Create an Elasticsearch sink connector#

The Elasticsearch sink connector enables you to move data from an Aiven for Apache Kafka® cluster to an Elasticsearch instance for further processing and analysis.

Warning

This article describes how to create a sink connector to Elasticsearch. Similar instructions are available also for OpenSearch® in the dedicated article.

Prerequisites#

To setup an Elasticsearch sink connector, you need an Aiven for Apache Kafka service with Kafka Connect enabled or a dedicated Aiven for Apache Kafka Connect cluster.

Furthermore you need to collect the following information about the target Elasticsearch service upfront:

  • ES_CONNECTION_URL: The Elasticsearch connection URL, in the form of https://HOST:PORT

  • ES_USERNAME: The Elasticsearch username to connect

  • ES_PASSWORD: The password for the username selected

  • TOPIC_LIST: The list of topics to sink divided by comma

  • APACHE_KAFKA_HOST: The hostname of the Apache Kafka service, only needed when using Avro as data format

  • SCHEMA_REGISTRY_PORT: The Apache Kafka’s schema registry port, only needed when using Avro as data format

  • SCHEMA_REGISTRY_USER: The Apache Kafka’s schema registry username, only needed when using Avro as data format

  • SCHEMA_REGISTRY_PASSWORD: The Apache Kafka’s schema registry user password, only needed when using Avro as data format

Note

If you’re using Aiven for Elasticsearch and Aiven for Apache Kafka® the above details are available in the Aiven console service Overview tab or via the dedicated avn service get command with the Aiven CLI.

The SCHEMA_REGISTRY related parameters are available in the Aiven for Apache Kafka® service page, Overview tab, and Schema Registry subtab

As of version 3.0, Aiven for Apache Kafka no longer supports Confluent Schema Registry. For more information, read the article describing the replacement, Karapace

Setup an Elasticsearch sink connector with Aiven Console#

The following example demonstrates how to setup a Elasticsearch sink connector for Apache Kafka using the Aiven Console.

Define a Kafka Connect configuration file#

Define the connector configurations in a file (we’ll refer to it with the name elasticsearch_sink.json) with the following content:

{
    "name":"CONNECTOR_NAME",
    "connector.class": "io.aiven.connect.elasticsearch.ElasticsearchSinkConnector",
    "topics": "TOPIC_LIST",
    "connection.url": "ES_CONNECTION_URL",
    "connection.username": "ES_USERNAME",
    "connection.password": "ES_PASSWORD",
    "type.name": "TYPE_NAME",
    "tasks.max":"1",
    "key.ignore": "true",
    "key.converter": "io.confluent.connect.avro.AvroConverter",
    "key.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
    "key.converter.basic.auth.credentials.source": "USER_INFO",
    "key.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
    "value.converter": "io.confluent.connect.avro.AvroConverter",
    "value.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
    "value.converter.basic.auth.credentials.source": "USER_INFO",
    "value.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD"
}

The configuration file contains the following entries:

  • name: the connector name

  • connection.url, connection.username, connection.password: sink Elasticsearch parameters collected in the prerequisite phase.

  • type.name: the Elasticsearch type name to be used when indexing.

  • key.ignore: boolean flag dictating if to ignore the message key. If set to true, the document ID is generated as message’s topic+partition+offset, the message key is used as ID otherwise.

  • tasks.max: maximum number of tasks to execute in parallel. By default this is 1.

  • key.converter and value.converter: defines the messages data format in the Apache Kafka topic. The io.confluent.connect.avro.AvroConverter converter translates messages from the Avro format. To retrieve the messages schema we use Aiven’s Karapace schema registry as specified by the schema.registry.url parameter and related credentials.

Note

The key.converter and value.converter sections are only needed when the source data is in Avro format. If omitted the messages will be read as binary format.

When using Avro as source data format, you need to set following parameters

  • value.converter.schema.registry.url: pointing to the Aiven for Apache Kafka schema registry URL in the form of https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT with the APACHE_KAFKA_HOST and SCHEMA_REGISTRY_PORT parameters retrieved in the previous step.

  • value.converter.basic.auth.credentials.source: to the value USER_INFO, since you’re going to login to the schema registry using username and password.

  • value.converter.schema.registry.basic.auth.user.info: passing the required schema registry credentials in the form of SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD with the SCHEMA_REGISTRY_USER and SCHEMA_REGISTRY_PASSWORD parameters retrieved in the previous step.

Create a Kafka Connect connector with Aiven Console#

To create the connector, access the Aiven Console and select the Aiven for Apache Kafka® or Aiven for Apache Kafka Connect® service where the connector needs to be defined, then:

  1. Click on the Connectors tab

  2. Clink on Create New Connector, the button is enabled only for services with Kafka Connect enabled.

  3. Select the Elasticsearch sink

  4. Under the Common tab, locate the Connector configuration text box and click on Edit

  5. Paste the connector configuration (stored in the elasticsearch_sink.json file) in the form

  6. Click on Apply

Note

The Aiven Console parses the configuration file and fills the relevant UI fields. You can review the UI fields across the various tab and change them if necessary. The changes will be reflected in JSON format in the Connector configuration text box.

  1. After all the settings are correctly configured, click on Create new connector

  2. Verify the connector status under the Connectors tab

  3. Verify the presence of the data in the target Elasticsearch service, the index name is equal to the Apache Kafka topic name

Note

Connectors can be created also using the dedicated Aiven CLI command.

Create daily Elasticsearch indices#

You might need to create a new Elasticsearch index on daily basis to store the Apache Kafka messages. Adding the following TimestampRouter transformation in the connector properties file provides a way to define the index name as concatenation of the topic name and message date.

"transforms": "TimestampRouter",
"transforms.TimestampRouter.topic.format": "${topic}-${timestamp}",
"transforms.TimestampRouter.timestamp.format": "yyyy-MM-dd",
"transforms.TimestampRouter.type": "org.apache.kafka.connect.transforms.TimestampRouter"

Warning

The current version of the Elasticsearch sink connector is not able to automatically create daily indices in Elasticsearch. Therefore you need to create the indices with the correct name before starting the sink connector. You can create Elasticsearch indices in many ways including CURL commands.

Example: Create an Elasticsearch sink connector on a topic with a JSON schema#

If you have a topic named iot_measurements containing the following data in JSON format, with a defined JSON schema:

{
    "schema": {
        "type":"struct",
        "fields":[{
            "type":"int64",
            "optional": false,
            "field": "iot_id"
            },{
            "type":"string",
            "optional": false,
            "field": "metric"
            },{
            "type":"int32",
            "optional": false,
            "field": "measurement"
            }]
    },
    "payload":{ "iot_id":1, "metric":"Temperature", "measurement":14}
}
{
    "schema": {
        "type":"struct",
        "fields":[{
            "type":"int64",
            "optional": false,
            "field": "iot_id"
            },{
            "type":"string",
            "optional": false,
            "field": "metric"
            },{
            "type":"int32",
            "optional": false,
            "field": "measurement"
            }]
    },
    "payload":{"iot_id":2, "metric":"Humidity", "measurement":60}}
}

Note

Since the JSON schema needs to be defined in every message, there is a big overhead to transmit the information. To achieve a better performance in term of information-message ratio you should use the Avro format together with the Karapace schema registry provided by Aiven

You can sink the iot_measurements topic to Elasticsearch with the following connector configuration, after replacing the placeholders for ES_CONNECTION_URL, ES_USERNAME and ES_PASSWORD:

{
    "name":"sink_iot_json_schema",
    "connector.class": "io.aiven.connect.elasticsearch.ElasticsearchSinkConnector",
    "topics": "iot_measurements",
    "connection.url": "ES_CONNECTION_URL",
    "connection.username": "ES_USERNAME",
    "connection.password": "ES_PASSWORD",
    "type.name": "iot_measurements",
    "tasks.max":"1",
    "key.ignore": "true",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter"
}

The configuration file contains the following peculiarities:

  • "topics": "iot_measurements": setting the topic to sink

  • "value.converter": "org.apache.kafka.connect.json.JsonConverter": the message value is in plain JSON format without a schema

  • "key.ignore": "true": the connector is ignoring the message key (empty), and generating documents with ID equal to topic+partition+offset

Example: Create an Elasticsearch sink connector on a topic in plain JSON format#

If you have a topic named students containing the following data in JSON format, without a defined schema:

Key: 1 Value: {"student_id":1, "student_name":"Carla"}
Key: 2 Value: {"student_id":2, "student_name":"Ugo"}
Key: 3 Value: {"student_id":3, "student_name":"Mary"}

You can sink the students topic to Elasticsearch with the following connector configuration, after replacing the placeholders for ES_CONNECTION_URL, ES_USERNAME and ES_PASSWORD:

{
    "name":"sink_students_json",
    "connector.class": "io.aiven.connect.elasticsearch.ElasticsearchSinkConnector",
    "topics": "students",
    "connection.url": "ES_CONNECTION_URL",
    "connection.username": "ES_USERNAME",
    "connection.password": "ES_PASSWORD",
    "type.name": "students",
    "tasks.max":"1",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "schema.ignore": "true"
}

The configuration file contains the following peculiarities:

  • "topics": "students": setting the topic to sink

  • "key.converter": "org.apache.kafka.connect.storage.StringConverter": the message key is a string

  • "value.converter": "org.apache.kafka.connect.json.JsonConverter": the message value is in plain JSON format without a schema

  • "value.converter.schemas.enable": "false": since the data in the value doesn’t have a schema, the connector shouldn’t try to read it and sets it to null

  • "schema.ignore": "true": since the value schema is null, the connector doesn’t infer it before pushing the data to Elasticsearch

Note

The Elasticsearch document ID is set as the message key


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