kaivalnp commented on code in PR #15979:
URL: https://github.com/apache/lucene/pull/15979#discussion_r3659863857


##########
lucene/core/src/test/org/apache/lucene/codecs/lucene106/dedup/TestLucene106DedupHnswVectorsFormat.java:
##########
@@ -0,0 +1,91 @@
+/*
+ * 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.codecs.lucene106.dedup;
+
+import java.io.IOException;
+import org.apache.lucene.codecs.Codec;
+import org.apache.lucene.codecs.KnnVectorsFormat;
+import org.apache.lucene.codecs.KnnVectorsReader;
+import org.apache.lucene.codecs.simpletext.SimpleTextKnnVectorsReader;
+import org.apache.lucene.index.CodecReader;
+import org.apache.lucene.index.LeafReader;
+import org.apache.lucene.tests.index.BaseKnnVectorsFormatTestCase;
+import org.apache.lucene.tests.util.TestUtil;
+import org.junit.Ignore;
+
+/**
+ * Runs the standard KNN vectors format suite against the de-duplicating HNSW 
format. De-duplication
+ * behavior itself is covered by {@link TestDedupFlatVectorsFormat}.
+ */
+public class TestLucene106DedupHnswVectorsFormat extends 
BaseKnnVectorsFormatTestCase {
+
+  private final KnnVectorsFormat format = new 
Lucene106DedupHnswVectorsFormat();
+
+  @Override
+  protected Codec getCodec() {
+    return TestUtil.alwaysKnnVectorsFormat(format);
+  }
+
+  @Override
+  protected boolean supportsFloatVectorFallback() {
+    return false; // stores raw vectors, no quantized fallback
+  }
+
+  @Override
+  protected void assertOffHeapByteSize(LeafReader r, String fieldName) throws 
IOException {
+    var fieldInfo = r.getFieldInfos().fieldInfo(fieldName);
+
+    if (r instanceof CodecReader codecReader) {
+      KnnVectorsReader knnVectorsReader = codecReader.getVectorReader();
+      knnVectorsReader = knnVectorsReader.unwrapReaderForField(fieldName);
+      var offHeap = knnVectorsReader.getOffHeapByteSize(fieldInfo);
+      long totalByteSize = 
offHeap.values().stream().mapToLong(Long::longValue).sum();
+      if (knnVectorsReader instanceof SimpleTextKnnVectorsReader) {
+        assertEquals(0L, offHeap.size()); // all vectors are in memory
+        assertEquals(0L, totalByteSize);
+      } else {
+        if (getNumVectors(knnVectorsReader, fieldInfo) == 0) {
+          assertEquals(0L, totalByteSize);
+        } else {
+          assertTrue(totalByteSize > 0);
+          assertTrue(offHeap.get("vdd") > 0L); // NOTE: different from vec
+
+          if (hasHNSW(knnVectorsReader, fieldInfo)) {
+            assertTrue(offHeap.get("vex") > 0L);
+          } else {
+            assertTrue(offHeap.get("vex") == null || offHeap.get("vex") == 0);
+          }
+        }
+      }
+    } else {
+      throw new AssertionError("unexpected:" + r.getClass());
+    }
+  }
+
+  /**
+   * This test indexes random vectors of small dimensions with high 
duplicates, checking that RAM
+   * usage is above a threshold. The RAM usage assumption breaks with the 
de-duplicating format.
+   */
+  @Override
+  @Ignore
+  public void testWriterRamEstimate() {}

Review Comment:
   This led to an interesting discovery -- the [`ramBytesUsed` for the HNSW 
writer](https://github.com/kaivalnp/lucene/blob/91936b9b43d7244b2663de254ef1c0715c59d754/lucene/core/src/java/org/apache/lucene/codecs/lucene99/Lucene99HnswVectorsWriter.java#L237-L245)
 is calculated by summing up the per-field writer values, and implicitly 
_assumes_ that the top-level writer uses trivial bytes (and is not counted), 
which is different for the de-duplicating format (we're attributing the raw 
de-duped vectors to the top-level writer, and only per-field mappings to 
field-level writers).
   
   I made some changes to remove this assumption and make both cases work with 
tests!



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