TerminusDB: An Open-Source Full-Featured In-Memory Graph Database for Knowledge Graph Representation
TerminusDB, hosted on GitHub, is a full-featured in-memory graph database that provides users with advanced tools for knowledge graph representation. As an open-source project, TerminusDB represents a significant move towards democratizing the technology and knowledge around data storage and management, opening up opportunities for developers around the world to interact and cooperate on data-centric projects.
Project Overview:
TerminusDB is designed with an ambitious goal: to provide an efficient, powerful, and easy-to-use graph database system that takes advantage of both in-memory computation and disk-based storage. This dual-approach tackles a significant problem in data management - avoiding the trade-off between speed and storage capacity. Targeting data scientists, developers, and organizations handling large and complex data sets, it aims to bring robust and real-time data handling capabilities, regardless of the scale or complexity involved.
Project Features:
A distinguishing feature of TerminusDB is its built-in comprehensive versioning system, enabling users to commit, branch, merge, push and pull data just like they would in a standard Git environment. It simplifies data lineage tracking and promotes collaboration in data-driven projects. TerminusDB also features WOQL (Web Object Query Language) which makes querying graph databases remarkably straightforward, facilitating the manipulation and retrieval of data.
Technology Stack:
Written primarily in Prolog, a language noted for its strong performance in pattern matching and symbolic representation, TerminusDB utilizes the strength of this language to manage complex data structures with relative ease. The choice of Prolog is a deliberate one, chosen for its powerful capabilities in the realm of artificial intelligence (AI) and logical programming.
Project Structure and Architecture:
The architecture of TerminusDB is layered and modular, with key components such as the storage engine, query planner, and transaction processor working in tandem to provide robust performance and scalability. The use of a graph-based data model enables effortless representation of complex data relationships, while the layered architecture ensures the different functional areas can be fine-tuned or updated in isolation, facilitating continuous improvement.
