Elasticsearch

The Apache Pekko Connectors Elasticsearch connector provides Apache Pekko Streams integration for Elasticsearch.

For more information about Elasticsearch, please visit the Elasticsearch documentation.

Project Info: Apache Pekko Connectors Elasticsearch
Artifact	org.apache.pekko pekko-connectors-elasticsearch 1.1.0 Snapshots are available
JDK versions	OpenJDK 8 OpenJDK 11 OpenJDK 17 OpenJDK 21
Scala versions	2.13.15, 2.12.20, 3.3.4
JPMS module name	pekko.stream.connectors.elasticsearch
License	Apache-2.0
API documentation	API (Scaladoc)
Forums	Github Discussions
Release notes	GitHub releases
Issues	Github issues
Sources	https://github.com/apache/pekko-connectors

Artifacts

sbt

val PekkoVersion = "1.1.3"
libraryDependencies ++= Seq(
  "org.apache.pekko" %% "pekko-connectors-elasticsearch" % "1.1.0",
  "org.apache.pekko" %% "pekko-stream" % PekkoVersion
)

Maven

<properties>
  <pekko.version>1.1.3</pekko.version>
  <scala.binary.version>2.13</scala.binary.version>
</properties>
<dependencies>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-connectors-elasticsearch_${scala.binary.version}</artifactId>
    <version>1.1.0</version>
  </dependency>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-stream_${scala.binary.version}</artifactId>
    <version>${pekko.version}</version>
  </dependency>
</dependencies>

Gradle

def versions = [
  PekkoVersion: "1.1.3",
  ScalaBinary: "2.13"
]
dependencies {
  implementation "org.apache.pekko:pekko-connectors-elasticsearch_${versions.ScalaBinary}:1.1.0"
  implementation "org.apache.pekko:pekko-stream_${versions.ScalaBinary}:${versions.PekkoVersion}"
}

The table below shows direct dependencies of this module and the second tab shows all libraries it depends on transitively.

Elasticsearch connection

The connection and credentials to authenticate with are configured with ElasticsearchConnectionSettings.

Scala

copysourceval connectionSettings = ElasticsearchConnectionSettings("http://localhost:9200")
  .withCredentials("user", "password")

Java

copysourceElasticsearchConnectionSettings connectionSettings =
    ElasticsearchConnectionSettings.create("http://localhost:9200")
        .withCredentials("user", "password");

Parameter	Default	Description
baseUrl	Empty	The base URL of Elasticsearch. Should not include a trailing slash.
username	None	The username to authenticate with
password	None	The password to authenticate with
headers	None	List of headers that should be sent with the http request.
connectionContext	None	The connectionContext that will be used with the http request. This can be used for TLS Auth instead of basic auth (username/password) by setting the SSLContext within the connectionContext.

Elasticsearch parameters

Any API method that allows reading from and writing to Elasticsearch takes an instance of ElasticsearchParamsElasticsearchParams.

ElasticsearchParams has be constructed based on the ElasticSearch API version that you’re targeting:

Scala

copysourceval elasticsearchParamsV5 = ElasticsearchParams.V5("index", "_doc")
val elasticsearchParamsV7 = ElasticsearchParams.V7("index")

Java

copysourceElasticsearchParams elasticsearchParamsV5 = ElasticsearchParams.V5("source", "_doc");
ElasticsearchParams elasticsearchParamsV7 = ElasticsearchParams.V7("source");

Elasticsearch as Source and Sink

You can stream messages from or to Elasticsearch using the ElasticsearchSourceElasticsearchSource, ElasticsearchFlowElasticsearchFlow or the ElasticsearchSinkElasticsearchSink.

Scala

copysourceimport spray.json._
import DefaultJsonProtocol._

case class Book(title: String, shouldSkip: Option[Boolean] = None, price: Int = 10)

implicit val format: JsonFormat[Book] = jsonFormat3(Book.apply)

Java

copysourcepublic static class Book {
  public String title;

  public Book() {}

  public Book(String title) {
    this.title = title;
  }
}

With typed source

Use ElasticsearchSource.typed and ElasticsearchSink.create to create source and sink. The data is converted to and from JSON by Spray JSON. The data is converted to and from JSON by Jackson’s ObjectMapper.

Scala

copysourceval copy = ElasticsearchSource
  .typed[Book](
    constructElasticsearchParams("source", "_doc", ApiVersion.V5),
    query = """{"match_all": {}}""",
    settings = baseSourceSettings)
  .map { (message: ReadResult[Book]) =>
    WriteMessage.createIndexMessage(message.id, message.source)
  }
  .runWith(
    ElasticsearchSink.create[Book](
      constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
      settings = baseWriteSettings))

Java

copysourceElasticsearchSourceSettings sourceSettings =
    ElasticsearchSourceSettings.create(connectionSettings).withApiVersion(ApiVersion.V5);
ElasticsearchWriteSettings sinkSettings =
    ElasticsearchWriteSettings.create(connectionSettings).withApiVersion(ApiVersion.V5);

Source<ReadResult<ElasticsearchTestBase.Book>, NotUsed> source =
    ElasticsearchSource.typed(
        constructElasticsearchParams("source", "_doc", ApiVersion.V5),
        "{\"match_all\": {}}",
        sourceSettings,
        ElasticsearchTestBase.Book.class);
CompletionStage<Done> f1 =
    source
        .map(m -> WriteMessage.createIndexMessage(m.id(), m.source()))
        .runWith(
            ElasticsearchSink.create(
                constructElasticsearchParams("sink2", "_doc", ApiVersion.V5),
                sinkSettings,
                new ObjectMapper()),
            system);

With JSON source

Use ElasticsearchSource.create and ElasticsearchSink.create to create source and sink.

Scala

copysourceval copy = ElasticsearchSource
  .create(
    constructElasticsearchParams("source", "_doc", ApiVersion.V5),
    query = """{"match_all": {}}""",
    settings = baseSourceSettings)
  .map { (message: ReadResult[spray.json.JsObject]) =>
    val book: Book = jsonReader[Book].read(message.source)
    WriteMessage.createIndexMessage(message.id, book)
  }
  .runWith(
    ElasticsearchSink.create[Book](
      constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
      settings = baseWriteSettings))

Java

copysourceElasticsearchSourceSettings sourceSettings =
    ElasticsearchSourceSettings.create(connectionSettings).withApiVersion(ApiVersion.V5);
ElasticsearchWriteSettings sinkSettings =
    ElasticsearchWriteSettings.create(connectionSettings).withApiVersion(ApiVersion.V5);

Source<ReadResult<Map<String, Object>>, NotUsed> source =
    ElasticsearchSource.create(
        constructElasticsearchParams("source", "_doc", ApiVersion.V5),
        "{\"match_all\": {}}",
        sourceSettings);
CompletionStage<Done> f1 =
    source
        .map(m -> WriteMessage.createIndexMessage(m.id(), m.source()))
        .runWith(
            ElasticsearchSink.create(
                constructElasticsearchParams("sink1", "_doc", ApiVersion.V5),
                sinkSettings,
                new ObjectMapper()),
            system);

Writing to Elasticsearch

In the above examples, WriteMessage is used as the input to ElasticsearchSink and ElasticsearchFlow. This means requesting index operation to Elasticsearch. It’s possible to request other operations using following message types:

Message factory	Description
WriteMessage.createIndexMessage	Create a new document. If id is specified and it already exists, replace the document and increment its version.
WriteMessage.createCreateMessage	Create a new document. If id already exists, the WriteResult will contain an error.
WriteMessage.createUpdateMessage	Update an existing document. If there is no document with the specified id, do nothing.
WriteMessage.createUpsertMessage	Update an existing document. If there is no document with the specified id, create a new document.
WriteMessage.createDeleteMessage	Delete an existing document. If there is no document with the specified id, do nothing.

Scala

copysourceval requests = List[WriteMessage[Book, NotUsed]](
  WriteMessage.createIndexMessage(id = "00001", source = Book("Book 1")),
  WriteMessage.createUpsertMessage(id = "00002", source = Book("Book 2")),
  WriteMessage.createUpsertMessage(id = "00003", source = Book("Book 3")),
  WriteMessage.createUpdateMessage(id = "00004", source = Book("Book 4")),
  WriteMessage.createCreateMessage(id = "00005", source = Book("Book 5")),
  WriteMessage.createDeleteMessage(id = "00002"))

val writeResults = Source(requests)
  .via(
    ElasticsearchFlow.create[Book](
      constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
      baseWriteSettings))
  .runWith(Sink.seq)

Java

copysource// Create, update, upsert and delete documents in sink8/book
List<WriteMessage<Book, NotUsed>> requests =
    Arrays.asList(
        WriteMessage.createIndexMessage("00001", new Book("Book 1")),
        WriteMessage.createUpsertMessage("00002", new Book("Book 2")),
        WriteMessage.createUpsertMessage("00003", new Book("Book 3")),
        WriteMessage.createUpdateMessage("00004", new Book("Book 4")),
        WriteMessage.createDeleteMessage("00002"));

Source.from(requests)
    .via(
        ElasticsearchFlow.create(
            constructElasticsearchParams("sink8", "_doc", ApiVersion.V5),
            ElasticsearchWriteSettings.create(connectionSettings).withApiVersion(ApiVersion.V5),
            new ObjectMapper()))
    .runWith(Sink.seq(), system)
    .toCompletableFuture()
    .get();

Source configuration

We can configure the source by ElasticsearchSourceSettings.

Scala

copysourceval sourceSettings = ElasticsearchSourceSettings(connectionSettings)
  .withBufferSize(10)
  .withScrollDuration(5.minutes)

Java

copysourceElasticsearchSourceSettings sourceSettings =
    ElasticsearchSourceSettings.create(connectionSettings).withBufferSize(10);

Parameter	Default	Description
connection		The connection details and credentials to authenticate against ElasticSearch. See ElasticsearchConnectionSettings
bufferSize	10	ElasticsearchSource retrieves messages from Elasticsearch by scroll scan. This buffer size is used as the scroll size.
includeDocumentVersion	false	Tell Elasticsearch to return the documents _version property with the search results. See Version and Optimistic Concurrenct Control to know about this property.
scrollDuration	5 min	ElasticsearchSource retrieves messages from Elasticsearch by scroll scan. This parameter is used as a scroll value. See Time units for supported units.
apiVersion	V7	Currently supports V5 and V7 (see below)

Sink and flow configuration

Sinks and flows are configured with ElasticsearchWriteSettings.

Scala

copysourceval sinkSettings =
  ElasticsearchWriteSettings(connectionSettings)
    .withBufferSize(10)
    .withVersionType("internal")
    .withRetryLogic(RetryAtFixedRate(maxRetries = 5, retryInterval = 1.second))
    .withApiVersion(ApiVersion.V5)

Java

copysourceElasticsearchWriteSettings settings =
    ElasticsearchWriteSettings.create(connectionSettings)
        .withBufferSize(10)
        .withVersionType("internal")
        .withRetryLogic(RetryAtFixedRate.create(5, Duration.ofSeconds(1)))
        .withApiVersion(ApiVersion.V5);

Parameter	Default	Description
connection		The connection details and credentials to authenticate against ElasticSearch. See ElasticsearchConnectionSettings
bufferSize	10	Flow and Sink batch messages to bulk requests when back-pressure applies.
versionType	None	If set, ElasticsearchSink uses the chosen versionType to index documents. See Version types for accepted settings.
retryLogic	No retries	See below
apiVersion	V7	Currently supports V5 and V7 (see below)
allowExplicitIndex	True	When set to False, the index name will be included in the URL instead of on each document (see below)

Retry logic

A bulk request might fail partially for some reason. To retry failed writes to Elasticsearch, a RetryLogic can be specified.

The provided implementations are:

• RetryAtFixedRate

Parameter	Description
maxRetries	The stage fails, if it gets this number of consecutive failures.
retryInterval	Failing writes are retried after this duration.

• RetryWithBackoff

Parameter	Description
maxRetries	The stage fails, if it gets this number of consecutive failures.
minBackoff	Initial backoff for failing writes.
maxBackoff	Maximum backoff for failing writes.

In case of write failures the order of messages downstream is guaranteed to be preserved.

Supported API versions

To support reading and writing to multiple versions of Elasticsearch, an ApiVersion can be specified.

This will be used to: 1. transform the bulk request into a format understood by the corresponding Elasticsearch server. 2. determine whether to include the index type mapping in the API calls. See removal of types

Currently V5 and V7 are supported specifically but this parameter does not need to match the server version exactly (for example, either V5 or V7 should work with Elasticsearch 6.x).

Allow explicit index

When using the _bulk API, Elasticsearch will reject requests that have an explicit index in the request body if explicit index names are not allowed. See URL-based access control

Elasticsearch as Flow

You can also build flow stages with ElasticsearchFlowElasticsearchFlow. The API is similar to creating Sinks.

Scala

copysourceval copy = ElasticsearchSource
  .typed[Book](
    constructElasticsearchParams("source", "_doc", ApiVersion.V5),
    query = """{"match_all": {}}""",
    settings = baseSourceSettings)
  .map { (message: ReadResult[Book]) =>
    WriteMessage.createIndexMessage(message.id, message.source)
  }
  .via(
    ElasticsearchFlow.create[Book](
      constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
      settings = baseWriteSettings))
  .runWith(Sink.seq)

Java

copysourceCompletionStage<List<WriteResult<Book, NotUsed>>> f1 =
    ElasticsearchSource.typed(
            constructElasticsearchParams("source", "_doc", ApiVersion.V5),
            "{\"match_all\": {}}",
            ElasticsearchSourceSettings.create(connectionSettings)
                .withApiVersion(ApiVersion.V5)
                .withBufferSize(5),
            Book.class)
        .map(m -> WriteMessage.createIndexMessage(m.id(), m.source()))
        .via(
            ElasticsearchFlow.create(
                constructElasticsearchParams("sink3", "_doc", ApiVersion.V5),
                ElasticsearchWriteSettings.create(connectionSettings)
                    .withApiVersion(ApiVersion.V5)
                    .withBufferSize(5),
                new ObjectMapper()))
        .runWith(Sink.seq(), system);

Storing documents from Strings

Elasticsearch requires the documents to be properly formatted JSON. If your data is available as JSON in Strings, you may use the pre-defined StringMessageWriter to avoid any conversions. For any other JSON technologies, implement a MessageWriter[T]MessageWriter<T>.

Scala

copysourceval write: Future[immutable.Seq[WriteResult[String, NotUsed]]] = Source(
  immutable.Seq(
    WriteMessage.createIndexMessage("1", Book("Das Parfum").toJson.compactPrint),
    WriteMessage.createIndexMessage("2", Book("Faust").toJson.compactPrint),
    WriteMessage.createIndexMessage("3", Book("Die unendliche Geschichte").toJson.compactPrint))).via(
  ElasticsearchFlow.create(
    constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
    settings = baseWriteSettings,
    StringMessageWriter))
  .runWith(Sink.seq)

Java

copysourceString indexName = "sink3-0";
CompletionStage<List<WriteResult<String, NotUsed>>> write =
    Source.from(
            Arrays.asList(
                WriteMessage.createIndexMessage("1", "{\"title\": \"Das Parfum\"}"),
                WriteMessage.createIndexMessage("2", "{\"title\": \"Faust\"}"),
                WriteMessage.createIndexMessage(
                    "3", "{\"title\": \"Die unendliche Geschichte\"}")))
        .via(
            ElasticsearchFlow.create(
                constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
                ElasticsearchWriteSettings.create(connectionSettings)
                    .withApiVersion(ApiVersion.V5)
                    .withBufferSize(5),
                StringMessageWriter.getInstance()))
        .runWith(Sink.seq(), system);

Passing data through ElasticsearchFlow

When streaming documents from Kafka, you might want to commit to Kafka AFTER the document has been written to Elastic.

Scala

copysource// We're going to pretend we got messages from kafka.
// After we've written them to Elastic, we want
// to commit the offset to Kafka

case class KafkaOffset(offset: Int)
case class KafkaMessage(book: Book, offset: KafkaOffset)

val messagesFromKafka = List(
  KafkaMessage(Book("Book 1"), KafkaOffset(0)),
  KafkaMessage(Book("Book 2"), KafkaOffset(1)),
  KafkaMessage(Book("Book 3"), KafkaOffset(2)))

var committedOffsets = Vector[KafkaOffset]()

def commitToKafka(offset: KafkaOffset): Unit =
  committedOffsets = committedOffsets :+ offset

val indexName = "sink6"
val kafkaToEs = Source(messagesFromKafka) // Assume we get this from Kafka
  .map { (kafkaMessage: KafkaMessage) =>
    val book = kafkaMessage.book
    val id = book.title

    // Transform message so that we can write to elastic
    WriteMessage.createIndexMessage(id, book).withPassThrough(kafkaMessage.offset)
  }
  .via( // write to elastic
    ElasticsearchFlow.createWithPassThrough[Book, KafkaOffset](
      constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
      settings = baseWriteSettings))
  .map { result =>
    if (!result.success) throw new Exception("Failed to write message to elastic")
    // Commit to kafka
    commitToKafka(result.message.passThrough)
  }
  .runWith(Sink.ignore)

kafkaToEs.futureValue shouldBe Done
// We're going to pretend we got messages from kafka.
// After we've written them to Elastic, we want
// to commit the offset to Kafka

case class KafkaOffset(offset: Int)
case class KafkaMessage(book: Book, offset: KafkaOffset)

val messagesFromKafka = List(
  KafkaMessage(Book("Book 1"), KafkaOffset(0)),
  KafkaMessage(Book("Book 2"), KafkaOffset(1)),
  KafkaMessage(Book("Book 3"), KafkaOffset(2)))

var committedOffsets = Vector[KafkaOffset]()

def commitToKafka(offset: KafkaOffset): Unit =
  committedOffsets = committedOffsets :+ offset

val indexName = "sink6-bulk"
val kafkaToEs = Source(messagesFromKafka) // Assume we get this from Kafka
  .map { (kafkaMessage: KafkaMessage) =>
    val book = kafkaMessage.book
    val id = book.title

    // Transform message so that we can write to elastic
    WriteMessage.createIndexMessage(id, book).withPassThrough(kafkaMessage.offset)
  }
  .grouped(2)
  .via( // write to elastic
    ElasticsearchFlow.createBulk[Book, KafkaOffset](
      constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
      settings = baseWriteSettings))
  .map(_.map { result =>
    if (!result.success) throw new Exception("Failed to write message to elastic")
    // Commit to kafka
    commitToKafka(result.message.passThrough)
  })
  .runWith(Sink.ignore)

kafkaToEs.futureValue shouldBe Done
// We're going to pretend we got messages from kafka.
// After we've written them to Elastic, we want
// to commit the offset to Kafka

case class KafkaOffset(offset: Int)
case class KafkaMessage(book: Book, offset: KafkaOffset)

val messagesFromKafka = List(
  KafkaMessage(Book("Book A", shouldSkip = Some(true)), KafkaOffset(0)),
  KafkaMessage(Book("Book 1"), KafkaOffset(1)),
  KafkaMessage(Book("Book 2"), KafkaOffset(2)),
  KafkaMessage(Book("Book B", shouldSkip = Some(true)), KafkaOffset(3)),
  KafkaMessage(Book("Book 3"), KafkaOffset(4)),
  KafkaMessage(Book("Book C", shouldSkip = Some(true)), KafkaOffset(5)))

var committedOffsets = Vector[KafkaOffset]()

def commitToKafka(offset: KafkaOffset): Unit =
  committedOffsets = committedOffsets :+ offset

val indexName = "sink6-nop"
val kafkaToEs = Source(messagesFromKafka) // Assume we get this from Kafka
  .map { (kafkaMessage: KafkaMessage) =>
    val book = kafkaMessage.book
    val id = book.title

    // Transform message so that we can write to elastic
    if (book.shouldSkip.getOrElse(false))
      WriteMessage.createNopMessage[Book]().withPassThrough(kafkaMessage.offset)
    else
      WriteMessage.createIndexMessage(id, book).withPassThrough(kafkaMessage.offset)
  }
  .via( // write to elastic
    ElasticsearchFlow.createWithPassThrough[Book, KafkaOffset](
      constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
      settings = baseWriteSettings))
  .map { result =>
    if (!result.success) throw new Exception("Failed to write message to elastic")
    // Commit to kafka
    commitToKafka(result.message.passThrough)
  }
  .runWith(Sink.ignore)

kafkaToEs.futureValue shouldBe Done
// We're going to pretend we got messages from kafka.
// After we've written them to Elastic, we want
// to commit the offset to Kafka

case class KafkaOffset(offset: Int)
case class KafkaMessage(book: Book, offset: KafkaOffset)

val messagesFromKafka = List(
  KafkaMessage(Book("Book 1", shouldSkip = Some(true)), KafkaOffset(0)),
  KafkaMessage(Book("Book 2", shouldSkip = Some(true)), KafkaOffset(1)),
  KafkaMessage(Book("Book 3", shouldSkip = Some(true)), KafkaOffset(2)))

var committedOffsets = Vector[KafkaOffset]()

def commitToKafka(offset: KafkaOffset): Unit =
  committedOffsets = committedOffsets :+ offset

val indexName = "sink6-none"
register(connectionSettings, indexName, "dummy", 10) // need to create index else exception in reading below

val kafkaToEs = Source(messagesFromKafka) // Assume we get this from Kafka
  .map { (kafkaMessage: KafkaMessage) =>
    val book = kafkaMessage.book
    val id = book.title

    // Transform message so that we can write to elastic
    if (book.shouldSkip.getOrElse(false))
      WriteMessage.createNopMessage[Book]().withPassThrough(kafkaMessage.offset)
    else
      WriteMessage.createIndexMessage(id, book).withPassThrough(kafkaMessage.offset)
  }
  .via( // write to elastic
    ElasticsearchFlow.createWithPassThrough[Book, KafkaOffset](
      constructElasticsearchParams(indexName, "_doc", ApiVersion.V5),
      settings = baseWriteSettings))
  .map { result =>
    if (!result.success) throw new Exception("Failed to write message to elastic")
    // Commit to kafka
    commitToKafka(result.message.passThrough)
  }
  .runWith(Sink.ignore)

kafkaToEs.futureValue shouldBe Done

Java

copysource// We're going to pretend we got messages from kafka.
// After we've written them to Elastic, we want
// to commit the offset to Kafka

List<KafkaMessage> messagesFromKafka =
    Arrays.asList(
        new KafkaMessage(new Book("Book 1"), new KafkaOffset(0)),
        new KafkaMessage(new Book("Book 2"), new KafkaOffset(1)),
        new KafkaMessage(new Book("Book 3"), new KafkaOffset(2)));

final KafkaCommitter kafkaCommitter = new KafkaCommitter();

CompletionStage<Done> kafkaToEs =
    Source.from(messagesFromKafka) // Assume we get this from Kafka
        .map(
            kafkaMessage -> {
              Book book = kafkaMessage.book;
              String id = book.title;

              // Transform message so that we can write to elastic
              return WriteMessage.createIndexMessage(id, book)
                  .withPassThrough(kafkaMessage.offset);
            })
        .via( // write to elastic
            ElasticsearchFlow.createWithPassThrough(
                constructElasticsearchParams("sink6", "_doc", ApiVersion.V5),
                ElasticsearchWriteSettings.create(connectionSettings)
                    .withApiVersion(ApiVersion.V5)
                    .withBufferSize(5),
                new ObjectMapper()))
        .map(
            result -> {
              if (!result.success())
                throw new RuntimeException("Failed to write message to elastic");
              // Commit to kafka
              kafkaCommitter.commit(result.message().passThrough());
              return NotUsed.getInstance();
            })
        .runWith(Sink.ignore(), system);

Specifying custom index-name for every document

When working with index-patterns using wildcards, you might need to specify a custom index-name for each document:

Scala

copysourceval customIndexName = "custom-index"

val writeCustomIndex = ElasticsearchSource
  .typed[Book](
    constructElasticsearchParams("source", "_doc", ApiVersion.V5),
    query = """{"match_all": {}}""",
    settings = baseSourceSettings)
  .map { (message: ReadResult[Book]) =>
    WriteMessage
      .createIndexMessage(message.id, message.source)
      .withIndexName(customIndexName) // Setting the index-name to use for this document
  }
  .runWith(
    ElasticsearchSink.create[Book](
      constructElasticsearchParams("this-is-not-the-index-we-are-using", "_doc", ApiVersion.V5),
      settings = baseWriteSettings))

Java

copysourceWriteMessage<String, NotUsed> msg =
    WriteMessage.createIndexMessage(doc).withIndexName("my-index");

Specifying custom metadata for every document

In some cases you might want to specify custom metadata per document you are inserting, for example a pipeline, this can be done like so:

Scala

copysourceval msg = WriteMessage
  .createIndexMessage(doc)
  .withCustomMetadata(Map("pipeline" -> "myPipeline"))

Java

copysourceMap<String, String> metadata = new HashMap<>();
metadata.put("pipeline", "myPipeline");
WriteMessage<String, NotUsed> msgWithMetadata =
    WriteMessage.createIndexMessage(doc).withCustomMetadata(metadata);

More custom searching

The easiest way of using Elasticsearch-source, is to just specify the query-param. Sometimes you need more control, like specifying which fields to return and so on. In such cases you can instead use ‘searchParams’ instead:

Scala

copysourcecase class TestDoc(id: String, a: String, b: Option[String], c: String)
// Search for docs and ask elastic to only return some fields

val readWithSearchParameters = ElasticsearchSource
  .typed[TestDoc](
    constructElasticsearchParams(indexName, typeName, ApiVersion.V5),
    searchParams = Map(
      "query" -> """ {"match_all": {}} """,
      "_source" -> """ ["id", "a", "c"] """),
    baseSourceSettings)
  .map { message =>
    message.source
  }
  .runWith(Sink.seq)

Java

copysource// Search for docs and ask elastic to only return some fields

Map<String, String> searchParams = new HashMap<>();
searchParams.put("query", "{\"match_all\": {}}");
searchParams.put("_source", "[\"id\", \"a\", \"c\"]");

List<TestDoc> result =
    ElasticsearchSource.<TestDoc>typed(
            constructElasticsearchParams(indexName, typeName, ApiVersion.V5),
            searchParams, // <-- Using searchParams
            ElasticsearchSourceSettings.create(connectionSettings)
                .withApiVersion(ApiVersion.V5),
            TestDoc.class,
            new ObjectMapper())
        .map(
            o -> {
              return o.source(); // These documents will only have property id, a and c (not
            })
        .runWith(Sink.seq(), system)
        .toCompletableFuture()
        .get();

Routing

Support for custom routing is available through the routing key. Add this key and the respective value in ‘searchParams’ map, to route your search directly to the shard that holds the document you are looking for and enjoy improved response times.

Sort

Support for sort is available through the sort key in searchParams map. If no sort is given, the source will use sort=_doc to maximize performance, as indicated by elasticsearch documentation.