Scale environments

Cloud Composer 1 | Cloud Composer 2

This page describes how to scale Cloud Composer environments.

Scale vertically and horizontally

In Cloud Composer 1, you don't define specific CPU and memory resources for Cloud Composer and Airflow components such as workers and schedulers. Instead, you specify the number and type of machines for nodes in your environment's cluster.

Note: During component distribution in Cloud Composer 1, some Airflow components might be placed unevenly over the available nodes. In such a scenario, the CPU or memory load of one or more nodes is higher compared to other nodes in the cluster.

Options for horizontal scaling:

• Adjust the number of nodes
• Adjust the number of schedulers

Options for vertical scaling:

• Adjust the machine type of the Cloud SQL instance
• Adjust the web server machine type

Adjust scheduler parameters

Your environment can run more than one Airflow scheduler at the same time. Use multiple schedulers to distribute load between several scheduler instances for better performance and reliability.

If your environment uses Airflow 2, You can specify a number of schedulers up to the number of nodes in your environment.

Increasing the number of schedulers does not always improve Airflow performance. For example, having only one scheduler might provide better performance than having two. This might happen when the extra scheduler is not utilized, and thus consumes resources of your environment without contributing to overall performance. The actual scheduler performance depends on the number of Airflow workers, the number of DAGs and tasks that run in your environment, and the configuration of both Airflow and the environment.

We recommend starting with two schedulers and then monitoring the performance of your environment. If you change the number of schedulers, you can always scale your environment back to the original number of schedulers.

For more information about configuring multiple schedulers, see Airflow documentation.

Console

1. In the Google Cloud console, go to the Environments page.

   Go to Environments

2. In the list of environments, click the name of your environment. The Environment details page opens.

3. Go to the Environment configuration tab.

4. In the Resources > Workloads configuration item, click Edit.

5. In the Resources > Number of schedulers item, click Edit.

6. In the Scheduler configuration pane, in the Number of schedulers field, specify the number of schedulers for your environment.

7. Click Save.

gcloud

The following Airflow scheduler parameters are available:

• --scheduler-count: the number of schedulers in your environment.

Run the following Google Cloud CLI command:

gcloud composer environments update ENVIRONMENT_NAME \
  --location LOCATION \
  --scheduler-count SCHEDULER_COUNT

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.

• LOCATION: the region where the environment is located.

Example:

gcloud composer environments update example-environment \
  --location us-central1 \
  --scheduler-count 2

API

1. Construct an environments.patch API request.

2. In this request:

   1. In the updateMask parameter, specify the config.workloadsConfig.schedulerCount mask.

   2. In the request body, specify the number of schedulers for your environment.

"config": {
  "workloadsConfig": {
    "scheduler": {
      "count": SCHEDULER_COUNT
    }
  }
}

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.

• LOCATION: the region where the environment is located.

• SCHEDULER_COUNT: the number of schedulers.

Example:

// PATCH https://composer.googleapis.com/v1/projects/example-project/
// locations/us-central1/environments/example-environment?updateMask=
// config.workloadsConfig.scheduler

"config": {
  "workloadsConfig": {
    "scheduler": {
      "count": 2
    }
  }
}

Terraform

The following fields in the workloads_config.scheduler block control the Airflow scheduler parameters. Each scheduler uses the specified amount of resources.

• scheduler.count: the number of schedulers in your environment.

resource "google_composer_environment" "example" {
  provider = google-beta
  name = "ENVIRONMENT_NAME"
  region = "LOCATION"

  config {

    workloads_config {
      scheduler {
        count = SCHEDULER_COUNT
      }
    }

  }
}

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.

• LOCATION: the region where the environment is located.

• SCHEDULER_COUNT: the number of schedulers.

Example:

resource "google_composer_environment" "example" {
  provider = google-beta
  name = "example-environment"
  region = "us-central1"

  config {

    workloads_config {
      scheduler {
        
        count = 2
      }
    }

  }
}

Adjust triggerer parameters

You can set the number of triggerers to zero, but you need at least one triggerer instance in your environment (or at least two in highly resilient environments), to use deferrable operators in your DAGs.

Depending on your environment's resilience mode, there are different possible configurations for the number of triggerers:

• Standard resilience: you can run up to 10 triggerers.
• High resilience: at least 2 triggerers, up to a maximum of 10.

Even if the number of triggerers is set to zero, a triggerer pod definition is created and visible in your environment's cluster, but no actual triggerer workloads are run.

You can also specify the amount of CPUs, memory, and disk space used by Airflow triggerers in your environment. In this way, you can increase performance of your environment, in addition to horizontal scaling provided by using multiple triggerers.

Note: For information about possible values and limits for resources, see the Resource limits section.

Console

1. In the Google Cloud console, go to the Environments page.

   Go to Environments

2. In the list of environments, click the name of your environment. The Environment details page opens.

3. Go to the Environment configuration tab.

4. In the Resources > Workloads configuration item, click Edit.

5. In the Workloads configuration pane adjust the parameters for Airflow triggerers:

   1. In the Triggerer section, in the Number of triggerers field, enter the number of triggerers in your environment.

      If you set at least one triggerer for your environment, also use the the CPU, and Memory fields to configure resource allocation for your triggerers.

   2. In the CPU and Memory, specify the number of CPUs, memory, and storage for Airflow triggerers. Each triggerer uses the specified amount of resources.

6. Click Save.

gcloud

The following Airflow triggerer parameters are available:

Caution: Always use all three arguments: --triggerer-cpu, --triggerer-memory, and --triggerer-count. If you omit a parameter, its current value is reset to the default: 0.5 vCPU, 0.5 GB memory, or the number of triggerers for your environment's resilience mode.

• --triggerer-count: the number of triggerers in your environment.

  • For standard resilience environments, use a value between 0 and 10.
  • For highly resilient environments, use 0, or a value between 2 and 10.

• --triggerer-cpu: the number of CPUs for an Airflow triggerer.

• --triggerer-memory: the amount of memory for an Airflow triggerer.

Run the following Google Cloud CLI command:

gcloud composer environments update ENVIRONMENT_NAME \
  --location LOCATION \
  --triggerer-count TRIGGERER_COUNT \
  --triggerer-cpu TRIGGERER_CPU \
  --triggerer-memory TRIGGERER_MEMORY

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• TRIGGERER_COUNT: the number of triggerers.
• TRIGGERER_CPU: the number of CPUs for a triggerer, in vCPU units.
• TRIGGERER_MEMORY: the amount of memory for a triggerer.

Note: The specified value for memory and disk space must be a whole number followed by a size unit of KB for kilobyte, MB for megabyte, or GB for gigabyte. The default size unit is GB. For example, 10GB produces a 10 gigabyte storage.

Examples:

• Scale to four triggerer instances:

  gcloud composer environments update example-environment \
    --location us-central1 \
    --triggerer-count 4 \
    --triggerer-cpu 1 \
    --triggerer-memory 1
  ```

- Disable triggerers by setting triggerer count to `0`. This operation
  doesn't require specifying CPU or memory for the triggerers.

```bash
  gcloud composer environments update example-environment \
    --location us-central1 \
    --triggerer-count 0
  ```

API

1. In the updateMask query parameter, specify the config.workloadsConfig.triggerer mask.

2. In the request body, specify all three parameters for triggerers.

"config": {
  "workloadsConfig": {
    "triggerer": {
      "count": TRIGGERER_COUNT,
      "cpu": TRIGGERER_CPU,
      "memoryGb": TRIGGERER_MEMORY
    }
  }
}

Replace the following:

• TRIGGERER_COUNT: the number of triggerers.

  • For standard resilience environments, use a value between 0 and 10.
  • For highly resilient environments, use 0, or a value between 2 and 10.

• TRIGGERER_CPU: the number of CPUs for a triggerer, in vCPU units.

• TRIGGERER_MEMORY: the amount of memory for a triggerer.

Examples:

• Disable triggerers by setting triggerer count to 0. This operation doesn't require specifying CPU or memory for the triggerers.

// PATCH https://composer.googleapis.com/v1/projects/example-project/
// locations/us-central1/environments/example-environment?updateMask=
// config.workloadsConfig.triggerer
"config": {
  "workloadsConfig": {
    "triggerer": {
      "count": 0
    }
  }
}

• Scale to four triggerer instances:

// PATCH https://composer.googleapis.com/v1/projects/example-project/
// locations/us-central1/environments/example-environment?updateMask=
// config.workloadsConfig.triggerer
"config": {
  "workloadsConfig": {
    "triggerer": {
      "count": 4,
      "cpu": 1,
      "memoryGb": 1
    }
  }
}

Terraform

The following fields in the workloads_config.triggerer block control the Airflow triggerer parameters. Each triggerer uses the specified amount of resources.

• triggerer.count: the number of triggerers in your environment.

  • For standard resilience environments, use a value between 0 and 10.
  • For highly resilient environments, use 0, or a value between 2 and 10.

• triggerer.cpu: the number of CPUs for an Airflow triggerer.

• triggerer.memory_gb: the amount of memory for an Airflow triggerer.

resource "google_composer_environment" "example" {
  provider = google-beta
  name = "ENVIRONMENT_NAME"
  region = "LOCATION"

  config {

    workloads_config {
      triggerer {
        count = TRIGGERER_COUNT
        cpu = TRIGGERER_CPU
        memory_gb = TRIGGERER_MEMORY
      }
    }

  }
}

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• TRIGGERER_COUNT: the number of triggerers.
• TRIGGERER_CPU: the number of CPUs for a triggerer, in vCPU units.
• TRIGGERER_MEMORY: the amount of memory for a triggerer, in GB.

Example:

resource "google_composer_environment" "example" {
  provider = google-beta
  name = "example-environment"
  region = "us-central1"

  config {

    workloads_config {
      triggerer {
        count = 1
        cpu = 0.5
        memory_gb = 0.5
      }
    }

  }
}

Adjust web server parameters

You can specify the amount of CPUs, memory, and disk space used by the Airflow web server in your environment. In this way, you can scale the performance of Airflow UI, for example, to match the demand coming from a large number of users or a large number of managed DAGs.

Note: For information about possible values and limits for resources, see the Resource limits section.

Console

1. In the Google Cloud console, go to the Environments page.

   Go to Environments

2. In the list of environments, click the name of your environment. The Environment details page opens.

3. Go to the Environment configuration tab.

4. In the Resources > Workloads configuration item, click Edit.

5. In the Workloads configuration pane adjust the parameters for the web server. In the CPU, Memory, and Storage fields, specify the number of CPUs, memory, and storage for the web server.

6. Click Save.

gcloud

The following Airflow web server parameters are available:

• --web-server-cpu: the number of CPUs for the Airflow web server.
• --web-server-memory: the amount of memory for the Airflow web server.
• --web-server-storage: the amount of disk space for the Airflow web server.

Run the following Google Cloud CLI command:

gcloud composer environments update ENVIRONMENT_NAME \
  --location LOCATION \
  --web-server-cpu WEB_SERVER_CPU \
  --web-server-memory WEB_SERVER_MEMORY \
  --web-server-storage WEB_SERVER_STORAGE

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• WEB_SERVER_CPU: the number of CPUs for web server, in vCPU units.
• WEB_SERVER_MEMORY: the amount of memory for web server.
• WEB_SERVER_STORAGE: the amount of memory for the web server.

Note: The specified value for memory and disk space must be a whole number followed by a size unit of KB for kilobyte, MB for megabyte, or GB for gigabyte. The default size unit is GB. For example, 10GB produces a 10 gigabyte storage.

Example:

gcloud composer environments update example-environment \
  --location us-central1 \
  --web-server-cpu 1 \
  --web-server-memory 2.5 \
  --web-server-storage 2

API

1. Construct an environments.patch API request.

2. In this request:

   1. In the updateMask parameter, specify the config.workloadsConfig.webServer mask to update all web server parameters. You can also update individual web server parameters by specifying a mask for those arameters: config.workloadsConfig.webServer.cpu, config.workloadsConfig.webServer.memoryGb, config.workloadsConfig.webServer.storageGb.

   2. In the request body, specify the new web server parameters.

"config": {
  "workloadsConfig": {
    "webServer": {
      "cpu": WEB_SERVER_CPU,
      "memoryGb": WEB_SERVER_MEMORY,
      "storageGb": WEB_SERVER_STORAGE
    }
  }
}

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• WEB_SERVER_CPU: the number of CPUs for the web server, in vCPU units.
• WEB_SERVER_MEMORY: the amount of memory for the web server, in GB.
• WEB_SERVER_STORAGE: the disk size for the web server, in GB.

Example:

// PATCH https://composer.googleapis.com/v1/projects/example-project/
// locations/us-central1/environments/example-environment?updateMask=
// config.workloadsConfig.webServer.cpu,
// config.workloadsConfig.webServer.memoryGb,
// config.workloadsConfig.webServer.storageGb

"config": {
  "workloadsConfig": {
    "webServer": {
      "cpu": 0.5,
      "memoryGb": 2.5,
      "storageGb": 2
    }
  }
}

Terraform

The following fields in the workloads_config.web_server block control the web server parameters.

• The web_server.cpu: the number of CPUs for the web server.
• The web_server.memory_gb: the amount of memory for the web server.
• The web_server.storage_gb: the amount of disk space for the web server.

resource "google_composer_environment" "example" {
  provider = google-beta
  name = "ENVIRONMENT_NAME"
  region = "LOCATION"

  config {

    workloads_config {
      web_server {
        cpu = WEB_SERVER_CPU
        memory_gb = WEB_SERVER_MEMORY
        storage_gb = WEB_SERVER_STORAGE
      }
    }

  }
}

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• WEB_SERVER_CPU: the number of CPUs for the web server, in vCPU units.
• WEB_SERVER_MEMORY: the amount of memory for the web server, in GB.
• WEB_SERVER_STORAGE: the disk size for the web server, in GB.

Example:

resource "google_composer_environment" "example" {
  provider = google-beta
  name = "example-environment"
  region = "us-central1"

  config {

    workloads_config {
      web_server {
        cpu = 0.5
        memory_gb = 1.875
        storage_gb = 1
      }
    }

  }
}

Adjust the environment size

The environment size controls the performance parameters of the managed Cloud Composer infrastructure that includes, for example, the Airflow database.

Consider selecting a larger environment size if you want to run a large number of DAGs and tasks.

Note: Environment size is different from the environment presets. Environment presets, which you can select when you create an environment, determine all limits, scale, and performance parameters of your environment, including the environment size. Environment size determines only the performance parameters of the managed Cloud Composer infrastructure of your environment.

Console

1. In the Google Cloud console, go to the Environments page.

   Go to Environments

2. In the list of environments, click the name of your environment. The Environment details page opens.

3. Go to the Environment configuration tab.

4. In the Resources > Workloads configuration item, click Edit.

5. In the Resources > Core infrastructure item, click Edit.

6. In the Core infrastructure pane, in the Environment size field, specify the environment size.

7. Click Save.

gcloud

The --environment-size argument controls the environment size:

gcloud composer environments update ENVIRONMENT_NAME \
    --location LOCATION \
    --environment-size ENVIRONMENT_SIZE

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• ENVIRONMENT_SIZE: small, medium, or large.

Example:

gcloud composer environments update example-environment \
    --location us-central1 \
    --environment-size medium

API

1. Create an environments.patch API request.

2. In this request:

   1. In the updateMask parameter, specify the config.environmentSize mask.

   2. In the request body, specify the environment size.

  "config": {
    "environmentSize": "ENVIRONMENT_SIZE"
  }

Replace the following:

• ENVIRONMENT_SIZE: the environment size, ENVIRONMENT_SIZE_SMALL, ENVIRONMENT_SIZE_MEDIUM, or ENVIRONMENT_SIZE_LARGE.

Example:

// PATCH https://composer.googleapis.com/v1/projects/example-project/
// locations/us-central1/environments/example-environment?updateMask=
// config.environmentSize

"config": {
  "environmentSize": "ENVIRONMENT_SIZE_MEDIUM"
}

Terraform

The environment_size field in the config block controls the environment size:

resource "google_composer_environment" "example" {
  provider = google-beta
  name = "ENVIRONMENT_NAME"
  region = "LOCATION"

  config {

    environment_size = "ENVIRONMENT_SIZE"

  }
}

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• ENVIRONMENT_SIZE: the environment size, ENVIRONMENT_SIZE_SMALL, ENVIRONMENT_SIZE_MEDIUM, or ENVIRONMENT_SIZE_LARGE.

Example:

resource "google_composer_environment" "example" {
  provider = google-beta
  name = "example-environment"
  region = "us-central1"

  config {

    environment_size = "ENVIRONMENT_SIZE_SMALL"

    }
  }
}

Adjust the number of nodes

You can change the number of nodes in your environment.

This number corresponds to the number of Airflow workers in your environment. In addition to running Airflow workers, your environment nodes also run Airflow schedulers and other environment components.

Console

1. In the Google Cloud console, go to the Environments page.

   Go to Environments

2. In the list of environments, click the name of your environment. The Environment details page opens.

3. Go to the Environment configuration tab.

4. In the Worker nodes > Node count item, click Edit.

5. In the Worker nodes configuration pane, in the Node count field, specify the number of nodes in your environment.

6. Click Save.

gcloud

The --node-count argument controls the number of nodes in your environment:

gcloud composer environments update ENVIRONMENT_NAME \
    --location LOCATION \
    --zone NODE_ZONE \
    --node-count NODE_COUNT

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• NODE_COUNT: the number of nodes. The minimum number of nodes is 3.
• NODE_ZONE: the Compute Engine zone for your environment VMs.

Example:

gcloud composer environments update example-environment \
    --location us-central1 \
    --zone us-central1-a \
    --node-count 6

API

1. Create an environments.patch API request.

2. In this request:

   1. In the updateMask parameter, specify the config.nodeCount mask.

   2. In the request body, specify the number of nodes for your environment.

  "config": {
    "nodeCount": NODE_COUNT
  }

Replace the following:

• NODE_COUNT: the number of nodes. The minimum number of nodes is 3.

Example:

// PATCH https://composer.googleapis.com/v1/projects/example-project/
// locations/us-central1/environments/example-environment?updateMask=
// config.nodeCount

"config": {
  "nodeCount": 6
}

Terraform

The node_count field in the node_config block specifies the number of nodes in your environment.

resource "google_composer_environment" "example" {

  config {
    node_config {
      node_count = NODE_COUNT
    }
}

Replace the following:

• NODE_COUNT: the number of nodes.

Example:

resource "google_composer_environment" "example" {
  name = "example-environment"
  region = "us-central1"

  config {

    node_config {
      node_count = 4
    }

}

Note: Changing the node count of an environment does not trigger the rebuilding of Airflow images used for the environment.

Adjust the machine type of the Cloud SQL instance

You can change the machine type of the Cloud SQL instance that stores the Airflow database of your environment.

Console

1. In the Google Cloud console, go to the Environments page.

   Go to Environments

2. In the list of environments, click the name of your environment. The Environment details page opens.

3. Go to the Environment configuration tab.

4. In the Resources > Cloud SQL machine type item, click Edit.

5. In the Cloud SQL configuration pane, in the Cloud SQL machine type drop-down list, select the machine type for the Cloud SQL instance of your environment.

6. Click Save.

gcloud

The --cloud-sql-machine-type arguments controls the machine type of the Cloud SQL instance in your environment.

Run the following Google Cloud CLI command:

gcloud composer environments update ENVIRONMENT_NAME \
  --location LOCATION \
  --cloud-sql-machine-type SQL_MACHINE_TYPE

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• SQL_MACHINE_TYPE: the machine type for the Cloud SQL instance.

Example:

gcloud composer environments update example-environment \
  --location us-central1 \
  --cloud-sql-machine-type db-n1-standard-2

API

1. Create an environments.patch API request.

2. In this request:

   1. In the updateMask parameter, specify the config.databaseConfig.machineType mask.

   2. In the request body, specify the machine type for the Cloud SQL instance.

{
  "config": {
    "databaseConfig": {
      "machineType": "SQL_MACHINE_TYPE"
    }
  }
}

Replace the following:

• SQL_MACHINE_TYPE: the machine type for the Cloud SQL instance.

Example:

// PATCH https://composer.googleapis.com/v1/projects/example-project/
// locations/us-central1/environments/example-environment?updateMask=
// config.databaseConfig.machineType

{
  "config": {
    "databaseConfig": {
      "machineType": "db-n1-standard-2"
    }
  }
}

Terraform

The machine_type field in the database_config block specifies the machine type for the Cloud SQL instance.

resource "google_composer_environment" "example" {

  config {
    database_config {
      machine_type = "SQL_MACHINE_TYPE"
    }
  }
}

Replace the following:

• SQL_MACHINE_TYPE: the machine type for the Cloud SQL instance.

Example:

resource "google_composer_environment" "example" {
  name = "example-environment"
  region = "us-central1"

  config {
    database_config {
      machine_type = "db-n1-standard-2"
    }
}

Adjust the web server machine type

You can change the machine type for the Airflow web server of your environment.

Console

1. In the Google Cloud console, go to the Environments page.

   Go to Environments

2. In the list of environments, click the name of your environment. The Environment details page opens.

3. Go to the Environment configuration tab.

4. In the Resources > Web server machine type item, click Edit.

5. In the Web server configuration pane, in the Web server machine type drop-down list, select the machine type for the Airflow web server.

6. Click Save.

gcloud

The --web-server-machine-type arguments controls the machine type of the Airflow web server instance in your environment.

Run the following Google Cloud CLI command:

gcloud composer environments update ENVIRONMENT_NAME \
  --location LOCATION \
  --web-server-machine-type WS_MACHINE_TYPE

Replace the following:

• ENVIRONMENT_NAME: the name of the environment.
• LOCATION: the region where the environment is located.
• WS_MACHINE_TYPE: the machine type for the Airflow web server instance.

Example:

gcloud composer environments update example-environment \
  --location us-central1 \
  --web-server-machine-type composer-n1-webserver-2

API

1. Create an environments.patch API request.

2. In this request:

   1. In the updateMask parameter, specify the config.webServerConfig.machineType mask.

   2. In the request body, specify the machine type for the web server.

{
  "config": {
    "webServerConfig": {
      "machineType": "WS_MACHINE_TYPE"
    }
  }
}

Replace the following:

• WS_MACHINE_TYPE: the machine type for the Airflow web server instance.

Example:

// PATCH https://composer.googleapis.com/v1/projects/example-project/
// locations/us-central1/environments/example-environment?updateMask=
// config.webServerConfig.machineType

{
  "config": {
    "webServerConfig": {
      "machineType": "composer-n1-webserver-2"
    }
  }
}

Terraform

The machine_type field in the web_server_config block specifies the machine type for the Airflow web server instance.

resource "google_composer_environment" "example" {

  config {
    web_server_config {
      machine_type = "WS_MACHINE_TYPE"
    }
  }
}

Replace the following:

• WS_MACHINE_TYPE: the machine type for the Airflow web server instance.

Example:

resource "google_composer_environment" "example" {
  name = "example-environment"
  region = "us-central1"

  config {
    web_server_config {
      machine_type = "composer-n1-webserver-2"
    }
}

What's next

• Environment scaling and performance
• Cloud Composer pricing
• Update environments
• Environment architecture