Merge branch 'main' of github.com:marciw/elasticsearch-java

Author: marciw

java-client/build.gradle.kts

@@ -56,6 +56,10 @@ forbiddenApis {
     suppressAnnotations = setOf("co.elastic.clients.util.AllowForbiddenApis")
 }
 
+tasks.compileJava {
+    options.release.set(17)
+}
+
 tasks.forbiddenApisMain {
     bundledSignatures = setOf("jdk-system-out")
 }
@@ -158,29 +162,6 @@ publishing {
                     developerConnection.set("scm:git:ssh://[email protected]:elastic/elasticsearch-java.git")
                     url.set("https://github.com/elastic/elasticsearch-java/")
                 }
-
-                withXml {
-                    // Note: org.elasticsearch.client is now an optional dependency, so the below is no more useful.
-                    // It's kept in case it ever comes back as a required dependency.
-
-                    // Set the version of dependencies of the org.elasticsearch.client group to the one that we are building.
-                    // Since the unified release process releases everything at once, this ensures all published artifacts depend
-                    // on the exact same version. This assumes of course that the binary API and the behavior of these dependencies
-                    // are the same as the one used in the dependency section below.
-                    val xPathFactory = javax.xml.xpath.XPathFactory.newInstance()
-                    val depSelector = xPathFactory.newXPath()
-                            .compile("/project/dependencies/dependency[groupId/text() = 'org.elasticsearch.client']")
-                    val versionSelector = xPathFactory.newXPath().compile("version")
-
-                    val deps = depSelector.evaluate(asElement().ownerDocument, javax.xml.xpath.XPathConstants.NODESET)
-                            as org.w3c.dom.NodeList
-
-                    for (i in 0 until deps.length) {
-                        val dep = deps.item(i)
-                        val version = versionSelector.evaluate(dep, javax.xml.xpath.XPathConstants.NODE) as org.w3c.dom.Element
-                        version.textContent = project.version.toString()
-                    }
-                }
             }
         }
     }
@@ -196,7 +177,7 @@ signing {
 dependencies {
     // Compile and test with the last 8.x version to make sure transition scenarios where
     // the Java API client coexists with a 8.x HLRC work fine
-    val elasticsearchVersion = "8.17.0"
+    val elasticsearchVersion = "9.0.0"
     val jacksonVersion = "2.18.3"
     val openTelemetryVersion = "1.29.0"
 
@@ -205,7 +186,9 @@ dependencies {
     compileOnly("org.elasticsearch.client", "elasticsearch-rest-client", elasticsearchVersion)
     testImplementation("org.elasticsearch.client", "elasticsearch-rest-client", elasticsearchVersion)
 
-    api("org.apache.httpcomponents.client5","httpclient5","5.4")
+    // Apache 2.0
+    // https://hc.apache.org/httpcomponents-client-ga/
+    api("org.apache.httpcomponents.client5","httpclient5","5.4.4")
 
     // Apache 2.0
     // https://search.maven.org/artifact/com.google.code.findbugs/jsr305
@@ -220,6 +203,10 @@ dependencies {
     // https://github.com/eclipse-ee4j/parsson
     api("org.eclipse.parsson:parsson:1.1.7")
 
+    // Apache 2.0
+    // http://commons.apache.org/logging/
+    api("commons-logging:commons-logging:1.3.5")
+
     // OpenTelemetry API for native instrumentation of the client.
     // Apache 2.0
     // https://github.com/open-telemetry/opentelemetry-java
@@ -288,6 +275,7 @@ class SpdxReporter(val dest: File) : ReportRenderer {
             "The Apache License, Version 2.0" to "Apache-2.0",
             "Apache License, Version 2.0" to "Apache-2.0",
             "The Apache Software License, Version 2.0" to "Apache-2.0",
+            "Apache-2.0" to "Apache-2.0",
             "MIT License" to "MIT",
             "BSD Zero Clause License" to "0BSD",
             "Eclipse Public License 2.0" to "EPL-2.0",

java-client/src/main/java/co/elastic/clients/elasticsearch/ElasticsearchAsyncClient.java

@@ -67,8 +67,6 @@
 import co.elastic.clients.elasticsearch.core.IndexResponse;
 import co.elastic.clients.elasticsearch.core.InfoRequest;
 import co.elastic.clients.elasticsearch.core.InfoResponse;
-import co.elastic.clients.elasticsearch.core.KnnSearchRequest;
-import co.elastic.clients.elasticsearch.core.KnnSearchResponse;
 import co.elastic.clients.elasticsearch.core.MgetRequest;
 import co.elastic.clients.elasticsearch.core.MgetResponse;
 import co.elastic.clients.elasticsearch.core.MsearchRequest;
@@ -3284,207 +3282,6 @@ public CompletableFuture<InfoResponse> info() {
 		return this.transport.performRequestAsync(InfoRequest._INSTANCE, InfoRequest._ENDPOINT, this.transportOptions);
 	}
 
-	// ----- Endpoint: knn_search
-
-	/**
-	 * Run a knn search.
-	 * <p>
-	 * NOTE: The kNN search API has been replaced by the <code>knn</code> option in
-	 * the search API.
-	 * <p>
-	 * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return
-	 * the matching documents. Given a query vector, the API finds the k closest
-	 * vectors and returns those documents as search hits.
-	 * <p>
-	 * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like
-	 * most kNN algorithms, HNSW is an approximate method that sacrifices result
-	 * accuracy for improved search speed. This means the results returned are not
-	 * always the true k closest neighbors.
-	 * <p>
-	 * The kNN search API supports restricting the search using a filter. The search
-	 * will return the top k documents that also match the filter query.
-	 * <p>
-	 * A kNN search response has the exact same structure as a search API response.
-	 * However, certain sections have a meaning specific to kNN search:
-	 * <ul>
-	 * <li>The document <code>_score</code> is determined by the similarity between
-	 * the query and document vector.</li>
-	 * <li>The <code>hits.total</code> object contains the total number of nearest
-	 * neighbor candidates considered, which is
-	 * <code>num_candidates * num_shards</code>. The
-	 * <code>hits.total.relation</code> will always be <code>eq</code>, indicating
-	 * an exact value.</li>
-	 * </ul>
-	 *
-	 * @see <a href=
-	 *      "https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-knn-search">Documentation
-	 *      on elastic.co</a>
-	 */
-
-	public <TDocument> CompletableFuture<KnnSearchResponse<TDocument>> knnSearch(KnnSearchRequest request,
-			Class<TDocument> tDocumentClass) {
-		@SuppressWarnings("unchecked")
-		JsonEndpoint<KnnSearchRequest, KnnSearchResponse<TDocument>, ErrorResponse> endpoint = (JsonEndpoint<KnnSearchRequest, KnnSearchResponse<TDocument>, ErrorResponse>) KnnSearchRequest._ENDPOINT;
-		endpoint = new EndpointWithResponseMapperAttr<>(endpoint,
-				"co.elastic.clients:Deserializer:_global.knn_search.Response.TDocument",
-				getDeserializer(tDocumentClass));
-
-		return this.transport.performRequestAsync(request, endpoint, this.transportOptions);
-	}
-
-	/**
-	 * Run a knn search.
-	 * <p>
-	 * NOTE: The kNN search API has been replaced by the <code>knn</code> option in
-	 * the search API.
-	 * <p>
-	 * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return
-	 * the matching documents. Given a query vector, the API finds the k closest
-	 * vectors and returns those documents as search hits.
-	 * <p>
-	 * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like
-	 * most kNN algorithms, HNSW is an approximate method that sacrifices result
-	 * accuracy for improved search speed. This means the results returned are not
-	 * always the true k closest neighbors.
-	 * <p>
-	 * The kNN search API supports restricting the search using a filter. The search
-	 * will return the top k documents that also match the filter query.
-	 * <p>
-	 * A kNN search response has the exact same structure as a search API response.
-	 * However, certain sections have a meaning specific to kNN search:
-	 * <ul>
-	 * <li>The document <code>_score</code> is determined by the similarity between
-	 * the query and document vector.</li>
-	 * <li>The <code>hits.total</code> object contains the total number of nearest
-	 * neighbor candidates considered, which is
-	 * <code>num_candidates * num_shards</code>. The
-	 * <code>hits.total.relation</code> will always be <code>eq</code>, indicating
-	 * an exact value.</li>
-	 * </ul>
-	 *
-	 * @param fn
-	 *            a function that initializes a builder to create the
-	 *            {@link KnnSearchRequest}
-	 * @see <a href=
-	 *      "https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-knn-search">Documentation
-	 *      on elastic.co</a>
-	 */
-
-	public final <TDocument> CompletableFuture<KnnSearchResponse<TDocument>> knnSearch(
-			Function<KnnSearchRequest.Builder, ObjectBuilder<KnnSearchRequest>> fn, Class<TDocument> tDocumentClass) {
-		return knnSearch(fn.apply(new KnnSearchRequest.Builder()).build(), tDocumentClass);
-	}
-
-	/**
-	 * Overload of {@link #knnSearch(KnnSearchRequest, Class)}, where Class is
-	 * defined as Void, meaning the documents will not be deserialized.
-	 */
-
-	public CompletableFuture<KnnSearchResponse<Void>> knnSearch(KnnSearchRequest request) {
-		@SuppressWarnings("unchecked")
-		JsonEndpoint<KnnSearchRequest, KnnSearchResponse<Void>, ErrorResponse> endpoint = (JsonEndpoint<KnnSearchRequest, KnnSearchResponse<Void>, ErrorResponse>) KnnSearchRequest._ENDPOINT;
-		return this.transport.performRequestAsync(request, endpoint, this.transportOptions);
-	}
-
-	/**
-	 * Overload of {@link #knnSearch(Function, Class)}, where Class is defined as
-	 * Void, meaning the documents will not be deserialized.
-	 */
-
-	public final CompletableFuture<KnnSearchResponse<Void>> knnSearch(
-			Function<KnnSearchRequest.Builder, ObjectBuilder<KnnSearchRequest>> fn) {
-		return knnSearch(fn.apply(new KnnSearchRequest.Builder()).build(), Void.class);
-	}
-
-	/**
-	 * Run a knn search.
-	 * <p>
-	 * NOTE: The kNN search API has been replaced by the <code>knn</code> option in
-	 * the search API.
-	 * <p>
-	 * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return
-	 * the matching documents. Given a query vector, the API finds the k closest
-	 * vectors and returns those documents as search hits.
-	 * <p>
-	 * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like
-	 * most kNN algorithms, HNSW is an approximate method that sacrifices result
-	 * accuracy for improved search speed. This means the results returned are not
-	 * always the true k closest neighbors.
-	 * <p>
-	 * The kNN search API supports restricting the search using a filter. The search
-	 * will return the top k documents that also match the filter query.
-	 * <p>
-	 * A kNN search response has the exact same structure as a search API response.
-	 * However, certain sections have a meaning specific to kNN search:
-	 * <ul>
-	 * <li>The document <code>_score</code> is determined by the similarity between
-	 * the query and document vector.</li>
-	 * <li>The <code>hits.total</code> object contains the total number of nearest
-	 * neighbor candidates considered, which is
-	 * <code>num_candidates * num_shards</code>. The
-	 * <code>hits.total.relation</code> will always be <code>eq</code>, indicating
-	 * an exact value.</li>
-	 * </ul>
-	 *
-	 * @see <a href=
-	 *      "https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-knn-search">Documentation
-	 *      on elastic.co</a>
-	 */
-
-	public <TDocument> CompletableFuture<KnnSearchResponse<TDocument>> knnSearch(KnnSearchRequest request,
-			Type tDocumentType) {
-		@SuppressWarnings("unchecked")
-		JsonEndpoint<KnnSearchRequest, KnnSearchResponse<TDocument>, ErrorResponse> endpoint = (JsonEndpoint<KnnSearchRequest, KnnSearchResponse<TDocument>, ErrorResponse>) KnnSearchRequest._ENDPOINT;
-		endpoint = new EndpointWithResponseMapperAttr<>(endpoint,
-				"co.elastic.clients:Deserializer:_global.knn_search.Response.TDocument",
-				getDeserializer(tDocumentType));
-
-		return this.transport.performRequestAsync(request, endpoint, this.transportOptions);
-	}
-
-	/**
-	 * Run a knn search.
-	 * <p>
-	 * NOTE: The kNN search API has been replaced by the <code>knn</code> option in
-	 * the search API.
-	 * <p>
-	 * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return
-	 * the matching documents. Given a query vector, the API finds the k closest
-	 * vectors and returns those documents as search hits.
-	 * <p>
-	 * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like
-	 * most kNN algorithms, HNSW is an approximate method that sacrifices result
-	 * accuracy for improved search speed. This means the results returned are not
-	 * always the true k closest neighbors.
-	 * <p>
-	 * The kNN search API supports restricting the search using a filter. The search
-	 * will return the top k documents that also match the filter query.
-	 * <p>
-	 * A kNN search response has the exact same structure as a search API response.
-	 * However, certain sections have a meaning specific to kNN search:
-	 * <ul>
-	 * <li>The document <code>_score</code> is determined by the similarity between
-	 * the query and document vector.</li>
-	 * <li>The <code>hits.total</code> object contains the total number of nearest
-	 * neighbor candidates considered, which is
-	 * <code>num_candidates * num_shards</code>. The
-	 * <code>hits.total.relation</code> will always be <code>eq</code>, indicating
-	 * an exact value.</li>
-	 * </ul>
-	 *
-	 * @param fn
-	 *            a function that initializes a builder to create the
-	 *            {@link KnnSearchRequest}
-	 * @see <a href=
-	 *      "https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-knn-search">Documentation
-	 *      on elastic.co</a>
-	 */
-
-	public final <TDocument> CompletableFuture<KnnSearchResponse<TDocument>> knnSearch(
-			Function<KnnSearchRequest.Builder, ObjectBuilder<KnnSearchRequest>> fn, Type tDocumentType) {
-		return knnSearch(fn.apply(new KnnSearchRequest.Builder()).build(), tDocumentType);
-	}
-
 	// ----- Endpoint: mget
 
 	/**