GrapheekDB was developed in 2014 by Raphaël Braud, a freelance developer from France. It was built for a recommendation system to extract the contents of documents, tokenizing their contents, and give recommendations of similar documents based on user queries. A graph database was chosen over a relational database to avoid multiple joins on tables of several million rows to improve performance. It was built with a specific purpose of recommending documents and has a python-like API (close to Django and Gremlin).
The Query interface is close to Germlin and Django frontend. The DBMS has methods for lookups on graphs that resemble Django lookups and methods for path traversals for inner and outer vertices and edges that resemble Germlin traversal methods. The DBMS also has aliasing and collecting methods as well as aggregation methods such as count and sum which are implemented using python's entity iterators.
While a graph database is index-free as it consists of direct pointers to its adjacent elements (a property known as adjacency), GrapheekDB does not need an index to find node and edge indices. However, the latest version of the DBMS does support nodes and edge indices for lookups on sparse graphs. The current version only supports "exact match indices" and performs a Depth-First-Search (DFS) in order to match indices. Storing the indices leads to a storage overhead and slows down writes in the DBMS.
https://bitbucket.org/nidusfr/grapheekdb
https://bitbucket.org/nidusfr/grapheekdb
https://bitbucket.org/nidusfr/grapheekdb/src/default/docs/index.rst
Raphaël Braud
2014
2015