Published in AWS-samples official Github repository:optimised-in-memory-graph-search Link to heading
I am excited to announce the release of my open-source implementation of a graph traversal engine with Amazon Web Services (AWS) Samples on GitHub. This project addresses some of the challenges and limitations inherent in existing graph database solutions, particularly when it comes to highly customized and conditional traversals.
The Challenge with Existing Solutions Link to heading
While there are many excellent, purpose-built managed graph database offerings available commercially, they often fall short in specific scenarios. One common issue is that graph query languages sometimes lack the necessary procedural functions for particular use cases. Additionally, progressive and incremental traversal and data streaming can become inefficient, especially when dealing with the latency and bandwidth limitations of the internet.
The Solution: A Self-Hosted Graph Traversal Engine Link to heading
To tackle these challenges, I led the development of a reference implementation of a containerized, self-hosted graph traversal engine. This solution is designed to maximize the use of RAM to locally cache and construct an easily traversable graph using hashmaps. By doing so, it allows for more efficient and customized graph traversals, tailored to specific needs without the overhead of internet-based data streaming.
Key Features and Benefits Link to heading
Local Caching: Utilizes RAM to cache data locally, ensuring fast access and traversal. Efficient Graph Construction: Uses hashmaps to build an easily traversable graph structure. Customization: Provides the flexibility to implement highly customized and conditional traversals that might not be possible with traditional graph query languages. Self-Hosted: Runs in a containerized environment, giving you control over the deployment and management of the graph traversal engine. Collaboration and Acknowledgements This project would not have been possible without the support and contributions of many talented individuals. I would like to extend my special thanks to Raja SP, Kenneth Ham, Guang Yang, Harshit Gupta, Sebastian Lee, Phil Scharff, Arden Packeer, Bijin Abraham Idicula, and Gregory Sukanto. Your insights and efforts have been invaluable.
Get Involved Link to heading
You can access the graph traversal engine on GitHub under AWS Samples here. I encourage you to explore the implementation, provide feedback, and contribute to its ongoing development. Together, we can continue to push the boundaries of what’s possible with graph databases and traversal engines.
Thank you for your interest and support. Let’s continue to innovate and solve complex challenges in the world of graph databases!