Create an Apache Kafka®-based Apache Flink® table¶

To build data pipelines, Apache Flink® requires source and target data structures to be mapped as Flink tables. This functionality can be achieved via the Aiven console or Aiven CLI.

A Flink table can be defined over an existing or new Aiven for Apache Kafka® topic to be able to source or sink streaming data. To define a table over an Apache Kafka® topic, the topic name, columns data format and connector type need to be defined, together with the Flink table name to use as reference when building data pipelines.

Warning

In order to define Flink’s tables an existing integration needs to be available between the Aiven for Flink service and one or more Aiven for Apache Kafka services.

Create an Apache Kafka-based Apache Flink table with Aiven Console¶

To create a Flink table based on an Aiven for Apache Kafka topic via Aiven console:

Navigate to the Aiven for Apache Flink service page, and open the Jobs and Data tab.
Select the Data Tables sub-tab and select the Aiven for Apache Kafka integration to use
Select the Aiven for Apache Kafka service and the topic to be used as source or target for the data pipeline. If you want to use a new topic not yet existing, write the topic name.

Warning

By default Flink will not be able to automatically create Apache Kafka topics while pushing the first record. To change this behaviour, enable in the Aiven for Apache Kafka target service the kafka.auto_create_topics_enable option in Advanced configuration section.

Select the Kafka connector type, between the Apache Kafka SQL Connector for standard topic reads/writes and the Upsert Kafka SQL Connector for changelog type of integration based on message key.

Note

For more information on the connector types and the requirements for each of them, see the articles on Kafka connector types and the requirements for each connector type.

Select the Key Data Format, if a value other than Key not used is selected, specify the fields from the SQL schema to be used as key. This setting is specifically needed to set message keys for topics acting as target of data pipelines.
Select the Value Data Format based on the message format in the Apache Kafka topic.

Note

For Key and Value data format the following options are available

Define the Flink table name; this name will represents the Flink reference to the topic and will be used during the data pipeline definition
Define the SQL schema: the SQL schema defines the fields retrieved from each message in a topic, additional transformations such as format casting or timestamp extraction, and watermark settings

Example: Define a Flink table using the standard connector over topic in JSON format¶

The Aiven for Apache Kafka service named demo-kafka contains a topic named metric-topic holding a stream of service metrics in JSON format like:

{'hostname': 'sleepy', 'cpu': 'cpu3', 'usage': 93.30629927475789, 'occurred_at': 1637775077782}
{'hostname': 'dopey', 'cpu': 'cpu4', 'usage': 88.39531418706092, 'occurred_at': 1637775078369}
{'hostname': 'happy', 'cpu': 'cpu2', 'usage': 77.90860728236156, 'occurred_at': 1637775078964}
{'hostname': 'dopey', 'cpu': 'cpu4', 'usage': 81.17372993952847, 'occurred_at': 1637775079054}

We can define a metrics_in Flink table with:

demo-kafka as integration service
metric-topic as Apache Kafka topic name
Apache Kafka SQL Connector since we want to threat every entry as unique events
Key not used as Key data format
JSON as Value data format
metrics_in as Flink table name
The following as SQL schema

cpu VARCHAR,
hostname VARCHAR,
usage DOUBLE,
occurred_at BIGINT,
time_ltz AS TO_TIMESTAMP_LTZ(occurred_at, 3),
WATERMARK FOR time_ltz AS time_ltz - INTERVAL '10' SECOND

Note

The SQL schema includes:

the message fields cpu, hostname, usage, occurred_at and the related data type. The order of fields in the SQL definition doesn’t need to follow the order presented in the payload.
the definition of the field time_ltz as transformation to TIMESTAMP(3) from the occurred_at timestamp in Linux format.
the WATERMARK definition

Example: Define a Flink table using the standard connector over topic in Avro format¶

In cases when target of the Flink data pipeline needs to write in Avro format to a topic named metric-topic-tgt within the Aiven for Apache Kafka service named demo-kafka.

We can define a metrics-out Flink table with:

demo-kafka as integration service
metric-topic-tgt as Apache Kafka topic name
Apache Kafka SQL Connector for the standard connection mode
Confluent Avro as Key data format
hostname as field to be used as key, the key in Apache Kafka is by default used for partition selection
Confluent Avro as Value data format
metrics-out as Flink table name
The following as SQL schema

cpu VARCHAR,
hostname VARCHAR,
usage DOUBLE

Note

The SQL schema includes the output message fields cpu, hostname, usage and the related data type.

Example: Define a Flink table using the upsert connector over topic in Avro format¶

In cases when target of the Flink pipeline needs to write in Avro format and upsert mode to a compacted topic named metric-topic-tgt within the Aiven for Apache Kafka service named demo-kafka.

We can define a metrics-out Flink table with:

demo-kafka as integration service
metric-topic-tgt as Apache Kafka topic name
Upsert Kafka SQL Connector for the changelog mode
Confluent Avro as Key data format

Note

Unlikely the standard Apache Kafka SQL connector, when using the Upsert Kafka SQL connector the key fields are not defined. They are derived by the PRIMARY KEY definition in the SQL schema.

Confluent Avro as Value data format
metrics-out as Flink table name
The following as SQL schema

cpu VARCHAR,
hostname VARCHAR,
max_usage DOUBLE,
PRIMARY KEY (cpu, hostname) NOT ENFORCED

Note

The SQL schema includes:

the output message fields cpu, hostname, max_usage and the related data type.
the PRIMARY KEY definition, driving the key part of the Apache Kafka message