Control the auto-adjustment of targetHits in ANN using post-filtering.

When searching the HNSW index in a post-filtering case,
targetHits is auto-adjusted in an effort to still expose targetHits hits to first-phase ranking after post-filtering.
The following formula is now used to ensure an upper bound of adjustedTargetHits,
avoiding that the search in the HNSW index takes too long.

adjustedTargetHits = min(targetHits / estimatedHitRatio, targetHits * targetHitsMaxAdjustmentFactor).

The target-hits-max-adjustment-factor can be set in a rank profile and overriden per query.
The value is in the range [1.0,inf], with the default being 20.0.

When setting this to 1.0, auto-adjustment of targetHits is effectively disabled.

1 files changed, 5 insertions, 1 deletions

diff --git a/container-search/src/test/java/com/yahoo/search/query/MatchingTestCase.java b/container-search/src/test/java/com/yahoo/search/query/MatchingTestCase.java
index e3a1eb18a33..37d0e9e1072 100644
--- a/container-search/src/test/java/com/yahoo/search/query/MatchingTestCase.java
+++ b/container-search/src/test/java/com/yahoo/search/query/MatchingTestCase.java
@@ -20,6 +20,7 @@ public class MatchingTestCase {
         assertNull(query.getRanking().getMatching().getMinHitsPerThread());
         assertNull(query.getRanking().getMatching().getPostFilterThreshold());
         assertNull(query.getRanking().getMatching().getApproximateThreshold());
+        assertNull(query.getRanking().getMatching().getTargetHitsMaxAdjustmentFactor());
     }
 
     @Test
@@ -30,13 +31,15 @@ public class MatchingTestCase {
                 "&ranking.matching.numSearchPartitions=13" +
                 "&ranking.matching.minHitsPerThread=3" +
                 "&ranking.matching.postFilterThreshold=0.8" +
-                "&ranking.matching.approximateThreshold=0.3");
+                "&ranking.matching.approximateThreshold=0.3" +
+                "&ranking.matching.targetHitsMaxAdjustmentFactor=2.5");
         assertEquals(Double.valueOf(0.7), query.getRanking().getMatching().getTermwiseLimit());
         assertEquals(Integer.valueOf(17), query.getRanking().getMatching().getNumThreadsPerSearch());
         assertEquals(Integer.valueOf(13), query.getRanking().getMatching().getNumSearchPartitions());
         assertEquals(Integer.valueOf(3), query.getRanking().getMatching().getMinHitsPerThread());
         assertEquals(Double.valueOf(0.8), query.getRanking().getMatching().getPostFilterThreshold());
         assertEquals(Double.valueOf(0.3), query.getRanking().getMatching().getApproximateThreshold());
+        assertEquals(Double.valueOf(2.5), query.getRanking().getMatching().getTargetHitsMaxAdjustmentFactor());
 
         query.prepare();
         assertEquals("0.7", query.getRanking().getProperties().get("vespa.matching.termwise_limit").get(0));
@@ -45,6 +48,7 @@ public class MatchingTestCase {
         assertEquals("3", query.getRanking().getProperties().get("vespa.matching.minhitsperthread").get(0));
         assertEquals("0.8", query.getRanking().getProperties().get("vespa.matching.global_filter.upper_limit").get(0));
         assertEquals("0.3", query.getRanking().getProperties().get("vespa.matching.global_filter.lower_limit").get(0));
+        assertEquals("2.5", query.getRanking().getProperties().get("vespa.matching.nns.target_hits_max_adjustment_factor").get(0));
     }
 
     @Test