Cluster
Bases: BaseModel, PulumiResource, TerraformResource
Databricks cluster
| ATTRIBUTE | DESCRIPTION |
|---|---|
access_controls |
List of access controls
TYPE:
|
apply_policy_default_values |
Whether to use policy default values for missing cluster attributes.
TYPE:
|
autoscale |
Autoscale specifications
TYPE:
|
autotermination_minutes |
Automatically terminate the cluster after being inactive for this time in minutes.
TYPE:
|
cluster_id |
Cluster ID. Used mostly when assigning a cluster to a job task.
TYPE:
|
custom_tags |
Additional tags for cluster resources. Databricks will tag all cluster resources (e.g., AWS EC2 instances and EBS volumes) with these tags in addition to default_tags. If a custom cluster tag has the same name as a default cluster tag, the custom tag is prefixed with an x_ when it is propagated. |
data_security_mode |
Select the security features of the cluster. Unity Catalog requires SINGLE_USER or USER_ISOLATION mode. If omitted, no security features are enabled. In the Databricks UI, this has been recently been renamed Access Mode and USER_ISOLATION has been renamed Shared, but use these terms here.
TYPE:
|
driver_instance_pool_id |
Similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.
TYPE:
|
driver_node_type_id |
The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.
TYPE:
|
enable_elastic_disk |
If you don’t want to allocate a fixed number of EBS volumes at cluster creation time, use autoscaling local storage. With autoscaling local storage, Databricks monitors the amount of free disk space available on your cluster’s Spark workers. If a worker begins to run too low on disk, Databricks automatically attaches a new EBS volume to the worker before it runs out of disk space. EBS volumes are attached up to a limit of 5 TB of total disk space per instance (including the instance’s local storage). To scale down EBS usage, make sure you have autotermination_minutes and autoscale attributes set.
TYPE:
|
enable_local_disk_encryption |
Some instance types you use to run clusters may have locally attached disks. Databricks may store shuffle data or temporary data on these locally attached disks. To ensure that all data at rest is encrypted for all storage types, including shuffle data stored temporarily on your cluster’s local disks, you can enable local disk encryption. When local disk encryption is enabled, Databricks generates an encryption key locally unique to each cluster node and uses it to encrypt all data stored on local disks. The scope of the key is local to each cluster node and is destroyed along with the cluster node itself. During its lifetime, the key resides in memory for encryption and decryption and is stored encrypted on the disk. Your workloads may run more slowly because of the performance impact of reading and writing encrypted data to and from local volumes. This feature is not available for all Azure Databricks subscriptions. Contact your Microsoft or Databricks account representative to request access.
TYPE:
|
idempotency_token |
An optional token to guarantee the idempotency of cluster creation requests. If an active cluster with the provided token already exists, the request will not create a new cluster, but it will return the existing running cluster's ID instead. If you specify the idempotency token, upon failure, you can retry until the request succeeds. Databricks platform guarantees to launch exactly one cluster with that idempotency token. This token should have at most 64 characters.
TYPE:
|
init_scripts |
List of init scripts specifications
TYPE:
|
instance_pool_id |
To reduce cluster start time, you can attach a cluster to a predefined pool of idle instances. When attached to a pool, a cluster allocates its driver and worker nodes from the pool. If the pool does not have sufficient idle resources to accommodate the cluster’s request, it expands by allocating new instances from the instance provider. When an attached cluster changes its state to TERMINATED, the instances it used are returned to the pool and reused by a different cluster.
TYPE:
|
is_pinned |
boolean value specifying if the cluster is pinned (not pinned by default). You must be a Databricks administrator to use this. The pinned clusters' maximum number is limited to 100, so apply may fail if you have more than that (this number may change over time, so check Databricks documentation for actual number).
TYPE:
|
libraries |
List of libraries specifications
TYPE:
|
lookup_existing |
Specifications for looking up existing resource. Other attributes will be ignored.
TYPE:
|
name |
Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.
TYPE:
|
node_type_id |
Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.
TYPE:
|
no_wait |
If true, the provider will not wait for the cluster to reach RUNNING state when creating the cluster, allowing cluster creation and library installation to continue asynchronously. Defaults to false (the provider will wait for cluster creation and library installation to succeed).
TYPE:
|
num_workers |
Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.
TYPE:
|
policy_id |
TYPE:
|
runtime_engine |
The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value
TYPE:
|
single_user_name |
The optional user name of the user to assign to an interactive cluster. This field is required when using data_security_mode set to SINGLE_USER or AAD Passthrough for Azure Data Lake Storage (ADLS) with a single-user cluster (i.e., not high-concurrency clusters).
TYPE:
|
spark_conf |
Map with key-value pairs to fine-tune Spark clusters, where you can provide custom Spark configuration properties in a cluster configuration. |
spark_env_vars |
Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers. |
spark_version |
Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.
TYPE:
|
ssh_public_keys |
SSH public key contents that will be added to each Spark node in this cluster. The corresponding private keys can be used to login with the user name ubuntu on port 2200. You can specify up to 10 keys. |
Examples:
from laktory import models
cluster = models.resources.databricks.Cluster(
name="default",
spark_version="14.0.x-scala2.12",
data_security_mode="USER_ISOLATION",
node_type_id="Standard_DS3_v2",
autoscale={
"min_workers": 1,
"max_workers": 4,
},
num_workers=0,
autotermination_minutes=30,
libraries=[{"pypi": {"package": "laktory==0.0.23"}}],
access_controls=[
{
"group_name": "role-engineers",
"permission_level": "CAN_RESTART",
}
],
is_pinned=True,
)
References
| ATTRIBUTE | DESCRIPTION |
|---|---|
additional_core_resources |
TYPE:
|
Bases: BaseModel
Cluster Init Script
| ATTRIBUTE | DESCRIPTION |
|---|---|
volumes |
Volumes file specification
TYPE:
|
workspace |
Workspace file specifications |
Bases: BaseModel
Cluster Init Script
| ATTRIBUTE | DESCRIPTION |
|---|---|
volumes |
Volumes file specification
TYPE:
|
workspace |
Workspace file specifications |
Bases: BaseModel
Cluster Library
| ATTRIBUTE | DESCRIPTION |
|---|---|
cran |
Cran library specifications
TYPE:
|
egg |
Egg filepath
TYPE:
|
jar |
Jar filepath
TYPE:
|
maven |
TYPE:
|
pypi |
Pypi library specifications
TYPE:
|
whl |
Wheel filepath
TYPE:
|