jtibshirani commented on a change in pull request #416:
URL: https://github.com/apache/lucene/pull/416#discussion_r758678766
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File path: lucene/core/src/java/org/apache/lucene/util/hnsw/HnswGraph.java
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@@ -56,31 +59,50 @@
public final class HnswGraph extends KnnGraphValues {
private final int maxConn;
+ private int numLevels; // the current number of levels in the graph
+ private int entryNode; // the current graph entry node on the top level
- // Each entry lists the top maxConn neighbors of a node. The nodes
correspond to vectors added to
- // HnswBuilder, and the
- // node values are the ordinals of those vectors.
- private final List<NeighborArray> graph;
+ // Nodes by level expressed as the level 0's nodes' ordinals.
+ // As level 0 contains all nodes, nodesByLevel.get(0) is null.
+ private final List<int[]> nodesByLevel;
+
+ // graph is a list of graph levels.
+ // Each level is represented as List<NeighborArray> – nodes' connections on
this level.
+ // Each entry in the list has the top maxConn neighbors of a node. The nodes
correspond to vectors
+ // added to HnswBuilder, and the node values are the ordinals of those
vectors.
+ // Thus, on all levels, neighbors expressed as the level 0's nodes' ordinals.
+ private final List<List<NeighborArray>> graph;
// KnnGraphValues iterator members
private int upto;
private NeighborArray cur;
- HnswGraph(int maxConn) {
- graph = new ArrayList<>();
- // Typically with diversity criteria we see nodes not fully occupied;
average fanout seems to be
- // about 1/2 maxConn. There is some indexing time penalty for
under-allocating, but saves RAM
- graph.add(new NeighborArray(Math.max(32, maxConn / 4)));
+ HnswGraph(int maxConn, int levelOfFirstNode) {
this.maxConn = maxConn;
+ this.numLevels = levelOfFirstNode + 1;
+ this.graph = new ArrayList<>(numLevels);
+ this.entryNode = 0;
+ for (int i = 0; i < numLevels; i++) {
+ graph.add(new ArrayList<>());
+ // Typically with diversity criteria we see nodes not fully occupied;
+ // average fanout seems to be about 1/2 maxConn.
+ // There is some indexing time penalty for under-allocating, but saves
RAM
+ graph.get(i).add(new NeighborArray(Math.max(32, maxConn / 4)));
+ }
+
+ this.nodesByLevel = new ArrayList<>(numLevels);
+ nodesByLevel.add(null); // we don't need this for 0th level, as it
contains all nodes
+ for (int l = 1; l < numLevels; l++) {
+ nodesByLevel.add(new int[] {0});
+ }
}
/**
- * Searches for the nearest neighbors of a query vector.
+ * Searches HNSW graph for the nearest neighbors of a query vector.
*
* @param query search query vector
* @param topK the number of nodes to be returned
- * @param numSeed the size of the queue maintained while searching, and
controls the number of
- * random entry points to sample
+ * @param numSeed the size of the queue maintained while searching
Review comment:
It works for me to have a separate discussion. Maybe at least in this PR
we can rename this to `numCandidates`, since the 'seed' naming no longer makes
sense?
As context, I still think it makes sense to remove the `numCandidates` vs.
`k` distinction in `HnswGraph`. The public signature `KnnVectorsReader#search`
does not include a notion of "num candidates", so users have no way to even use
this distinction. I'd be in favor of removing it from `HnswGraph`, then having
a follow-up discussion about whether the vector search APIs should handle
`numCandidates` vs `k`.
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