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Vector test tools (#128934)

Browse source 
This adds some testing tools for verifying vector recall and latency
directly without having to spin up an entire ES node and running a rally
track.

Its pretty barebones and takes inspiration from lucene-util, but I
wanted access to our own formats and tooling to make our lives easier.

Here is an example config file. This will build the initial index, run
queries at num_candidates: 50, then again at num_candidates 100 (without
reindexing, and re-using the cached nearest neighbors).

```
[{
  "doc_vectors" : "path",
  "query_vectors" : "path",
  "num_docs" : 10000,
  "num_queries" : 10,
  "index_type" : "hnsw",
  "num_candidates" : 50,
  "k" : 10,
  "hnsw_m" : 16,
  "hnsw_ef_construction" : 200,
  "index_threads" : 4,
  "reindex" : true,
  "force_merge" : false,
  "vector_space" : "maximum_inner_product",
  "dimensions" : 768
},
{
"doc_vectors" : "path",
"query_vectors" : "path",
"num_docs" : 10000,
"num_queries" : 10,
"index_type" : "hnsw",
"num_candidates" : 100,
"k" : 10,
"hnsw_m" : 16,
"hnsw_ef_construction" : 200,
"vector_space" : "maximum_inner_product",
"dimensions" : 768
}
]
```

To execute:

```
./gradlew :qa:vector:checkVec --args="/Path/to/knn_tester_config.json"
```

Calling `./gradlew :qa:vector:checkVecHelp` gives some guidance on how
to use it, additionally providing a way to run it via java directly
(useful to bypass gradlew guff).
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/*
* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
* or more contributor license agreements. Licensed under the "Elastic License
* 2.0", the "GNU Affero General Public License v3.0 only", and the "Server Side
* Public License v 1"; you may not use this file except in compliance with, at
* your election, the "Elastic License 2.0", the "GNU Affero General Public
* License v3.0 only", or the "Server Side Public License, v 1".
*/
apply plugin: 'elasticsearch.java'
apply plugin: 'elasticsearch.build'
tasks.named("dependencyLicenses").configure {
mapping from: /lucene-.*/, to: 'lucene'
}
tasks.named('forbiddenApisMain').configure {
enabled = false
}
dependencies {
api "org.apache.lucene:lucene-core:${versions.lucene}"
api "org.apache.lucene:lucene-queries:${versions.lucene}"
api "org.apache.lucene:lucene-codecs:${versions.lucene}"
implementation project(':libs:logging')
implementation project(':server')
}
/**
* Task to run the KnnIndexTester with the provided parameters.
*/
tasks.register("checkVec", JavaExec) {
group = "Execution"
description = "Runs KnnIndexTester with the provided parameters to validate recall and performance."
classpath = sourceSets.main.runtimeClasspath
mainClass.set("org.elasticsearch.test.knn.KnnIndexTester")
// Configure logging to console
systemProperty "es.logger.out", "console"
systemProperty "es.logger.level", "INFO" // Change to DEBUG if needed
if (buildParams.getRuntimeJavaVersion().map { it.majorVersion.toInteger() }.get() >= 21) {
jvmArgs '-Xms4g', '-Xmx4g', '--add-modules=jdk.incubator.vector', '--enable-native-access=ALL-UNNAMED', '-Djava.util.concurrent.ForkJoinPool.common.parallelism=8', '-XX:+UnlockDiagnosticVMOptions', '-XX:+DebugNonSafepoints', '-XX:+HeapDumpOnOutOfMemoryError'
}
}
tasks.register("checkVecHelp", JavaExec) {
group = "Help"
description = "Prints help for the KnnIndexTester task."
classpath = sourceSets.main.runtimeClasspath
mainClass.set("org.elasticsearch.test.knn.KnnIndexTester")
args = ["--help"]
doLast {
println """
=============================================================================
KnnIndexTester Help
=============================================================================
Run with Gradle:
----------------
# Using default configuration file
./gradlew :qa:vector:checkVec
# Using custom configuration file
./gradlew :qa:vector:checkVec --args="path/to/your/config.json"
# Adjust heap size
./gradlew :qa:vector:checkVec -Dorg.gradle.jvmargs="-Xmx8g" --args="path/to/your/config.json"
# Set environment variable for more extensive JVM options
export GRADLE_OPTS="-Xmx8g -XX:+UseG1GC -XX:MaxGCPauseMillis=100"
./gradlew :qa:vector:checkVec
Run directly with Java:
----------------------
# Generate classpath (run once to create the file)
./gradlew :qa:vector:printClasspath
# Then use the classpath file with java
java -cp "\$(cat build/vector_classpath.txt)" \\
--add-modules=jdk.incubator.vector \\
--enable-native-access=ALL-UNNAMED \\
-Djava.util.concurrent.ForkJoinPool.common.parallelism=8 \\
-Xmx4g \\
-Xms4g \\\\
org.elasticsearch.test.knn.KnnIndexTester path/to/your/config.json
"""
}
}
tasks.register("printClasspath") {
group = "Help"
description = "Prints the classpath needed to run KnnIndexTester directly with java"
doLast {
def classpathFile = new File("${buildDir}/vector_classpath.txt")
classpathFile.parentFile.mkdirs()
classpathFile.text = sourceSets.main.runtimeClasspath.asPath
println "Classpath written to: ${classpathFile.absolutePath}"
}
}

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---
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Apache Lucene
Copyright 2014 The Apache Software Foundation
This product includes software developed at
The Apache Software Foundation (http://www.apache.org/).
Includes software from other Apache Software Foundation projects,
including, but not limited to:
- Apache Ant
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ICU4J, (under analysis/icu) is licensed under an MIT styles license
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Some data files (under analysis/icu/src/data) are derived from Unicode data such
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Brics Automaton (under core/src/java/org/apache/lucene/util/automaton) is
BSD-licensed, created by Anders Møller. See http://www.brics.dk/automaton/
The levenshtein automata tables (under core/src/java/org/apache/lucene/util/automaton) were
automatically generated with the moman/finenight FSA library, created by
Jean-Philippe Barrette-LaPierre. This library is available under an MIT license,
see http://sites.google.com/site/rrettesite/moman and
http://bitbucket.org/jpbarrette/moman/overview/
The class org.apache.lucene.util.WeakIdentityMap was derived from
the Apache CXF project and is Apache License 2.0.
The Google Code Prettify is Apache License 2.0.
See http://code.google.com/p/google-code-prettify/
JUnit (junit-4.10) is licensed under the Common Public License v. 1.0
See http://junit.sourceforge.net/cpl-v10.html
This product includes code (JaspellTernarySearchTrie) from Java Spelling Checkin
g Package (jaspell): http://jaspell.sourceforge.net/
License: The BSD License (http://www.opensource.org/licenses/bsd-license.php)
The snowball stemmers in
analysis/common/src/java/net/sf/snowball
were developed by Martin Porter and Richard Boulton.
The snowball stopword lists in
analysis/common/src/resources/org/apache/lucene/analysis/snowball
were developed by Martin Porter and Richard Boulton.
The full snowball package is available from
http://snowball.tartarus.org/
The KStem stemmer in
analysis/common/src/org/apache/lucene/analysis/en
was developed by Bob Krovetz and Sergio Guzman-Lara (CIIR-UMass Amherst)
under the BSD-license.
The Arabic,Persian,Romanian,Bulgarian, Hindi and Bengali analyzers (common) come with a default
stopword list that is BSD-licensed created by Jacques Savoy. These files reside in:
analysis/common/src/resources/org/apache/lucene/analysis/ar/stopwords.txt,
analysis/common/src/resources/org/apache/lucene/analysis/fa/stopwords.txt,
analysis/common/src/resources/org/apache/lucene/analysis/ro/stopwords.txt,
analysis/common/src/resources/org/apache/lucene/analysis/bg/stopwords.txt,
analysis/common/src/resources/org/apache/lucene/analysis/hi/stopwords.txt,
analysis/common/src/resources/org/apache/lucene/analysis/bn/stopwords.txt
See http://members.unine.ch/jacques.savoy/clef/index.html.
The German,Spanish,Finnish,French,Hungarian,Italian,Portuguese,Russian and Swedish light stemmers
(common) are based on BSD-licensed reference implementations created by Jacques Savoy and
Ljiljana Dolamic. These files reside in:
analysis/common/src/java/org/apache/lucene/analysis/de/GermanLightStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/de/GermanMinimalStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/es/SpanishLightStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/fi/FinnishLightStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/fr/FrenchLightStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/fr/FrenchMinimalStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/hu/HungarianLightStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/it/ItalianLightStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/pt/PortugueseLightStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/ru/RussianLightStemmer.java
analysis/common/src/java/org/apache/lucene/analysis/sv/SwedishLightStemmer.java
The Stempel analyzer (stempel) includes BSD-licensed software developed
by the Egothor project http://egothor.sf.net/, created by Leo Galambos, Martin Kvapil,
and Edmond Nolan.
The Polish analyzer (stempel) comes with a default
stopword list that is BSD-licensed created by the Carrot2 project. The file resides
in stempel/src/resources/org/apache/lucene/analysis/pl/stopwords.txt.
See http://project.carrot2.org/license.html.
The SmartChineseAnalyzer source code (smartcn) was
provided by Xiaoping Gao and copyright 2009 by www.imdict.net.
WordBreakTestUnicode_*.java (under modules/analysis/common/src/test/)
is derived from Unicode data such as the Unicode Character Database.
See http://unicode.org/copyright.html for more details.
The Morfologik analyzer (morfologik) includes BSD-licensed software
developed by Dawid Weiss and Marcin Miłkowski (http://morfologik.blogspot.com/).
Morfologik uses data from Polish ispell/myspell dictionary
(http://www.sjp.pl/slownik/en/) licenced on the terms of (inter alia)
LGPL and Creative Commons ShareAlike.
Morfologic includes data from BSD-licensed dictionary of Polish (SGJP)
(http://sgjp.pl/morfeusz/)
Servlet-api.jar and javax.servlet-*.jar are under the CDDL license, the original
source code for this can be found at http://www.eclipse.org/jetty/downloads.php
===========================================================================
Kuromoji Japanese Morphological Analyzer - Apache Lucene Integration
===========================================================================
This software includes a binary and/or source version of data from
mecab-ipadic-2.7.0-20070801
which can be obtained from
http://atilika.com/releases/mecab-ipadic/mecab-ipadic-2.7.0-20070801.tar.gz
or
http://jaist.dl.sourceforge.net/project/mecab/mecab-ipadic/2.7.0-20070801/mecab-ipadic-2.7.0-20070801.tar.gz
===========================================================================
mecab-ipadic-2.7.0-20070801 Notice
===========================================================================
Nara Institute of Science and Technology (NAIST),
the copyright holders, disclaims all warranties with regard to this
software, including all implied warranties of merchantability and
fitness, in no event shall NAIST be liable for
any special, indirect or consequential damages or any damages
whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortuous action, arising out
of or in connection with the use or performance of this software.
A large portion of the dictionary entries
originate from ICOT Free Software. The following conditions for ICOT
Free Software applies to the current dictionary as well.
Each User may also freely distribute the Program, whether in its
original form or modified, to any third party or parties, PROVIDED
that the provisions of Section 3 ("NO WARRANTY") will ALWAYS appear
on, or be attached to, the Program, which is distributed substantially
in the same form as set out herein and that such intended
distribution, if actually made, will neither violate or otherwise
contravene any of the laws and regulations of the countries having
jurisdiction over the User or the intended distribution itself.
NO WARRANTY
The program was produced on an experimental basis in the course of the
research and development conducted during the project and is provided
to users as so produced on an experimental basis. Accordingly, the
program is provided without any warranty whatsoever, whether express,
implied, statutory or otherwise. The term "warranty" used herein
includes, but is not limited to, any warranty of the quality,
performance, merchantability and fitness for a particular purpose of
the program and the nonexistence of any infringement or violation of
any right of any third party.
Each user of the program will agree and understand, and be deemed to
have agreed and understood, that there is no warranty whatsoever for
the program and, accordingly, the entire risk arising from or
otherwise connected with the program is assumed by the user.
Therefore, neither ICOT, the copyright holder, or any other
organization that participated in or was otherwise related to the
development of the program and their respective officials, directors,
officers and other employees shall be held liable for any and all
damages, including, without limitation, general, special, incidental
and consequential damages, arising out of or otherwise in connection
with the use or inability to use the program or any product, material
or result produced or otherwise obtained by using the program,
regardless of whether they have been advised of, or otherwise had
knowledge of, the possibility of such damages at any time during the
project or thereafter. Each user will be deemed to have agreed to the
foregoing by his or her commencement of use of the program. The term
"use" as used herein includes, but is not limited to, the use,
modification, copying and distribution of the program and the
production of secondary products from the program.
In the case where the program, whether in its original form or
modified, was distributed or delivered to or received by a user from
any person, organization or entity other than ICOT, unless it makes or
grants independently of ICOT any specific warranty to the user in
writing, such person, organization or entity, will also be exempted
from and not be held liable to the user for any such damages as noted
above as far as the program is concerned.

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/*
* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
* or more contributor license agreements. Licensed under the "Elastic License
* 2.0", the "GNU Affero General Public License v3.0 only", and the "Server Side
* Public License v 1"; you may not use this file except in compliance with, at
* your election, the "Elastic License 2.0", the "GNU Affero General Public
* License v3.0 only", or the "Server Side Public License, v 1".
*/
module org.elasticsearch.test.knn {
requires org.elasticsearch.base;
requires org.elasticsearch.server;
requires org.elasticsearch.xcontent;
requires org.apache.lucene.core;
requires org.apache.lucene.codecs;
requires org.apache.lucene.queries;
requires org.elasticsearch.logging;
requires java.management;
requires jdk.management;
}

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/*
* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
* or more contributor license agreements. Licensed under the "Elastic License
* 2.0", the "GNU Affero General Public License v3.0 only", and the "Server Side
* Public License v 1"; you may not use this file except in compliance with, at
* your election, the "Elastic License 2.0", the "GNU Affero General Public
* License v3.0 only", or the "Server Side Public License, v 1".
*/
package org.elasticsearch.test.knn;
import org.apache.lucene.index.VectorEncoding;
import org.apache.lucene.index.VectorSimilarityFunction;
import org.elasticsearch.common.Strings;
import org.elasticsearch.core.PathUtils;
import org.elasticsearch.xcontent.ObjectParser;
import org.elasticsearch.xcontent.ParseField;
import org.elasticsearch.xcontent.ToXContentObject;
import org.elasticsearch.xcontent.XContentBuilder;
import org.elasticsearch.xcontent.XContentParser;
import java.io.IOException;
import java.nio.file.Path;
import java.util.Locale;
/**
* Command line arguments for the KNN index tester.
* This class encapsulates all the parameters required to run the KNN index tests.
*/
record CmdLineArgs(
Path docVectors,
Path queryVectors,
int numDocs,
int numQueries,
KnnIndexTester.IndexType indexType,
int numCandidates,
int k,
int nProbe,
int ivfClusterSize,
int overSamplingFactor,
int hnswM,
int hnswEfConstruction,
int searchThreads,
int indexThreads,
boolean reindex,
boolean forceMerge,
VectorSimilarityFunction vectorSpace,
int quantizeBits,
VectorEncoding vectorEncoding,
int dimensions
) implements ToXContentObject {
static final ParseField DOC_VECTORS_FIELD = new ParseField("doc_vectors");
static final ParseField QUERY_VECTORS_FIELD = new ParseField("query_vectors");
static final ParseField NUM_DOCS_FIELD = new ParseField("num_docs");
static final ParseField NUM_QUERIES_FIELD = new ParseField("num_queries");
static final ParseField INDEX_TYPE_FIELD = new ParseField("index_type");
static final ParseField NUM_CANDIDATES_FIELD = new ParseField("num_candidates");
static final ParseField K_FIELD = new ParseField("k");
static final ParseField N_PROBE_FIELD = new ParseField("n_probe");
static final ParseField IVF_CLUSTER_SIZE_FIELD = new ParseField("ivf_cluster_size");
static final ParseField OVER_SAMPLING_FACTOR_FIELD = new ParseField("over_sampling_factor");
static final ParseField HNSW_M_FIELD = new ParseField("hnsw_m");
static final ParseField HNSW_EF_CONSTRUCTION_FIELD = new ParseField("hnsw_ef_construction");
static final ParseField SEARCH_THREADS_FIELD = new ParseField("search_threads");
static final ParseField INDEX_THREADS_FIELD = new ParseField("index_threads");
static final ParseField REINDEX_FIELD = new ParseField("reindex");
static final ParseField FORCE_MERGE_FIELD = new ParseField("force_merge");
static final ParseField VECTOR_SPACE_FIELD = new ParseField("vector_space");
static final ParseField QUANTIZE_BITS_FIELD = new ParseField("quantize_bits");
static final ParseField VECTOR_ENCODING_FIELD = new ParseField("vector_encoding");
static final ParseField DIMENSIONS_FIELD = new ParseField("dimensions");
static CmdLineArgs fromXContent(XContentParser parser) throws IOException {
Builder builder = PARSER.apply(parser, null);
return builder.build();
}
static final ObjectParser<CmdLineArgs.Builder, Void> PARSER = new ObjectParser<>("cmd_line_args", true, Builder::new);
static {
PARSER.declareString(Builder::setDocVectors, DOC_VECTORS_FIELD);
PARSER.declareString(Builder::setQueryVectors, QUERY_VECTORS_FIELD);
PARSER.declareInt(Builder::setNumDocs, NUM_DOCS_FIELD);
PARSER.declareInt(Builder::setNumQueries, NUM_QUERIES_FIELD);
PARSER.declareString(Builder::setIndexType, INDEX_TYPE_FIELD);
PARSER.declareInt(Builder::setNumCandidates, NUM_CANDIDATES_FIELD);
PARSER.declareInt(Builder::setK, K_FIELD);
PARSER.declareInt(Builder::setNProbe, N_PROBE_FIELD);
PARSER.declareInt(Builder::setIvfClusterSize, IVF_CLUSTER_SIZE_FIELD);
PARSER.declareInt(Builder::setOverSamplingFactor, OVER_SAMPLING_FACTOR_FIELD);
PARSER.declareInt(Builder::setHnswM, HNSW_M_FIELD);
PARSER.declareInt(Builder::setHnswEfConstruction, HNSW_EF_CONSTRUCTION_FIELD);
PARSER.declareInt(Builder::setSearchThreads, SEARCH_THREADS_FIELD);
PARSER.declareInt(Builder::setIndexThreads, INDEX_THREADS_FIELD);
PARSER.declareBoolean(Builder::setReindex, REINDEX_FIELD);
PARSER.declareBoolean(Builder::setForceMerge, FORCE_MERGE_FIELD);
PARSER.declareString(Builder::setVectorSpace, VECTOR_SPACE_FIELD);
PARSER.declareInt(Builder::setQuantizeBits, QUANTIZE_BITS_FIELD);
PARSER.declareString(Builder::setVectorEncoding, VECTOR_ENCODING_FIELD);
PARSER.declareInt(Builder::setDimensions, DIMENSIONS_FIELD);
}
@Override
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
builder.startObject();
if (docVectors != null) {
builder.field(DOC_VECTORS_FIELD.getPreferredName(), docVectors.toString());
}
if (queryVectors != null) {
builder.field(QUERY_VECTORS_FIELD.getPreferredName(), queryVectors.toString());
}
builder.field(NUM_DOCS_FIELD.getPreferredName(), numDocs);
builder.field(NUM_QUERIES_FIELD.getPreferredName(), numQueries);
builder.field(INDEX_TYPE_FIELD.getPreferredName(), indexType.name().toLowerCase(Locale.ROOT));
builder.field(NUM_CANDIDATES_FIELD.getPreferredName(), numCandidates);
builder.field(K_FIELD.getPreferredName(), k);
builder.field(N_PROBE_FIELD.getPreferredName(), nProbe);
builder.field(IVF_CLUSTER_SIZE_FIELD.getPreferredName(), ivfClusterSize);
builder.field(OVER_SAMPLING_FACTOR_FIELD.getPreferredName(), overSamplingFactor);
builder.field(HNSW_M_FIELD.getPreferredName(), hnswM);
builder.field(HNSW_EF_CONSTRUCTION_FIELD.getPreferredName(), hnswEfConstruction);
builder.field(SEARCH_THREADS_FIELD.getPreferredName(), searchThreads);
builder.field(INDEX_THREADS_FIELD.getPreferredName(), indexThreads);
builder.field(REINDEX_FIELD.getPreferredName(), reindex);
builder.field(FORCE_MERGE_FIELD.getPreferredName(), forceMerge);
builder.field(VECTOR_SPACE_FIELD.getPreferredName(), vectorSpace.name().toLowerCase(Locale.ROOT));
builder.field(QUANTIZE_BITS_FIELD.getPreferredName(), quantizeBits);
builder.field(VECTOR_ENCODING_FIELD.getPreferredName(), vectorEncoding.name().toLowerCase(Locale.ROOT));
builder.field(DIMENSIONS_FIELD.getPreferredName(), dimensions);
return builder.endObject();
}
@Override
public String toString() {
return Strings.toString(this, false, false);
}
static class Builder {
private Path docVectors;
private Path queryVectors;
private int numDocs = 1000;
private int numQueries = 100;
private KnnIndexTester.IndexType indexType = KnnIndexTester.IndexType.HNSW;
private int numCandidates = 1000;
private int k = 10;
private int nProbe = 10;
private int ivfClusterSize = 1000;
private int overSamplingFactor = 1;
private int hnswM = 16;
private int hnswEfConstruction = 200;
private int searchThreads = 1;
private int indexThreads = 1;
private boolean reindex = false;
private boolean forceMerge = false;
private VectorSimilarityFunction vectorSpace = VectorSimilarityFunction.EUCLIDEAN;
private int quantizeBits = 8;
private VectorEncoding vectorEncoding = VectorEncoding.FLOAT32;
private int dimensions;
public Builder setDocVectors(String docVectors) {
this.docVectors = PathUtils.get(docVectors);
return this;
}
public Builder setQueryVectors(String queryVectors) {
this.queryVectors = PathUtils.get(queryVectors);
return this;
}
public Builder setNumDocs(int numDocs) {
this.numDocs = numDocs;
return this;
}
public Builder setNumQueries(int numQueries) {
this.numQueries = numQueries;
return this;
}
public Builder setIndexType(String indexType) {
this.indexType = KnnIndexTester.IndexType.valueOf(indexType.toUpperCase(Locale.ROOT));
return this;
}
public Builder setNumCandidates(int numCandidates) {
this.numCandidates = numCandidates;
return this;
}
public Builder setK(int k) {
this.k = k;
return this;
}
public Builder setNProbe(int nProbe) {
this.nProbe = nProbe;
return this;
}
public Builder setIvfClusterSize(int ivfClusterSize) {
this.ivfClusterSize = ivfClusterSize;
return this;
}
public Builder setOverSamplingFactor(int overSamplingFactor) {
this.overSamplingFactor = overSamplingFactor;
return this;
}
public Builder setHnswM(int hnswM) {
this.hnswM = hnswM;
return this;
}
public Builder setHnswEfConstruction(int hnswEfConstruction) {
this.hnswEfConstruction = hnswEfConstruction;
return this;
}
public Builder setSearchThreads(int searchThreads) {
this.searchThreads = searchThreads;
return this;
}
public Builder setIndexThreads(int indexThreads) {
this.indexThreads = indexThreads;
return this;
}
public Builder setReindex(boolean reindex) {
this.reindex = reindex;
return this;
}
public Builder setForceMerge(boolean forceMerge) {
this.forceMerge = forceMerge;
return this;
}
public Builder setVectorSpace(String vectorSpace) {
this.vectorSpace = VectorSimilarityFunction.valueOf(vectorSpace.toUpperCase(Locale.ROOT));
return this;
}
public Builder setQuantizeBits(int quantizeBits) {
this.quantizeBits = quantizeBits;
return this;
}
public Builder setVectorEncoding(String vectorEncoding) {
this.vectorEncoding = VectorEncoding.valueOf(vectorEncoding.toUpperCase(Locale.ROOT));
return this;
}
public Builder setDimensions(int dimensions) {
this.dimensions = dimensions;
return this;
}
public CmdLineArgs build() {
if (docVectors == null) {
throw new IllegalArgumentException("Document vectors path must be provided");
}
if (dimensions <= 0) {
throw new IllegalArgumentException("dimensions must be a positive integer");
}
return new CmdLineArgs(
docVectors,
queryVectors,
numDocs,
numQueries,
indexType,
numCandidates,
k,
nProbe,
ivfClusterSize,
overSamplingFactor,
hnswM,
hnswEfConstruction,
searchThreads,
indexThreads,
reindex,
forceMerge,
vectorSpace,
quantizeBits,
vectorEncoding,
dimensions
);
}
}
}

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/*
* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
* or more contributor license agreements. Licensed under the "Elastic License
* 2.0", the "GNU Affero General Public License v3.0 only", and the "Server Side
* Public License v 1"; you may not use this file except in compliance with, at
* your election, the "Elastic License 2.0", the "GNU Affero General Public
* License v3.0 only", or the "Server Side Public License, v 1".
*/
package org.elasticsearch.test.knn;
import com.sun.management.ThreadMXBean;
import org.apache.lucene.codecs.Codec;
import org.apache.lucene.codecs.KnnVectorsFormat;
import org.apache.lucene.codecs.lucene101.Lucene101Codec;
import org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsFormat;
import org.elasticsearch.common.Strings;
import org.elasticsearch.common.logging.LogConfigurator;
import org.elasticsearch.core.PathUtils;
import org.elasticsearch.index.codec.vectors.ES813Int8FlatVectorFormat;
import org.elasticsearch.index.codec.vectors.ES814HnswScalarQuantizedVectorsFormat;
import org.elasticsearch.index.codec.vectors.IVFVectorsFormat;
import org.elasticsearch.index.codec.vectors.es818.ES818BinaryQuantizedVectorsFormat;
import org.elasticsearch.index.codec.vectors.es818.ES818HnswBinaryQuantizedVectorsFormat;
import org.elasticsearch.logging.Level;
import org.elasticsearch.xcontent.XContentParser;
import org.elasticsearch.xcontent.XContentParserConfiguration;
import org.elasticsearch.xcontent.XContentType;
import java.io.InputStream;
import java.lang.management.ThreadInfo;
import java.nio.file.Files;
import java.nio.file.Path;
import java.util.ArrayList;
import java.util.List;
import java.util.Locale;
/**
* A utility class to create and test KNN indices using Lucene.
* It supports various index types (HNSW, FLAT, IVF) and configurations.
*/
public class KnnIndexTester {
static final Level LOG_LEVEL = Level.DEBUG;
static final SysOutLogger logger = new SysOutLogger();
static {
LogConfigurator.loadLog4jPlugins();
LogConfigurator.configureESLogging(); // native access requires logging to be initialized
}
static final String INDEX_DIR = "target/knn_index";
enum IndexType {
HNSW,
FLAT,
IVF
}
private static String formatIndexPath(CmdLineArgs args) {
List<String> suffix = new ArrayList<>();
if (args.indexType() == IndexType.FLAT) {
suffix.add("flat");
} else if (args.indexType() == IndexType.IVF) {
suffix.add("ivf");
suffix.add(Integer.toString(args.ivfClusterSize()));
} else {
suffix.add(Integer.toString(args.hnswM()));
suffix.add(Integer.toString(args.hnswEfConstruction()));
if (args.quantizeBits() < 32) {
suffix.add(Integer.toString(args.quantizeBits()));
}
}
return INDEX_DIR + "/" + args.docVectors().getFileName() + "-" + String.join("-", suffix) + ".index";
}
static Codec createCodec(CmdLineArgs args) {
final KnnVectorsFormat format;
if (args.indexType() == IndexType.IVF) {
format = new IVFVectorsFormat(args.ivfClusterSize());
} else {
if (args.quantizeBits() == 1) {
if (args.indexType() == IndexType.FLAT) {
format = new ES818BinaryQuantizedVectorsFormat();
} else {
format = new ES818HnswBinaryQuantizedVectorsFormat(args.hnswM(), args.hnswEfConstruction(), 1, null);
}
} else if (args.quantizeBits() < 32) {
if (args.indexType() == IndexType.FLAT) {
format = new ES813Int8FlatVectorFormat(null, args.quantizeBits(), true);
} else {
format = new ES814HnswScalarQuantizedVectorsFormat(
args.hnswM(),
args.hnswEfConstruction(),
null,
args.quantizeBits(),
true
);
}
} else {
format = new Lucene99HnswVectorsFormat(args.hnswM(), args.hnswEfConstruction(), 1, null);
}
}
return new Lucene101Codec() {
@Override
public KnnVectorsFormat getKnnVectorsFormatForField(String field) {
return format;
}
};
}
/**
* Main method to run the KNN index tester.
* It parses command line arguments, creates the index, and runs searches if specified.
*
* @param args Command line arguments
* @throws Exception If an error occurs during index creation or search
*/
public static void main(String[] args) throws Exception {
if (args.length != 1 || args[0].equals("--help") || args[0].equals("-h")) {
// printout an example configuration formatted file and indicate that it is required
System.out.println("Usage: java -cp <your-classpath> org.elasticsearch.test.knn.KnnIndexTester <config-file>");
System.out.println("Where <config-file> is a JSON file containing one or more configurations for the KNN index tester.");
System.out.println("An example configuration object: ");
System.out.println(
Strings.toString(
new CmdLineArgs.Builder().setDimensions(64)
.setDocVectors("/doc/vectors/path")
.setQueryVectors("/query/vectors/path")
.build(),
true,
true
)
);
return;
}
String jsonConfig = args[0];
// Parse command line arguments
Path jsonConfigPath = PathUtils.get(jsonConfig);
if (Files.exists(jsonConfigPath) == false) {
throw new IllegalArgumentException("JSON config file does not exist: " + jsonConfigPath);
}
// Parse the JSON config file to get command line arguments
// This assumes that CmdLineArgs.fromXContent is implemented to parse the JSON file
List<CmdLineArgs> cmdLineArgsList = new ArrayList<>();
try (
InputStream jsonStream = Files.newInputStream(jsonConfigPath);
XContentParser parser = XContentType.JSON.xContent().createParser(XContentParserConfiguration.EMPTY, jsonStream)
) {
// check if the parser is at the start of an object if so, we only have one set of arguments
if (parser.currentToken() == null && parser.nextToken() == XContentParser.Token.START_OBJECT) {
cmdLineArgsList.add(CmdLineArgs.fromXContent(parser));
} else if (parser.currentToken() == XContentParser.Token.START_ARRAY) {
// if the parser is at the start of an array, we have multiple sets of arguments
while (parser.nextToken() != XContentParser.Token.END_ARRAY) {
cmdLineArgsList.add(CmdLineArgs.fromXContent(parser));
}
} else {
throw new IllegalArgumentException("Invalid JSON format in config file: " + jsonConfigPath);
}
}
FormattedResults formattedResults = new FormattedResults();
for (CmdLineArgs cmdLineArgs : cmdLineArgsList) {
Results result = new Results(cmdLineArgs.indexType().name().toLowerCase(Locale.ROOT), cmdLineArgs.numDocs());
System.out.println("Running KNN index tester with arguments: " + cmdLineArgs);
Codec codec = createCodec(cmdLineArgs);
Path indexPath = PathUtils.get(formatIndexPath(cmdLineArgs));
if (cmdLineArgs.reindex() || cmdLineArgs.forceMerge()) {
KnnIndexer knnIndexer = new KnnIndexer(
cmdLineArgs.docVectors(),
indexPath,
codec,
cmdLineArgs.indexThreads(),
cmdLineArgs.vectorEncoding(),
cmdLineArgs.dimensions(),
cmdLineArgs.vectorSpace(),
cmdLineArgs.numDocs()
);
if (Files.exists(indexPath) == false) {
if (cmdLineArgs.reindex() == false) {
throw new IllegalArgumentException("Index path does not exist: " + indexPath);
}
if (cmdLineArgs.forceMerge()) {
throw new IllegalArgumentException("Force merging without an existing index in: " + indexPath);
}
}
if (cmdLineArgs.reindex()) {
knnIndexer.createIndex(result);
}
if (cmdLineArgs.forceMerge()) {
knnIndexer.forceMerge(result);
} else {
knnIndexer.numSegments(result);
}
}
if (cmdLineArgs.queryVectors() != null) {
KnnSearcher knnSearcher = new KnnSearcher(indexPath, cmdLineArgs);
knnSearcher.runSearch(result);
}
formattedResults.results.add(result);
}
System.out.println("Results:");
System.out.println(formattedResults);
}
static class FormattedResults {
List<Results> results = new ArrayList<>();
@Override
public String toString() {
if (results.isEmpty()) {
return "No results available.";
}
// Define column headers
String[] headers = {
"index_type",
"num_docs",
"index_time(ms)",
"force_merge_time(ms)",
"num_segments",
"latency(ms)",
"net_cpu_time(ms)",
"avg_cpu_count",
"QPS",
"recall",
"visited" };
// Calculate appropriate column widths based on headers and data
int[] widths = calculateColumnWidths(headers);
StringBuilder sb = new StringBuilder();
// Format and append header
sb.append(formatRow(headers, widths));
sb.append("\n");
// Add separator line
for (int width : widths) {
sb.append("-".repeat(width)).append(" ");
}
sb.append("\n");
// Format and append each row of data
for (Results result : results) {
String[] rowData = {
result.indexType,
Integer.toString(result.numDocs),
Long.toString(result.indexTimeMS),
Long.toString(result.forceMergeTimeMS),
Integer.toString(result.numSegments),
String.format(Locale.ROOT, "%.2f", result.avgLatency),
String.format(Locale.ROOT, "%.2f", result.netCpuTimeMS),
String.format(Locale.ROOT, "%.2f", result.avgCpuCount),
String.format(Locale.ROOT, "%.2f", result.qps),
String.format(Locale.ROOT, "%.2f", result.avgRecall),
String.format(Locale.ROOT, "%.2f", result.averageVisited) };
sb.append(formatRow(rowData, widths));
sb.append("\n");
}
return sb.toString();
}
// Helper method to format a single row with proper column widths
private String formatRow(String[] values, int[] widths) {
StringBuilder row = new StringBuilder();
for (int i = 0; i < values.length; i++) {
// Left-align text column (index_type), right-align numeric columns
String format = (i == 0) ? "%-" + widths[i] + "s" : "%" + widths[i] + "s";
row.append(Strings.format(format, values[i]));
// Add separation between columns
if (i < values.length - 1) {
row.append(" ");
}
}
return row.toString();
}
// Calculate appropriate column widths based on headers and data
private int[] calculateColumnWidths(String[] headers) {
int[] widths = new int[headers.length];
// Initialize widths with header lengths
for (int i = 0; i < headers.length; i++) {
widths[i] = headers[i].length();
}
// Update widths based on data
for (Results result : results) {
String[] values = {
result.indexType,
Integer.toString(result.numDocs),
Long.toString(result.indexTimeMS),
Long.toString(result.forceMergeTimeMS),
Integer.toString(result.numSegments),
String.format(Locale.ROOT, "%.2f", result.avgLatency),
String.format(Locale.ROOT, "%.2f", result.netCpuTimeMS),
String.format(Locale.ROOT, "%.2f", result.avgCpuCount),
String.format(Locale.ROOT, "%.2f", result.qps),
String.format(Locale.ROOT, "%.2f", result.avgRecall),
String.format(Locale.ROOT, "%.2f", result.averageVisited) };
for (int i = 0; i < values.length; i++) {
widths[i] = Math.max(widths[i], values[i].length());
}
}
return widths;
}
}
static class Results {
final String indexType;
final int numDocs;
long indexTimeMS;
long forceMergeTimeMS;
int numSegments;
double avgLatency;
double qps;
double avgRecall;
double averageVisited;
double netCpuTimeMS;
double avgCpuCount;
Results(String indexType, int numDocs) {
this.indexType = indexType;
this.numDocs = numDocs;
}
}
static final class SysOutLogger {
void warn(String message) {
if (LOG_LEVEL.ordinal() >= Level.WARN.ordinal()) {
System.out.println(message);
}
}
void warn(String message, Object... params) {
if (LOG_LEVEL.ordinal() >= Level.WARN.ordinal()) {
System.out.println(String.format(Locale.ROOT, message, params));
}
}
void info(String message) {
if (LOG_LEVEL.ordinal() >= Level.INFO.ordinal()) {
System.out.println(message);
}
}
void info(String message, Object... params) {
if (LOG_LEVEL.ordinal() >= Level.INFO.ordinal()) {
System.out.println(String.format(Locale.ROOT, message, params));
}
}
void debug(String message) {
if (LOG_LEVEL.ordinal() >= Level.DEBUG.ordinal()) {
System.out.println(message);
}
}
void debug(String message, Object... params) {
if (LOG_LEVEL.ordinal() >= Level.DEBUG.ordinal()) {
System.out.println(String.format(Locale.ROOT, message, params));
}
}
void trace(String message) {
if (LOG_LEVEL == Level.TRACE) {
System.out.println(message);
}
}
void trace(String message, Object... params) {
if (LOG_LEVEL == Level.TRACE) {
System.out.println(String.format(Locale.ROOT, message, params));
}
}
}
static final class ThreadDetails {
private static final ThreadMXBean threadBean = (ThreadMXBean) java.lang.management.ManagementFactory.getThreadMXBean();
public final long[] threadIDs;
public final long[] cpuTimesNS;
public final ThreadInfo[] threadInfos;
public final long ns;
ThreadDetails() {
ns = System.nanoTime();
threadIDs = threadBean.getAllThreadIds();
cpuTimesNS = threadBean.getThreadCpuTime(threadIDs);
threadInfos = threadBean.getThreadInfo(threadIDs);
}
}
}

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/*
* @notice
* 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.
* a copy and modification from Lucene util
* Modifications copyright (C) 2025 Elasticsearch B.V.
*/
package org.elasticsearch.test.knn;
import org.apache.lucene.codecs.Codec;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.FieldType;
import org.apache.lucene.document.KnnByteVectorField;
import org.apache.lucene.document.KnnFloatVectorField;
import org.apache.lucene.document.StoredField;
import org.apache.lucene.index.ConcurrentMergeScheduler;
import org.apache.lucene.index.DirectoryReader;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriterConfig;
import org.apache.lucene.index.VectorEncoding;
import org.apache.lucene.index.VectorSimilarityFunction;
import org.apache.lucene.store.FSDirectory;
import org.apache.lucene.util.PrintStreamInfoStream;
import org.elasticsearch.common.io.Channels;
import java.io.IOException;
import java.io.UncheckedIOException;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.channels.FileChannel;
import java.nio.file.Files;
import java.nio.file.Path;
import java.util.ArrayList;
import java.util.List;
import java.util.Objects;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicInteger;
import static org.elasticsearch.test.knn.KnnIndexTester.logger;
class KnnIndexer {
private static final double WRITER_BUFFER_MB = 128;
static final String ID_FIELD = "id";
static final String VECTOR_FIELD = "vector";
private final Path docsPath;
private final Path indexPath;
private final VectorEncoding vectorEncoding;
private final int dim;
private final VectorSimilarityFunction similarityFunction;
private final Codec codec;
private final int numDocs;
private final int numIndexThreads;
KnnIndexer(
Path docsPath,
Path indexPath,
Codec codec,
int numIndexThreads,
VectorEncoding vectorEncoding,
int dim,
VectorSimilarityFunction similarityFunction,
int numDocs
) {
this.docsPath = docsPath;
this.indexPath = indexPath;
this.codec = codec;
this.numIndexThreads = numIndexThreads;
this.vectorEncoding = vectorEncoding;
this.dim = dim;
this.similarityFunction = similarityFunction;
this.numDocs = numDocs;
}
void numSegments(KnnIndexTester.Results result) {
try (FSDirectory dir = FSDirectory.open(indexPath); IndexReader reader = DirectoryReader.open(dir)) {
result.numSegments = reader.leaves().size();
} catch (IOException e) {
throw new UncheckedIOException("Failed to get segment count for index at " + indexPath, e);
}
}
void createIndex(KnnIndexTester.Results result) throws IOException, InterruptedException, ExecutionException {
IndexWriterConfig iwc = new IndexWriterConfig().setOpenMode(IndexWriterConfig.OpenMode.CREATE);
iwc.setCodec(codec);
iwc.setRAMBufferSizeMB(WRITER_BUFFER_MB);
iwc.setUseCompoundFile(false);
iwc.setMaxFullFlushMergeWaitMillis(0);
FieldType fieldType = switch (vectorEncoding) {
case BYTE -> KnnByteVectorField.createFieldType(dim, similarityFunction);
case FLOAT32 -> KnnFloatVectorField.createFieldType(dim, similarityFunction);
};
iwc.setInfoStream(new PrintStreamInfoStream(System.out) {
@Override
public boolean isEnabled(String component) {
return Objects.equals(component, "IVF");
}
});
logger.debug(
"KnnIndexer: using codec=%s, vectorEncoding=%s, dim=%d, similarityFunction=%s",
codec.getName(),
vectorEncoding,
dim,
similarityFunction
);
if (Files.exists(indexPath)) {
logger.debug("KnnIndexer: existing index at %s", indexPath);
} else {
Files.createDirectories(indexPath);
}
long start = System.nanoTime();
try (
FSDirectory dir = FSDirectory.open(indexPath);
IndexWriter iw = new IndexWriter(dir, iwc);
FileChannel in = FileChannel.open(docsPath)
) {
long docsPathSizeInBytes = in.size();
if (docsPathSizeInBytes % ((long) dim * vectorEncoding.byteSize) != 0) {
throw new IllegalArgumentException(
"docsPath \"" + docsPath + "\" does not contain a whole number of vectors? size=" + docsPathSizeInBytes
);
}
logger.info(
"docsPathSizeInBytes=%d, dim=%d, vectorEncoding=%s, byteSize=%d",
docsPathSizeInBytes,
dim,
vectorEncoding,
vectorEncoding.byteSize
);
VectorReader inReader = VectorReader.create(in, dim, vectorEncoding);
try (ExecutorService exec = Executors.newFixedThreadPool(numIndexThreads, r -> new Thread(r, "KnnIndexer-Thread"))) {
AtomicInteger numDocsIndexed = new AtomicInteger();
List<Future<?>> threads = new ArrayList<>();
for (int i = 0; i < numIndexThreads; i++) {
Thread t = new IndexerThread(iw, inReader, dim, vectorEncoding, fieldType, numDocsIndexed, numDocs);
t.setDaemon(true);
threads.add(exec.submit(t));
}
for (Future<?> t : threads) {
t.get();
}
}
logger.debug("all indexing threads finished, now IndexWriter.commit()");
iw.commit();
ConcurrentMergeScheduler cms = (ConcurrentMergeScheduler) iwc.getMergeScheduler();
cms.sync();
}
long elapsed = System.nanoTime() - start;
logger.debug("Indexing took %d ms for %d docs", TimeUnit.NANOSECONDS.toMillis(elapsed), numDocs);
result.indexTimeMS = TimeUnit.NANOSECONDS.toMillis(elapsed);
}
void forceMerge(KnnIndexTester.Results results) throws Exception {
IndexWriterConfig iwc = new IndexWriterConfig().setOpenMode(IndexWriterConfig.OpenMode.APPEND);
iwc.setInfoStream(new PrintStreamInfoStream(System.out) {
@Override
public boolean isEnabled(String component) {
return Objects.equals(component, "IVF");
}
});
iwc.setCodec(codec);
logger.debug("KnnIndexer: forceMerge in %s", indexPath);
long startNS = System.nanoTime();
try (IndexWriter iw = new IndexWriter(FSDirectory.open(indexPath), iwc)) {
iw.forceMerge(1);
}
long endNS = System.nanoTime();
long elapsedNSec = (endNS - startNS);
logger.info("forceMerge took %d ms", TimeUnit.NANOSECONDS.toMillis(elapsedNSec));
results.forceMergeTimeMS = TimeUnit.NANOSECONDS.toMillis(elapsedNSec);
}
static class IndexerThread extends Thread {
private final IndexWriter iw;
private final AtomicInteger numDocsIndexed;
private final int numDocsToIndex;
private final FieldType fieldType;
private final VectorEncoding vectorEncoding;
private final byte[] byteVectorBuffer;
private final float[] floatVectorBuffer;
private final VectorReader in;
private IndexerThread(
IndexWriter iw,
VectorReader in,
int dims,
VectorEncoding vectorEncoding,
FieldType fieldType,
AtomicInteger numDocsIndexed,
int numDocsToIndex
) {
this.iw = iw;
this.in = in;
this.vectorEncoding = vectorEncoding;
this.fieldType = fieldType;
this.numDocsIndexed = numDocsIndexed;
this.numDocsToIndex = numDocsToIndex;
switch (vectorEncoding) {
case BYTE -> {
byteVectorBuffer = new byte[dims];
floatVectorBuffer = null;
}
case FLOAT32 -> {
floatVectorBuffer = new float[dims];
byteVectorBuffer = null;
}
default -> throw new IllegalArgumentException("unexpected vector encoding: " + vectorEncoding);
}
}
@Override
public void run() {
try {
_run();
} catch (IOException ioe) {
throw new UncheckedIOException(ioe);
}
}
private void _run() throws IOException {
while (true) {
int id = numDocsIndexed.getAndIncrement();
if (id >= numDocsToIndex) {
break;
}
Document doc = new Document();
switch (vectorEncoding) {
case BYTE -> {
in.next(byteVectorBuffer);
doc.add(new KnnByteVectorField(VECTOR_FIELD, byteVectorBuffer, fieldType));
}
case FLOAT32 -> {
in.next(floatVectorBuffer);
doc.add(new KnnFloatVectorField(VECTOR_FIELD, floatVectorBuffer, fieldType));
}
}
if ((id + 1) % 25000 == 0) {
logger.debug("Done indexing " + (id + 1) + " documents.");
}
doc.add(new StoredField(ID_FIELD, id));
iw.addDocument(doc);
}
}
}
static class VectorReader {
final float[] target;
final ByteBuffer bytes;
final FileChannel input;
long position;
static VectorReader create(FileChannel input, int dim, VectorEncoding vectorEncoding) throws IOException {
int bufferSize = dim * vectorEncoding.byteSize;
if (input.size() % ((long) dim * vectorEncoding.byteSize) != 0) {
throw new IllegalArgumentException(
"vectors file \"" + input + "\" does not contain a whole number of vectors? size=" + input.size()
);
}
return new VectorReader(input, dim, bufferSize);
}
VectorReader(FileChannel input, int dim, int bufferSize) throws IOException {
this.bytes = ByteBuffer.wrap(new byte[bufferSize]).order(ByteOrder.LITTLE_ENDIAN);
this.input = input;
this.target = new float[dim];
reset();
}
void reset() throws IOException {
position = 0;
input.position(position);
}
private void readNext() throws IOException {
int bytesRead = Channels.readFromFileChannel(this.input, position, bytes);
if (bytesRead < bytes.capacity()) {
position = 0;
bytes.position(0);
// wrap around back to the start of the file if we hit the end:
logger.warn("VectorReader hit EOF when reading " + this.input + "; now wrapping around to start of file again");
this.input.position(position);
bytesRead = Channels.readFromFileChannel(this.input, position, bytes);
if (bytesRead < bytes.capacity()) {
throw new IllegalStateException(
"vector file " + input + " doesn't even have enough bytes for a single vector? got bytesRead=" + bytesRead
);
}
}
position += bytesRead;
bytes.position(0);
}
synchronized void next(float[] dest) throws IOException {
readNext();
bytes.asFloatBuffer().get(dest);
}
synchronized void next(byte[] dest) throws IOException {
readNext();
bytes.get(dest);
}
}
}

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/*
* @notice
* 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.
* a copy and modification from Lucene util
* Modifications copyright (C) 2025 Elasticsearch B.V.
*/
package org.elasticsearch.test.knn;
import org.apache.lucene.index.DirectoryReader;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.StoredFields;
import org.apache.lucene.index.VectorEncoding;
import org.apache.lucene.index.VectorSimilarityFunction;
import org.apache.lucene.queries.function.FunctionQuery;
import org.apache.lucene.queries.function.valuesource.ByteKnnVectorFieldSource;
import org.apache.lucene.queries.function.valuesource.ByteVectorSimilarityFunction;
import org.apache.lucene.queries.function.valuesource.ConstKnnByteVectorValueSource;
import org.apache.lucene.queries.function.valuesource.ConstKnnFloatValueSource;
import org.apache.lucene.queries.function.valuesource.FloatKnnVectorFieldSource;
import org.apache.lucene.queries.function.valuesource.FloatVectorSimilarityFunction;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.search.TotalHits;
import org.apache.lucene.store.Directory;
import org.apache.lucene.store.FSDirectory;
import org.apache.lucene.store.MMapDirectory;
import org.elasticsearch.core.PathUtils;
import org.elasticsearch.index.mapper.vectors.DenseVectorFieldMapper;
import org.elasticsearch.search.profile.query.QueryProfiler;
import org.elasticsearch.search.vectors.ESKnnByteVectorQuery;
import org.elasticsearch.search.vectors.ESKnnFloatVectorQuery;
import org.elasticsearch.search.vectors.IVFKnnFloatVectorQuery;
import org.elasticsearch.search.vectors.QueryProfilerProvider;
import org.elasticsearch.search.vectors.RescoreKnnVectorQuery;
import java.io.IOException;
import java.io.OutputStream;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.IntBuffer;
import java.nio.channels.FileChannel;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.attribute.FileTime;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Objects;
import java.util.Set;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.ForkJoinPool;
import java.util.concurrent.TimeUnit;
import static org.apache.lucene.search.DocIdSetIterator.NO_MORE_DOCS;
import static org.elasticsearch.test.knn.KnnIndexTester.logger;
import static org.elasticsearch.test.knn.KnnIndexer.ID_FIELD;
import static org.elasticsearch.test.knn.KnnIndexer.VECTOR_FIELD;
class KnnSearcher {
private final Path docPath;
private final Path indexPath;
private final Path queryPath;
private final int numDocs;
private final int numQueryVectors;
private final long randomSeed = 42;
private final float selectivity = 1f;
private final int topK;
private final int efSearch;
private final int nProbe;
private final KnnIndexTester.IndexType indexType;
private final int dim;
private final VectorSimilarityFunction similarityFunction;
private final VectorEncoding vectorEncoding;
private final float overSamplingFactor;
private final int searchThreads;
KnnSearcher(Path indexPath, CmdLineArgs cmdLineArgs) {
this.docPath = cmdLineArgs.docVectors();
this.indexPath = indexPath;
this.queryPath = cmdLineArgs.queryVectors();
this.numDocs = cmdLineArgs.numDocs();
this.numQueryVectors = cmdLineArgs.numQueries();
this.topK = cmdLineArgs.k();
this.dim = cmdLineArgs.dimensions();
this.similarityFunction = cmdLineArgs.vectorSpace();
this.vectorEncoding = cmdLineArgs.vectorEncoding();
this.overSamplingFactor = cmdLineArgs.overSamplingFactor();
if (numQueryVectors <= 0) {
throw new IllegalArgumentException("numQueryVectors must be > 0");
}
this.efSearch = cmdLineArgs.numCandidates();
this.nProbe = cmdLineArgs.nProbe();
this.indexType = cmdLineArgs.indexType();
this.searchThreads = cmdLineArgs.searchThreads();
}
void runSearch(KnnIndexTester.Results finalResults) throws IOException {
TopDocs[] results = new TopDocs[numQueryVectors];
int[][] resultIds = new int[numQueryVectors][];
long elapsed, totalCpuTimeMS, totalVisited = 0;
try (
FileChannel input = FileChannel.open(queryPath);
ExecutorService executorService = Executors.newFixedThreadPool(searchThreads, r -> new Thread(r, "KnnSearcher-Thread"))
) {
long queryPathSizeInBytes = input.size();
logger.info(
"queryPath size: "
+ queryPathSizeInBytes
+ " bytes, assuming vector count is "
+ (queryPathSizeInBytes / ((long) dim * vectorEncoding.byteSize))
);
KnnIndexer.VectorReader targetReader = KnnIndexer.VectorReader.create(input, dim, vectorEncoding);
long startNS;
try (MMapDirectory dir = new MMapDirectory(indexPath)) {
try (DirectoryReader reader = DirectoryReader.open(dir)) {
IndexSearcher searcher = searchThreads > 1 ? new IndexSearcher(reader, executorService) : new IndexSearcher(reader);
byte[] targetBytes = new byte[dim];
float[] target = new float[dim];
// warm up
for (int i = 0; i < numQueryVectors; i++) {
if (vectorEncoding.equals(VectorEncoding.BYTE)) {
targetReader.next(targetBytes);
doVectorQuery(targetBytes, searcher);
} else {
targetReader.next(target);
doVectorQuery(target, searcher);
}
}
targetReader.reset();
startNS = System.nanoTime();
KnnIndexTester.ThreadDetails startThreadDetails = new KnnIndexTester.ThreadDetails();
for (int i = 0; i < numQueryVectors; i++) {
if (vectorEncoding.equals(VectorEncoding.BYTE)) {
targetReader.next(targetBytes);
results[i] = doVectorQuery(targetBytes, searcher);
} else {
targetReader.next(target);
results[i] = doVectorQuery(target, searcher);
}
}
KnnIndexTester.ThreadDetails endThreadDetails = new KnnIndexTester.ThreadDetails();
elapsed = TimeUnit.NANOSECONDS.toMillis(System.nanoTime() - startNS);
long startCPUTimeNS = 0;
long endCPUTimeNS = 0;
for (int i = 0; i < startThreadDetails.threadInfos.length; i++) {
if (startThreadDetails.threadInfos[i].getThreadName().startsWith("KnnSearcher-Thread")) {
startCPUTimeNS += startThreadDetails.cpuTimesNS[i];
}
}
for (int i = 0; i < endThreadDetails.threadInfos.length; i++) {
if (endThreadDetails.threadInfos[i].getThreadName().startsWith("KnnSearcher-Thread")) {
endCPUTimeNS += endThreadDetails.cpuTimesNS[i];
}
}
totalCpuTimeMS = TimeUnit.NANOSECONDS.toMillis(endCPUTimeNS - startCPUTimeNS);
// Fetch, validate and write result document ids.
StoredFields storedFields = reader.storedFields();
for (int i = 0; i < numQueryVectors; i++) {
totalVisited += results[i].totalHits.value();
resultIds[i] = getResultIds(results[i], storedFields);
}
logger.info(
"completed %d searches in %d ms: %d QPS CPU time=%dms",
numQueryVectors,
elapsed,
(1000L * numQueryVectors) / elapsed,
totalCpuTimeMS
);
}
}
}
logger.info("checking results");
int[][] nn = getOrCalculateExactNN();
finalResults.avgRecall = checkResults(resultIds, nn, topK);
finalResults.qps = (1000f * numQueryVectors) / elapsed;
finalResults.avgLatency = (float) elapsed / numQueryVectors;
finalResults.averageVisited = (double) totalVisited / numQueryVectors;
finalResults.netCpuTimeMS = (double) totalCpuTimeMS / numQueryVectors;
finalResults.avgCpuCount = (double) totalCpuTimeMS / elapsed;
}
private int[][] getOrCalculateExactNN() throws IOException {
// look in working directory for cached nn file
String hash = Integer.toString(
Objects.hash(
docPath,
indexPath,
queryPath,
numDocs,
numQueryVectors,
topK,
similarityFunction.ordinal(),
selectivity,
randomSeed
),
36
);
String nnFileName = "nn-" + hash + ".bin";
Path nnPath = PathUtils.get("target/" + nnFileName);
if (Files.exists(nnPath) && isNewer(nnPath, docPath, indexPath, queryPath)) {
logger.info("read pre-cached exact match vectors from cache file \"" + nnPath + "\"");
return readExactNN(nnPath);
} else {
logger.info("computing brute-force exact KNN matches for " + numQueryVectors + " query vectors from \"" + queryPath + "\"");
long startNS = System.nanoTime();
// TODO: enable computing NN from high precision vectors when
// checking low-precision recall
int[][] nn;
if (vectorEncoding.equals(VectorEncoding.BYTE)) {
nn = computeExactNNByte(queryPath);
} else {
nn = computeExactNN(queryPath);
}
writeExactNN(nn, nnPath);
long elapsedMS = TimeUnit.NANOSECONDS.toMillis(System.nanoTime() - startNS); // ns -> ms
logger.info("computed " + numQueryVectors + " exact matches in " + elapsedMS + " ms");
return nn;
}
}
private boolean isNewer(Path path, Path... others) throws IOException {
FileTime modified = Files.getLastModifiedTime(path);
for (Path other : others) {
if (Files.getLastModifiedTime(other).compareTo(modified) >= 0) {
return false;
}
}
return true;
}
TopDocs doVectorQuery(byte[] vector, IndexSearcher searcher) throws IOException {
Query knnQuery;
if (overSamplingFactor > 1f) {
throw new IllegalArgumentException("oversampling factor > 1 is not supported for byte vectors");
}
if (indexType == KnnIndexTester.IndexType.IVF) {
throw new IllegalArgumentException("IVF index type does not support byte vectors");
} else {
knnQuery = new ESKnnByteVectorQuery(
VECTOR_FIELD,
vector,
topK,
efSearch,
null,
DenseVectorFieldMapper.FilterHeuristic.ACORN.getKnnSearchStrategy()
);
}
QueryProfiler profiler = new QueryProfiler();
TopDocs docs = searcher.search(knnQuery, this.topK);
QueryProfilerProvider queryProfilerProvider = (QueryProfilerProvider) knnQuery;
queryProfilerProvider.profile(profiler);
return new TopDocs(new TotalHits(profiler.getVectorOpsCount(), docs.totalHits.relation()), docs.scoreDocs);
}
TopDocs doVectorQuery(float[] vector, IndexSearcher searcher) throws IOException {
Query knnQuery;
int topK = this.topK;
int efSearch = this.efSearch;
if (overSamplingFactor > 1f) {
// oversample the topK results to get more candidates for the final result
topK = (int) Math.ceil(topK * overSamplingFactor);
efSearch = Math.max(topK, efSearch);
}
if (indexType == KnnIndexTester.IndexType.IVF) {
knnQuery = new IVFKnnFloatVectorQuery(VECTOR_FIELD, vector, topK, efSearch, null, nProbe);
} else {
knnQuery = new ESKnnFloatVectorQuery(
VECTOR_FIELD,
vector,
topK,
efSearch,
null,
DenseVectorFieldMapper.FilterHeuristic.ACORN.getKnnSearchStrategy()
);
}
if (overSamplingFactor > 1f) {
// oversample the topK results to get more candidates for the final result
knnQuery = new RescoreKnnVectorQuery(VECTOR_FIELD, vector, similarityFunction, this.topK, knnQuery);
}
QueryProfiler profiler = new QueryProfiler();
TopDocs docs = searcher.search(knnQuery, this.topK);
QueryProfilerProvider queryProfilerProvider = (QueryProfilerProvider) knnQuery;
queryProfilerProvider.profile(profiler);
return new TopDocs(new TotalHits(profiler.getVectorOpsCount(), docs.totalHits.relation()), docs.scoreDocs);
}
private static float checkResults(int[][] results, int[][] nn, int topK) {
int totalMatches = 0;
int totalResults = results.length * topK;
for (int i = 0; i < results.length; i++) {
totalMatches += compareNN(nn[i], results[i], topK);
}
return totalMatches / (float) totalResults;
}
private static int compareNN(int[] expected, int[] results, int topK) {
int matched = 0;
Set<Integer> expectedSet = new HashSet<>();
Set<Integer> alreadySeen = new HashSet<>();
for (int i = 0; i < topK; i++) {
expectedSet.add(expected[i]);
}
for (int docId : results) {
if (alreadySeen.add(docId) == false) {
throw new IllegalStateException("duplicate docId=" + docId);
}
if (expectedSet.contains(docId)) {
++matched;
}
}
return matched;
}
private int[][] readExactNN(Path nnPath) throws IOException {
int[][] result = new int[numQueryVectors][];
try (FileChannel in = FileChannel.open(nnPath)) {
IntBuffer intBuffer = in.map(FileChannel.MapMode.READ_ONLY, 0, (long) numQueryVectors * topK * Integer.BYTES)
.order(ByteOrder.LITTLE_ENDIAN)
.asIntBuffer();
for (int i = 0; i < numQueryVectors; i++) {
result[i] = new int[topK];
intBuffer.get(result[i]);
}
}
return result;
}
private void writeExactNN(int[][] nn, Path nnPath) throws IOException {
logger.info("writing true nearest neighbors to cache file \"" + nnPath + "\"");
ByteBuffer tmp = ByteBuffer.allocate(nn[0].length * Integer.BYTES).order(ByteOrder.LITTLE_ENDIAN);
try (OutputStream out = Files.newOutputStream(nnPath)) {
for (int i = 0; i < numQueryVectors; i++) {
tmp.asIntBuffer().put(nn[i]);
out.write(tmp.array());
}
}
}
private int[][] computeExactNN(Path queryPath) throws IOException {
int[][] result = new int[numQueryVectors][];
try (Directory dir = FSDirectory.open(indexPath); DirectoryReader reader = DirectoryReader.open(dir)) {
List<Callable<Void>> tasks = new ArrayList<>();
try (FileChannel qIn = FileChannel.open(queryPath)) {
KnnIndexer.VectorReader queryReader = KnnIndexer.VectorReader.create(qIn, dim, VectorEncoding.FLOAT32);
for (int i = 0; i < numQueryVectors; i++) {
float[] queryVector = new float[dim];
queryReader.next(queryVector);
tasks.add(new ComputeNNFloatTask(i, topK, queryVector, result, reader, similarityFunction));
}
ForkJoinPool.commonPool().invokeAll(tasks);
}
return result;
}
}
private int[][] computeExactNNByte(Path queryPath) throws IOException {
int[][] result = new int[numQueryVectors][];
try (Directory dir = FSDirectory.open(indexPath); DirectoryReader reader = DirectoryReader.open(dir)) {
List<Callable<Void>> tasks = new ArrayList<>();
try (FileChannel qIn = FileChannel.open(queryPath)) {
KnnIndexer.VectorReader queryReader = KnnIndexer.VectorReader.create(qIn, dim, VectorEncoding.BYTE);
for (int i = 0; i < numQueryVectors; i++) {
byte[] queryVector = new byte[dim];
queryReader.next(queryVector);
tasks.add(new ComputeNNByteTask(i, queryVector, result, reader, similarityFunction));
}
ForkJoinPool.commonPool().invokeAll(tasks);
}
return result;
}
}
static class ComputeNNFloatTask implements Callable<Void> {
private final int queryOrd;
private final float[] query;
private final int[][] result;
private final IndexReader reader;
private final VectorSimilarityFunction similarityFunction;
private final int topK;
ComputeNNFloatTask(
int queryOrd,
int topK,
float[] query,
int[][] result,
IndexReader reader,
VectorSimilarityFunction similarityFunction
) {
this.queryOrd = queryOrd;
this.query = query;
this.result = result;
this.reader = reader;
this.similarityFunction = similarityFunction;
this.topK = topK;
}
@Override
public Void call() {
IndexSearcher searcher = new IndexSearcher(reader);
try {
var queryVector = new ConstKnnFloatValueSource(query);
var docVectors = new FloatKnnVectorFieldSource(VECTOR_FIELD);
Query query = new FunctionQuery(new FloatVectorSimilarityFunction(similarityFunction, queryVector, docVectors));
var topDocs = searcher.search(query, topK);
result[queryOrd] = getResultIds(topDocs, reader.storedFields());
if ((queryOrd + 1) % 10 == 0) {
logger.info(" exact knn scored " + (queryOrd + 1));
}
} catch (IOException e) {
throw new RuntimeException(e);
}
return null;
}
}
static class ComputeNNByteTask implements Callable<Void> {
private final int queryOrd;
private final byte[] query;
private final int[][] result;
private final IndexReader reader;
private final VectorSimilarityFunction similarityFunction;
ComputeNNByteTask(int queryOrd, byte[] query, int[][] result, IndexReader reader, VectorSimilarityFunction similarityFunction) {
this.queryOrd = queryOrd;
this.query = query;
this.result = result;
this.reader = reader;
this.similarityFunction = similarityFunction;
}
@Override
public Void call() {
IndexSearcher searcher = new IndexSearcher(reader);
int topK = result[0].length;
try {
var queryVector = new ConstKnnByteVectorValueSource(query);
var docVectors = new ByteKnnVectorFieldSource(VECTOR_FIELD);
Query query = new FunctionQuery(new ByteVectorSimilarityFunction(similarityFunction, queryVector, docVectors));
var topDocs = searcher.search(query, topK);
result[queryOrd] = getResultIds(topDocs, reader.storedFields());
if ((queryOrd + 1) % 10 == 0) {
logger.info(" exact knn scored " + (queryOrd + 1));
}
} catch (IOException e) {
throw new RuntimeException(e);
}
return null;
}
}
static int[] getResultIds(TopDocs topDocs, StoredFields storedFields) throws IOException {
int[] resultIds = new int[topDocs.scoreDocs.length];
int i = 0;
for (ScoreDoc doc : topDocs.scoreDocs) {
if (doc.doc != NO_MORE_DOCS) {
// there is a bug somewhere that can result in doc=NO_MORE_DOCS! I think it happens
// in some degenerate case (like input query has NaN in it?) that causes no results to
// be returned from HNSW search?
resultIds[i++] = Integer.parseInt(storedFields.document(doc.doc).get(ID_FIELD));
}
}
return resultIds;
}
}

2
server/src/main/java/module-info.java
View file

@ -479,4 +479,6 @@ module org.elasticsearch.server {
exports org.elasticsearch.lucene.util.automaton; exports org.elasticsearch.lucene.util.automaton;
exports org.elasticsearch.index.codec.perfield; exports org.elasticsearch.index.codec.perfield;
exports org.elasticsearch.lucene.search; exports org.elasticsearch.lucene.search;
exports org.elasticsearch.index.codec.vectors to org.elasticsearch.test.knn;
exports org.elasticsearch.index.codec.vectors.es818 to org.elasticsearch.test.knn;
} }

1
server/src/main/java/org/elasticsearch/index/codec/vectors/IVFVectorsReader.java
View file

@ -51,6 +51,7 @@ public abstract class IVFVectorsReader extends KnnVectorsReader {
protected final IntObjectHashMap<FieldEntry> fields; protected final IntObjectHashMap<FieldEntry> fields;
private final FlatVectorsReader rawVectorsReader; private final FlatVectorsReader rawVectorsReader;
@SuppressWarnings("this-escape")
protected IVFVectorsReader(SegmentReadState state, FlatVectorsReader rawVectorsReader) throws IOException { protected IVFVectorsReader(SegmentReadState state, FlatVectorsReader rawVectorsReader) throws IOException {
this.state = state; this.state = state;
this.fieldInfos = state.fieldInfos; this.fieldInfos = state.fieldInfos;

1
server/src/main/java/org/elasticsearch/index/codec/vectors/IVFVectorsWriter.java
View file

@ -55,6 +55,7 @@ public abstract class IVFVectorsWriter extends KnnVectorsWriter {
private final FlatVectorsWriter rawVectorDelegate; private final FlatVectorsWriter rawVectorDelegate;
private final SegmentWriteState segmentWriteState; private final SegmentWriteState segmentWriteState;
@SuppressWarnings("this-escape")
protected IVFVectorsWriter(SegmentWriteState state, FlatVectorsWriter rawVectorDelegate) throws IOException { protected IVFVectorsWriter(SegmentWriteState state, FlatVectorsWriter rawVectorDelegate) throws IOException {
this.segmentWriteState = state; this.segmentWriteState = state;
this.rawVectorDelegate = rawVectorDelegate; this.rawVectorDelegate = rawVectorDelegate;

1
server/src/main/java/org/elasticsearch/index/codec/vectors/es816/ES816BinaryQuantizedVectorsReader.java
View file

@ -69,6 +69,7 @@ public class ES816BinaryQuantizedVectorsReader extends FlatVectorsReader impleme
private final FlatVectorsReader rawVectorsReader; private final FlatVectorsReader rawVectorsReader;
private final ES816BinaryFlatVectorsScorer vectorScorer; private final ES816BinaryFlatVectorsScorer vectorScorer;
@SuppressWarnings("this-escape")
ES816BinaryQuantizedVectorsReader( ES816BinaryQuantizedVectorsReader(
SegmentReadState state, SegmentReadState state,
FlatVectorsReader rawVectorsReader, FlatVectorsReader rawVectorsReader,

1
server/src/main/java/org/elasticsearch/index/codec/vectors/es818/DirectIOLucene99FlatVectorsReader.java
View file

@ -65,6 +65,7 @@ public class DirectIOLucene99FlatVectorsReader extends FlatVectorsReader impleme
private final IndexInput vectorData; private final IndexInput vectorData;
private final FieldInfos fieldInfos; private final FieldInfos fieldInfos;
@SuppressWarnings("this-escape")
public DirectIOLucene99FlatVectorsReader(SegmentReadState state, FlatVectorsScorer scorer) throws IOException { public DirectIOLucene99FlatVectorsReader(SegmentReadState state, FlatVectorsScorer scorer) throws IOException {
super(scorer); super(scorer);
int versionMeta = readMetadata(state); int versionMeta = readMetadata(state);

1
server/src/main/java/org/elasticsearch/index/codec/vectors/es818/ES818BinaryQuantizedVectorsReader.java
View file

@ -70,6 +70,7 @@ public class ES818BinaryQuantizedVectorsReader extends FlatVectorsReader impleme
private final FlatVectorsReader rawVectorsReader; private final FlatVectorsReader rawVectorsReader;
private final ES818BinaryFlatVectorsScorer vectorScorer; private final ES818BinaryFlatVectorsScorer vectorScorer;
@SuppressWarnings("this-escape")
ES818BinaryQuantizedVectorsReader( ES818BinaryQuantizedVectorsReader(
SegmentReadState state, SegmentReadState state,
FlatVectorsReader rawVectorsReader, FlatVectorsReader rawVectorsReader,

1
server/src/main/java/org/elasticsearch/index/codec/vectors/es818/ES818BinaryQuantizedVectorsWriter.java
View file

@ -84,6 +84,7 @@ public class ES818BinaryQuantizedVectorsWriter extends FlatVectorsWriter {
* *
* @param vectorsScorer the scorer to use for scoring vectors * @param vectorsScorer the scorer to use for scoring vectors
*/ */
@SuppressWarnings("this-escape")
protected ES818BinaryQuantizedVectorsWriter( protected ES818BinaryQuantizedVectorsWriter(
ES818BinaryFlatVectorsScorer vectorsScorer, ES818BinaryFlatVectorsScorer vectorsScorer,
FlatVectorsWriter rawVectorDelegate, FlatVectorsWriter rawVectorDelegate,

2
settings.gradle
View file

@ -171,3 +171,5 @@ if (extraProjects.exists()) {
addSubProjects('', extraProjectDir) addSubProjects('', extraProjectDir)
} }
} }
include 'qa:vector'