OrientDB has the flexibility of the Document databases and the power of the Graph databases to manage relationships. It can work in schema-less mode, schema-full or a mix of both. It can store up to 150,000 records per second on common hardware. OrientDB has been designed to be very fast. It inherits the best features and concepts from the Object Databases, Graph DBMS and the modern NoSQL engines. 
 Source code location: <A HREF="http://code.google.com/p/orient/" target="_blank">http://code.google.com/p/orient/</A>

OrientDB has the flexibility of the Document databases and the power of the Graph databases to manage relationships. It can work in schema-less mode, schema-full or a mix of both. It can store up to 150,000 records per second on common hardware. OrientDB has been designed to be very fast. It inherits the best features and concepts from the Object Databases, Graph DBMS and the modern NoSQL engines.

OrientDB - The NoSQL Graph-Document DBMS

SurrealDB is the ultimate mult--modal database for tomorrow's applications. SurrealDB is an end-to-end cloud-native database designed for modern applications, including web, mobile, serverless, Jamstack, backend, and traditional applications. With SurrealDB, you can simplify your database and API infrastructure, reduce development time, and build secure, performant apps quickly and cost-effectively.
<ul dir="auto">
<li>Reduces development time: SurrealDB simplifies your database and API stack by removing the need for most server-side components, allowing you to build secure, performant apps faster and cheaper.</li>
<li>Real-time collaborative API backend service:&nbsp;SurrealDB functions as both a database and an API backend service, enabling real-time collaboration.</li>
<li>Support for multiple querying languages:&nbsp;SurrealDB supports SQL querying from client devices, GraphQL, ACID transactions, WebSocket connections, structured and unstructured data, graph querying, full-text indexing, and geospatial querying.</li>
<li>Granular access control: SurrealDB provides row-level permissions-based access control, giving you the ability to manage data access with precision.</li>
<li class="task-list-item">Record links and directed typed graph connections</li>
<li>Incrementally computed views for pre-computed advanced analytics</li>
<li>Connect and query directly from web-browsers and client devices</li>
<li>Store and model data in any way with tables, documents, and graph</li>
<li>Simple schema definition for frontend and backend development</li>
<li>Use embedded JavaScript functions for custom advanced functionality</li>
</ul>

SurrealDB is an end-to-end cloud-native database designed for modern applications, including web, mobile, serverless, Jamstack, backend, and traditional applications.

SurrealDB - A scalable, distributed, collaborative, document-graph database, for the realtime web

FlockDB is much simpler than other graph databases such as neo4j because it tries to solve fewer problems. It scales horizontally and is designed for on-line, low-latency, high throughput environments such as web-sites. Twitter uses FlockDB to store social graphs (who follows whom, who blocks whom) and secondary indices. As of April 2010, the Twitter FlockDB cluster stores 13+ billion edges and sustains peak traffic of 20k writes/second and 100k reads/second.

FlockDB - A distributed, fault-tolerant graph database from Twitter

ArangoDB is a multi-purpose open-source database with flexible data model for documents, graphs, and key-values. Build high performance application using a convenient sql-like query language or JavaScript/Ruby extensions. Its key features are  Schema-free, Convenient querying using AQL, Extendable through JS, Space efficiency, Supports modern storage hardware, like SSD and large caches and lot more.

ArangoDB - The Multi-purpose NoSQL DB

STINGER is a package designed to support streaming graph analytics by using in-memory parallel computation to accelerate the computation. STINGER is composed of the core data structure and the STINGER server, algorithms, and an RPC server that can be used to run queries and serve visualizations. 
 
The data structure used is similar to an adjacency list except that the adjacencies of a vertex are stored in a linked list of semi-dense blocks of edges rather than a linked list of individual edges. Also, the vertex array contains more metadata per each vertex than just a pointer to the linked of edge blocks.

STINGER is a package designed to support streaming graph analytics by using in-memory parallel computation to accelerate the computation. STINGER is composed of the core data structure and the STINGER server, algorithms, and an RPC server that can be used to run queries and serve visualizations. 

STINGER - In-memory graph store and dynamic graph analysis platform

Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop graph applications.
 
You can ingest data from sources like Kafka, SQL, or plain CSV files. Memgraph provides a standard interface to query your data with Cypher, a widely-used and declarative query language that is easy to write, understand and optimize for performance. This is achieved by using the property graph data model, which stores data in terms of objects, their attributes, and the relationships that connect them. This is a natural and effective way to model many real-world problems without relying on complex SQL schemas.
 
Memgraph is a platform for graph applications, not just for graph analytics. From Social networks to user event tracking, permissions modeling, recommendation systems, and so much more, you can build it on Memgraph.

Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop graph applications.



Memgraph - Build modern, graph-based applications on top of your streaming data in minutes

Graphlite is an embedded graph databases for Python. Graphlite aims to change that by building a simple and fast graph layer over SQLite. Similar to FlockDB, Graphlite only stores adjacency lists, but they can be queried in the style of normal graph databases, e.g. with traversals.

Graphlite - Embedded graph databases for Python

Eclipse JNoSQL is a Java framework that streamlines the integration of Java applications with NoSQL databases. It defines a set of APIs and provides a standard implementation for most NoSQL databases. This clearly helps to achieve very low coupling with the underlying NoSQL technologies used in applications.
 
It provide a set of APIs that defines communication with NoSQL databases. Compared with traditional the RDBMS world, they are like the JDBC API. It contains four modules, one for each NoSQL database type: Key-Value, Column Family, Document, and Graph.

Eclipse JNoSQL is a Java framework that streamlines the integration of Java applications with NoSQL databases. It defines a set of APIs and provides a standard implementation for most NoSQL databases. This clearly helps to achieve very low coupling with the underlying NoSQL technologies used in applications.

Eclipse JNoSQL - Framework which has the goal to help Java developers to create Jakarta EE applications with NoSQL

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