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Managing machine learning models in Managed Service for ClickHouse®

Written by
Updated at May 13, 2025

Managed Service for ClickHouse® allows you to analyze data by applying CatBoost machine learning models without additional tools.

To apply a model, add it to your cluster and call it in an SQL query using the built-in catboostEvaluate() function. After running this query, you get model predictions for each row of input data.

Read more about the catboostEvaluate() function in the ClickHouse® documentation.

Before adding a model

Managed Service for ClickHouse® only works with readable models uploaded to Yandex Object Storage:

  1. To link your service account to a cluster, assign the iam.serviceAccounts.user role or higher to your Yandex Cloud account.
  2. Upload the trained model file to Yandex Object Storage.
  3. Connect the service account to the cluster. You will use your service account to configure permissions to access the model file.
  4. Assign the storage.viewer role to the service account.
  5. In the bucket's ACL, add the READ permission to the service account.
  6. Get a link to the model file.

Getting a list of models in a cluster

  1. In the management console, go to the folder page and select Managed Service for ClickHouse.
  2. Click the cluster name and select the Machine learning tab in the left-hand panel.

If you do not have the Yandex Cloud (CLI) command line interface yet, install and initialize it.

The folder specified when creating the CLI profile is used by default. To change the default folder, use the yc config set folder-id <folder_ID> command. You can specify a different folder using the --folder-name or --folder-id parameter.

To get a list of models in a cluster, run the command:

yc managed-clickhouse ml-model list --cluster-name=<cluster_name>

You can request the cluster name with the list of clusters in the folder.

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Use the MlModel.List method and send the following request, e.g., via cURL:

    curl \
    --request GET \
    --header "Authorization: Bearer $IAM_TOKEN" \
    --url 'https://mdb.api.cloud.yandex.net/managed-clickhouse/v1/clusters/<cluster_ID>/mlModels'

    You can request the cluster ID with the list of clusters in the folder.

  3. View the server response to make sure the request was successful.

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Clone the cloudapi repository:

    cd ~/ && git clone --depth=1 https://github.com/yandex-cloud/cloudapi

    Below, we assume the repository contents are stored in the ~/cloudapi/ directory.

  3. Use the MlModelService.List call and send the following request, e.g., via gRPCurl:

    grpcurl \
    -format json \
    -import-path ~/cloudapi/ \
    -import-path ~/cloudapi/third_party/googleapis/ \
    -proto ~/cloudapi/yandex/cloud/mdb/clickhouse/v1/ml_model_service.proto \
    -rpc-header "Authorization: Bearer $IAM_TOKEN" \
    -d '{
            "cluster_id": "<cluster_ID>"
        }' \
    mdb.api.cloud.yandex.net:443 \
    yandex.cloud.mdb.clickhouse.v1.MlModelService.List

    You can request the cluster ID with the list of clusters in the folder.

  4. View the server response to make sure the request was successful.

Getting detailed information about a model

  1. In the management console, go to the folder page and select Managed Service for ClickHouse.
  2. Click the cluster name and select the Machine learning tab in the left-hand panel.

If you do not have the Yandex Cloud (CLI) command line interface yet, install and initialize it.

The folder specified when creating the CLI profile is used by default. To change the default folder, use the yc config set folder-id <folder_ID> command. You can specify a different folder using the --folder-name or --folder-id parameter.

To get model details, run this command:

yc managed-clickhouse ml-model get <model_name> \
  --cluster-name=<cluster_name>

You can request the model name with a list of cluster models and the cluster name with a list of clusters in the folder.

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Use the MlModel.Get method and send the following request, e.g., via cURL:

    curl \
    --request GET \
    --header "Authorization: Bearer $IAM_TOKEN" \
    --url 'https://mdb.api.cloud.yandex.net/managed-clickhouse/v1/clusters/<cluster_ID>/mlModels/<model_name>'

    You can request the cluster ID with a list of clusters in the folder and model name with a list of models in the cluster.

  3. View the server response to make sure the request was successful.

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Clone the cloudapi repository:

    cd ~/ && git clone --depth=1 https://github.com/yandex-cloud/cloudapi

    Below, we assume the repository contents are stored in the ~/cloudapi/ directory.

  3. Use the MlModelService.Get call and send the following request, e.g., via gRPCurl:

    grpcurl \
    -format json \
    -import-path ~/cloudapi/ \
    -import-path ~/cloudapi/third_party/googleapis/ \
    -proto ~/cloudapi/yandex/cloud/mdb/clickhouse/v1/ml_model_service.proto \
    -rpc-header "Authorization: Bearer $IAM_TOKEN" \
    -d '{
            "cluster_id": "<cluster_ID>",
            "ml_model_name": "<model_name>"
        }' \
    mdb.api.cloud.yandex.net:443 \
    yandex.cloud.mdb.clickhouse.v1.MlModelService.Get

    You can request the cluster ID with a list of clusters in the folder and model name with a list of models in the cluster.

  4. View the server response to make sure the request was successful.

Creating a model

Note

The only supported model type is CatBoost: ML_MODEL_TYPE_CATBOOST.

  1. Select the cluster:

    1. In the management console, navigate to the folder page and select Managed Service for ClickHouse.
    2. Click the cluster name and select the Machine learning tab in the left-hand panel.
    3. Click Create model.

  2. Configure the model parameters:

    • Type: ML_MODEL_TYPE_CATBOOST.
    • Name: Model name. Model name is one of the arguments of the catboostEvaluate() function, which is used to call the model in ClickHouse®.
    • URL: Model address in Yandex Object Storage.

  3. Click Create and wait for the model to be created.

If you do not have the Yandex Cloud (CLI) command line interface yet, install and initialize it.

The folder specified when creating the CLI profile is used by default. To change the default folder, use the yc config set folder-id <folder_ID> command. You can specify a different folder using the --folder-name or --folder-id parameter.

To create a model, run this command:

yc managed-clickhouse ml-model create <model_name> \
  --cluster-name=<cluster_name> \
  --type=ML_MODEL_TYPE_CATBOOST \
  --uri=<link_to_model_file_in_Object_Storage>

You can request the cluster name with the list of clusters in the folder.

  1. Open the current Terraform configuration file that defines your infrastructure.

    For more information about creating this file, see Creating clusters.

  2. To the Managed Service for ClickHouse® cluster description, add the ml_model block with a description of the added machine learning model:

    resource "yandex_mdb_clickhouse_cluster" "<cluster_name>" {
  ...
  ml_model {
    name = "<model_name>"
    type = "ML_MODEL_TYPE_CATBOOST"
    uri  = "<link_to_model_file_in_Object_Storage>"
  }
}

  3. Make sure the settings are correct.

    1. In the command line, navigate to the directory that contains the current Terraform configuration files defining the infrastructure.

    2. Run this command:

      terraform validate

      Terraform will show any errors found in your configuration files.

  4. Confirm updating the resources.

    1. Run this command to view the planned changes:

      terraform plan

      If you described the configuration correctly, the terminal will display a list of the resources to update and their parameters. This is a verification step that does not apply changes to your resources.

    2. If everything looks correct, apply the changes:

      1. Run this command:

        terraform apply

      2. Confirm updating the resources.

      3. Wait for the operation to complete.

For more information, see the Terraform provider documentation.

Time limits

A Terraform provider sets the timeout for Managed Service for ClickHouse® cluster operations:

  • Creating a cluster, including by restoring one from a backup: 60 minutes.
  • Editing a cluster: 90 minutes.
  • Deleting a cluster: 30 minutes.

Operations exceeding the set timeout are interrupted.

How do I change these limits?

Add the timeouts block to the cluster description, for example:

resource "yandex_mdb_clickhouse_cluster" "<cluster_name>" {
  ...
  timeouts {
    create = "1h30m" # 1 hour 30 minutes
    update = "2h"    # 2 hours
    delete = "30m"   # 30 minutes
  }
}

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Use the MlModel.Create method and send the following request, e.g., via cURL:

    curl \
    --request POST \
    --header "Authorization: Bearer $IAM_TOKEN" \
    --header "Content-Type: application/json" \
    --url 'https://{{ api-host-mdb }/managed-clickhouse/v1/clusters/<cluster_ID>/mlModels' \
    --data '{
              "mlModelName": "<model_name>",
              "type": "ML_MODEL_TYPE_CATBOOST",
              "uri": "<file_link>"
            }'

    Where:

    • mlModelName: Model name.
    • type: Model type, always takes the ML_MODEL_TYPE_CATBOOST value.
    • uri: Link to the model file in Object Storage.

    You can request the cluster ID with the list of clusters in the folder.

  3. View the server response to make sure the request was successful.

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Clone the cloudapi repository:

    cd ~/ && git clone --depth=1 https://github.com/yandex-cloud/cloudapi

    Below, we assume the repository contents are stored in the ~/cloudapi/ directory.

  3. Use the MlModelService.Create call and send the following request, e.g., via gRPCurl:

    grpcurl \
    -format json \
    -import-path ~/cloudapi/ \
    -import-path ~/cloudapi/third_party/googleapis/ \
    -proto ~/cloudapi/yandex/cloud/mdb/clickhouse/v1/ml_model_service.proto \
    -rpc-header "Authorization: Bearer $IAM_TOKEN" \
    -d '{
            "cluster_id": "<cluster_ID>",
            "ml_model_name": "<model_name>",
            "type": "ML_MODEL_TYPE_CATBOOST",
            "uri": "<file_link>"
        }' \
    mdb.api.cloud.yandex.net:443 \
    yandex.cloud.mdb.clickhouse.v1.MlModelService.Create

    Where:

    • ml_model_name: Model name.
    • type: Model type, always takes the ML_MODEL_TYPE_CATBOOST value.
    • uri: Link to the model file in Object Storage.

    You can request the cluster ID with the list of clusters in the folder.

  4. View the server response to make sure the request was successful.

Applying a model

To apply the model to data stored in a ClickHouse® cluster:

  1. Connect to the cluster.

  2. Execute an SQL query in the format:

    SELECT 
    catboostEvaluate('<path_to_model_file>', 
                  <column_1_name>,
                  <column_2_name>,
                  ...
                  <column_N_name>)
FROM <table_name>

As the catboostEvaluate() function arguments, specify the following:

  • Path to the model file in /var/lib/clickhouse/models/<model_name>.bin format.
  • Names of columns containing the input data.

The result of the query execution will be a column with model predictions for each row of the source table.

Updating a model

Managed Service for ClickHouse® does not track changes in the model file located in the Yandex Object Storage bucket.

To update the contents of a model that is already connected to the cluster:

  1. Upload the file with the current model to Yandex Object Storage.
  2. Get a link to this file.
  3. Change the parameters of the model connected to Managed Service for ClickHouse® by providing a new link to the model file.
  1. In the management console, go to the folder page and select Managed Service for ClickHouse.
  2. Click the cluster name and select the Machine learning tab in the left-hand panel.
  3. Select the appropriate model, click , and select Edit.

If you do not have the Yandex Cloud (CLI) command line interface yet, install and initialize it.

The folder specified when creating the CLI profile is used by default. To change the default folder, use the yc config set folder-id <folder_ID> command. You can specify a different folder using the --folder-name or --folder-id parameter.

To change the link to the model file in the Yandex Object Storage bucket, run the command:

yc managed-clickhouse ml-model update <model_name> \
  --cluster-name=<cluster_name> \
  --uri=<new_link_to_file_in_Object_Storage>

You can request the model name with a list of cluster models and the cluster name with a list of clusters in the folder.

  1. Open the current Terraform configuration file that defines your infrastructure.

    For more information about creating this file, see Creating clusters.

  2. In the Managed Service for ClickHouse® cluster description, change the uri parameter value under ml_model:

    resource "yandex_mdb_clickhouse_cluster" "<cluster_name>" {
...
  ml_model {
    name = "<model_name>"
    type = "ML_MODEL_TYPE_CATBOOST"
    uri  = "<new_link_to_model_file_in_Object_Storage>"
  }
}

  3. Make sure the settings are correct.

    1. In the command line, navigate to the directory that contains the current Terraform configuration files defining the infrastructure.

    2. Run this command:

      terraform validate

      Terraform will show any errors found in your configuration files.

  4. Confirm updating the resources.

    1. Run this command to view the planned changes:

      terraform plan

      If you described the configuration correctly, the terminal will display a list of the resources to update and their parameters. This is a verification step that does not apply changes to your resources.

    2. If everything looks correct, apply the changes:

      1. Run this command:

        terraform apply

      2. Confirm updating the resources.

      3. Wait for the operation to complete.

For more information, see the Terraform provider documentation.

Time limits

A Terraform provider sets the timeout for Managed Service for ClickHouse® cluster operations:

  • Creating a cluster, including by restoring one from a backup: 60 minutes.
  • Editing a cluster: 90 minutes.
  • Deleting a cluster: 30 minutes.

Operations exceeding the set timeout are interrupted.

How do I change these limits?

Add the timeouts block to the cluster description, for example:

resource "yandex_mdb_clickhouse_cluster" "<cluster_name>" {
  ...
  timeouts {
    create = "1h30m" # 1 hour 30 minutes
    update = "2h"    # 2 hours
    delete = "30m"   # 30 minutes
  }
}

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Use the MlModel.Update method and send the following request, e.g., via cURL:

    Warning

    The API method will assign default values to all the parameters of the object you are modifying unless you explicitly provide them in your request. To avoid this, list the settings you want to change in the updateMask parameter as a single comma-separated string.

    curl \
    --request PATCH \
    --header "Authorization: Bearer $IAM_TOKEN" \
    --header "Content-Type: application/json" \
    --url 'https://{{ api-host-mdb }/managed-clickhouse/v1/clusters/<cluster_ID>/mlModels/<model_name>' \
    --data '{
              "updateMask": "uri",
              "uri": "<file_link>"
            }'

    Where:

    • updateMask: List of parameters to update as a single string, separated by commas.

      Here only one parameter is specified: uri.

    • uri: Link to the new model file in Object Storage.

    You can request the cluster ID with the list of clusters in the folder.

  3. View the server response to make sure the request was successful.

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Clone the cloudapi repository:

    cd ~/ && git clone --depth=1 https://github.com/yandex-cloud/cloudapi

    Below, we assume the repository contents are stored in the ~/cloudapi/ directory.

  3. Use the MlModelService.Update call and send the following request, e.g., via gRPCurl:

    Warning

    The API method will assign default values to all the parameters of the object you are modifying unless you explicitly provide them in your request. To avoid this, list the settings you want to change in the update_mask parameter as an array of paths[] strings.

    Format for listing settings
    "update_mask": {
    "paths": [
        "<setting_1>",
        "<setting_2>",
        ...
        "<setting_N>"
    ]
}

    grpcurl \
    -format json \
    -import-path ~/cloudapi/ \
    -import-path ~/cloudapi/third_party/googleapis/ \
    -proto ~/cloudapi/yandex/cloud/mdb/clickhouse/v1/ml_model_service.proto \
    -rpc-header "Authorization: Bearer $IAM_TOKEN" \
    -d '{
            "cluster_id": "<cluster_ID>",
            "ml_model_name": "<schema_name>",
            "update_mask": {
              "paths": ["uri"]
            },
            "uri": "<file_link>"
        }' \
    mdb.api.cloud.yandex.net:443 \
    yandex.cloud.mdb.clickhouse.v1.MlModelService.Create

    Where:

    • ml_model_name: Model name.

    • update_mask: List of parameters to update as an array of paths[] strings.

      Here only one parameter is specified: uri.

    • uri: Link to the new model file in Object Storage.

    You can request the cluster ID with the list of clusters in the folder.

  4. View the server response to make sure the request was successful.

Disabling a model

Note

After disabling a model, the corresponding object is kept in the Yandex Object Storage bucket. If you no longer need this model object, you can delete it.

  1. In the management console, go to the folder page and select Managed Service for ClickHouse.
  2. Click the cluster name and select the Machine learning tab in the left-hand panel.
  3. Select the appropriate model, click , and select Delete.

If you do not have the Yandex Cloud (CLI) command line interface yet, install and initialize it.

The folder specified when creating the CLI profile is used by default. To change the default folder, use the yc config set folder-id <folder_ID> command. You can specify a different folder using the --folder-name or --folder-id parameter.

To disable a model, run the command:

yc managed-clickhouse ml-model delete <model_name> \
  --cluster-name=<cluster_name>

You can request the model name with a list of cluster models and the cluster name with a list of clusters in the folder.

  1. Open the current Terraform configuration file that defines your infrastructure.

    For more information about creating this file, see Creating clusters.

  2. Delete the description block of the appropriate ml_model model from the Managed Service for ClickHouse® cluster description.

  3. Make sure the settings are correct.

    1. In the command line, navigate to the directory that contains the current Terraform configuration files defining the infrastructure.

    2. Run this command:

      terraform validate

      Terraform will show any errors found in your configuration files.

  4. Confirm updating the resources.

    1. Run this command to view the planned changes:

      terraform plan

      If you described the configuration correctly, the terminal will display a list of the resources to update and their parameters. This is a verification step that does not apply changes to your resources.

    2. If everything looks correct, apply the changes:

      1. Run this command:

        terraform apply

      2. Confirm updating the resources.

      3. Wait for the operation to complete.

For more information, see the Terraform provider documentation.

Time limits

A Terraform provider sets the timeout for Managed Service for ClickHouse® cluster operations:

  • Creating a cluster, including by restoring one from a backup: 60 minutes.
  • Editing a cluster: 90 minutes.
  • Deleting a cluster: 30 minutes.

Operations exceeding the set timeout are interrupted.

How do I change these limits?

Add the timeouts block to the cluster description, for example:

resource "yandex_mdb_clickhouse_cluster" "<cluster_name>" {
  ...
  timeouts {
    create = "1h30m" # 1 hour 30 minutes
    update = "2h"    # 2 hours
    delete = "30m"   # 30 minutes
  }
}

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Use the MlModel.Delete method and send the following request, e.g., via cURL:

    curl \
    --request DELETE \
    --header "Authorization: Bearer $IAM_TOKEN" \
    --url 'https://mdb.api.cloud.yandex.net/managed-clickhouse/v1/clusters/<cluster_ID>/mlModels/<model_name>'

    You can request the cluster ID with a list of clusters in the folder and model name with a list of models in the cluster.

  3. View the server response to make sure the request was successful.

  1. Get an IAM token for API authentication and put it into the environment variable:

    export IAM_TOKEN="<IAM_token>"

  2. Clone the cloudapi repository:

    cd ~/ && git clone --depth=1 https://github.com/yandex-cloud/cloudapi

    Below, we assume the repository contents are stored in the ~/cloudapi/ directory.

  3. Use the MlModelService.Delete call and send the following request, e.g., via gRPCurl:

    grpcurl \
    -format json \
    -import-path ~/cloudapi/ \
    -import-path ~/cloudapi/third_party/googleapis/ \
    -proto ~/cloudapi/yandex/cloud/mdb/clickhouse/v1/ml_model_service.proto \
    -rpc-header "Authorization: Bearer $IAM_TOKEN" \
    -d '{
            "cluster_id": "<cluster_ID>",
            "ml_model_name": "<schema_name>"
        }' \
    mdb.api.cloud.yandex.net:443 \
    yandex.cloud.mdb.clickhouse.v1.MlModelService.Delete

    You can request the cluster ID with the list of clusters in the folder.

  4. View the server response to make sure the request was successful.

Example

If you do not have a suitable data set or model to process it, you can test machine learning in Managed Service for ClickHouse® using this example. We prepared a data file for it and trained a model to analyze it. You can upload data to ClickHouse® and see model predictions for different rows of the table.

Note

In this example, we are going to use public data from the Amazon Employee Access Challenge. The model is trained to predict values in the ACTION column. The same data and model are used on GitHub.

To upload data to ClickHouse® and test the model:

  1. In the management console, add the test model:

    • Type: ML_MODEL_TYPE_CATBOOST.
    • Name: ml_test.
    • URL: https://storage.yandexcloud.net/managed-clickhouse/catboost_model.bin.

  2. Download the file with data to analyze.

  3. Connect to the cluster.

  4. Create a test table:

    CREATE TABLE
            ml_test_table (date Date MATERIALIZED today(), 
                          ACTION UInt8, 
                          RESOURCE UInt32, 
                          MGR_ID UInt32, 
                          ROLE_ROLLUP_1 UInt32, 
                          ROLE_ROLLUP_2 UInt32, 
                          ROLE_DEPTNAME UInt32, 
                          ROLE_TITLE UInt32, 
                          ROLE_FAMILY_DESC UInt32, 
                          ROLE_FAMILY UInt32, 
                          ROLE_CODE UInt32) 
            ENGINE = MergeTree() 
PARTITION BY date 
ORDER BY date;

  5. Upload the data to the table:

    INSERT INTO ml_test_table FROM INFILE '<file_path>/train.csv' FORMAT CSVWithNames;

  6. Test the model:

    • Get predicted values in the ACTION column for the first 10 rows in the table:

      SELECT
    catboostEvaluate('/var/lib/clickhouse/models/ml_test.bin',
                    RESOURCE,
                    MGR_ID,
                    ROLE_ROLLUP_1,
                    ROLE_ROLLUP_2,
                    ROLE_DEPTNAME,
                    ROLE_TITLE,
                    ROLE_FAMILY_DESC,
                    ROLE_FAMILY,
                    ROLE_CODE) > 0 AS prediction,
    ACTION AS target
FROM ml_test_table
LIMIT 10;

    • Get the probability prediction for the first 10 rows in the table:

      SELECT
    catboostEvaluate('/var/lib/clickhouse/models/ml_test.bin',
                    RESOURCE,
                    MGR_ID,
                    ROLE_ROLLUP_1,
                    ROLE_ROLLUP_2,
                    ROLE_DEPTNAME,
                    ROLE_TITLE,
                    ROLE_FAMILY_DESC,
                    ROLE_FAMILY,
                    ROLE_CODE) AS prediction,
    1. / (1 + exp(-prediction)) AS probability,
    ACTION AS target
FROM ml_test_table
LIMIT 10;

ClickHouse® is a registered trademark of ClickHouse, Inc.

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