The technology behind GitHub’s new code search | The GitHub Blog

A look at what went into building the world's largest public code search index.

**Hasnain says:**

The appendix is the best part! Great insight into a tricky engineering problem, which lays out the constraints and some parts of the solution that were considered. Would love to see the code be open sourced at some point.

“To determine the optimal ingest order, we need a way to tell how similar one repository is to another (similar in terms of their content), so we invented a new probabilistic data structure to do this in the same class of data structures as MinHash and HyperLogLog. This data structure, which we call a geometric filter, allows computing set similarity and the symmetric difference between sets with logarithmic space. In this case, the sets we’re comparing are the contents of each repository as represented by (path, blob_sha) tuples. Armed with that knowledge, we can construct a graph where the vertices are repositories and edges are weighted with this similarity metric. Calculating a minimum spanning tree of this graph (with similarity as cost) and then doing a level order traversal of the tree gives us an ingest order where we can make best use of delta encoding. Really though, this graph is enormous (millions of nodes, trillions of edges), so our MST algorithm computes an approximation that only takes a few minutes to calculate and provides 90% of the delta compression benefits we’re going for.”