The capacity to quickly store and analyze highly related data has led to graph databases’ meteoric popularity in the past few years. Applications like social networks, recommendation engines, and fraud detection benefit greatly from graph databases, which differ from conventional relational databases’ ability to depict complicated relationships between elements.
What are Graph Databases?
Graph databases are a subset of NoSQL databases that store and display data using graph structures. They include “nodes,” which stand in for things, and “edges,” which show their connections. Using a graph-based method, queries of highly connected data may be efficiently and flexibly executed, facilitating the discovery of patterns and insights.
Advantages
There are three main benefits to using a graph database instead of a relational one.
- High Flexibility: The graph database’s schema can be changed to suit different applications without impacting current functions, making it very flexible.
- High Performance: A graph database maintains superior performance even when dealing with intricate transactions. Deep analytics work in a similar manner.
- Graph databases are more efficient than relational databases due to the shortness of graph queries and the speed with which relationships can be traversed.
Everyone can view and contribute to the source code of an open-source graph database. The code is published on GitHub. In 2024, companies could benefit from the following open-source graph databases:
As a native graph database incorporating a graph model down to the storage level, Neo4j is among the most well-known and long-running open-source graph databases, boasting over 12,000 stars on GitHub. It offers ACID transactions, cluster support, and runtime failover.
WhiteDB is a C-based NoSQL database package that runs entirely in main memory and is very lightweight. No server process is available. There is no need for sockets because WhiteDB and the application program read and write data directly from and to shared memory.
ArangoDB is a free and open-source graph database that prioritizes speed and scalability. More than 13,000 people have given it a star on GitHub. It combines an integrated search engine, native graphing, JSON compatibility, and various data access patterns with a single query language.
Stardog is a platform for knowledge graphs that combines features of graph databases and semantic reasoning. It has sophisticated tools for creating and querying knowledge graphs and supports SPARQL.
A native graph database with over 19,000 stars on GitHub, Dgraph supports native GraphQL. This open-source database is known for its speed, scalability, distributed nature, and high availability. Dgraph instantly resolves queries through graph navigation and is designed to handle big data volumes.
Memgraph is an open-source, in-memory graph database with over 2000 stars on GitHub. It may be used either on-premises or in the cloud. Memgraph Platform is an on-premises option for businesses, while Memgraph Cloud is a completely managed service that eliminates the need for an administrator.
Orly is a non-relational database designed to handle massive user loads quickly and efficiently. Because of its speed and high concurrency, we won’t need memcache anymore; it gives a single path to data.
Graph Engine (GE) is a distributed graph processing engine that runs in memory. It is supported by a general compute engine and strongly typed RAM storage. Over a network of computers, the distributed RAM store offers a high-performance key-value store that can be accessed from anywhere in the world. Using the RAM store, GE makes it possible to access massive dispersed data sets quickly and arbitrarily.
Aerospike Graph, a distributed graph database built on top of the Aerospike Database, enables users to store and query massive volumes of data without sacrificing performance. This open-source database employs Apache TinkerPop, a graph computing platform, to perform online transactions and analytical graph queries.
With over 4,500 stars on GitHub, OrientDB is a dependable graph database that is quick, adaptable, and extensible. This multi-model database supports graph, document, full-text, and geographic models and doesn’t necessitate costly runtime JOINs. Additionally, this database supports ACID and SQL transactions.
One free RDF triplestore that supports desktop computers is GraphDB Lite, which can hold up to 100 million triples. The JAVA platform makes it easy to deploy this GraphDB version. Instead of relying on indexes stored in files, SPARQL 1.1 queries are executed entirely in memory. Inferencing reasoning operations are GraphDB Lite-compatible.
Cayley, an open-source graph database inspired by Google’s Knowledge Graph, has received over 14,000 stars on GitHub. The Go-based database has RDF support and can be used on top of any pre-existing SQL or NoSQL database. The user can add new query languages or custom logic to Cayley, and the database can be connected to any programming language. This makes Cayley highly modular. Multiple query languages, including GraphQL and MQL, are built into it.
The MapGraph API simplifies the process of creating GPU-based graph analytics with great performance. The API’s basis is the Gather-Apply-Scatter (GAS) model, which is employed in GraphLab. The CUDA kernels in MapGraph employ various advanced techniques, including vertex-degree-dependent dynamic parallelism granularity and frontier compaction, to execute high-performance computations and effectively use GPUs’ high memory bandwidth.
The distributed graph store Weaver features horizontal scalability, excellent performance, and good consistency. Through a straightforward Python API, Weaver allows users to perform transactional graph modifications and searches.
Virtuoso, an open-source graph database developed by OpenLink, has over 800 stars on GitHub. A data virtualization platform and a DBMS supporting many models are part of its functionality. This database is highly available, fast, and scalable; it doesn’t rely on complicated queries or heavy usage to do so.
Filament is a navigational query style-based graph persistence framework with accompanying toolkits. The storage model can be customized, but it stores graph objects and their properties in basic relational databases by default.
The Linux Foundation is behind the JanusGraph project, which has over 5,000 stars on GitHub. With its multi-machine cluster and highly scalable graph database, it can manage massive graphs with billions of nodes and edges, handle graph queries, and perform complicated traversals.
Titan is a transaction database for graphs that can scale to store and query data from distributed graphs with hundreds of billions of edges and vertices. Titan can handle hundreds of users running complicated graph traversals in real-time.
Directed hypergraphs are the basis of HyperGraphDB, an open-source database that may be expanded upon. It has robust data modeling, knowledge representation capabilities, and customizable indexing. It can read and write data concurrently without blocking and is completely transactional and multi-threaded. This database has more than 190 stars on GitHub.
Storage for massive amounts of dispersed, highly connected, semi-structured data is made easy with Sones GraphDB, an object-oriented graph database.
