Skip to main content
Version: Next

MySQL to ClickHouse Example

Here we use an example to introduce how to use Apache InLong creating MySQL -> ClickHouse data synchronization.


Install InLong

Before we begin, we need to install InLong. Here we provide two ways:

Add Connectors

Download the connectors corresponding to Flink version, and after decompression, place sort-connector-jdbc-[version]-SNAPSHOT.jar in /inlong-sort/connectors/ directory.

Install ClickHouse

docker run -d --rm --net=host --name clickhouse -e CLICKHOUSE_USER=admin -e CLICKHOUSE_PASSWORD=inlong -e CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT=1 clickhouse/clickhouse-server:22.8

Cluster Initialize

When all containers are successfully started, you can access the InLong dashboard address http://localhost, and use the following default account to log in.

User: admin
Password: inlong

Create Cluster Tag

Click [Clusters] -> [ClusterTags] -> [Create] on the page to specify the cluster label name and person in charge.

Create Cluster Tag


default_cluster is the default ClusterTags reported by each component. If you decide to use a different name, make sure to update the corresponding tag configuration accordingly.

Register Pulsar Cluster

Click [Clusters] -> [Cluster] -> [Create] on the page to register Pulsar Cluster.

Create Pulsar


The ClusterTags selects the newly created default_cluster, the Pulsar cluster deployed by docker:

Service URL is pulsar://pulsar:6650, Admin URL is http://pulsar:8080.

Register ClickHouse DataNodes

Click [DataNodes] -> [Create] on the page to register ClickHouse DataNodes.

Create Clickhouse Datanode

Create Task

Create Data Streams Group

Click [Synchronization][Create] on the page and input the Group ID and Steam ID:

Create Group_Stream

Create Data Source

In the data source, click [New][MySQL] to configure the source name, address, databases and tables information.

Create Source

  • Please create the test.source_table database table in advance, the schema is: CREATE TABLE test.source_table (id INT PRIMARY KEY, name VARCHAR(50));

Create Data Sink

In the data target, click [New][ClickHouse] to configure the name, DB name, table name, and created ck data node.

Create Sink

Configuration Fields

Configure fields mapping in [Source Field] and [Target Field] respectively, and click [Submit] after completion.

Create Fields

Approve Data Stream

Click [Approval] -> [MyApproval] -> [Approval] -> [Ok].


Back to [Synchronization] page, wait for [success].

Test Data

Send Data


# MySQL info

# Insert data in a loop
for ((i=1; i<=1000; i++))
# Generate data

# Build an insert SQL
query="INSERT INTO $DB_TABLE (id, name) VALUES ($id, '$name');"

# Execute insert SQL
mysql -h $DB_HOST -u $DB_USER -p$DB_PASS $DB_NAME -e "$query"

Insert 1000 pieces of data in total:


Verify Data

Then enter the ClickHouse container and view the source table data:


You can also view audit data on the page:



ClickHouse fails to write data, you can view the error on the Flink page and check the permissions of the user and table engine used.