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Connect to ClickHouse database

Connecting MindsDB to your ClickHouse database can be done in two ways:

MindsDB Studio

Using MindsDB Studio, you can connect to the ClickHouse database with a few clicks.

Connect to database

  1. From the left navigation menu, select Database Integration.
  2. Click on the ADD DATABASE button.
  3. In the Connect to Database modal window:
    1. Select ClickHouse as Supported Database.
    2. Add the Database name.
    3. Add the Hostname.
    4. Add Port.
    5. Add ClickHouse user.
    6. Add Password for ClickHouse user.
    7. Click on CONNECT.

Connect to ClickHouse

Create new Datasource

  1. Click on the NEW DATASOURCE button.
  2. In the Datasource from DB integration modal window:
    1. Add Datasource Name.
    2. Add Database name.
    3. Add SELECT Query e.g (SELECT * FROM my_database)
    4. Click on CREATE.

Create ClickHouse Datasource

That's it 🎉 🏆 💻

You have successfully connected to ClickHouse from MindsDB Studio. The next step is to train the Machine Learning model.

ClickHouse client

How to extend MindsDB configuration

Our suggestion is to always use MindsDB Studio to connect MindsDB to your database. If you still want to extend the configuration without using MindsDB Studio follow the configuration example.

After adding the required configuration, you will need to start MindsDB and provide the path to the newly created config.json:

python3 -m mindsdb --api=http,mysql --config=config.json

The --api parameter specifies the type of API to use -- in this case HTTP and MySQL. The --config parameter specifies the location of the configuration file.

Start MindsDB with config

If MindsDB is successfully connected to your ClickHouse database, it will create a new database mindsdb and new table predictors. After starting the server, you can run a SELECT query from your ClickHouse client to make sure integration has been successful.

SELECT * FROM mindsdb.predictors;

SELECT from MindsDB predictors table

That's it 🎉 🏆 💻

You have successfully connected MindsDB Server and ClickHouse. The next step is to train the Machine Learning model.