Supported machine types

Dataproc clusters are built on Compute Engine instances. Machine types define the virtualized hardware resources available to an instance. Compute Engine offers both predefined machine types and custom machine types. Dataproc clusters can use both predefined and custom types for both master and/or worker nodes.

Dataproc clusters support the following Compute Engine predefined machine types (machine type availability varies by region):

• General purpose machine types, which include N1, N2, N2D, E2, C3, C4, and N4 machine types (Dataproc also supports N1, N2, N2D, E2, C3, C4, and N4 custom machine types).

  Limitations:

  • the n1-standard-1 machine type is not supported for 2.0+ images (the n1-standard-1 machine type is not recommended for pre-2.0 images—instead, use a machine type with higher memory.
  • Shared-core machine types are not supported, which include the following unsupported machine types:
    • E2: e2-micro, e2-small, and e2-medium shared-core machine types, and
    • N1: f1-micro and g1-small shared-core machine types.
  • Dataproc selects hyperdisk-balanced as the boot-disk type if machine type is C3, C4 or N4.
• Compute-optimized machine types, which include C2 and C2D machine types.
• Memory-optimized machine types, which include M1 and M2 machine types.
• ARM machine types, which include T2A machine types.

Custom machine types

Dataproc supports N1 series custom machine types.

Custom machine types are ideal for the following workloads:

• Workloads that are not a good fit for the predefined machine types.
• Workloads that require more processing power or more memory, but don't need all of the upgrades that are provided by the next machine type level.

For example, if you have a workload that needs more processing power than that provided by an n1-standard-4 instance, but the next step up, an n1-standard-8 instance, provides too much capacity. With custom machine types, you can create Dataproc clusters with master and/or worker nodes in the middle range, with 6 virtual CPUs and 25 GB of memory.

Specify a custom machine type

Custom machine types use a special machine type specification and are subject to limitations. For example, the custom machine type specification for a custom VM with 6 virtual CPUs and 22.5 GB of memory is custom-6-23040.

The numbers in the machine type specification correspond to the number of virtual CPUs (vCPUs)in the machine (6) and the amount of memory (23040). The amount of memory is calculated by multiplying the amount of memory in gigabytes by 1024 (see Expressing memory in GB or MB). In this example, 22.5 (GB) is multiplied by 1024: 22.5 * 1024 = 23040.

You use the above syntax to specify the custom machine type with your clusters. You can set the machine type for either master or worker nodes or both when you create a cluster. If you set both, the master node can use a custom machine type that is different from the custom machine type used by workers. The machine type used by any secondary workers follow the settings for primary workers and cannot be separately set (see Secondary workers - preemptible and non-preemptible VMs).

Custom machine type pricing

Custom machine type pricing is based on the resources used in a custom machine. Dataproc pricing is added to the cost of compute resources, and is based on the total number of virtual CPUs (vCPUs) used in a cluster.

Create a Dataproc cluster with a specified machine type

gcloud dataproc clusters create test-cluster /
    --master-machine-type custom-6-23040 /
    --worker-machine-type custom-6-23040 /
    other args

...
properties:
  distcp:mapreduce.map.java.opts: -Xmx1638m
  distcp:mapreduce.map.memory.mb: '2048'
  distcp:mapreduce.reduce.java.opts: -Xmx4915m
  distcp:mapreduce.reduce.memory.mb: '6144'
  mapred:mapreduce.map.cpu.vcores: '1'
  mapred:mapreduce.map.java.opts: -Xmx1638m
...

POST /v1/projects/my-project-id/regions/is-central1/clusters/
{
  "projectId": "my-project-id",
  "clusterName": "test-cluster",
  "config": {
    "configBucket": "",
    "gceClusterConfig": {
      "subnetworkUri": "default",
      "zoneUri": "us-central1-a"
    },
    "masterConfig": {
      "numInstances": 1,
      "machineTypeUri": "n1-highmem-4",
      "diskConfig": {
        "bootDiskSizeGb": 500,
        "numLocalSsds": 0
      }
    },
    "workerConfig": {
      "numInstances": 2,
      "machineTypeUri": "n1-highmem-4",
      "diskConfig": {
        "bootDiskSizeGb": 500,
        "numLocalSsds": 0
      }
    }
  }
}

Create a Dataproc cluster with custom machine type with extended memory

Dataproc supports custom machine types with extended memory beyond the 6.5GB per vCPU limit (see Extended Memory Pricing).

Console

Click Extend memory when customizing Machine type memory in the Master node and/or Worker nodes section from the Configure nodes panel on the Dataproc Create a cluster page in the Google Cloud console.

gcloud Command

To create a cluster from the gcloud command line with custom CPUs with extended memory, add a -ext suffix to the ‑‑master-machine-type and/or ‑‑worker-machine-type flags.

Example

The following gcloud command-line sample creates a Dataproc cluster with 1 CPU and 50 GB memory (50 * 1024 = 51200) in each node:

gcloud dataproc clusters create test-cluster /
    --master-machine-type custom-1-51200-ext /
    --worker-machine-type custom-1-51200-ext /
    other args

API

The following sample JSON snippet from a Dataproc REST API clusters.create request specifies 1 CPU and 50 GB memory (50 * 1024 = 51200) in each node:

...
    "masterConfig": {
      "numInstances": 1,
      "machineTypeUri": "custom-1-51200-ext",
    ...
    },
    "workerConfig": {
      "numInstances": 2,
      "machineTypeUri": "custom-1-51200-ext",
     ...
...

ARM machine types

Dataproc supports creating a cluster with nodes that use ARM machine types, such as the T2A machine type.

Requirements and limitations:

• The Dataproc image must be compatible with ARM chipset (currently, only the Dataproc 2.1-ubuntu20-arm image is compatible with the ARM CHIPSET). Note that this image does not support many optional and initialization-action components (see 2.1.x release versions).
• Since one image must be specified for a cluster, the master, worker, and secondary-worker nodes must use an ARM machine type that is compatible with the selected Dataproc ARM image.
• Dataproc features that are not compatible with ARM machine types are not available (for example, local SSDs are not supported by T2A machine types).

Create a Dataproc cluster with an ARM machine type

gcloud dataproc clusters create cluster-name \
    --region=REGION \
    --image-version=2.1-ubuntu20-arm \
    --master-machine-type=t2a-standard-4 \
    --worker-machine-type=t2a-standard-4

POST /v1/projects/my-project-id/regions/is-central1/clusters/
{
  "projectId": "my-project-id",
  "clusterName": "sample-cluster",
  "config": {
    "configBucket": "",
    "gceClusterConfig": {
      "subnetworkUri": "default",
      "zoneUri": "us-central1-a"
    },
    "masterConfig": {
      "numInstances": 1,
      "machineTypeUri": "t2a-standard-4",
      "diskConfig": {
        "bootDiskSizeGb": 500,
      }
    },
    "workerConfig": {
      "numInstances": 2,
      "machineTypeUri": "t2a-standard-4",
      "diskConfig": {
        "bootDiskSizeGb": 500,
        "numLocalSsds": 0
      }
    },
    "softwareConfig": {
      "imageVersion": "2.1-ubuntu20-arm"
    }
  }
}