Clusters API

The Clusters API allows you to create, start, edit, list, terminate, and delete clusters. The maximum allowed size of a request to the Clusters API is 10MB.

Cluster lifecycle methods require a cluster ID, which is returned from Create. To obtain a list of clusters, invoke List.

Databricks maps cluster node instance types to compute units known as DBUs. See the instance type pricing page for a list of the supported instance types and their corresponding DBUs. For instance provider information, see AWS instance type specifications and pricing.

Databricks always provides one year’s deprecation notice before ceasing support for an instance type.

Create

Endpoint	HTTP Method
`2.0/clusters/create`	`POST`

Create a new Apache Spark cluster. This method acquires new instances from the cloud provider if necessary. This method is asynchronous; the returned cluster_id can be used to poll the cluster state. When this method returns, the cluster is in a PENDING state. The cluster is usable once it enters a RUNNING state. See ClusterState.

Note

Databricks may not be able to acquire some of the requested nodes, due to cloud provider limitations (account limits, spot price, …) or transient network issues. If Databricks acquires at least 85% of the requested on-demand nodes, cluster creation will succeed. Otherwise the cluster will terminate with an informative error message.

An example request:

{
  "cluster_name": "my-cluster",
  "spark_version": "5.3.x-scala2.11",
  "node_type_id": "i3.xlarge",
  "spark_conf": {
    "spark.speculation": true
  },
  "aws_attributes": {
    "availability": "SPOT",
    "zone_id": "us-west-2a"
  },
  "num_workers": 25
}

Below is an example for an autoscaling cluster. This cluster will start with 2 nodes, the minimum.

{
  "cluster_name": "autoscaling-cluster",
  "spark_version": "5.3.x-scala2.11",
  "node_type_id": "i3.xlarge",
  "autoscale" : {
    "min_workers": 2,
    "max_workers": 50
  }
}

Request Structure

Field Name	Type	Description
`num_workers` OR `autoscale`	`INT32` OR AutoScale	If `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. Note: When reading the properties of a cluster, this field reflects the desired number of workers rather than the actual number of workers. For instance, if a cluster is resized from 5 to 10 workers, this field will immediately be updated to reflect the target size of 10 workers, whereas the workers listed in `executors` will gradually increase from 5 to 10 as the new nodes are provisioned. If `autoscale`, parameters needed in order to automatically scale clusters up and down based on load. Note: autoscaling works best with Databricks Runtime 3.0 or above.
cluster_name	`STRING`	Cluster name requested by the user. This doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.
spark_version	`STRING`	The runtime version of the cluster. You can retrieve a list of available runtime versions by using the Runtime Versions API call. This field is required.
spark_conf	SparkConfPair	An object containing a set of optional, user-specified Spark configuration key-value pairs. You can also pass in a string of extra JVM options to the driver and the executors via `spark.driver.extraJavaOptions` and `spark.executor.extraJavaOptions` respectively. Example Spark confs: `{"spark.speculation": true, "spark.streaming.ui.retainedBatches": 5}` or `{"spark.driver.extraJavaOptions": "-verbose:gc -XX:+PrintGCDetails"}`
aws_attributes	AwsAttributes	Attributes related to clusters running on Amazon Web Services. If not specified at cluster creation, a set of default values will be used.
node_type_id	`STRING`	This field encodes, through a single value, the resources available to each of the Spark nodes in this cluster. For example, the Spark nodes can be provisioned and optimized for memory or compute intensive workloads A list of available node types can be retrieved by using the List Node Types API call. This field is required.
driver_node_type_id	`STRING`	The node type of the Spark driver. This field is optional; if unset, the driver node type will be set as the same value as `node_type_id` defined above.
ssh_public_keys	An array of `STRING`	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`. Up to 10 keys can be specified.
custom_tags	An array of ClusterTag	Additional tags for cluster resources. Databricks will tag all cluster resources (e.g., AWS instances and EBS volumes) with these tags in addition to `default_tags`. Notes: Tags are not supported on legacy node types such as compute-optimized and memory-optimized Databricks allows at most 45 custom tags
cluster_log_conf	ClusterLogConf	The configuration for delivering Spark logs to a long-term storage destination. Only one destination can be specified for one cluster. If the conf is given, the logs will be delivered to the destination every `5 mins`. The destination of driver logs is `<destination>/<cluster-ID>/driver`, while the destination of executor logs is `<destination>/<cluster-ID>/executor`.
init_scripts	An array of InitScriptInfo	The configuration for storing init scripts. Any number of destinations can be specified. The scripts are executed sequentially in the order provided. If `cluster_log_conf` is specified, init script logs are sent to `<destination>/<cluster-ID>/init_scripts`.
docker_image	DockerImage	Docker image for a custom container.
spark_env_vars	SparkEnvPair	An object containing a set of optional, user-specified environment variable key-value pairs. Key-value pairs of the form (X,Y) are exported as is (i.e., `export X='Y'`) while launching the driver and workers. In order to specify an additional set of `SPARK_DAEMON_JAVA_OPTS`, we recommend appending them to `$SPARK_DAEMON_JAVA_OPTS` as shown in the example below. This ensures that all default databricks managed environmental variables are included as well. Example Spark environment variables: `{"SPARK_WORKER_MEMORY": "28000m", "SPARK_LOCAL_DIRS": "/local_disk0"}` or `{"SPARK_DAEMON_JAVA_OPTS": "$SPARK_DAEMON_JAVA_OPTS -Dspark.shuffle.service.enabled=true"}`
autotermination_minutes	`INT32`	Automatically terminates the cluster after it is inactive for this time in minutes. If not set, this cluster will not be automatically terminated. 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	`BOOL`	Autoscaling Local Storage: when enabled, this cluster will dynamically acquire additional disk space when its Spark workers are running low on disk space. This feature requires specific AWS permissions to function correctly - refer to Autoscaling local storage for details.
instance_pool_id	`STRING`	The optional ID of the instance pool to which the cluster belongs. Refer to Instance Pools API for details.
idempotency_token	`STRING`	An optional token that can be used 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 ID of the existing cluster instead. The existence of a cluster with the same token is not checked against terminated clusters. If you specify the idempotency token, upon failure you can retry until the request succeeds. Databricks will guarantee that exactly one cluster will be launched with that idempotency token. This token should have at most 64 characters.

Response Structure

Field Name	Type	Description
cluster_id	`STRING`	Canonical identifier for the cluster.

Edit

Endpoint	HTTP Method
`2.0/clusters/edit`	`POST`

Edit the configuration of a cluster to match the provided attributes and size.

You can edit a cluster if it is in a RUNNING or TERMINATED state. If you edit a cluster while it is in a RUNNING state, it will be restarted so that the new attributes can take effect. If you edit a cluster while it is 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. An attempt to edit 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.

An example request:

{
 "cluster_id": "1202-211320-brick1",
 "num_workers": 10,
 "spark_version": "5.3.x-scala2.11",
 "node_type_id": "i3.2xlarge"
}

Request Structure

Field Name	Type	Description
`num_workers` OR `autoscale`	`INT32` OR AutoScale	If `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. Note: When reading the properties of a cluster, this field reflects the desired number of workers rather than the actual number of workers. For instance, if a cluster is resized from 5 to 10 workers, this field will immediately be updated to reflect the target size of 10 workers, whereas the workers listed in `executors` will gradually increase from 5 to 10 as the new nodes are provisioned. If `autoscale`, parameters needed in order to automatically scale clusters up and down based on load. Autoscaling works best with Databricks Runtime 3.0 or above.
cluster_id	`STRING`	Canonical identifier for the cluster. This field is required.
cluster_name	`STRING`	Cluster name requested by the user. This doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.
spark_version	`STRING`	The runtime version of the cluster. You can retrieve a list of available runtime versions by using the Runtime Versions API call. This field is required.
spark_conf	SparkConfPair	An object containing a set of optional, user-specified Spark configuration key-value pairs. You can also pass in a string of extra JVM options to the driver and the executors via `spark.driver.extraJavaOptions` and `spark.executor.extraJavaOptions` respectively. Example Spark confs: `{"spark.speculation": true, "spark.streaming.ui.retainedBatches": 5}` or `{"spark.driver.extraJavaOptions": "-verbose:gc -XX:+PrintGCDetails"}`
aws_attributes	AwsAttributes	Attributes related to clusters running on Amazon Web Services. If not specified at cluster creation, a set of default values will be used.
node_type_id	`STRING`	This field encodes, through a single value, the resources available to each of the Spark nodes in this cluster. For example, the Spark nodes can be provisioned and optimized for memory or compute intensive workloads A list of available node types can be retrieved by using the List Node Types API call. This field is required.
driver_node_type_id	`STRING`	The node type of the Spark driver. This field is optional; if unset, the driver node type will be set as the same value as `node_type_id` defined above.
ssh_public_keys	An array of `STRING`	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`. Up to 10 keys can be specified.
custom_tags	An array of ClusterTag	Additional tags for cluster resources. Databricks will tag all cluster resources (e.g., AWS instances and EBS volumes) with these tags in addition to `default_tags`. Notes: Tags are not supported on legacy node types such as compute-optimized and memory-optimized Databricks allows at most 45 custom tags
cluster_log_conf	ClusterLogConf	The configuration for delivering Spark logs to a long-term storage destination. Only one destination can be specified for one cluster. If the conf is given, the logs will be delivered to the destination every `5 mins`. The destination of driver logs is `<destination>/<cluster-ID>/driver`, while the destination of executor logs is `<destination>/<cluster-ID>/executor`.
init_scripts	An array of InitScriptInfo	The configuration for storing init scripts. Any number of destinations can be specified. The scripts are executed sequentially in the order provided. If `cluster_log_conf` is specified, init script logs are sent to `<destination>/<cluster-ID>/init_scripts`.
docker_image	DockerImage	Docker image for a custom container.
spark_env_vars	SparkEnvPair	An object containing a set of optional, user-specified environment variable key-value pairs. Key-value pairs of the form (X,Y) are exported as is (i.e., `export X='Y'`) while launching the driver and workers. In order to specify an additional set of `SPARK_DAEMON_JAVA_OPTS`, we recommend appending them to `$SPARK_DAEMON_JAVA_OPTS` as shown in the example below. This ensures that all default databricks managed environmental variables are included as well. Example Spark environment variables: `{"SPARK_WORKER_MEMORY": "28000m", "SPARK_LOCAL_DIRS": "/local_disk0"}` or `{"SPARK_DAEMON_JAVA_OPTS": "$SPARK_DAEMON_JAVA_OPTS -Dspark.shuffle.service.enabled=true"}`
autotermination_minutes	`INT32`	Automatically terminates the cluster after it is inactive for this time in minutes. If not set, this cluster will not be automatically terminated. 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	`BOOL`	Autoscaling Local Storage: when enabled, this cluster will dynamically acquire additional disk space when its Spark workers are running low on disk space. This feature requires specific AWS permissions to function correctly - refer to Autoscaling local storage for details.
instance_pool_id	`STRING`	The optional ID of the instance pool to which the cluster belongs. Refer to Pools for details.

Start

Endpoint	HTTP Method
`2.0/clusters/start`	`POST`

Start a terminated Spark cluster given its ID. This is similar to createCluster, except:

The terminated cluster ID and attributes are preserved.
The cluster starts with the last specified cluster size. If the terminated cluster was an autoscaling cluster, the cluster starts with the minimum number of nodes.
If the cluster is not in a TERMINATED state, nothing will happen.
Clusters launched to run a job cannot be started.

An example request:

{
  "cluster_id": "1202-211320-brick1"
}

Request Structure

Field Name	Type	Description
cluster_id	`STRING`	The cluster to be started. This field is required.

Restart

Endpoint	HTTP Method
`2.0/clusters/restart`	`POST`

Restart a Spark cluster given its ID. If the cluster is not in a RUNNING state, nothing will happen.

An example request:

{
  "cluster_id": "1202-211320-brick1"
}

Request Structure

Field Name	Type	Description
cluster_id	`STRING`	The cluster to be started. This field is required.

Resize

Endpoint	HTTP Method
`2.0/clusters/resize`	`POST`

Resize a cluster to have a desired number of workers. This will fail unless the cluster is in a RUNNING state.

An example request:

{
  "cluster_id": "1202-211320-brick1",
  "num_workers": 30
}

Request Structure

Field Name Type Description

num_workers OR autoscale

INT32 OR AutoScale

If 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.

Note: When reading the properties of a cluster, this field reflects the desired number of workers rather than the actual number of workers. For instance, if a cluster is resized from 5 to 10 workers, this field will immediately be updated to reflect the target size of 10 workers, whereas the workers listed in executors will gradually increase from 5 to 10 as the new nodes are provisioned.

If autoscale, parameters needed in order to automatically scale clusters up and down based on load. Note: autoscaling works best with Databricks Runtime 3.0 or above.

cluster_id STRING The cluster to be resized. This field is required.

Delete (Terminate)

Endpoint	HTTP Method
`2.0/clusters/delete`	`POST`

Terminate a Spark cluster given its ID. The cluster is removed asynchronously. Once the termination has completed, the cluster will be in a TERMINATED state. If the cluster is already in a TERMINATING or TERMINATED state, nothing will happen.

30 days after a cluster is terminated, it is permanently deleted.

An example request:

{
  "cluster_id": "1202-211320-brick1"
}

Request Structure

Field Name	Type	Description
cluster_id	`STRING`	The cluster to be terminated. This field is required.

Permanent Delete

Endpoint	HTTP Method
`2.0/clusters/permanent-delete`	`POST`

Permanently delete a cluster. If the cluster is running, it is terminated and its resources are asynchronously removed. If the cluster is terminated, then it is immediately removed.

You cannot perform any action, including retrieve the cluster’s permissions, on a permanently deleted cluster. A permanently deleted cluster is also no longer returned in the cluster list.

An example request:

{
  "cluster_id": "1202-211320-brick1"
}

Request Structure

Field Name	Type	Description
cluster_id	`STRING`	The cluster to be permanently deleted. This field is required.

Get

Endpoint	HTTP Method
`2.0/clusters/get`	`GET`

Retrieve the information for a cluster given its identifier. Clusters can be described while they are running or up to 30 days after they are terminated.

An example request:

/clusters/get?cluster_id=1202-211320-brick1

Request Structure

Field Name	Type	Description
cluster_id	`STRING`	The cluster about which to retrieve information. This field is required.

Response Structure

Field Name	Type	Description
`num_workers` OR `autoscale`	`INT32` OR AutoScale	If `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. Note: When reading the properties of a cluster, this field reflects the desired number of workers rather than the actual number of workers. For instance, if a cluster is resized from 5 to 10 workers, this field will immediately be updated to reflect the target size of 10 workers, whereas the workers listed in `executors` will gradually increase from 5 to 10 as the new nodes are provisioned. If `autoscale`, parameters needed in order to automatically scale clusters up and down based on load. Note: autoscaling works best with Databricks Runtime 3.0 or above.
cluster_id	`STRING`	Canonical identifier for the cluster. This ID is retained during cluster restarts and resizes, while each new cluster has a globally unique ID.
creator_user_name	`STRING`	Creator user name. The field won’t be included in the response if the user has already been deleted.
driver	SparkNode	Node on which the Spark driver resides. The driver node contains the Spark master and the Databricks application that manages the per-notebook Spark REPLs.
executors	An array of SparkNode	Nodes on which the Spark executors reside.
spark_context_id	`INT64`	A canonical SparkContext identifier. This value does change when the Spark driver restarts. The pair `(cluster_id, spark_context_id)` is a globally unique identifier over all Spark contexts.
jdbc_port	`INT32`	Port on which Spark JDBC server is listening in the driver node. No service will be listening on on this port in executor nodes.
cluster_name	`STRING`	Cluster name requested by the user. This doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.
spark_version	`STRING`	The runtime version of the cluster. You can retrieve a list of available runtime versions by using the Runtime Versions API call.
spark_conf	SparkConfPair	An object containing a set of optional, user-specified Spark configuration key-value pairs. You can also pass in a string of extra JVM options to the driver and the executors via `spark.driver.extraJavaOptions` and `spark.executor.extraJavaOptions` respectively. Example Spark confs: `{"spark.speculation": true, "spark.streaming.ui.retainedBatches": 5}` or `{"spark.driver.extraJavaOptions": "-verbose:gc -XX:+PrintGCDetails"}`
aws_attributes	AwsAttributes	Attributes related to clusters running on Amazon Web Services. If not specified at cluster creation, a set of default values will be used.
node_type_id	`STRING`	This field encodes, through a single value, the resources available to each of the Spark nodes in this cluster. For example, the Spark nodes can be provisioned and optimized for memory or compute intensive workloads A list of available node types can be retrieved by using the List Node Types API call. This field is required.
driver_node_type_id	`STRING`	The node type of the Spark driver. This field is optional; if unset, the driver node type will be set as the same value as `node_type_id` defined above.
ssh_public_keys	An array of `STRING`	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`. Up to 10 keys can be specified.
custom_tags	An array of ClusterTag	Additional tags for cluster resources. Databricks will tag all cluster resources with these tags in addition to `default_tags`. Notes: Tags are not supported on legacy node types such as compute-optimized and memory-optimized Databricks allows at most 45 custom tags
cluster_log_conf	ClusterLogConf	The configuration for delivering Spark logs to a long-term storage destination. Only one destination can be specified for one cluster. If the conf is given, the logs will be delivered to the destination every `5 mins`. The destination of driver logs is `<destination>/<cluster-ID>/driver`, while the destination of executor logs is `<destination>/<cluster-ID>/executor`.
init_scripts	An array of InitScriptInfo	The configuration for storing init scripts. Any number of destinations can be specified. The scripts are executed sequentially in the order provided. If `cluster_log_conf` is specified, init script logs are sent to that location.
docker_image	DockerImage	Docker image for a custom container.
spark_env_vars	SparkEnvPair	An object containing a set of optional, user-specified environment variable key-value pairs. Key-value pairs of the form (X,Y) are exported as is (i.e., `export X='Y'`) while launching the driver and workers. In order to specify an additional set of `SPARK_DAEMON_JAVA_OPTS`, we recommend appending them to `$SPARK_DAEMON_JAVA_OPTS` as shown in the example below. This ensures that all default databricks managed environmental variables are included as well. Example Spark environment variables: `{"SPARK_WORKER_MEMORY": "28000m", "SPARK_LOCAL_DIRS": "/local_disk0"}` or `{"SPARK_DAEMON_JAVA_OPTS": "$SPARK_DAEMON_JAVA_OPTS -Dspark.shuffle.service.enabled=true"}`
autotermination_minutes	`INT32`	Automatically terminates the cluster after it is inactive for this time in minutes. If not set, this cluster will not be automatically terminated. 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	`BOOL`	Autoscaling Local Storage: when enabled, this cluster will dynamically acquire additional disk space when its Spark workers are running low on disk space. This feature requires specific AWS permissions to function correctly - refer to Autoscaling local storage for details.
instance_pool_id	`STRING`	The optional ID of the instance pool to which the cluster belongs. Refer to Pools for details.
cluster_source	ClusterSource	Determines whether the cluster was created by a user through the UI, by the Databricks Jobs scheduler, or through an API request.
state	ClusterState	State of the cluster.
state_message	`STRING`	A message associated with the most recent state transition (e.g., the reason why the cluster entered a `TERMINATED` state).
start_time	`INT64`	Time (in epoch milliseconds) when the cluster creation request was received (when the cluster entered a `PENDING` state).
terminated_time	`INT64`	Time (in epoch milliseconds) when the cluster was terminated, if applicable.
last_state_loss_time	`INT64`	Time when the cluster driver last lost its state (due to a restart or driver failure).
last_activity_time	`INT64`	Time (in epoch milliseconds) when the cluster was last active. A cluster is active if there is at least one command that has not finished on the cluster. This field is available after the cluster has reached a `RUNNING` state. Updates to this field are made as best-effort attempts. Certain versions of Spark do not support reporting of cluster activity. Refer to Automatic termination for details.
cluster_memory_mb	`INT64`	Total amount of cluster memory, in megabytes.
cluster_cores	`FLOAT`	Number of CPU cores available for this cluster. This can be fractional, e.g. 7.5 cores, since certain node types are configured to share cores between Spark nodes on the same instance.
default_tags	An array of ClusterTag	Tags that are added by Databricks regardless of any `custom_tags`, including: Vendor: Databricks Creator: <username_of_creator> ClusterName: <name_of_cluster> ClusterId: <id_of_cluster> Name: <Databricks internal use>
cluster_log_status	LogSyncStatus	Cluster log delivery status.
termination_reason	TerminationReason	Information about why the cluster was terminated. This field only appears when the cluster is in a `TERMINATING` or `TERMINATED` state.

Pin

Note

You must be a Databricks administrator to invoke this API.

Endpoint	HTTP Method
`2.0/clusters/pin`	`POST`

Ensure that an all-purpose cluster configuration is retained even after a cluster has been terminated for more than 30 days. Pinning ensures that the cluster is always returned by the List API. Pinning a cluster that is already pinned has no effect.

An example request:

{
  "cluster_id": "1202-211320-brick1"
}

Request Structure

Field Name	Type	Description
cluster_id	`STRING`	The cluster to pin. This field is required.

Unpin

Note

You must be a Databricks administrator to invoke this API.

Endpoint	HTTP Method
`2.0/clusters/unpin`	`POST`

Allows the cluster to eventually be removed from the list returned by the List API. Unpinning a cluster that is not pinned has no effect.

An example request:

{
  "cluster_id": "1202-211320-brick1"
}

Request Structure

Field Name	Type	Description
cluster_id	`STRING`	The cluster to unpin. This field is required.

List

Endpoint	HTTP Method
`2.0/clusters/list`	`GET`

Return information about all pinned clusters, active clusters, up to 70 of the most recently terminated all-purpose clusters in the past 30 days, and up to 30 of the most recently terminated job clusters in the past 30 days. For example, if there is 1 pinned cluster, 4 active clusters, 45 terminated all-purpose clusters in the past 30 days, and 50 terminated job clusters in the past 30 days, then this API returns the 1 pinned cluster, 4 active clusters, all 45 terminated all-purpose clusters, and the 30 most recently terminated job clusters.

Response Structure

Field Name	Type	Description
clusters	An array of ClusterInfo	A list of clusters.

List Node Types

Endpoint	HTTP Method
`2.0/clusters/list-node-types`	`GET`

Return a list of supported Spark node types. These node types can be used to launch a cluster.

Response Structure

Field Name	Type	Description
node_types	An array of NodeType	The list of available Spark node types.

Runtime Versions

Endpoint	HTTP Method
`2.0/clusters/spark-versions`	`GET`

Return the list of available runtime versions. These versions can be used to launch a cluster.

Response Structure

Field Name	Type	Description
versions	An array of SparkVersion	All the available runtime versions.

List Zones

Endpoint	HTTP Method
`2.0/clusters/list-zones`	`GET`

Return a list of availability zones where clusters can be created in (ex: us-west-2a). These zones can be used to launch a cluster.

Response Structure

Field Name	Type	Description
zones	An array of `STRING`	The list of available zones (e.g., [‘us-west-2c’, ‘us-east-2’]).
default_zone	`STRING`	The availability zone if no `zone_id` is provided in the cluster creation request.

Events

Endpoint	HTTP Method
`2.0/clusters/events`	`POST`

Retrieve a list of events about the activity of a cluster. This API is paginated. If there are more events to read, the response includes all the parameters necessary to request the next page of events.

An example request:

{
  "cluster_id": "1202-211320-brick1"
}

An example response:

{
  "events": [{
    "cluster_id": "1202-211320-brick1",
    "timestamp": 1534371918659,
    "type": "TERMINATING",
    "details": {
      "reason": {
        "code": "INACTIVITY",
        "parameters": {
          "inactivity_duration_min": "120"
        }
      }
    }
  }, {
    "cluster_id": "1202-211320-brick1",
    "timestamp": 1534358289590,
    "type": "RUNNING",
    "details": {
      "current_num_workers": 2,
      "target_num_workers": 2
    }
  }, {
    "cluster_id": "1202-211320-brick1",
    "timestamp": 1533225298406,
    "type": "RESTARTING",
    "details": {
      "user": "admin"
    }
  }],
  "next_page": {
    "cluster_id": "0802-034608-aloe926",
    "end_time": 1534371918659,
    "offset": 50
  },
  "total_count": 55
}

Example request to retrieve the next page of events:

{
  "cluster_id": "1202-211320",
  "start_time": 1534371918659
}

Request Structure

Retrieve events pertaining to a specific cluster.

Field Name	Type	Description
cluster_id	`STRING`	The ID of the cluster to retrieve events about. This field is required.
start_time	`INT64`	The start time in epoch milliseconds. If empty, returns events starting from the beginning of time.
end_time	`INT64`	The end time in epoch milliseconds. If empty, returns events up to the current time.
order	ListOrder	The order to list events in; either `ASC` or `DESC`. Defaults to `DESC`.
event_types	An array of ClusterEventType	An optional set of event types to filter on. If empty, all event types are returned.
offset	`INT64`	The offset in the result set. Defaults to 0 (no offset). When an offset is specified and the results are requested in descending order, the `end_time` field is required.
limit	`INT64`	The maximum number of events to include in a page of events. Defaults to 50, and maximum allowed value is 500.

Response Structure

Field Name	Type	Description
events	An array of ClusterEvent	This list of matching events.
next_page	Request Structure	The parameters required to retrieve the next page of events. Omitted if there are no more events to read.
total_count	`INT64`	The total number of events filtered by the `start_time`, `end_time`, and `event_types.`

Data Structures

AutoScale

Range defining the min and max number of cluster workers.

Field Name	Type	Description
min_workers	`INT32`	The minimum number of workers to which the cluster can scale down when underutilized. It is also the initial number of workers the cluster will have after creation.
max_workers	`INT32`	The maximum number of workers to which the cluster can scale up when overloaded. `max_workers` must be strictly greater than `min_workers`.

ClusterInfo

Metadata about a cluster.

Field Name	Type	Description
`num_workers` OR `autoscale`	`INT32` OR AutoScale	If `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. Note: When reading the properties of a cluster, this field reflects the desired number of workers rather than the actual number of workers. For instance, if a cluster is resized from 5 to 10 workers, this field will immediately be updated to reflect the target size of 10 workers, whereas the workers listed in `executors` will gradually increase from 5 to 10 as the new nodes are provisioned. If `autoscale`, parameters needed in order to automatically scale clusters up and down based on load. Note: autoscaling works best with Databricks Runtime 3.0 or above.
cluster_id	`STRING`	Canonical identifier for the cluster. This ID is retained during cluster restarts and resizes, while each new cluster has a globally unique ID.
creator_user_name	`STRING`	Creator user name. The field won’t be included in the response if the user has already been deleted.
driver	SparkNode	Node on which the Spark driver resides. The driver node contains the Spark master and the Databricks application that manages the per-notebook Spark REPLs.
executors	An array of SparkNode	Nodes on which the Spark executors reside.
spark_context_id	`INT64`	A canonical SparkContext identifier. This value does change when the Spark driver restarts. The pair `(cluster_id, spark_context_id)` is a globally unique identifier over all Spark contexts.
jdbc_port	`INT32`	Port on which Spark JDBC server is listening in the driver node. No service will be listening on on this port in executor nodes.
cluster_name	`STRING`	Cluster name requested by the user. This doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.
spark_version	`STRING`	The runtime version of the cluster. You can retrieve a list of available runtime versions by using the Runtime Versions API call.
spark_conf	SparkConfPair	An object containing a set of optional, user-specified Spark configuration key-value pairs. You can also pass in a string of extra JVM options to the driver and the executors via `spark.driver.extraJavaOptions` and `spark.executor.extraJavaOptions` respectively. Example Spark confs: `{"spark.speculation": true, "spark.streaming.ui.retainedBatches": 5}` or `{"spark.driver.extraJavaOptions": "-verbose:gc -XX:+PrintGCDetails"}`
aws_attributes	AwsAttributes	Attributes related to clusters running on Amazon Web Services. If not specified at cluster creation, a set of default values will be used.
node_type_id	`STRING`	This field encodes, through a single value, the resources available to each of the Spark nodes in this cluster. For example, the Spark nodes can be provisioned and optimized for memory or compute intensive workloads. A list of available node types can be retrieved by using the List Node Types API call.
driver_node_type_id	`STRING`	The node type of the Spark driver. This field is optional; if unset, the driver node type will be set as the same value as `node_type_id` defined above.
ssh_public_keys	An array of `STRING`	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`. Up to 10 keys can be specified.
custom_tags	An array of ClusterTag	Additional tags for cluster resources. Databricks tags all cluster resources with these tags in addition to `default_tags`. Notes: Tags are not supported on legacy node types such as compute-optimized and memory-optimized Databricks allows at most 45 custom tags
cluster_log_conf	ClusterLogConf	The configuration for delivering Spark logs to a long-term storage destination. Only one destination can be specified for one cluster. If the conf is given, the logs will be delivered to the destination every `5 mins`. The destination of driver logs is `<destination>/<cluster-ID>/driver`, while the destination of executor logs is `<destination>/<cluster-ID>/executor`.
init_scripts	An array of InitScriptInfo	The configuration for storing init scripts. Any number of destinations can be specified. The scripts are executed sequentially in the order provided. If `cluster_log_conf` is specified, init script logs are sent to `<destination>/<cluster-ID>/init_scripts`.
docker_image	DockerImage	Docker image for a custom container.
spark_env_vars	SparkEnvPair	An object containing a set of optional, user-specified environment variable key-value pairs. Key-value pairs of the form (X,Y) are exported as is (i.e., `export X='Y'`) while launching the driver and workers. To specify an additional set of `SPARK_DAEMON_JAVA_OPTS`, we recommend appending them to `$SPARK_DAEMON_JAVA_OPTS` as shown in the example below. This ensures that all default databricks managed environmental variables are included as well. Example Spark environment variables: `{"SPARK_WORKER_MEMORY": "28000m", "SPARK_LOCAL_DIRS": "/local_disk0"}` or `{"SPARK_DAEMON_JAVA_OPTS": "$SPARK_DAEMON_JAVA_OPTS -Dspark.shuffle.service.enabled=true"}`
autotermination_minutes	`INT32`	Automatically terminates the cluster after it is inactive for this time in minutes. If not set, this cluster will not be automatically terminated. 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	`BOOL`	Autoscaling Local Storage: when enabled, this cluster will dynamically acquire additional disk space when its Spark workers are running low on disk space. This feature requires specific AWS permissions to function correctly - refer to Autoscaling local storage for details.
instance_pool_id	`STRING`	The optional ID of the instance pool to which the cluster belongs. Refer to Pools for details.
cluster_source	ClusterSource	Determines whether the cluster was created by a user through the UI, by the Databricks Jobs scheduler, or through an API request.
state	ClusterState	State of the cluster.
state_message	`STRING`	A message associated with the most recent state transition (e.g., the reason why the cluster entered a `TERMINATED` state). This field is unstructured, and its exact format is subject to change.
start_time	`INT64`	Time (in epoch milliseconds) when the cluster creation request was received (when the cluster entered a `PENDING` state).
terminated_time	`INT64`	Time (in epoch milliseconds) when the cluster was terminated, if applicable.
last_state_loss_time	`INT64`	Time when the cluster driver last lost its state (due to a restart or driver failure).
last_activity_time	`INT64`	Time (in epoch milliseconds) when the cluster was last active. A cluster is active if there is at least one command that has not finished on the cluster. This field is available after the cluster has reached a `RUNNING` state. Updates to this field are made as best-effort attempts. Certain versions of Spark do not support reporting of cluster activity. Refer to Automatic termination for details.
cluster_memory_mb	`INT64`	Total amount of cluster memory, in megabytes.
cluster_cores	`FLOAT`	Number of CPU cores available for this cluster. This can be fractional, e.g. 7.5 cores, since certain node types are configured to share cores between Spark nodes on the same instance.
default_tags	An array of ClusterTag	Tags that are added by Databricks by default, regardless of any `custom_tags` that may have been added. These include: Vendor: Databricks Creator: <username_of_creator> ClusterName: <name_of_cluster> ClusterId: <id_of_cluster> Name: <Databricks internal use>
cluster_log_status	LogSyncStatus	Cluster log delivery status.
termination_reason	TerminationReason	Information about why the cluster was terminated. This field only appears when the cluster is in a `TERMINATING` or `TERMINATED` state.

ClusterEvent

Cluster event information.

Field Name	Type	Description
cluster_id	`STRING`	Canonical identifier for the cluster. This field is required.
timestamp	`INT64`	The timestamp when the event occurred, stored as the number of milliseconds since the unix epoch. Assigned by the Timeline service.
type	ClusterEventType	The event type. This field is required.
details	EventDetails	The event details. This field is required.

ClusterEventType

Type of a cluster event.

Event Type	Description
CREATING	Indicates that the cluster is being created.
DID_NOT_EXPAND_DISK	Indicates that a disk is low on space, but adding disks would put it over the max capacity.
EXPANDED_DISK	Indicates that a disk was low on space and the disks were expanded.
FAILED_TO_EXPAND_DISK	Indicates that a disk was low on space and disk space could not be expanded.
INIT_SCRIPTS_STARTING	Indicates that the cluster scoped init script has started.
INIT_SCRIPTS_FINISHED	Indicates that the cluster scoped init script has finished.
STARTING	Indicates that the cluster is being started.
RESTARTING	Indicates that the cluster is being started.
TERMINATING	Indicates that the cluster is being terminated.
EDITED	Indicates that the cluster has been edited.
RUNNING	Indicates the cluster has finished being created. Includes the number of nodes in the cluster and a failure reason if some nodes could not be acquired.
RESIZING	Indicates a change in the target size of the cluster (upsize or downsize).
UPSIZE_COMPLETED	Indicates that nodes finished being added to the cluster. Includes the number of nodes in the cluster and a failure reason if some nodes could not be acquired.
NODES_LOST	Indicates that some nodes were lost from the cluster.
DRIVER_HEALTHY	Indicates that the driver is healthy and the cluster is ready for use.
DRIVER_UNAVAILABLE	Indicates that the driver is unavailable.
SPARK_EXCEPTION	Indicates that a Spark exception was thrown from the driver.
DRIVER_NOT_RESPONDING	Indicates that the driver is up but is not responsive, likely due to GC.
DBFS_DOWN	Indicates that the driver is up but DBFS is down.
METASTORE_DOWN	Indicates that the driver is up but the metastore is down.
AUTOSCALING_STATS_REPORT	Usage report containing the total and unused instance minutes of the autoscaling cluster over the last hour.
NODE_BLACKLISTED	Indicates that a node has been blacklisted by Spark.
PINNED	Indicates that the cluster was pinned.
UNPINNED	Indicates that the cluster was unpinned.

EventDetails

Details about a cluster event.

Field Name	Type	Description
current_num_workers	`INT32`	The number of nodes in the cluster.
target_num_workers	`INT32`	The targeted number of nodes in the cluster.
previous_attributes	AwsAttributes	The cluster attributes before a cluster was edited.
attributes	AwsAttributes	For created clusters, the attributes of the cluster. For edited clusters, the new attributes of the cluster.
previous_cluster_size	ClusterSize	The size of the cluster before an edit or resize.
cluster_size	ClusterSize	The cluster size that was set in the cluster creation or edit.
cause	ResizeCause	The cause of a change in target size.
reason	TerminationReason	A termination reason: On a `TERMINATED` event, the reason for the termination. On a `RESIZE_COMPLETE` event, indicates the reason that we failed to acquire some nodes.
user	`STRING`	The user that caused the event to occur. (Empty if it was done by Databricks.)

AwsAttributes

Attributes set during cluster creation related to Amazon Web Services.

Field Name	Type	Description
first_on_demand	`INT32`	The first `first_on_demand` nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, `first_on_demand` nodes will be placed on on-demand instances and the remainder will be placed on `availability` instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster.
availability	AwsAvailability	Availability type used for all subsequent nodes past the `first_on_demand` ones. Note: If `first_on_demand` is zero, this availability type will be used for the entire cluster.
zone_id	`STRING`	Identifier for the availability zone/datacenter in which the cluster resides. This string will be of a form like “us-west-2a”. The provided availability zone must be in the same region as the Databricks deployment. For example, “us-west-2a” is not a valid zone ID if the Databricks deployment resides in the “us-east-1” region. This is an optional field at cluster creation, and if not specified, a default zone will be used. The list of available zones as well as the default value can be found by using the List Zones API.
instance_profile_arn	`STRING`	Nodes for this cluster will only be placed on AWS instances with this instance profile. If omitted, nodes will be placed on instances without an instance profile. The instance profile must have previously been added to the Databricks environment by an account administrator. This feature may only be available to certain customer plans.
spot_bid_price_percent	`INT32`	The max price for AWS spot instances, as a percentage of the corresponding instance type’s on-demand price. For example, if this field is set to 50, and the cluster needs a new `i3.xlarge` spot instance, then the max price is half of the price of on-demand `i3.xlarge` instances. Similarly, if this field is set to 200, the max price is twice the price of on-demand `i3.xlarge` instances. If not specified, the default value is 100. When spot instances are requested for this cluster, only spot instances whose max price percentage matches this field will be considered. For safety, we enforce this field to be no more than 10000.
ebs_volume_type	EbsVolumeType	The type of EBS volumes that will be launched with this cluster.
ebs_volume_count	`INT32`	The number of volumes launched for each instance. You can choose up to 10 volumes. This feature is only enabled for supported node types. Legacy node types cannot specify custom EBS volumes. For node types with no instance store, at least one EBS volume needs to be specified; otherwise, cluster creation will fail. These EBS volumes will be mounted at `/ebs0`, `/ebs1`, and etc. Instance store volumes will be mounted at `/local_disk0`, `/local_disk1`, and etc. If EBS volumes are attached, Databricks will configure Spark to use only the EBS volumes for scratch storage because heterogeneously sized scratch devices can lead to inefficient disk utilization. If no EBS volumes are attached, Databricks will configure Spark to use instance store volumes. If EBS volumes are specified, then the Spark configuration `spark.local.dir` will be overridden.
ebs_volume_size	`INT32`	The size of each EBS volume (in GiB) launched for each instance. For general purpose SSD, this value must be within the range 100 - 4096. For throughput optimized HDD, this value must be within the range 500 - 4096. Custom EBS volumes cannot be specified for the legacy node types (memory-optimized and compute-optimized).

ClusterAttributes

Common set of attributes set during cluster creation. These attributes cannot be changed over the lifetime of a cluster.

Field Name	Type	Description
cluster_name	`STRING`	Cluster name requested by the user. This doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.
spark_version	`STRING`	The runtime version of the cluster, e.g. “5.0.x-scala2.11”. You can retrieve a list of available runtime versions by using the Runtime Versions API call.
spark_conf	SparkConfPair	An object containing a set of optional, user-specified Spark configuration key-value pairs. You can also pass in a string of extra JVM options to the driver and the executors via `spark.driver.extraJavaOptions` and `spark.executor.extraJavaOptions` respectively. Example Spark confs: `{"spark.speculation": true, "spark.streaming.ui.retainedBatches": 5}` or `{"spark.driver.extraJavaOptions": "-verbose:gc -XX:+PrintGCDetails"}`
aws_attributes	AwsAttributes	Attributes related to clusters running on Amazon Web Services. If not specified at cluster creation, a set of default values will be used.
node_type_id	`STRING`	This field encodes, through a single value, the resources available to each of the Spark nodes in this cluster. For example, the Spark nodes can be provisioned and optimized for memory or compute intensive workloads A list of available node types can be retrieved by using the List Node Types API call.
driver_node_type_id	`STRING`	The node type of the Spark driver. This field is optional; if unset, the driver node type will be set as the same value as `node_type_id` defined above.
ssh_public_keys	An array of `STRING`	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`. Up to 10 keys can be specified.
custom_tags	An array of ClusterTag	Additional tags for cluster resources. Databricks tags all cluster resources (e.g., AWS instances and EBS volumes) with these tags in addition to `default_tags`. Notes: Tags are not supported on legacy node types such as compute-optimized and memory-optimized Databricks allows at most 45 custom tags
cluster_log_conf	ClusterLogConf	The configuration for delivering Spark logs to a long-term storage destination. Only one destination can be specified for one cluster. If the conf is given, the logs will be delivered to the destination every `5 mins`. The destination of driver logs is `<destination>/<cluster-ID>/driver`, while the destination of executor logs is `<destination>/<cluster-ID>/executor`.
init_scripts	An array of InitScriptInfo	The configuration for storing init scripts. Any number of destinations can be specified. The scripts are executed sequentially in the order provided. If `cluster_log_conf` is specified, init script logs are sent to `<destination>/<cluster-ID>/init_scripts`.
docker_image	DockerImage	Docker image for a custom container.
spark_env_vars	SparkEnvPair	An object containing a set of optional, user-specified environment variable key-value pairs. Key-value pairs of the form (X,Y) are exported as is (i.e., `export X='Y'`) while launching the driver and workers. In order to specify an additional set of `SPARK_DAEMON_JAVA_OPTS`, we recommend appending them to `$SPARK_DAEMON_JAVA_OPTS` as shown in the example below. This ensures that all default databricks managed environmental variables are included as well. Example Spark environment variables: `{"SPARK_WORKER_MEMORY": "28000m", "SPARK_LOCAL_DIRS": "/local_disk0"}` or `{"SPARK_DAEMON_JAVA_OPTS": "$SPARK_DAEMON_JAVA_OPTS -Dspark.shuffle.service.enabled=true"}`
autotermination_minutes	`INT32`	Automatically terminates the cluster after it is inactive for this time in minutes. If not set, this cluster will not be automatically terminated. 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	`BOOL`	Autoscaling Local Storage: when enabled, this cluster will dynamically acquire additional disk space when its Spark workers are running low on disk space. This feature requires specific AWS permissions to function correctly. Refer to Autoscaling local storage for details.
instance_pool_id	`STRING`	The optional ID of the instance pool to which the cluster belongs. Refer to Pools for details.
cluster_source	ClusterSource	Determines whether the cluster was created by a user through the UI, created by the Databricks Jobs scheduler, or through an API request.

ClusterSize

Cluster size specification.

Field Name Type Description

num_workers OR autoscale

INT32 OR AutoScale

If 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.

When reading the properties of a cluster, this field reflects the desired number of workers rather than the actual number of workers. For instance, if a cluster is resized from 5 to 10 workers, this field will immediately be updated to reflect the target size of 10 workers, whereas the workers listed in executors will gradually increase from 5 to 10 as the new nodes are provisioned.

If autoscale, parameters needed in order to automatically scale clusters up and down based on load. Note: autoscaling works best with Databricks Runtime 3.0 or above.

ListOrder

Generic ordering enum for list-based queries.

Order	Description
DESC	Descending order
ASC	Ascending order

ResizeCause

Reason why a cluster was resized.

Cause	Description
AUTOSCALE	Automatically resized based on load.
USER_REQUEST	User requested a new size.
AUTORECOVERY	Autorecovery monitor resized the cluster after it lost a node.

ClusterLogConf

Path to cluster log.

Field Name Type Description

dbfs OR s3

DbfsStorageInfo

S3StorageInfo

DBFS location of cluster log. destination must be provided. For example, { "dbfs" : { "destination" : "dbfs:/home/cluster_log" } }

S3 location of cluster log. destination and either region or endpoint must be provided. For example, { "s3": { "destination" : "s3://cluster_log_bucket/prefix", "region" : "us-west-2" } }

InitScriptInfo

Path to an init script.

Field Name Type Description

dbfs OR s3

DbfsStorageInfo

S3StorageInfo

DBFS location of init script. destination must be provided. For example, { "dbfs" : { "destination" : "dbfs:/home/init_script" } }

S3 location of init script. destination and either region or endpoint must be provided. For example, { "s3": { "destination" : "s3://init_script_bucket/prefix", "region" : "us-west-2" } }

ClusterTag

Cluster tag definition.

Field Name	Type	Description
key	`STRING`	The key of the tag. The key length must be between 1 and 127 UTF-8 characters, inclusive. For a list of all restrictions, see AWS Tag Restrictions: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/Using_Tags.html#tag-restrictions
value	`STRING`	The value of the tag. The value length must be less than or equal to 255 UTF-8 characters. For a list of all restrictions, see AWS Tag Restrictions: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/Using_Tags.html#tag-restrictions

DbfsStorageInfo

DBFS storage information.

Field Name	Type	Description
destination	`STRING`	DBFS destination, e.g. `dbfs:/my/path`

DockerImage

Docker image connection information.

Field	Type	Description
url	string	URL for the Docker image.
basic_auth	DockerBasicAuth	Basic authentication information for Docker repository.

DockerBasicAuth

Docker repository basic authentication information.

Field	Description
username	User name for the Docker repository.
password	Password for the Docker repository.

LogSyncStatus

Log delivery status.

Field Name	Type	Description
last_attempted	`INT64`	The timestamp of last attempt. If the last attempt fails, `last_exception` will contain the exception in the last attempt.
last_exception	`STRING`	The exception thrown in the last attempt, it would be null (omitted in the response) if there is no exception in last attempted.

NodeType

Description of a Spark node type including both the dimensions of the node and the instance type on which it will be hosted.

Field Name	Type	Description
node_type_id	`STRING`	Unique identifier for this node type. This field is required.
memory_mb	`INT32`	Memory (in MB) available for this node type. This field is required.
num_cores	`FLOAT`	Number of CPU cores available for this node type. This can be fractional, e.g., 2.5 cores, if the the number of cores on a machine instance is not divisible by the number of Spark nodes on that machine. This field is required.
description	`STRING`	A string description associated with this node type. This field is required.
instance_type_id	`STRING`	An identifier for the type of hardware that this node runs on. This field is required.
is_deprecated	`BOOL`	Whether the node type is deprecated. Non-deprecated node types offer greater performance.
node_info	ClusterCloudProviderNodeInfo	Node type info reported by the cloud provider.

ClusterCloudProviderNodeInfo

Information about an instance supplied by a cloud provider.

Field Name	Type	Description
status	ClusterCloudProviderNodeStatus	Status as reported by the cloud provider.
available_core_quota	`INT32`	Available CPU core quota.
total_core_quota	`INT32`	Total CPU core quota.

ClusterCloudProviderNodeStatus

Status of an instance supplied by a cloud provider.

Status	Description
NotEnabledOnSubscription	Node type not available for subscription.
NotAvailableInRegion	Node type not available in region.

ParameterPair

Parameter that provides additional information about why a cluster was terminated.

Field Name	Type	Description
key	TerminationParameter	Type of termination information.
value	`STRING`	The termination information.

SparkConfPair

Spark configuration key-value pairs.

Field Name	Type	Description
key	`STRING`	The configuration property name.
value	`STRING`	The configuration property value.

SparkEnvPair

Spark environment variable key-value pairs.

Important

When specifying environment variables in a job cluster, the fields in this data structure accept only Latin characters (ASCII character set). Using non-ASCII characters will return an error. Examples of invalid, non-ASCII characters are Chinese, Japanese kanjis, and emojis.

Field Name	Type	Description
key	`STRING`	The environment variable name.
value	`STRING`	The environment variable value.

SparkNode

Spark driver or executor configuration.

Field Name	Type	Description
private_ip	`STRING`	Private IP address (typically a 10.x.x.x address) of the Spark node. This is different from the private IP address of the host instance.
public_dns	`STRING`	Public DNS address of this node. This address can be used to access the Spark JDBC server on the driver node. To communicate with the JDBC server, traffic must be manually authorized by adding security group rules to the “worker-unmanaged” security group via the AWS console.
node_id	`STRING`	Globally unique identifier for this node.
instance_id	`STRING`	Globally unique identifier for the host instance from the cloud provider.
start_timestamp	`INT64`	The timestamp (in millisecond) when the Spark node is launched.
node_aws_attributes	SparkNodeAwsAttributes	Attributes specific to AWS for a Spark node.
host_private_ip	`STRING`	The private IP address of the host instance.

SparkVersion

Databricks Runtime version of the cluster.

Field Name	Type	Description
key	`STRING`	Databricks Runtime version key, for example `6.0.x-scala2.11`. The value that should be provided as the `spark_version` when creating a new cluster. The exact runtime version may change over time for a “wildcard” version (that is, `6.0.x-scala2.11` is a “wildcard” version) with minor bug fixes.
name	`STRING`	A descriptive name for the runtime version, for example “Databricks Runtime 6.0”.

TerminationReason

Reason why a cluster was terminated.

Field Name	Type	Description
code	TerminationCode	Status code indicating why a cluster was terminated.
type	TerminationType	Reason indicating why a cluster was terminated.
parameters	An array of ParameterPair	List of parameters that provide additional information about why a cluster was terminated.

PoolClusterTerminationCode

Status code indicating why the cluster was terminated due to a pool failure.

Code	Description
INSTANCE_POOL_MAX_CAPACITY_FAILURE	The pool max capacity has been reached.
INSTANCE_POOL_NOT_FOUND_FAILURE	The pool specified by the cluster is no longer active or doesn’t exist.

ClusterSource

Service that created the cluster.

Service	Description
UI	Cluster created through the UI.
JOB	Cluster created by the Databricks job scheduler.
API	Cluster created through an API call.

ClusterState

State of a cluster. The allowable state transitions are as follows:

PENDING -> RUNNING
PENDING -> TERMINATING
RUNNING -> RESIZING
RUNNING -> RESTARTING
RUNNING -> TERMINATING
RESTARTING -> RUNNING
RESTARTING -> TERMINATING
RESIZING -> RUNNING
RESIZING -> TERMINATING
TERMINATING -> TERMINATED

State	Description
PENDING	Indicates that a cluster is in the process of being created.
RUNNING	Indicates that a cluster has been started and is ready for use.
RESTARTING	Indicates that a cluster is in the process of restarting.
RESIZING	Indicates that a cluster is in the process of adding or removing nodes.
TERMINATING	Indicates that a cluster is in the process of being destroyed.
TERMINATED	Indicates that a cluster has been successfully destroyed.
ERROR	This state is not used anymore. It was used to indicate a cluster that failed to be created. Terminating and Terminated are used instead.
UNKNOWN	Indicates that a cluster is in an unknown state. A cluster should never be in this state.

TerminationCode

Status code indicating why the cluster was terminated.

Code	Description
USER_REQUEST	A user terminated the cluster directly. Parameters should include a `username` field that indicates the specific user who terminated the cluster.
JOB_FINISHED	The cluster was launched by a job and terminated when the job completed.
INACTIVITY	The cluster was terminated since it was idle.
CLOUD_PROVIDER_SHUTDOWN	The instance that hosted the Spark driver was terminated by the cloud provider. In AWS, for example, AWS may retire instances and directly shut them down. Parameters should include an `aws_instance_state_reason` field indicating the AWS-provided reason why the instance was terminated.
COMMUNICATION_LOST	Databricks lost connection to services on the driver instance. For example, this can happen when problems arise in cloud networking infrastructure, or when the instance itself becomes unhealthy.
CLOUD_PROVIDER_LAUNCH_FAILURE	Databricks experienced a cloud provider failure when requesting instances to launch clusters. For example, AWS limits the number of running instances and EBS volumes. If you ask Databricks to launch a cluster that requires instances or EBS volumes that exceed your AWS limit, the cluster will fail with this status code. Parameters should include one of `aws_api_error_code`, `aws_instance_state_reason`, or `aws_spot_request_status` to indicate the AWS-provided reason why Databricks could not request the required instances for the cluster.
SPARK_STARTUP_FAILURE	The Spark cluster failed to initialize. Possible reasons may include failure to create the environment for Spark or issues launching the Spark master and worker processes.
INVALID_ARGUMENT	Cannot launch the cluster because the user specified an invalid argument. For example, the user might specify an invalid runtime version for the cluster.
UNEXPECTED_LAUNCH_FAILURE	While launching this cluster, Databricks failed to complete critical setup steps, terminating the cluster.
INTERNAL_ERROR	Databricks encountered an unexpected error that forced the running cluster to be terminated. Contact Databricks support for additional details.
SPARK_ERROR	The Spark driver failed to start. Possible reasons may include incompatible libraries and initialization scripts that corrupted the Spark container.
METASTORE_COMPONENT_UNHEALTHY	The cluster failed to start because the external metastore could not be reached. Refer to Troubleshooting.
DBFS_COMPONENT_UNHEALTHY	The cluster failed to start because Databricks File System (DBFS) could not be reached.
DRIVER_UNREACHABLE	Databricks was not able to access the Spark driver, because it was not reachable.
DRIVER_UNRESPONSIVE	Databricks was not able to access the Spark driver, because it was unresponsive.
INSTANCE_UNREACHABLE	Databricks was not able to access instances in order to start the cluster. This can be a transient networking issue. If the problem persists, this usually indicates a networking environment misconfiguration.
CONTAINER_LAUNCH_FAILURE	Databricks was unable to launch containers on worker nodes for the cluster. Have your admin check your network configuration.
INSTANCE_POOL_CLUSTER_FAILURE	Pool backed cluster specific failure. Refer to Pools for details.
REQUEST_REJECTED	Databricks cannot handle the request at this moment. Try again later and contact Databricks if the problem persists.
INIT_SCRIPT_FAILURE	Databricks cannot load and run a cluster-scoped init script on one of the cluster’s nodes, or the init script terminates with a non-zero exit code. Refer to Cluster-scoped init script logs.
TRIAL_EXPIRED	The Databricks trial subscription expired.

TerminationType

Reason why the cluster was terminated.

Type	Description
SUCCESS	Termination succeeded.
CLIENT_ERROR	Non-retryable. Client must fix parameters before reattempting the cluster creation.
SERVICE_FAULT	Databricks service issue. Client can retry.
CLOUD_FAILURE	Cloud provider infrastructure issue. Client can retry after the underlying issue is resolved.

TerminationParameter

Key that provides additional information about why a cluster was terminated.

Key	Description
username	The username of the user who terminated the cluster.
aws_api_error_code	The AWS provided error code describing why cluster nodes could not be provisioned. For example, `InstanceLimitExceeded` indicates that the limit of EC2 instances for a specific instance type has been exceeded. For reference, see: https://docs.aws.amazon.com/AWSEC2/latest/APIReference/query-api-troubleshooting.html.
aws_instance_state_reason	The AWS provided state reason describing why the driver node was terminated. For example, `Client.VolumeLimitExceeded` indicates that the limit of EBS volumes or total EBS volume storage has been exceeded. For reference, see https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_StateReason.html.
aws_spot_request_status	Describes why a spot request could not be fulfilled. For example, `price-too-low` indicates that the max price was below the current spot price. For reference, see: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-bid-status.html#spot-instance-bid-status-understand.
aws_spot_request_fault_code	Provides additional details when a spot request fails. For example `InsufficientFreeAddressesInSubnet` indicates the subnet does not have free IP addresses to accommodate the new instance. For reference, see https://docs.aws.amazon.com/cli/latest/reference/ec2/describe-spot-instance-requests.html.
aws_impaired_status_details	The AWS provided status check which failed and induced a node loss. This status may correspond to a failed instance or system check. For reference, see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/monitoring-system-instance-status-check.html.
aws_instance_status_event	The AWS provided scheduled event (e.g. reboot) which induced a node loss. For reference, see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/monitoring-instances-status-check_sched.html.
aws_error_message	Human-readable context of various failures from AWS. This field is unstructured, and its exact format is subject to change.
databricks_error_message	Additional context that may explain the reason for cluster termination. This field is unstructured, and its exact format is subject to change.
inactivity_duration_min	An idle cluster was shut down after being inactive for this duration.
instance_id	The ID of the instance that was hosting the Spark driver.
instance_pool_id	The ID of the instance pool the cluster is using.
instance_pool_error_code	The error code for cluster failures specific to a pool.

S3StorageInfo

S3 storage information.

Field Name	Type	Description
destination	`STRING`	S3 destination, e.g. `s3://my-bucket/some-prefix` You must configure the cluster with an instance profile and the instance profile must have write access to the destination. You cannot use AWS keys.
region	`STRING`	S3 region, e.g. `us-west-2`. Either `region` or `endpoint` must be set. If both are set, `endpoint` is used.
endpoint	`STRING`	S3 endpoint, e.g. `https://s3-us-west-2.amazonaws.com`. Either `region` or `endpoint` needs to be set. If both are set, `endpoint` is used.
enable_encryption	`BOOL`	(Optional)Enable server side encryption, `false` by default.
encryption_type	`STRING`	(Optional) The encryption type, it could be `sse-s3` or `sse-kms`. It is used only when encryption is enabled and the default type is `sse-s3`.
kms_key	`STRING`	(Optional) KMS key used if encryption is enabled and encryption type is set to `sse-kms`.
canned_acl	`STRING`	(Optional) Set canned access control list, e.g. `bucket-owner-full-control`. If `canned_cal` is set, the cluster instance profile must have `s3:PutObjectAcl` permission on the destination bucket and prefix. The full list of possible canned ACL can be found at https://docs.aws.amazon.com/AmazonS3/latest/dev/acl-overview.html#canned-acl. By default only the object owner gets full control. If you are using cross account role for writing data, you may want to set `bucket-owner-full-control` to make bucket owner able to read the logs.

SparkNodeAwsAttributes

Attributes specific to AWS for a Spark node.

Field Name	Type	Description
is_spot	`BOOL`	Whether this node is on an Amazon spot instance.

AwsAvailability

The set of AWS availability types supported when setting up nodes for a cluster.

Type	Description
SPOT	Use spot instances.
ON_DEMAND	Use on-demand instances.
SPOT_WITH_FALLBACK	Preferably use spot instances, but fall back to on-demand instances if spot instances cannot be acquired (e.g., if AWS spot prices are too high).

EbsVolumeType

EBS volume types that Databricks supports. See Amazon EBS Product Details for details.

Type	Description
GENERAL_PURPOSE_SSD	Provision extra storage using AWS gp2 EBS volumes.
THROUGHPUT_OPTIMIZED_HDD	Provision extra storage using AWS st1 volumes.