Cluster API

The Cluster API allows you to create/edit/delete clusters via the API. For the cost information, please see the Databricks pricing page. The maximum allowed size of a request to the Cluster API is 10MB.


Create

Endpoint HTTP Method
2.0/clusters/create POST

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

Note: Databricks may not be able to acquire some of the requested nodes, due to cloud provider limitations or transient network issues. If is unable to acquire a sufficient number of the requested nodes, cluster creation will terminate with an informative error message.

An example request:

{
  "cluster_name": "my-cluster",
  "spark_version": "3.4.x-scala2.11",
  "node_type_id": "Standard_D3_v2",
  "spark_conf": {
    "spark.speculation": true
  },
  "num_workers": 25
}

See below as an example for an autoscaling cluster. Note that this cluster will start with 2 nodes, the minimum.

{
  "cluster_name": "autoscaling-cluster",
  "spark_version": "3.4.x-scala2.11",
  "node_type_id": "Standard_D3_v2",
  "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 current 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 spark_info 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 DB runtime versions 3.0 or later.

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 Spark version of the cluster. A list of available Spark versions can be retrieved by using the Spark Versions API call. This field is required.
spark_conf An array of SparkConfPair

An object containing a set of optional, user-specified Spark configuration key-value pairs. Users 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"}

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. Note that 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., VMs disk volumes) with these tags in addition to default_tags.

Currently Databricks allows up to 9 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 $destination/$clusterId/driver, while the destination of executor logs is $destination/$clusterId/executor.
spark_env_vars An array of SparkEnvPair

An object containing a set of optional, user-specified environment variable key-value pairs. Please note that key-value pair of the form (X,Y) will be 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. Users can also set this value to 0 to explicitly disable automatic termination.

Response Structure

Field Name Type Description
cluster_id STRING  

Edit

Endpoint HTTP Method
2.0/clusters/edit POST

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

A cluster can be edited if it is in a RUNNING or TERMINATED state. If a cluster is edited while in a RUNNING state, it will be restarted so that the new attributes can take effect. If a cluster is edited 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. 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": "3.4.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 current 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 spark_info 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 DB runtime versions 3.0 or later.

cluster_id STRING 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 Spark version of the cluster. A list of available Spark versions can be retrieved by using the Spark Versions API call. This field is required.
spark_conf An array of SparkConfPair

An object containing a set of optional, user-specified Spark configuration key-value pairs. Users 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"}

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. Note that 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.
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 $destination/$clusterId/driver, while the destination of executor logs is $destination/$clusterId/executor.
spark_env_vars An array of SparkEnvPair

An object containing a set of optional, user-specified environment variable key-value pairs. Please note that key-value pair of the form (X,Y) will be 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. Users can also set this value to 0 to explicitly disable automatic termination.

Start

Endpoint HTTP Method
2.0/clusters/start POST
Starts a terminated Spark cluster given its id. This works similar to createCluster except:
  • The previous cluster id and attributes are preserved.

  • The cluster starts with the last specified cluster size.
    • If the previous cluster was an autoscaling cluster, the current cluster starts with the minimum number of nodes.
  • If the cluster is not currently 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

Restarts a Spark cluster given its id. If the cluster is not currently 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

Resizes 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 current 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 spark_info 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 DB runtime versions 3.0 or later.

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

Delete

Endpoint HTTP Method
2.0/clusters/delete POST

Removes a Spark cluster given its id. The cluster is removed asynchronously. Once the deletion has completed, the cluster will be in a TERMINATED state. If the cluster is already in a TERMINATING or TERMINATED 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 deleted. This field is required.

Get

Endpoint HTTP Method
2.0/clusters/get GET

Retrieves the information for a cluster given its identifier. Clusters can be described while they are running, or up to 60 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 current 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 spark_info 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 DB runtime versions 3.0 or later.

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 nod. No service will be listeningon 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 Spark version of the cluster. A list of available Spark versions can be retrieved by using the Spark Versions API call.
spark_conf An array of SparkConfPair

An object containing a set of optional, user-specified Spark configuration key-value pairs. Users 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"}

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. Note that 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.
cluster_log_conf ClusterLogConf The configuration for delivering spark logs to a long-term storage destination. Dbfs is supported. 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 $destination/$clusterId/driver, while the destination of executor logs is $destination/$clusterId/executor.
spark_env_vars An array of SparkEnvPair

An object containing a set of optional, user-specified environment variable key-value pairs. Please note that key-value pair of the form (X,Y) will be 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. Users can also set this value to 0 to explicitly disable automatic termination.
state ClusterState Current 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. Please 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. Note that 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.
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.

List

Endpoint HTTP Method
2.0/clusters/list GET

Returns information about all currently active clusters, and up to 100 most recently terminated clusters in the past 7 days. For example, if there are 5 active clusters and 101 terminated clusters in the past 7 days, it returns the 5 active clusters and 100 terminated clusters. If there are 5 active clusters and 99 terminated clusters in the past 7 days, it returns 5 active clusters and all the 99 terminated clusters.

Response Structure

Field Name Type Description
clusters An array of ClusterInfo  

List Node Types

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

Returns 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.

Spark Versions

Endpoint HTTP Method
2.0/clusters/spark-versions GET

Returns the list of available Spark versions. These versions can be used to launch a cluster.

Response Structure

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

Data Structures

AutoScale

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. Note that max_workers must be strictly greater than min_workers.

ClusterInfo

Describes all of the metadata about a single Spark cluster in Databricks.

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 current 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 spark_info 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 DB runtime versions 3.0 or later.

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 nod. No service will be listeningon 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 Spark version of the cluster. A list of available Spark versions can be retrieved by using the Spark Versions API call.
spark_conf An array of SparkConfPair

An object containing a set of optional, user-specified Spark configuration key-value pairs. Users 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"}

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. Note that 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.
cluster_log_conf ClusterLogConf The configuration for delivering spark logs to a long-term storage destination. Dbfs is supported. 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 $destination/$clusterId/driver, while the destination of executor logs is $destination/$clusterId/executor.
spark_env_vars An array of SparkEnvPair

An object containing a set of optional, user-specified environment variable key-value pairs. Please note that key-value pair of the form (X,Y) will be 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. Users can also set this value to 0 to explicitly disable automatic termination.
state ClusterState Current 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. Please 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. Note that 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>
  • Environment: <Databricks internal use>
  • databricks-instance-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.

ClusterLogConf

Cluster log delivery config

Field Name Type Description
dbfs DbfsStorageInfo For dbfs, destination must be provided. For example, { "dbfs" : { "destination" : "dbfs:/home/cluster_log" } }

ClusterTag

Field Name Type Description
key STRING

The key of the tag. The key must:

  • Be between 1 and 512 characters long
  • Not contain any of the characters <>%*&+?\/
  • Not begin with “azure”, “microsoft”, or “windows”
value STRING The value of the tag. The value length must be less than or equal to 256 UTF-8 characters.

DbfsStorageInfo

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

LogSyncStatus

The 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

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

ParameterPair

Field Name Type Description
key TerminationParameter  
value STRING  

SparkConfPair

Spark configuration key-value pairs

Field Name Type Description
key STRING  
value STRING  

SparkEnvPair

Spark environment variable key-value pairs

Field Name Type Description
key STRING  
value STRING  

SparkNode

Describes a specific Spark driver or executor.

Field Name Type Description
private_ip STRING Private IP address (typically a 10.x.x.x address) of the Spark node. Note that 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.
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.
host_private_ip STRING The private IP address of the host instance.

SparkVersion

Field Name Type Description
key STRING Spark version key, for example “3.4.x-scala2.11”. This is the value which should be provided as the “spark_version” when creating a new cluster. Note that the exact Spark version may change over time for a “wildcard” version with minor bug fixes.
name STRING A descriptive name for this Spark version, for example “Spark 2.1”.

TerminationReason

Field Name Type Description
code TerminationCode status code indicating why the cluster was terminated
parameters An array of ParameterPair list of parameters that provide additional information about why the cluster was terminated

ClusterSource

Indicates the service that created the cluster.

UI Cluster created through the UI.
JOB Cluster created by the Databricks Job Scheduler.
API Cluster created through an API call.

ClusterState

The state of a Cluster. The current 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
PENDING Indicates a cluster that is in progress of being created.
RUNNING Indicates a cluster that 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 a cluster which has been successfully destroyed.
ERROR This state is not used anymore. It was used to indicate a cluster which failed to be created. Terminating and Terminated are used instead.
UNKNOWN Indicates a cluster which is an unknown state. A cluster should never be in this state.

TerminationCode

The status code indicating why the cluster was terminated

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 This cluster was launched by a Job, and terminated when the Job completed.
INACTIVITY This cluster was terminated since it was idle.
CLOUD_PROVIDER_SHUTDOWN The instance that hosted the spark driver was terminated by the cloud provider.
COMMUNICATION_LOST Databricks may lose connection to services on the driver instance. One such case is when problems arise in cloud networking infrastructure, or when the instance itself becomes unhealty.
CLOUD_PROVIDER_LAUNCH_FAILURE Databricks may hit cloud provider failures when requesting instances to launch clusters.
SPARK_STARTUP_FAILURE The Spark driver failed to start. Possible reasons may include incompatible libraries and initialization scripts that corrupted the Spark container.
INVALID_ARGUMENT Cannot launch the cluster because the user specified an invalid argument. For example, the use might specify an invalid spark 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 which forced the running cluster to be terminated. Please contact Databricks support for additional details.
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.
REQUEST_REJECTED Databricks cannot handle the request at this moment. Please try again later and contact Databricks if the problem persists.

TerminationParameter

Possible keys that provide additional information as to why a cluster was terminated.

username The username of the user who terminated the cluster.
databricks_error_message Additional context that may explain the reason for cluster termination.
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.
azure_error_code The Azure provided error code describing why cluster nodes could not be provisioned. For reference, see: https://docs.microsoft.com/en-us/azure/virtual-machines/windows/error-messages
azure_error_message Provides human-readable context of various failures from Azure.