databricks_cluster Data Source

October 29, 2025 · View on GitHub

Retrieves information about a databricks_cluster using its id. This could be retrieved programmatically using databricks_clusters data source.

-> This data source can only be used with a workspace-level provider!

Example Usage

Retrieve attributes of each SQL warehouses in a workspace

data "databricks_clusters" "all" {
}

data "databricks_cluster" "all" {
  for_each   = data.databricks_clusters.all.ids
  cluster_id = each.value
}

Multiple clusters with the same name

When fetching a cluster whose name is not unique (including terminated but not permanently deleted clusters), you must use the cluster_id argument to uniquely identify the cluster. Combine this data source with databricks_clusters to get the cluster_id of the cluster you want to fetch.

data "databricks_clusters" "my_cluster" {
  cluster_name_contains = "my-cluster"
  filter_by {
    cluster_states = ["RUNNING"]
    # Filter additionally on cluster sources if needed:
    # cluster_sources = ["API"] # if created by Terraform or another API-based tool
    # cluster_sources = ["UI"] # if created in the Databricks web interface
  }
}

data "databricks_cluster" "my_cluster" {
  cluster_id = tolist(data.databricks_clusters.my_cluster.ids)[0]
}

Argument Reference

  • cluster_id - (Required if cluster_name isn't specified) The id of the cluster.
  • cluster_name - (Required if cluster_id isn't specified) The exact name of the cluster to search. Can only be specified if there is exactly one cluster with the provided name.
  • provider_config - (Optional) Configure the provider for management through account provider. This block consists of the following fields:
    • workspace_id - (Required) Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.

Attribute Reference

This data source exports the following attributes:

  • id - cluster ID
  • cluster_info block, consisting of following fields:
    • cluster_name - Cluster name, which doesn’t have to be unique.
    • spark_version - Runtime version of the cluster.
    • runtime_engine - The type of runtime of the cluster
    • driver_node_type_id - The node type of the Spark driver.
    • node_type_id - Any supported databricks_node_type id.
    • instance_pool_id The pool of idle instances the cluster is attached to.
    • driver_instance_pool_id - similar to instance_pool_id, but for driver node.
    • policy_id - Identifier of Cluster Policy to validate cluster and preset certain defaults.
    • autotermination_minutes - Automatically terminate the cluster after being inactive for this time in minutes. If specified, the threshold must be between 10 and 10000 minutes. You can also set this value to 0 to explicitly disable automatic termination.
    • enable_elastic_disk - Use autoscaling local storage.
    • enable_local_disk_encryption - Enable local disk encryption.
    • data_security_mode - Security features of the cluster. Unity Catalog requires SINGLE_USER or USER_ISOLATION mode. LEGACY_PASSTHROUGH for passthrough cluster and LEGACY_TABLE_ACL for Table ACL cluster. Default to NONE, i.e. no security feature enabled.
    • single_user_name - The optional user name of the user to assign to an interactive cluster. This field is required when using standard AAD Passthrough for Azure Data Lake Storage (ADLS) with a single-user cluster (i.e., not high-concurrency clusters).
    • idempotency_token - An optional token to guarantee the idempotency of cluster creation requests.
    • ssh_public_keys - SSH public key contents that will be added to each Spark node in this cluster.
    • 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.
    • custom_tags - Additional tags for cluster resources.
    • spark_conf - Map with key-value pairs to fine-tune Spark clusters.

The following resources are often used in the same context: