Update cluster configuration. — edit

Updates the configuration of a cluster to match the provided attributes and size. A cluster can be updated if it is in a RUNNING or TERMINATED state.

Usage

edit_cluster(
  client,
  cluster_id,
  spark_version,
  apply_policy_default_values = NULL,
  autoscale = NULL,
  autotermination_minutes = NULL,
  aws_attributes = NULL,
  azure_attributes = NULL,
  cluster_log_conf = NULL,
  cluster_name = NULL,
  cluster_source = NULL,
  custom_tags = NULL,
  data_security_mode = NULL,
  docker_image = NULL,
  driver_instance_pool_id = NULL,
  driver_node_type_id = NULL,
  enable_elastic_disk = NULL,
  enable_local_disk_encryption = NULL,
  gcp_attributes = NULL,
  init_scripts = NULL,
  instance_pool_id = NULL,
  node_type_id = NULL,
  num_workers = NULL,
  policy_id = NULL,
  runtime_engine = NULL,
  single_user_name = NULL,
  spark_conf = NULL,
  spark_env_vars = NULL,
  ssh_public_keys = NULL,
  workload_type = NULL
)

clustersEdit(
  client,
  cluster_id,
  spark_version,
  apply_policy_default_values = NULL,
  autoscale = NULL,
  autotermination_minutes = NULL,
  aws_attributes = NULL,
  azure_attributes = NULL,
  cluster_log_conf = NULL,
  cluster_name = NULL,
  cluster_source = NULL,
  custom_tags = NULL,
  data_security_mode = NULL,
  docker_image = NULL,
  driver_instance_pool_id = NULL,
  driver_node_type_id = NULL,
  enable_elastic_disk = NULL,
  enable_local_disk_encryption = NULL,
  gcp_attributes = NULL,
  init_scripts = NULL,
  instance_pool_id = NULL,
  node_type_id = NULL,
  num_workers = NULL,
  policy_id = NULL,
  runtime_engine = NULL,
  single_user_name = NULL,
  spark_conf = NULL,
  spark_env_vars = NULL,
  ssh_public_keys = NULL,
  workload_type = NULL
)

Arguments

client: Required. Instance of DatabricksClient()
cluster_id: Required. ID of the cluser.
spark_version: Required. The Spark version of the cluster, e.g.
apply_policy_default_values: This field has no description yet.
autoscale: Parameters needed in order to automatically scale clusters up and down based on load.
autotermination_minutes: Automatically terminates the cluster after it is inactive for this time in minutes.
aws_attributes: Attributes related to clusters running on Amazon Web Services.
azure_attributes: Attributes related to clusters running on Microsoft Azure.
cluster_log_conf: The configuration for delivering spark logs to a long-term storage destination.
cluster_name: Cluster name requested by the user.
cluster_source: Determines whether the cluster was created by a user through the UI, created by the Databricks Jobs Scheduler, or through an API request.
custom_tags: Additional tags for cluster resources.
data_security_mode: Data security mode decides what data governance model to use when accessing data from a cluster.
docker_image: This field has no description yet.
driver_instance_pool_id: The optional ID of the instance pool for the driver of the cluster belongs.
driver_node_type_id: The node type of the Spark driver.
enable_elastic_disk: Autoscaling Local Storage: when enabled, this cluster will dynamically acquire additional disk space when its Spark workers are running low on disk space.
enable_local_disk_encryption: Whether to enable LUKS on cluster VMs' local disks.
gcp_attributes: Attributes related to clusters running on Google Cloud Platform.
init_scripts: The configuration for storing init scripts.
instance_pool_id: The optional ID of the instance pool to which the cluster belongs.
node_type_id: This field encodes, through a single value, the resources available to each of the Spark nodes in this cluster.
num_workers: Number of worker nodes that this cluster should have.
policy_id: The ID of the cluster policy used to create the cluster if applicable.
runtime_engine: Decides which runtime engine to be use, e.g.
single_user_name: Single user name if data_security_mode is SINGLE_USER.
spark_conf: An object containing a set of optional, user-specified Spark configuration key-value pairs.
spark_env_vars: An object containing a set of optional, user-specified environment variable key-value pairs.
ssh_public_keys: SSH public key contents that will be added to each Spark node in this cluster.
workload_type: This field has no description yet.

Details

If a cluster is updated while in a RUNNING state, it will be restarted so that the new attributes can take effect.

If a cluster is updated while in a TERMINATED state, it will remain TERMINATED. The next time it is started using the clusters/start API, the new attributes will take effect. Any attempt to update a cluster in any other state will be rejected with an INVALID_STATE error code.

Clusters created by the Databricks Jobs service cannot be edited.