JSON Format

Format: Serialization Schema Format: Deserialization Schema

The JSON format allows to read and write JSON data based on an JSON schema. Currently, the JSON schema is derived from table schema.

The JSON format supports append-only streams, unless you’re using a connector that explicitly support retract streams and/or upsert streams like the Upsert Kafka connector. If you need to write retract streams and/or upsert streams, we suggest you to look at CDC JSON formats like Debezium JSON and Canal JSON.

Dependencies

In order to use the Json format 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-json</artifactId>
  <version>1.15.0</version>
</dependency>

How to create a table with JSON format

Here is an example to create a table using Kafka connector and JSON format.

CREATE TABLE user_behavior (
  user_id BIGINT,
  item_id BIGINT,
  category_id BIGINT,
  behavior STRING,
  ts TIMESTAMP(3)
) WITH (
 'connector' = 'kafka',
 'topic' = 'user_behavior',
 'properties.bootstrap.servers' = 'localhost:9092',
 'properties.group.id' = 'testGroup',
 'format' = 'json',
 'json.fail-on-missing-field' = 'false',
 'json.ignore-parse-errors' = 'true'
)

Format Options

Data Type Mapping

Currently, the JSON schema is always derived from table schema. Explicitly defining an JSON schema is not supported yet.

Flink JSON format uses jackson databind API to parse and generate JSON string.

The following table lists the type mapping from Flink type to JSON type.