mayya-sharipova opened a new pull request, #14380:
URL: https://github.com/apache/lucene/pull/14380
Backport for #14331
Currently when doing merging of HNSW graphs incrementally, we first
initialize a graph from the biggest segment, and for other segments, we rebuild
the graphs completely by going through a segment's vector values one by one,
searching for it in the new graph to find best neighbours to connect it with.
This PR proposes more efficient merging based on the idea if we know where
we want to insert a node, we have a good idea of where we want to insert its
neighbours. Similarly to the current approach, we initialize a new graph from
the biggest segment. For all other segments, we find a smaller set of nodes
that "covers" their graph, and we insert that set as usual. For other nodes,
outside of J sets, we do lighter searches with pre-calculated eps.
This allows substantial speedups in merging (up to 2x in force-merge).
The algorithm is based on the following steps:
1. Get all graphs that don't have deletions and sort them by size
(descending).
2. Copy the largest graph to the new graph (`gL`).
3. For each remaining small graph (`gS`):
- Find the nodes that best cover `gS` (join set `j`). These nodes will be
inserted into `gL` as usual: by searching `gL` to find the best candidates
(`w`) to which connect the nodes.
- For each remaining node in `gS` do "lighter" searches:
- We provide `eps` to search in `gL`. We form `eps` by the union of the
node's neighbors in `gS` and the node's neighbors' neighbors in `gL`. We also
limit `beamWidth` (`efConstruction` ) to `M * 3`.
Algorithm designed by Thomas Veasey
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