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 supports the following Compute Engine predefined machine types in clusters:
- General purpose machine types, which include N1, N2, N2D, and E2 machine types:
Dataproc also supports N1, N2, N2D, and E2 custom machine types.
Compute-optimized machine types, which include C2 machine types.
Memory-optimized machine types, which include M1 and M2 machine types.
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.
Specifying 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).
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 command
Run the gcloud dataproc clusters create command with the following flags to create a Dataproc cluster with master and/or worker machine types:- The
--master-machine-type machine-typeflag allows you to set the predefined or custom machine type used by the master VM instance in your cluster (or master instances if you create a HA cluster) - The
--worker-machine-type custom-machine-typeflag allows you to set the predefined or custom machine type used by the worker VM instances in your cluster
Example:
gcloud dataproc clusters create test-cluster / --master-machine-type custom-6-23040 / --worker-machine-type custom-6-23040 / other argsOnce the Dataproc cluster starts, cluster details are displayed in the terminal window. The following is a partial sample listing of cluster properties displayed in the terminal window:
... 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 ...
REST API
To create a cluster with custom machine types, set themachineTypeUri in the masterConfig and/or workerConfig
InstanceGroupConfig
in the
cluster.create
API request.
Example:
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
}
}
}
}
Console
From the Configure nodes panel of the Dataproc Create a cluster page in the Google Cloud console, select machine family, series and type for the cluster's master and worker nodes.
CPU Extended Memory
Dataproc supports custom machine types with extended memory beyond the 6.5GB per vCPU limit (see Extended Memory Pricing).
Using Extended Memory
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
REST 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",
...
...
Console
Click Extend memory when customizing Machine type memory in the Master node and/or Worker nodes section on the Dataproc Create a cluster page on Google Cloud console.
For more information
See Creating a VM Instance with a Custom Machine Type.