Updating an Apache Airflow™ cluster

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

The folder specified in the CLI profile is used by default. You can specify a different folder using the --folder-name or --folder-id parameter.

To change the cluster settings:

1. View the description of the update cluster CLI command:

   yc managed-airflow cluster update --help
   

2. Provide a list of settings to update in the update cluster command:

   yc managed-airflow managed-airflow cluster update <cluster_name_or_ID> \
      --new-name <new_cluster_name> \
      --description <cluster_description> \
      --labels <label_list> \
      --service-account-id <service_account_ID> \
      --security-group-ids <security_group_IDs> \
      --webserver count=<number_of_instances>,`
                 `resource-preset-id=<resource_ID> \
      --scheduler count=<number_of_instances>,`
                 `resource-preset-id=<resource_ID> \
      --worker min-count=<minimum_number_of_instances>,`
              `max-count=<maximum_number_of_instances>,`
              `resource-preset-id=<resource_ID> \
      --triggerer count=<number_of_instances>,`
                 `resource-preset-id=<resource_ID> \
      --deb-packages <list_of_deb_packages> \
      --pip-packages <list_of_pip_packages> \
      --dags-bucket <bucket_name> \
      --deletion-protection \
      --lockbox-secrets-backend \
      --log-enabled \
      --log-folder-id <folder_ID> \
      --log-min-level <logging_level>
   

   Where:

   • --name: Cluster name.

   • --description: Cluster description.

   • --labels: List of labels. Provide labels in <key>=<value> format.

   • --admin-password: Admin user password. The password must be not less than 8 characters long and contain at least:

     • One uppercase letter
     • One lowercase letter
     • One digit
     • One special character

   • --service-account-id: Service account ID.

   • --subnet-ids: List of subnet IDs.

   • --security-group-ids: List of security group IDs.

   • --webserver, --scheduler, --worker, --triggerer: Managed Service for Apache Airflow™ component configuration:

     • count: Number of instances in the cluster for the web server, scheduler, and trigger.

     • min-count, max-count: Minimum and maximum number of instances in the cluster for the worker.

     • resource-preset-id: ID of the web server, scheduler, worker, and trigger computing resources. The possible values are:

       • c1-m4: 1 vCPU, 4 GB RAM.
       • c2-m8: 2 vCPUs, 8 GB RAM.
       • c4-m16: 4 vCPUs, 16 GB RAM.
       • c8-m32: 8 vCPUs, 32 GB RAM.

   • --deb-packages, --pip-packages: Lists of deb and pip packages enabling you to install additional libraries and applications in the cluster for running DAG files:

     If required, you can set version restrictions for the installed packages, for example:

     --pip-packages "pandas==2.0.2,scikit-learn>=1.0.0,clickhouse-driver~=0.2.0"
     

     The package name format and version are defined by the install command: pip install for pip packages and apt install for deb packages.

   • --dags-bucket: Name of the bucket to store DAG files in.

   • --deletion-protection: Enables cluster protection against accidental deletion.

     With deletion protection enabled, you will still be able to manually connect to the cluster and delete it.

   • --lockbox-secrets-backend: Enables using secrets in Yandex Lockbox to store Apache Airflow™ configuration data, variables, and connection parameters.

   • --airflow-config: Apache Airflow™ additional properties. Provide them in <configuration_section>.<key>=<value> format, such as the following:

     --airflow-config core.load_examples=False
     

   • Logging parameters:

     • --log-enabled: Enables logging. Logs generated by Apache Airflow™ will be sent to Yandex Cloud Logging.
     • --log-folder-id: Folder ID. Logs will be written to the default log group for this folder.
     • --log-group-id: Custom log group ID.
     • --log-min-level: Minimum logging level. Possible values: TRACE, DEBUG, INFO (default), WARN, ERROR, and FATAL.

     You can specify only one of the parameters: --log-folder-id or --log-group-id.

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

1. Open the current Terraform configuration file with an infrastructure plan.

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

2. To change cluster settings, update the values of the required fields in the configuration file.

   Alert

   Do not change the cluster name and password using Terraform. This will delete the existing cluster and create a new one.

   Here is an example of the configuration file structure:

   resource "yandex_airflow_cluster" "<cluster_name>" {
     name        = "<cluster_name>"
     description = "<cluster_description>"
   
     labels = { <label_list> }
   
     admin_password     = "<administrator_password>"
     service_account_id = "<service_account_ID>"
     subnet_ids         = ["<list_of_subnet_IDs>"]
     security_group_ids = ["<list_of_security_group_IDs>"]
   
     webserver = {
       count              = <number_of_instances>
       resource_preset_id = "<resource_ID>"
     }
   
     scheduler = {
       count              = <number_of_instances>
       resource_preset_id = "<resource_ID>"
     }
   
     worker = {
       min_count          = <minimum_number_of_instances>
       max_count          = <maximum_number_of_instances>
       resource_preset_id = "<resource_ID>"
     }
   
     triggerer = {
       count              = <number_of_instances>
       resource_preset_id = "<resource_ID>"
     }
   
     pip_packages = ["list_of_pip_packages"]
     deb_packages = ["list_of_deb_packages"]
   
     code_sync = {
       s3 = {
         bucket = "<bucket_name>"
       }
     }
   
     deletion_protection = <deletion_protection>
   
     lockbox_secrets_backend = {
       enabled = <usage_of_secrets>
     }
   
     airflow_config = {
       <configuration_section> = {
         <key> = "<value>"
       }
     }
   
     logging = {
       enabled   = <use_of_logging>
       folder_id = "<folder_ID>"
       min_level = "<logging_level>"
     }
   }
   
   resource "yandex_vpc_network" "<network_name>" { name = "<network_name>" }
   
   resource "yandex_vpc_subnet" "<subnet_name>" {
     name           = "<subnet_name>"
     zone           = "<availability_zone>"
     network_id     = "<network_ID>"
     v4_cidr_blocks = ["<range>"]
   }
   

   Where:

   • name: Cluster name.

   • description: Cluster description.

   • labels: List of labels. Provide labels in <key> = "<value>" format.

   • admin_password: Admin user password. The password must be not less than 8 characters long and contain at least:

     • One uppercase letter
     • One lowercase letter
     • One digit
     • One special character

   • service_account_id: Service account ID.

   • subnet_ids: List of subnet IDs.

   • security_group_ids: List of security group IDs.

   • webserver, scheduler, worker, triggerer: Managed Service for Apache Airflow™ component configuration:

     • count: Number of instances in the cluster for the web server, scheduler, and trigger.

     • min_count, max_count: Minimum and maximum number of instances in the cluster for the worker.

     • resource_preset_id: ID of the web server, scheduler, worker, and trigger computing resources. The possible values are:

       • c1-m4: 1 vCPU, 4 GB RAM.
       • c2-m8: 2 vCPUs, 8 GB RAM.
       • c4-m16: 4 vCPUs, 16 GB RAM.
       • c8-m32: 8 vCPUs, 32 GB RAM.

   • deb_packages, pip_packages: Lists of deb and pip packages enabling you to install additional libraries and applications in the cluster for running DAG files.

     If required, you can set version restrictions for the installed packages, for example:

     pip_packages = ["pandas==2.0.2","scikit-learn>=1.0.0","clickhouse-driver~=0.2.0"]
     

     The package name format and version are defined by the install command: pip install for pip packages and apt install for deb packages.

   • code_sync.s3.bucket: Name of the bucket to store DAG files in.

   • deletion_protection: Enables cluster protection against accidental deletion. Possible values: true or false.

     With deletion protection enabled, you will still be able to manually connect to the cluster and delete it.

   • lockbox_secrets_backend.enabled: Enables using secrets in Yandex Lockbox to store Apache Airflow™ configuration data, variables, and connection parameters. Possible values: true or false.

   • airflow_config: Apache Airflow™ additional properties, e.g., core for configuration section, load_examples for key, and False for value.

   • logging: Logging parameters:

     • enabled: Enables logging. Logs generated by Apache Airflow™ components will be sent to Yandex Cloud Logging. Possible values: true or false.
     • folder_id: Folder ID. Logs will be written to the default log group for this folder.
     • log_group_id: Custom log group ID.
     • min_level: Minimum logging level. Possible values: TRACE, DEBUG, INFO (default), WARN, ERROR, and FATAL.

     You can specify only one of the parameters: folder_id or log_group_id.

3. Make sure the settings are correct.

   1. Using the command line, navigate to the folder that contains the up-to-date Terraform configuration files with an infrastructure plan.

   2. Run the command:

      terraform validate
      

      If there are errors in the configuration files, Terraform will point to them.

4. Confirm updating the resources.

   1. Run the command to view planned changes:

      terraform plan
      

      If the resource configuration descriptions are correct, the terminal will display a list of the resources to modify and their parameters. This is a test step. No resources are updated.

   2. If you are happy with the planned changes, apply them:

      1. Run the command:

         terraform apply
         

      2. Confirm the update of resources.

      3. Wait for the operation to complete.

For more information, see the Terraform provider documentation.

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

   export IAM_TOKEN="<IAM_token>"
   

2. Create a file named body.json and add the following contents to it:

   {
     "updateMask": "<list_of_parameters_to_change>",
     "name": "<cluster_name>",
     "description": "<cluster_description>",
     "labels": { <label_list> },
     "configSpec": {
       "airflow": {
         "config": { <list_of_properties> }
       },
       "webserver": {
         "count": "<number_of_instances>",
         "resources": {
           "resourcePresetId": "<resource_ID>"
         }
       },
       "scheduler": {
         "count": "<number_of_instances>",
         "resources": {
           "resourcePresetId": "<resource_ID>"
         }
       },
       "triggerer": {
         "count": "<number_of_instances>",
         "resources": {
           "resourcePresetId": "<resource_ID>"
         }
       },
       "worker": {
         "minCount": "<minimum_number_of_instances>",
         "maxCount": "<maximum_number_of_instances>",
         "resources": {
           "resourcePresetId": "<resource_ID>"
         }
       },
       "dependencies": {
         "pipPackages": [ <list_of_pip_packages> ],
         "debPackages": [ <list_of_deb_packages> ]
       },
       "lockbox": {
         "enabled": <logging_usage>
       }
     },
     "codeSync": {
       "s3": {
         "bucket": "<bucket_name>"
       }
     },
     "networkSpec": {
       "securityGroupIds": [ <list_of_security_group_IDs> ]
     },
     "deletionProtection": <deletion_protection>,
     "serviceAccountId": "<service_account_ID>",
     "logging": {
       "enabled": <logging_usage>,
       "minLevel": "<logging_level>",
       // Specify either `folderId` or `logGroupId`.
       "folderId": "<folder_ID>",
       "logGroupId": "<log_group_ID>",
     }
   }
   

   Where:

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

     Warning

     When updating a cluster, all parameters of the object being changed that were not explicitly set in the request will be overridden with their default values. To avoid this, list the settings you want to change in the updateMask parameter.

   • name: Cluster name.

   • description: Cluster description.

   • labels: List of labels. Provide labels in "<key>": "<value>" format.

   • config: Cluster configuration:

     • airflow.config: Apache Airflow™ additional properties. Provide them in "<configuration_section>.<key>": "<value>" format, such as the following:

       "airflow": {
         "config": {
           "core.load_examples": "False"
         }
       }
       

     • webserver, scheduler, triggerer, and worker: Configuration of Managed Service for Apache Airflow™ components:

       • count: Number of instances in the cluster for the web server, scheduler, and trigger.

       • minCount and maxCount: Minimum and maximum number of instances in the cluster for the worker.

       • resources.resourcePresetId: ID of the web server, scheduler, worker, and trigger computing resources. The possible values are:

         • c1-m4 for 1 vCPU and 4 GB RAM
         • c2-m8 for 2 vCPUs and 8 GB RAM
         • c4-m16 for 4 vCPUs and 16 GB RAM
         • c8-m32 for 8 vCPUs and 32 GB RAM

     • dependencies: Lists of packages enabling you to install additional libraries and applications for running DAG files in the cluster:

       • pipPackages: List of pip packages.
       • debPackages: List of deb packages.

       If required, you can set version restrictions for the installed packages, for example:

       "dependencies": {
         "pipPackages": [
           "pandas==2.0.2",
           "scikit-learn>=1.0.0",
           "clickhouse-driver~=0.2.0"
         ]
       }
       

       The package name format and version are defined by the install command: pip install for pip packages and apt install for deb packages.

     • lockbox.enabled: Enables using secrets in Yandex Lockbox to store Apache Airflow™ configuration data, variables, and connection parameters. The possible values are true or false.

   • network.securityGroupIds: List of security group IDs.

   • codeSync.s3.bucket: Name of the bucket to store DAG files in.

   • deletionProtection: Enables cluster protection against accidental deletion. The possible values are true or false.

     With deletion protection enabled, you will still be able to manually connect to the cluster and delete it.

   • serviceAccountId: ID of the previously created service account.

   • logging: Logging parameters:

     • enabled: Enables logging. Logs generated by Apache Airflow™ components will be sent to Yandex Cloud Logging. The possible values are true or false.
     • minLevel: Minimum logging level. Possible values: TRACE, DEBUG, INFO, WARN, ERROR, and FATAL.
     • folderId: Folder ID. Logs will be written to the default log group for this folder.
     • logGroupId: Custom log group ID.

     You can only specify either folderId or logGroupId.

3. Use the Cluster.update method and make a request, e.g., via cURL:

   curl \
       --request PATCH \
       --header "Authorization: Bearer $IAM_TOKEN" \
       --url 'https://airflow.api.cloud.yandex.net/managed-airflow/v1/clusters/<cluster_ID>'
       --data '@body.json'
   

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

4. 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. Create a file named body.json and add the following contents to it:

   {
     "cluster_id": "<cluster_ID>",
     "update_mask": "<list_of_parameters_to_change>",
     "name": "<cluster_name>",
     "description": "<cluster_description>",
     "labels": { <label_list> },
     "config_spec": {
       "airflow": {
         "config": { <list_of_properties> }
       },
       "webserver": {
         "count": "<number_of_instances>",
         "resources": {
           "resource_preset_id": "<resource_ID>"
         }
       },
       "scheduler": {
         "count": "<number_of_instances>",
         "resources": {
           "resource_preset_id": "<resource_ID>"
         }
       },
       "triggerer": {
         "count": "<number_of_instances>",
         "resources": {
           "resource_preset_id": "<resource_ID>"
         }
       },
       "worker": {
         "min_count": "<minimum_number_of_instances>",
         "max_count": "<maximum_number_of_instances>",
         "resources": {
           "resource_preset_id": "<resource_ID>"
         }
       },
       "dependencies": {
         "pip_packages": [ <list_of_pip_packages> ],
         "deb_packages": [ <list_of_deb_packages> ]
       },
       "lockbox": {
         "enabled": <logging_usage>
       }
     },
     "code_sync": {
       "s3": {
         "bucket": "<bucket_name>"
       }
     },
     "network_spec": {
       "security_group_ids": [ <list_of_security_group_IDs> ]
     },
     "deletion_protection": <deletion_protection>,
     "service_account_id": "<service_account_ID>",
     "logging": {
       "enabled": <logging_usage>,
       "min_level": "<logging_level>",
       // Specify either `folderId` or `logGroupId`.
       "folder_id": "<folder_ID>",
       "log_group_id": "<log_group_ID>",
     }
   }
   

   Where:

   • cluster_id: Cluster ID. You can retrieve it with a list of clusters in a folder.

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

     Format for listing settings

     "update_mask": {
         "paths": [
             "<setting_1>",
             "<setting_2>",
             ...
             "<setting_N>"
         ]
     }
     

     Warning

     When updating a cluster, all parameters of the object being changed that were not explicitly set in the request will be overridden with their default values. To avoid this, list the settings you want to change in the update_mask parameter.

   • name: Cluster name.

   • description: Cluster description.

   • labels: List of labels. Provide labels in "<key>": "<value>" format.

   • config_spec: Cluster configuration:

     • airflow.config: Apache Airflow™ additional properties. Provide them in "<configuration_section>.<key>": "<value>" format, such as the following:

       "airflow": {
         "config": {
           "core.load_examples": "False"
         }
       }
       

     • webserver, scheduler, triggerer, and worker: Configuration of Managed Service for Apache Airflow™ components:

       • count: Number of instances in the cluster for the web server, scheduler, and trigger.

       • min_count and max_count: Minimum and maximum number of instances in the cluster for the worker.

       • resources.resource_preset_id: ID of the web server, scheduler, worker, and trigger computing resources. The possible values are:

         • c1-m4 for 1 vCPU and 4 GB RAM
         • c2-m8 for 2 vCPUs and 8 GB RAM
         • c4-m16 for 4 vCPUs and 16 GB RAM
         • c8-m32 for 8 vCPUs and 32 GB RAM

     • dependencies: Lists of packages enabling you to install additional libraries and applications for running DAG files in the cluster:

       • pip_packages: List of pip packages.
       • deb_packages: List of deb packages.

       If required, you can set version restrictions for the installed packages, for example:

       "dependencies": {
         "pip_packages": [
           "pandas==2.0.2",
           "scikit-learn>=1.0.0",
           "clickhouse-driver~=0.2.0"
         ]
       }
       

       The package name format and version are defined by the install command: pip install for pip packages and apt install for deb packages.

     • lockbox.enabled: Enables using secrets in Yandex Lockbox to store Apache Airflow™ configuration data, variables, and connection parameters. The possible values are true or false.

   • network_spec.security_group_ids: List of security group IDs.

   • code_sync.s3.bucket: Name of the bucket to store DAG files in.

   • deletion_protection: Enables cluster protection against accidental deletion. The possible values are true or false.

     With deletion protection enabled, you will still be able to manually connect to the cluster and delete it.

   • service_account_id: ID of the previously created service account.

   • logging: Logging parameters:

     • enabled: Enables logging. Logs generated by Apache Airflow™ components will be sent to Yandex Cloud Logging. The possible values are true or false.
     • min_level: Minimum logging level. Possible values: TRACE, DEBUG, INFO, WARN, ERROR, and FATAL.
     • folder_id: Folder ID. Logs will be written to the default log group for this folder.
     • log_group_id: Custom log group ID.

     You can only specify either folder_id or log_group_id.

4. Use the ClusterService/Update call and make a request, e.g., via gRPCurl:

   grpcurl \
       -format json \
       -import-path ~/cloudapi/ \
       -import-path ~/cloudapi/third_party/googleapis/ \
       -proto ~/cloudapi/yandex/cloud/airflow/v1/cluster_service.proto \
       -rpc-header "Authorization: Bearer $IAM_TOKEN" \
       -d @ \
       airflow.api.cloud.yandex.net:443 \
       yandex.cloud.airflow.v1.ClusterService.Update \
       < body.json
   

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