Connect Apache Kafka® to Aiven for ClickHouse®#

You can integrate Aiven for ClickHouse® with either Aiven for Apache Kafka® service located in the same project, or an external Apache Kafka endpoint. A single Aiven for ClickHouse instance can connect to multiple Kafka clusters with different authentication mechanism and credentials.

Behind the scenes the integration between Aiven for ClickHouse and Apache Kafka services relies on ClickHouse Kafka Engine.

Note

Aiven for ClickHouse service integrations are available for Startup plans and higher.

Prerequisites#

You will need

Aiven for ClickHouse service
Aiven for Apache Kafka service or a self-hosted Apache Kafka service

Tip

If you use the self-hosted Apache Kafka service, an external Apache Kafka endpoint should be configured in Integration endpoints.
At least one topic in the Apache Kafka service

Variables#

The following variables will be used later in the code snippets:

Variable	Description
`CLICKHOUSE_SERVICE_NAME`	Name of your Aiven for ClickHouse service.
`KAFKA_SERVICE_NAME`	Name of the Apache Kafka service you use for the integration.
`PROJECT`	Name of Aiven project where your services are located.
`CONNECTOR_TABLE_NAME`	Name of the Kafka engine virtual table that is used as a connector.
`DATA_FORMAT`	Input/output data format in which data is accepted into Aiven for ClickHouse. See Reference.
`CONSUMER_GROUP_NAME`	Name of the consumer group. Each message is delivered once per consumer group.

Create an integration#

To connect Aiven for ClickHouse and Aiven for Apache Kafka by enabling a data service integration, see Create data service integrations.

The newly created database name has the following format: service_KAFKA_SERVICE_NAME, where KAFKA_SERVICE_NAME is the name of your Apache Kafka service.

Note

During this step we only created an empty database in Aiven for ClickHouse, but we didn’t create any tables yet. Creation of the virtual connector table is done by setting specific integration configuration, see the section below.

Update Apache Kafka integration settings#

Next step is to configure the topic and data format options for the integration. This will create a virtual table in Aiven for ClickHouse that can receive and send messages from multiple topics. You can have as many of such tables as you need.

For each table, there are mandatory and optional setting to be defined.

Mandatory settings#

For each table, you need to define the following:

name - name of the connector table
columns - array of columns, with names and types
topics - array of topics, where you want to bring the data from
data_format - your preferred format for data input, see Formats for ClickHouse®-Kafka® data exchange
group_name - consumer group name, that will be created on your behalf

JSON format

{
    "tables": [
        {
            "name": "CONNECTOR_TABLE_NAME",
            "columns": [
                {"name": "id", "type": "UInt64"},
                {"name": "name", "type": "String"}
            ],
            "topics": [{"name": "topic1"}, {"name": "topic2"}],
            "data_format": "DATA_FORMAT",
            "group_name": "CONSUMER_NAME"
        }
    ]
}

Optional settings#

For each table, you can define the following optional settings:

Name	Description	Default value	Allowed values	Minimum value	Maximum value
`auto_offset_reset`	Action to take when there is no initial offset in the offset store or the desired offset is out of range	`earliest`	`smallest`, `earliest`, `beginning`, `largest`, `latest`, `end`	–	–
`date_time_input_format`	Method to read `DateTime` from text input formats	`basic`	`basic`, `best_effort`, `best_effort_us`	–	–
`handle_error_mode`	Method to handle errors for the Kafka engine	`default`	`default`, `stream`	–	–
`max_block_size`	Number of rows collected by poll(s) for flushing data from Kafka	`0`	`0` - `1_000_000_000`	`0`	`1_000_000_000`
`max_rows_per_message`	Maximum number of rows produced in one Kafka message for row-based formats	`1`	`1` - `1_000_000_000`	`1`	`1_000_000_000`
`num_consumers`	Number of consumers per table per replica	`1`	`1` - `10`	`1`	`10`
`poll_max_batch_size`	Maximum amount of messages to be polled in a single Kafka poll	`0`	`0` - `1_000_000_000`	`0`	`1_000_000_000`
`skip_broken_messages`	Minimum number of broken messages from Kafka topic per block to be skipped	`0`	`0` - `1_000_000_000`	`0`	`1_000_000_000`

JSON format

{
    "tables": [
        {
            "name": "CONNECTOR_TABLE_NAME",
            "columns": [
                {"name": "id", "type": "UInt64"},
                {"name": "name", "type": "String"}
            ],
            "topics": [{"name": "topic1"}, {"name": "topic2"}],
            "data_format": "DATA_FORMAT",
            "group_name": "CONSUMER_NAME",
            "auto_offset_reset": "earliest"
        }
    ]
}

Configure integration with CLI#

Currently the configurations can be set only with the help of CLI command avn service integration-update

Follow these instructions:

Get the service integration id by requesting the full list of integrations. Replace PROJECT, CLICKHOUSE_SERVICE_NAME and KAFKA_SERVICE_NAME with the names of your services:

avn service integration-list                        \
--project PROJECT_NAME                                   \
CLICKHOUSE_SERVICE_NAME | grep KAFKA_SERVICE_NAME

Update the configuration settings using the service integration id retrieved in the previous step and your integration settings. Replace SERVICE_INTEGRATION_ID, CONNECTOR_TABLE_NAME, DATA_FORMAT and CONSUMER_NAME with your values:

avn service integration-update SERVICE_INTEGRATION_ID \
--project PROJECT_NAME                                \
--user-config-json '{
    "tables": [
        {
            "name": "CONNECTOR_TABLE_NAME",
            "columns": [
                {"name": "id", "type": "UInt64"},
                {"name": "name", "type": "String"}
            ],
            "topics": [{"name": "topic1"}, {"name": "topic2"}],
            "data_format": "DATA_FORMAT",
            "group_name": "CONSUMER_NAME"
        }
    ]
}'

Read and store data#

In Aiven for ClickHouse you can consume messages by running SELECT command. Replace KAFKA_SERVICE_NAME and CONNECTOR_TABLE_NAME with your values and run:

SELECT * FROM service_KAFKA_SERVICE_NAME.CONNECTOR_TABLE_NAME

However, the messages are only read once (per consumer group). If you want to store the messages for later, you can send them into a separate ClickHouse table with the help of a materialized view.

For example, run to creating a destination table:

CREATE TABLE destination (id UInt64, name String)
ENGINE = ReplicatedMergeTree()
ORDER BY id;

Add a materialised view to bring the data from the connector:

CREATE MATERIALIZED VIEW materialised_view TO destination AS
SELECT *
FROM service_KAFKA_SERVICE_NAME.CONNECTOR_TABLE_NAME;

Now the messages consumed from the Apache Kafka topic will be read automatically and sent into the destination table directly.

Write data back to the topic#

You can also bring the entries from ClickHouse table into the Apache Kafka topic. Replace KAFKA_SERVICE_NAME and CONNECTOR_TABLE_NAME with your values:

INSERT INTO service_KAFKA_SERVICE_NAME.CONNECTOR_TABLE_NAME(id, name)
VALUES (1, 'Michelangelo')

Warning

Writing to more than one topic is not supported.

Reference#

When connecting ClickHouse® to Kafka® using Aiven integrations, data exchange requires using specific formats. Check the supported formats for input and output data in Formats for ClickHouse®-Kafka® data exchange.