mikemccand commented on a change in pull request #2022:
URL: https://github.com/apache/lucene-solr/pull/2022#discussion_r519881970



##########
File path: lucene/core/src/java/org/apache/lucene/util/hnsw/HnswGraph.java
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@@ -0,0 +1,235 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.lucene.util.hnsw;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Random;
+import java.util.Set;
+import java.util.TreeSet;
+
+import org.apache.lucene.index.KnnGraphValues;
+import org.apache.lucene.index.VectorValues;
+
+import static org.apache.lucene.search.DocIdSetIterator.NO_MORE_DOCS;
+import static org.apache.lucene.util.VectorUtil.dotProduct;
+import static org.apache.lucene.util.VectorUtil.squareDistance;
+
+/**
+ * Navigable Small-world graph. Provides efficient approximate nearest neighbor
+ * search for high dimensional vectors.  See <a 
href="https://doi.org/10.1016/j.is.2013.10.006";>Approximate nearest
+ * neighbor algorithm based on navigable small world graphs [2014]</a> and <a
+ * href="https://arxiv.org/abs/1603.09320";>this paper [2018]</a> for details.
+ *
+ * This implementation is actually more like the one in the same authors' 
earlier 2014 paper in that
+ * there is no hierarchy (just one layer), and no fanout restriction on the 
graph: nodes are allowed to accumulate
+ * an unbounded number of outbound links, but it does incorporate some of the 
innovations of the later paper, like
+ * using a priority queue to perform a beam search while traversing the graph. 
The nomenclature is a bit different
+ * here from what's used in those papers:
+ *
+ * <h3>Hyperparameters</h3>
+ * <ul>
+ *   <li><code>numSeed</code> is the equivalent of <code>m</code> in the 2012 
paper; it controls the number of random entry points to sample.</li>
+ *   <li><code>beamWidth</code> in {@link HnswGraphBuilder} has the same 
meaning as <code>efConst</code> in the 2016 paper. It is the number of
+ *   nearest neighbor candidates to track while searching the graph for each 
newly inserted node.</li>
+ *   <li><code>maxConn</code> has the same meaning as <code>M</code> in the 
later paper; it controls how many of the <code>efConst</code> neighbors are
+ *   connected to the new node</li>
+ *   <li><code>fanout</code> the fanout parameter of {@link 
VectorValues#search(float[], int, int)}
+ *   is used to control the values of <code>numSeed</code> and 
<code>topK</code> that are passed to this API.
+ *   Thus <code>fanout</code> is like a combination of <code>ef</code> (search 
beam width) from the 2016 paper and <code>m</code> from the 2014 paper.
+ *   </li>
+ * </ul>
+ *
+ * <p>Note: The graph may be searched by multiple threads concurrently, but 
updates are not thread-safe. Also note: there is no notion of
+ * deletions. Document searching built on top of this must do its own 
deletion-filtering.</p>
+ */
+public final class HnswGraph {
+
+  // each entry lists the neighbors of a node, in node order
+  private final List<List<Neighbor>> graph;
+
+  HnswGraph() {

Review comment:
       Yeah, +1 for fast follow!




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