyGraph to your existing data stores, create graph schemas, and start querying. The system can be deployed via Docker, AWS AMI, GCP Marketplace, or within a VPC or data center, providing full data control. For query compatibility, PuppyGraph supports both Gremlin and OpenCypher standards.

Conclusion

Open-source graph databases combine the benefits of open source philosophy and graph databases to bring the value of graphs to more users. These systems allow organizations to leverage the power of graph-based data modeling without being locked into proprietary solutions, while benefiting from community-driven development and improvements. This makes them an ideal starting point for developers to learn graph database concepts and experiment with implementations before potentially scaling up to enterprise solutions or building their own products.

Interested in trying out PuppyGraph for free? Download the forever free PuppyGraph Developer Edition, or book a demo with our engineering team.

Sa Wang is a Software Engineer with exceptional math abilities and strong coding skills. He earned his Bachelor's degree in Computer Science from Fudan University and has been studying Mathematical Logic in the Philosophy Department at Fudan University, expecting to receive his Master's degree in Philosophy in June this year. He and his team won a gold medal in the Jilin regional competition of the China Collegiate Programming Contest and received a first-class award in the Shanghai regional competition of the National Student Math Competition.

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  • Designed for production
  • Available via AWS AMI & Docker install
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