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Elasticsearch SQL Connector #

Sink: Batch Sink: Streaming Append & Upsert Mode

The Elasticsearch connector allows for writing into an index of the Elasticsearch engine. This document describes how to setup the Elasticsearch Connector to run SQL queries against Elasticsearch.

The connector can operate in upsert mode for exchanging UPDATE/DELETE messages with the external system using the primary key defined on the DDL.

If no primary key is defined on the DDL, the connector can only operate in append mode for exchanging INSERT only messages with external system.

Dependencies #

In order to use the Elasticsearch connector the following dependencies are required for both projects using a build automation tool (such as Maven or SBT) and SQL Client with SQL JAR bundles.

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-connector-elasticsearch6_2.11</artifactId>
  <version>1.14.4</version>
</dependency>

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-connector-elasticsearch7_2.11</artifactId>
  <version>1.14.4</version>
</dependency>

How to create an Elasticsearch table #

The example below shows how to create an Elasticsearch sink table:

CREATE TABLE myUserTable (
  user_id STRING,
  user_name STRING
  uv BIGINT,
  pv BIGINT,
  PRIMARY KEY (user_id) NOT ENFORCED
) WITH (
  'connector' = 'elasticsearch-7',
  'hosts' = 'http://localhost:9200',
  'index' = 'users'
);

Connector Options #

Features #

Key Handling #

Elasticsearch sink can work in either upsert mode or append mode, it depends on whether primary key is defined. If primary key is defined, Elasticsearch sink works in upsert mode which can consume queries containing UPDATE/DELETE messages. If primary key is not defined, Elasticsearch sink works in append mode which can only consume queries containing INSERT only messages.

In Elasticsearch connector, the primary key is used to calculate the Elasticsearch document id, which is a string of up to 512 bytes. It cannot have whitespaces. The Elasticsearch connector generates a document ID string for every row by concatenating all primary key fields in the order defined in the DDL using a key delimiter specified by document-id.key-delimiter. Certain types are not allowed as primary key field as they do not have a good string representation, e.g. BYTES, ROW, ARRAY, MAP, etc. If no primary key is specified, Elasticsearch will generate a document id automatically.

See CREATE TABLE DDL for more details about PRIMARY KEY syntax.

Dynamic Index #

Elasticsearch sink supports both static index and dynamic index.

If you want to have a static index, the index option value should be a plain string, e.g. 'myusers', all the records will be consistently written into “myusers” index.

If you want to have a dynamic index, you can use {field_name} to reference a field value in the record to dynamically generate a target index. You can also use '{field_name|date_format_string}' to convert a field value of TIMESTAMP/DATE/TIME type into the format specified by the date_format_string. The date_format_string is compatible with Java’s DateTimeFormatter. For example, if the option value is 'myusers-{log_ts|yyyy-MM-dd}', then a record with log_ts field value 2020-03-27 12:25:55 will be written into “myusers-2020-03-27” index.

Data Type Mapping #

Elasticsearch stores document in a JSON string. So the data type mapping is between Flink data type and JSON data type. Flink uses built-in 'json' format for Elasticsearch connector. Please refer to JSON Format page for more type mapping details.

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