Jobs API

The Jobs API allows you to create, edit, and delete jobs. The maximum allowed size of a request to the Jobs API is 10MB. See Create a High Concurrency cluster for a how-to guide on this API.

For details about updates to the Jobs API that support orchestration of multiple tasks with Databricks jobs, see Jobs API updates.

Note

If you receive a 500-level error when making Jobs API requests, Databricks recommends retrying requests for up to 10 min (with a minimum 30 second interval between retries).

Important

To access Databricks REST APIs, you must authenticate.

Create

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

Create a new job.

Example

This example creates a job that runs a JAR task at 10:15pm each night.

Request

curl --netrc --request POST \
https://<databricks-instance>/api/2.0/jobs/create \
--data @create-job.json \
| jq .

create-job.json:

{
  "name": "Nightly model training",
  "new_cluster": {
    "spark_version": "7.3.x-scala2.12",
    "node_type_id": "r3.xlarge",
    "aws_attributes": {
      "availability": "ON_DEMAND"
    },
    "num_workers": 10
  },
  "libraries": [
    {
      "jar": "dbfs:/my-jar.jar"
    },
    {
      "maven": {
        "coordinates": "org.jsoup:jsoup:1.7.2"
      }
    }
  ],
  "email_notifications": {
    "on_start": [],
    "on_success": [],
    "on_failure": []
  },
  "timeout_seconds": 3600,
  "max_retries": 1,
  "schedule": {
    "quartz_cron_expression": "0 15 22 * * ?",
    "timezone_id": "America/Los_Angeles"
  },
  "spark_jar_task": {
    "main_class_name": "com.databricks.ComputeModels"
  }
}

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
The contents of create-job.json with fields that are appropriate for your solution.

This example uses a .netrc file and jq.

Response

{
  "job_id": 1
}

Request structure

Important

When you run a job on a new jobs cluster, the job is treated as a Jobs Compute (automated) workload subject to Jobs Compute pricing.
When you run a job on an existing all-purpose cluster, it is treated as an All-Purpose Compute (interactive) workload subject to All-Purpose Compute pricing.

Field Name	Type	Description
existing_cluster_id OR new_cluster	`STRING` OR NewCluster	If existing_cluster_id, the ID of an existing cluster that will be used for all runs of this job. When running jobs on an existing cluster, you may need to manually restart the cluster if it stops responding. We suggest running jobs on new clusters for greater reliability. If new_cluster, a description of a cluster that will be created for each run.
notebook_task OR spark_jar_task OR spark_python_task OR spark_submit_task	NotebookTask OR SparkJarTask OR SparkPythonTask OR SparkSubmitTask	If notebook_task, indicates that this job should run a notebook. This field may not be specified in conjunction with spark_jar_task. If spark_jar_task, indicates that this job should run a JAR. If spark_python_task, indicates that this job should run a Python file. If spark_submit_task, indicates that this job should be launched by the spark submit script.
name	`STRING`	An optional name for the job. The default value is `Untitled`.
libraries	An array of Library	An optional list of libraries to be installed on the cluster that will execute the job. The default value is an empty list.
email_notifications	JobEmailNotifications	An optional set of email addresses notified when runs of this job begin and complete and when this job is deleted. The default behavior is to not send any emails.
timeout_seconds	`INT32`	An optional timeout applied to each run of this job. The default behavior is to have no timeout.
max_retries	`INT32`	An optional maximum number of times to retry an unsuccessful run. A run is considered to be unsuccessful if it completes with the `FAILED` result_state or `INTERNAL_ERROR` `life_cycle_state`. The value -1 means to retry indefinitely and the value 0 means to never retry. The default behavior is to never retry.
min_retry_interval_millis	`INT32`	An optional minimal interval in milliseconds between the start of the failed run and the subsequent retry run. The default behavior is that unsuccessful runs are immediately retried.
retry_on_timeout	`BOOL`	An optional policy to specify whether to retry a job when it times out. The default behavior is to not retry on timeout.
schedule	CronSchedule	An optional periodic schedule for this job. The default behavior is that the job runs when triggered by clicking Run Now in the Jobs UI or sending an API request to `runNow`.
max_concurrent_runs	`INT32`	An optional maximum allowed number of concurrent runs of the job. Set this value if you want to be able to execute multiple runs of the same job concurrently. This is useful for example if you trigger your job on a frequent schedule and want to allow consecutive runs to overlap with each other, or if you want to trigger multiple runs which differ by their input parameters. This setting affects only new runs. For example, suppose the job’s concurrency is 4 and there are 4 concurrent active runs. Then setting the concurrency to 3 won’t kill any of the active runs. However, from then on, new runs are skipped unless there are fewer than 3 active runs. This value cannot exceed 1000. Setting this value to 0 causes all new runs to be skipped. The default behavior is to allow only 1 concurrent run.

Response structure

Field Name	Type	Description
job_id	`INT64`	The canonical identifier for the newly created job.

List

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

List all jobs.

Example

Request

curl --netrc --request GET \
https://<databricks-instance>/api/2.0/jobs/list \
| jq .

Replace <databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.

This example uses a .netrc file and jq.

Response

{
  "jobs": [
    {
      "job_id": 1,
      "settings": {
        "name": "Nightly model training",
        "new_cluster": {
          "spark_version": "7.3.x-scala2.12",
          "node_type_id": "r3.xlarge",
          "aws_attributes": {
            "availability": "ON_DEMAND"
          },
          "num_workers": 10
        },
        "libraries": [
          {
            "jar": "dbfs:/my-jar.jar"
          },
          {
            "maven": {
              "coordinates": "org.jsoup:jsoup:1.7.2"
            }
          }
        ],
        "email_notifications": {
          "on_start": [],
          "on_success": [],
          "on_failure": []
        },
        "timeout_seconds": 100000000,
        "max_retries": 1,
        "schedule": {
          "quartz_cron_expression": "0 15 22 * * ?",
          "timezone_id": "America/Los_Angeles",
          "pause_status": "UNPAUSED"
        },
        "spark_jar_task": {
          "main_class_name": "com.databricks.ComputeModels"
        }
      },
      "created_time": 1457570074236
    }
  ]
}

Response structure

Field Name	Type	Description
jobs	An array of Job	The list of jobs.

Delete

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

Delete a job and send an email to the addresses specified in JobSettings.email_notifications. No action occurs if the job has already been removed. After the job is removed, neither its details nor its run history is visible in the Jobs UI or API. The job is guaranteed to be removed upon completion of this request. However, runs that were active before the receipt of this request may still be active. They will be terminated asynchronously.

Example

curl --netrc --request POST \
https://<databricks-instance>/api/2.0/jobs/delete \
--data '{ "job_id": <job-id> }'

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
<job-id> with the ID of the job, for example 123.

This example uses a .netrc file.

Request structure

Field Name	Type	Description
job_id	`INT64`	The canonical identifier of the job to delete. This field is required.

Get

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

Retrieve information about a single job.

Example

Request

curl --netrc --request GET \
'https://<databricks-instance>/api/2.0/jobs/get?job_id=<job-id>' \
| jq .

Or:

curl --netrc --get \
https://<databricks-instance>/api/2.0/jobs/get \
--data job_id=<job-id> \
| jq .

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
<job-id> with the ID of the job, for example 123.

This example uses a .netrc file and jq.

Response

{
  "job_id": 1,
  "settings": {
    "name": "Nightly model training",
    "new_cluster": {
      "spark_version": "7.3.x-scala2.12",
      "node_type_id": "r3.xlarge",
      "aws_attributes": {
        "availability": "ON_DEMAND"
      },
      "num_workers": 10
    },
    "libraries": [
      {
        "jar": "dbfs:/my-jar.jar"
      },
      {
        "maven": {
          "coordinates": "org.jsoup:jsoup:1.7.2"
        }
      }
    ],
    "email_notifications": {
      "on_start": [],
      "on_success": [],
      "on_failure": []
    },
    "timeout_seconds": 100000000,
    "max_retries": 1,
    "schedule": {
      "quartz_cron_expression": "0 15 22 * * ?",
      "timezone_id": "America/Los_Angeles",
      "pause_status": "UNPAUSED"
    },
    "spark_jar_task": {
      "main_class_name": "com.databricks.ComputeModels"
    }
  },
  "created_time": 1457570074236
}

Request structure

Field Name	Type	Description
job_id	`INT64`	The canonical identifier of the job to retrieve information about. This field is required.

Response structure

Field Name	Type	Description
job_id	`INT64`	The canonical identifier for this job.
creator_user_name	`STRING`	The creator user name. This field won’t be included in the response if the user has been deleted.
settings	JobSettings	Settings for this job and all of its runs. These settings can be updated using the Reset or Update endpoints.
created_time	`INT64`	The time at which this job was created in epoch milliseconds (milliseconds since 1/1/1970 UTC).

Reset

Endpoint	HTTP Method
`2.0/jobs/reset`	`POST`

Overwrite all settings for a specific job. Use the Update endpoint to update job settings partially.

Example

This example request makes job 2 identical to job 1 in the create example.

curl --netrc --request POST \
https://<databricks-instance>/api/2.0/jobs/reset \
--data @reset-job.json \
| jq .

reset-job.json:

{
  "job_id": 2,
  "new_settings": {
    "name": "Nightly model training",
    "new_cluster": {
      "spark_version": "7.3.x-scala2.12",
      "node_type_id": "r3.xlarge",
      "aws_attributes": {
        "availability": "ON_DEMAND"
      },
      "num_workers": 10
    },
    "libraries": [
      {
        "jar": "dbfs:/my-jar.jar"
      },
      {
        "maven": {
          "coordinates": "org.jsoup:jsoup:1.7.2"
        }
      }
    ],
    "email_notifications": {
      "on_start": [],
      "on_success": [],
      "on_failure": []
    },
    "timeout_seconds": 100000000,
    "max_retries": 1,
    "schedule": {
      "quartz_cron_expression": "0 15 22 * * ?",
      "timezone_id": "America/Los_Angeles",
      "pause_status": "UNPAUSED"
    },
    "spark_jar_task": {
      "main_class_name": "com.databricks.ComputeModels"
    }
  }
}

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
The contents of reset-job.json with fields that are appropriate for your solution.

This example uses a .netrc file and jq.

Request structure

Field Name Type Description

job_id INT64 The canonical identifier of the job to reset. This field is required.

new_settings

JobSettings

The new settings of the job. These settings completely replace the old settings.

Changes to the field JobSettings.timeout_seconds are applied to active runs. Changes to other fields are applied to future runs only.

Update

Endpoint	HTTP Method
`2.0/jobs/update`	`POST`

Add, change, or remove specific settings of an existing job. Use the Reset endpoint to overwrite all job settings.

Example

This example request removes libraries and adds email notification settings to job 1 defined in the create example.

curl --netrc --request POST \
https://<databricks-instance>/api/2.0/jobs/update \
--data @update-job.json \
| jq .

update-job.json:

{
  "job_id": 1,
  "new_settings": {
    "existing_cluster_id": "1201-my-cluster",
    "email_notifications": {
      "on_start": [ "someone@example.com" ],
      "on_success": [],
      "on_failure": []
    }
  },
  "fields_to_remove": ["libraries"]
}

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
The contents of update-job.json with fields that are appropriate for your solution.

This example uses a .netrc file and jq.

Request structure

Field Name Type Description

job_id INT64 The canonical identifier of the job to update. This field is required.

new_settings

JobSettings

The new settings for the job. Any top-level fields specified in new_settings are completely replaced. Partially updating nested fields is not supported.

Changes to the field JobSettings.timeout_seconds are applied to active runs. Changes to other fields are applied to future runs only.

fields_to_remove An array of STRING Remove top-level fields in the job settings. Removing nested fields is not supported. This field is optional.

Run now

Important

You can create jobs only in a Data Science & Engineering workspace.
A workspace is limited to 1000 concurrent job runs. A 429 Too Many Requests response is returned when you request a run that cannot be started immediately.
The number of jobs a workspace can create in an hour is limited to 5000 (includes “run now” and “runs submit”). This limit also affects jobs created by the REST API and notebook workflows.

Endpoint	HTTP Method
`2.0/jobs/run-now`	`POST`

Run a job now and return the run_id of the triggered run.

Tip

If you invoke Create together with Run now, you can use the Runs submit endpoint instead, which allows you to submit your workload directly without having to create a job.

Example

curl --netrc --request POST \
https://<databricks-instance>/api/2.0/jobs/run-now \
--data @run-job.json \
| jq .

run-job.json:

An example request for a notebook job:

{
  "job_id": 1,
  "notebook_params": {
    "name": "john doe",
    "age": "35"
  }
}

An example request for a JAR job:

{
  "job_id": 2,
  "jar_params": [ "john doe", "35" ]
}

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
The contents of run-job.json with fields that are appropriate for your solution.

This example uses a .netrc file and jq.

Request structure

Field Name	Type	Description
job_id	`INT64`
jar_params	An array of `STRING`	A list of parameters for jobs with JAR tasks, e.g. `"jar_params": ["john doe", "35"]`. The parameters will be used to invoke the main function of the main class specified in the Spark JAR task. If not specified upon `run-now`, it will default to an empty list. jar_params cannot be specified in conjunction with notebook_params. The JSON representation of this field (i.e. `{"jar_params":["john doe","35"]}`) cannot exceed 10,000 bytes.
notebook_params	A map of ParamPair	A map from keys to values for jobs with notebook task, e.g. `"notebook_params": {"name": "john doe", "age": "35"}`. The map is passed to the notebook and is accessible through the dbutils.widgets.get function. If not specified upon `run-now`, the triggered run uses the job’s base parameters. You cannot specify notebook_params in conjunction with jar_params. The JSON representation of this field (i.e. `{"notebook_params":{"name":"john doe","age":"35"}}`) cannot exceed 10,000 bytes.
python_params	An array of `STRING`	A list of parameters for jobs with Python tasks, e.g. `"python_params": ["john doe", "35"]`. The parameters will be passed to Python file as command-line parameters. If specified upon `run-now`, it would overwrite the parameters specified in job setting. The JSON representation of this field (i.e. `{"python_params":["john doe","35"]}`) cannot exceed 10,000 bytes.
spark_submit_params	An array of `STRING`	A list of parameters for jobs with spark submit task, e.g. `"spark_submit_params": ["--class", "org.apache.spark.examples.SparkPi"]`. The parameters will be passed to spark-submit script as command-line parameters. If specified upon `run-now`, it would overwrite the parameters specified in job setting. The JSON representation of this field cannot exceed 10,000 bytes.

Response structure

Field Name	Type	Description
run_id	`INT64`	The globally unique ID of the newly triggered run.
number_in_job	`INT64`	The sequence number of this run among all runs of the job.

Runs submit

Important

You can create jobs only in a Data Science & Engineering workspace.
A workspace is limited to 1000 concurrent job runs. A 429 Too Many Requests response is returned when you request a run that cannot be started immediately.
The number of jobs a workspace can create in an hour is limited to 5000 (includes “run now” and “runs submit”). This limit also affects jobs created by the REST API and notebook workflows.

Endpoint	HTTP Method
`2.0/jobs/runs/submit`	`POST`

Submit a one-time run. This endpoint allows you to submit a workload directly without creating a job. Runs submitted using this endpoint don’t display in the UI. Use the jobs/runs/get API to check the run state after the job is submitted.

Example

Request

curl --netrc --request POST \
https://<databricks-instance>/api/2.0/jobs/runs/submit \
--data @submit-job.json \
| jq .

submit-job.json:

{
  "run_name": "my spark task",
  "new_cluster": {
    "spark_version": "7.3.x-scala2.12",
    "node_type_id": "r3.xlarge",
    "aws_attributes": {
      "availability": "ON_DEMAND"
    },
    "num_workers": 10
  },
  "libraries": [
    {
      "jar": "dbfs:/my-jar.jar"
    },
    {
      "maven": {
        "coordinates": "org.jsoup:jsoup:1.7.2"
      }
    }
  ],
  "spark_jar_task": {
    "main_class_name": "com.databricks.ComputeModels"
  }
}

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
The contents of submit-job.json with fields that are appropriate for your solution.

This example uses a .netrc file and jq.

Response

{
  "run_id": 123
}

Request structure

Important

When you run a job on a new jobs cluster, the job is treated as a Jobs Compute (automated) workload subject to Jobs Compute pricing.
When you run a job on an existing all-purpose cluster, it is treated as an All-Purpose Compute (interactive) workload subject to All-Purpose Compute pricing.

Field Name	Type	Description
existing_cluster_id OR new_cluster	`STRING` OR NewCluster	If existing_cluster_id, the ID of an existing cluster that will be used for all runs of this job. When running jobs on an existing cluster, you may need to manually restart the cluster if it stops responding. We suggest running jobs on new clusters for greater reliability. If new_cluster, a description of a cluster that will be created for each run.
notebook_task OR spark_jar_task OR spark_python_task OR spark_submit_task	NotebookTask OR SparkJarTask OR SparkPythonTask OR SparkSubmitTask	If notebook_task, indicates that this job should run a notebook. This field may not be specified in conjunction with spark_jar_task. If spark_jar_task, indicates that this job should run a JAR. If spark_python_task, indicates that this job should run a Python file. If spark_submit_task, indicates that this job should be launched by the spark submit script.
run_name	`STRING`	An optional name for the run. The default value is `Untitled`.
libraries	An array of Library	An optional list of libraries to be installed on the cluster that will execute the job. The default value is an empty list.
timeout_seconds	`INT32`	An optional timeout applied to each run of this job. The default behavior is to have no timeout.
idempotency_token	`STRING`	An optional token that can be used to guarantee the idempotency of job run requests. If an active run with the provided token already exists, the request will not create a new run, but will return the ID of the existing run instead. If you specify the idempotency token, upon failure you can retry until the request succeeds. Databricks guarantees that exactly one run will be launched with that idempotency token. This token should have at most 64 characters. For more information, see How to ensure idempotency for jobs.

Response structure

Field Name	Type	Description
run_id	`INT64`	The canonical identifier for the newly submitted run.

Runs list

Endpoint	HTTP Method
`2.0/jobs/runs/list`	`GET`

List runs in descending order by start time.

Note

Runs are automatically removed after 60 days. If you to want to reference them beyond 60 days, you should save old run results before they expire. To export using the UI, see Export job run results. To export using the Jobs API, see Runs export.

Example

Request

curl --netrc --request GET \
'https://<databricks-instance>/api/2.0/jobs/runs/list?job_id=<job-id>&active_only=<true-false>&offset=<offset>&limit=<limit>&run_type=<run-type>' \
| jq .

Or:

curl --netrc --get \
https://<databricks-instance>/api/2.0/jobs/runs/list \
--data 'job_id=<job-id>&active_only=<true-false>&offset=<offset>&limit=<limit>&run_type=<run-type>' \
| jq .

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
<job-id> with the ID of the job, for example 123.
<true-false> with true or false.
<offset> with the offset value.
<limit> with the limit value.
<run-type> with the run_type value.

This example uses a .netrc file and jq.

Response

{
  "runs": [
    {
      "job_id": 1,
      "run_id": 452,
      "number_in_job": 5,
      "state": {
        "life_cycle_state": "RUNNING",
        "state_message": "Performing action"
      },
      "task": {
        "notebook_task": {
          "notebook_path": "/Users/donald@duck.com/my-notebook"
        }
      },
      "cluster_spec": {
        "existing_cluster_id": "1201-my-cluster"
      },
      "cluster_instance": {
        "cluster_id": "1201-my-cluster",
        "spark_context_id": "1102398-spark-context-id"
      },
      "overriding_parameters": {
        "jar_params": ["param1", "param2"]
      },
      "start_time": 1457570074236,
      "end_time": 1457570075149,
      "setup_duration": 259754,
      "execution_duration": 3589020,
      "cleanup_duration": 31038,
      "trigger": "PERIODIC"
    }
  ],
  "has_more": true
}

Request structure

Field Name	Type	Description
active_only OR completed_only	`BOOL` OR `BOOL`	If active_only is `true`, only active runs are included in the results; otherwise, lists both active and completed runs. An active run is a run in the `PENDING`, `RUNNING`, or `TERMINATING` RunLifecycleState. This field cannot be `true` when completed_only is `true`. If completed_only is `true`, only completed runs are included in the results; otherwise, lists both active and completed runs. This field cannot be `true` when active_only is `true`.
job_id	`INT64`	The job for which to list runs. If omitted, the Jobs service will list runs from all jobs.
offset	`INT32`	The offset of the first run to return, relative to the most recent run.
limit	`INT32`	The number of runs to return. This value should be greater than 0 and less than 1000. The default value is 20. If a request specifies a limit of 0, the service will instead use the maximum limit.
run_type	`STRING`	The type of runs to return. For a description of run types, see Run.

Response structure

Field Name	Type	Description
runs	An array of Run	A list of runs, from most recently started to least.
has_more	`BOOL`	If true, additional runs matching the provided filter are available for listing.

Runs get

Endpoint	HTTP Method
`2.0/jobs/runs/get`	`GET`

Retrieve the metadata of a run.

Note

Runs are automatically removed after 60 days. If you to want to reference them beyond 60 days, you should save old run results before they expire. To export using the UI, see Export job run results. To export using the Jobs API, see Runs export.

Example

Request

curl --netrc --request GET \
'https://<databricks-instance>/api/2.0/jobs/runs/get?run_id=<run-id>' \
| jq .

Or:

curl --netrc --get \
https://<databricks-instance>/api/2.0/jobs/runs/get \
--data run_id=<run-id> \
| jq .

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
<run-id> with the ID of the run, for example 123.

This example uses a .netrc file and jq.

Response

{
  "job_id": 1,
  "run_id": 452,
  "number_in_job": 5,
  "state": {
    "life_cycle_state": "RUNNING",
    "state_message": "Performing action"
  },
  "task": {
    "notebook_task": {
      "notebook_path": "/Users/someone@example.com/my-notebook"
    }
  },
  "cluster_spec": {
    "existing_cluster_id": "1201-my-cluster"
  },
  "cluster_instance": {
    "cluster_id": "1201-my-cluster",
    "spark_context_id": "1102398-spark-context-id"
  },
  "overriding_parameters": {
    "jar_params": ["param1", "param2"]
  },
  "start_time": 1457570074236,
  "end_time": 1457570075149,
  "setup_duration": 259754,
  "execution_duration": 3589020,
  "cleanup_duration": 31038,
  "trigger": "PERIODIC"
}

Request structure

Field Name	Type	Description
run_id	`INT64`	The canonical identifier of the run for which to retrieve the metadata. This field is required.

Response structure

Field Name	Type	Description
job_id	`INT64`	The canonical identifier of the job that contains this run.
run_id	`INT64`	The canonical identifier of the run. This ID is unique across all runs of all jobs.
number_in_job	`INT64`	The sequence number of this run among all runs of the job. This value starts at 1.
original_attempt_run_id	`INT64`	If this run is a retry of a prior run attempt, this field contains the run_id of the original attempt; otherwise, it is the same as the run_id.
state	RunState	The result and lifecycle states of the run.
schedule	CronSchedule	The cron schedule that triggered this run if it was triggered by the periodic scheduler.
task	JobTask	The task performed by the run, if any.
cluster_spec	ClusterSpec	A snapshot of the job’s cluster specification when this run was created.
cluster_instance	ClusterInstance	The cluster used for this run. If the run is specified to use a new cluster, this field will be set once the Jobs service has requested a cluster for the run.
overriding_parameters	RunParameters	The parameters used for this run.
start_time	`INT64`	The time at which this run was started in epoch milliseconds (milliseconds since 1/1/1970 UTC). This may not be the time when the job task starts executing, for example, if the job is scheduled to run on a new cluster, this is the time the cluster creation call is issued.
end_time	`INT64`	The time at which this run ended in epoch milliseconds (milliseconds since 1/1/1970 UTC). This field will be set to 0 if the job is still running.
setup_duration	`INT64`	The time it took to set up the cluster in milliseconds. For runs that run on new clusters this is the cluster creation time, for runs that run on existing clusters this time should be very short.
execution_duration	`INT64`	The time in milliseconds it took to execute the commands in the JAR or notebook until they completed, failed, timed out, were cancelled, or encountered an unexpected error.
cleanup_duration	`INT64`	The time in milliseconds it took to terminate the cluster and clean up any associated artifacts. The total duration of the run is the sum of the setup_duration, the execution_duration, and the cleanup_duration.
trigger	TriggerType	The type of trigger that fired this run.
creator_user_name	`STRING`	The creator user name. This field won’t be included in the response if the user has been deleted
run_page_url	`STRING`	The URL to the detail page of the run.

Runs export

Endpoint	HTTP Method
`2.0/jobs/runs/export`	`GET`

Export and retrieve the job run task.

Note

Only notebook runs can be exported in HTML format. Exporting runs of other types will fail.

Example

Request

curl --netrc --request GET \
'https://<databricks-instance>/api/2.0/jobs/runs/export?run_id=<run-id>' \
| jq .

Or:

curl --netrc --get \
https://<databricks-instance>/api/2.0/jobs/runs/export \
--data run_id=<run-id> \
| jq .

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
<run-id> with the ID of the run, for example 123.

This example uses a .netrc file and jq.

Response

{
  "views": [ {
    "content": "<!DOCTYPE html><html><head>Head</head><body>Body</body></html>",
    "name": "my-notebook",
    "type": "NOTEBOOK"
  } ]
}

To extract the HTML notebook from the JSON response, download and run this Python script.

Note

The notebook body in the __DATABRICKS_NOTEBOOK_MODEL object is encoded.

Request structure

Field Name	Type	Description
run_id	`INT64`	The canonical identifier for the run. This field is required.
views_to_export	ViewsToExport	Which views to export (CODE, DASHBOARDS, or ALL). Defaults to CODE.

Response structure

Field Name	Type	Description
views	An array of ViewItem	The exported content in HTML format (one for every view item).

Runs cancel

Endpoint	HTTP Method
`2.0/jobs/runs/cancel`	`POST`

Cancel a run. The run is canceled asynchronously, so when this request completes, the run may still be running. The run will be terminated shortly. If the run is already in a terminal life_cycle_state, this method is a no-op.

This endpoint validates that the run_id parameter is valid and for invalid parameters returns HTTP status code 400.

Example

curl --netrc --request POST \
https://<databricks-instance>/api/2.0/jobs/runs/cancel \
--data '{ "run_id": <run-id> }'

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
<run-id> with the ID of the run, for example 123.

This example uses a .netrc file.

Request structure

Field Name	Type	Description
run_id	`INT64`	This field is required.

Runs get output

Endpoint	HTTP Method
`2.0/jobs/runs/get-output`	`GET`

Retrieve the output and metadata of a run. When a notebook task returns a value through the dbutils.notebook.exit() call, you can use this endpoint to retrieve that value. Databricks restricts this API to return the first 5 MB of the output. For returning a larger result, you can store job results in a cloud storage service.

This endpoint validates that the run_id parameter is valid and for invalid parameters returns HTTP status code 400.

Runs are automatically removed after 60 days. If you to want to reference them beyond 60 days, you should save old run results before they expire. To export using the UI, see Export job run results. To export using the Jobs API, see Runs export.

Example

Request

curl --netrc --request GET \
'https://<databricks-instance>/api/2.0/jobs/runs/get-output?run_id=<run-id>' \
| jq .

Or:

curl --netrc --get \
https://<databricks-instance>/api/2.0/jobs/runs/get-output \
--data run_id=<run-id> \
| jq .

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
<run-id> with the ID of the run, for example 123.

This example uses a .netrc file and jq.

Response

{
  "metadata": {
    "job_id": 1,
    "run_id": 452,
    "number_in_job": 5,
    "state": {
      "life_cycle_state": "TERMINATED",
      "result_state": "SUCCESS",
      "state_message": ""
    },
    "task": {
      "notebook_task": {
        "notebook_path": "/Users/someone@example.com/my-notebook"
      }
    },
    "cluster_spec": {
      "existing_cluster_id": "1201-my-cluster"
    },
    "cluster_instance": {
      "cluster_id": "1201-my-cluster",
      "spark_context_id": "1102398-spark-context-id"
    },
    "overriding_parameters": {
      "jar_params": ["param1", "param2"]
    },
    "start_time": 1457570074236,
    "setup_duration": 259754,
    "execution_duration": 3589020,
    "cleanup_duration": 31038,
    "trigger": "PERIODIC"
  },
  "notebook_output": {
    "result": "the maybe truncated string passed to dbutils.notebook.exit()"
  }
}

Request structure

Field Name	Type	Description
run_id	`INT64`	The canonical identifier for the run. This field is required.

Response structure

Field Name Type Description

notebook_output OR error

NotebookOutput OR STRING

If notebook_output, the output of a notebook task, if available. A notebook task that terminates (either successfully or with a failure) without calling dbutils.notebook.exit() is considered to have an empty output. This field will be set but its result value will be empty.

If error, an error message indicating why output is not available. The message is unstructured, and its exact format is subject to change.

metadata Run All details of the run except for its output.

Runs delete

Endpoint	HTTP Method
`2.0/jobs/runs/delete`	`POST`

Delete a non-active run. Returns an error if the run is active.

Example

curl --netrc --request POST \
https://<databricks-instance>/api/2.0/jobs/runs/delete \
--data '{ "run_id": <run-id> }'

Replace:

<databricks-instance> with the Databricks workspace instance name, for example dbc-a1b2345c-d6e7.cloud.databricks.com.
<run-id> with the ID of the run, for example 123.

This example uses a .netrc file.

Request structure

Field Name	Type	Description
run_id	`INT64`	The canonical identifier of the run for which to retrieve the metadata.

Data structures

In this section:

ClusterInstance

Identifiers for the cluster and Spark context used by a run. These two values together identify an execution context across all time.

Field Name Type Description

cluster_id

STRING

The canonical identifier for the cluster used by a run. This field is always available for runs on existing clusters. For runs on new clusters, it becomes available once the cluster is created. This value can be used to view logs by browsing to /#setting/sparkui/$cluster_id/driver-logs. The logs will continue to be available after the run completes.

The response won’t include this field if the identifier is not available yet.

spark_context_id

STRING

The canonical identifier for the Spark context used by a run. This field will be filled in once the run begins execution. This value can be used to view the Spark UI by browsing to /#setting/sparkui/$cluster_id/$spark_context_id. The Spark UI will continue to be available after the run has completed.

The response won’t include this field if the identifier is not available yet.

ClusterSpec

Important

When you run a job on a new jobs cluster, the job is treated as a Jobs Compute (automated) workload subject to Jobs Compute pricing.
When you run a job on an existing all-purpose cluster, it is treated as an All-Purpose Compute (interactive) workload subject to All-Purpose Compute pricing.

Field Name Type Description

existing_cluster_id OR new_cluster

STRING OR NewCluster

If existing_cluster_id, the ID of an existing cluster that will be used for all runs of this job. When running jobs on an existing cluster, you may need to manually restart the cluster if it stops responding. We suggest running jobs on new clusters for greater reliability.

If new_cluster, a description of a cluster that will be created for each run.

libraries An array of Library An optional list of libraries to be installed on the cluster that will execute the job. The default value is an empty list.

CronSchedule

Field Name	Type	Description
quartz_cron_expression	`STRING`	A Cron expression using Quartz syntax that describes the schedule for a job. See Cron Trigger for details. This field is required.
timezone_id	`STRING`	A Java timezone ID. The schedule for a job will be resolved with respect to this timezone. See Java TimeZone for details. This field is required.
pause_status	`STRING`	Indicate whether this schedule is paused or not. Either “PAUSED” or “UNPAUSED”.

Job

Field Name	Type	Description
job_id	`INT64`	The canonical identifier for this job.
creator_user_name	`STRING`	The creator user name. This field won’t be included in the response if the user has already been deleted.
run_as	`STRING`	The user name that the job will run as. `run_as` is based on the current job settings, and is set to the creator of the job if job access control is disabled, or the `is_owner` permission if job access control is enabled.
settings	JobSettings	Settings for this job and all of its runs. These settings can be updated using the `resetJob` method.
created_time	`INT64`	The time at which this job was created in epoch milliseconds (milliseconds since 1/1/1970 UTC).

JobEmailNotifications

Important

The on_start, on_success, and on_failure fields 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
on_start	An array of `STRING`	A list of email addresses to be notified when a run begins. If not specified on job creation, reset, or update, the list is empty, and notifications are not sent.
on_success	An array of `STRING`	A list of email addresses to be notified when a run successfully completes. A run is considered to have completed successfully if it ends with a `TERMINATED` `life_cycle_state` and a `SUCCESSFUL` result_state. If not specified on job creation, reset, or update, the list is empty, and notifications are not sent.
on_failure	An array of `STRING`	A list of email addresses to be notified when a run unsuccessfully completes. A run is considered to have completed unsuccessfully if it ends with an `INTERNAL_ERROR` `life_cycle_state` or a `SKIPPED`, `FAILED`, or `TIMED_OUT` result_state. If this is not specified on job creation, reset, or update the list will be empty, and notifications are not sent.
no_alert_for_skipped_runs	`BOOL`	If true, do not send email to recipients specified in `on_failure` if the run is skipped.

JobSettings

Important

When you run a job on a new jobs cluster, the job is treated as a Jobs Compute (automated) workload subject to Jobs Compute pricing.
When you run a job on an existing all-purpose cluster, it is treated as an All-Purpose Compute (interactive) workload subject to All-Purpose Compute pricing.

Settings for a job. These settings can be updated using the resetJob method.

Field Name	Type	Description
existing_cluster_id OR new_cluster	`STRING` OR NewCluster	If existing_cluster_id, the ID of an existing cluster that will be used for all runs of this job. When running jobs on an existing cluster, you may need to manually restart the cluster if it stops responding. We suggest running jobs on new clusters for greater reliability. If new_cluster, a description of a cluster that will be created for each run.
notebook_task OR spark_jar_task OR spark_python_task OR spark_submit_task	NotebookTask OR SparkJarTask OR SparkPythonTask OR SparkSubmitTask	If notebook_task, indicates that this job should run a notebook. This field may not be specified in conjunction with spark_jar_task. If spark_jar_task, indicates that this job should run a JAR. If spark_python_task, indicates that this job should run a Python file. If spark_submit_task, indicates that this job should be launched by the spark submit script.
name	`STRING`	An optional name for the job. The default value is `Untitled`.
libraries	An array of Library	An optional list of libraries to be installed on the cluster that will execute the job. The default value is an empty list.
email_notifications	JobEmailNotifications	An optional set of email addresses that will be notified when runs of this job begin or complete as well as when this job is deleted. The default behavior is to not send any emails.
timeout_seconds	`INT32`	An optional timeout applied to each run of this job. The default behavior is to have no timeout.
max_retries	`INT32`	An optional maximum number of times to retry an unsuccessful run. A run is considered to be unsuccessful if it completes with the `FAILED` result_state or `INTERNAL_ERROR` `life_cycle_state`. The value -1 means to retry indefinitely and the value 0 means to never retry. The default behavior is to never retry.
min_retry_interval_millis	`INT32`	An optional minimal interval in milliseconds between attempts. The default behavior is that unsuccessful runs are immediately retried.
retry_on_timeout	`BOOL`	An optional policy to specify whether to retry a job when it times out. The default behavior is to not retry on timeout.
schedule	CronSchedule	An optional periodic schedule for this job. The default behavior is that the job will only run when triggered by clicking “Run Now” in the Jobs UI or sending an API request to `runNow`.
max_concurrent_runs	`INT32`	An optional maximum allowed number of concurrent runs of the job. Set this value if you want to be able to execute multiple runs of the same job concurrently. This is useful for example if you trigger your job on a frequent schedule and want to allow consecutive runs to overlap with each other, or if you want to trigger multiple runs which differ by their input parameters. This setting affects only new runs. For example, suppose the job’s concurrency is 4 and there are 4 concurrent active runs. Then setting the concurrency to 3 won’t kill any of the active runs. However, from then on, new runs will be skipped unless there are fewer than 3 active runs. This value cannot exceed 1000. Setting this value to 0 causes all new runs to be skipped. The default behavior is to allow only 1 concurrent run.

JobTask

Field Name

Type

Description

notebook_task OR spark_jar_task OR spark_python_task OR spark_submit_task

NotebookTask OR SparkJarTask OR SparkPythonTask OR SparkSubmitTask

If notebook_task, indicates that this job should run a notebook. This field may not be specified in conjunction with spark_jar_task.

If spark_jar_task, indicates that this job should run a JAR.

If spark_python_task, indicates that this job should run a Python file.

If spark_submit_task, indicates that this job should be launched by the spark submit script.

NewCluster

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 current number of workers. For example, 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, the required parameters to automatically scale clusters up and down based on load.
spark_version	`STRING`	The Spark version of the cluster. A list of available Spark versions can be retrieved 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	ClusterTag	An object containing a set of tags for cluster resources. Databricks tags all cluster resources (such as AWS instances and EBS volumes) with these tags in addition to default_tags. Note: 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 scripts 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`.
spark_env_vars	SparkEnvPair	An object containing a set of optional, user-specified environment variable key-value pairs. Key-value pair 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 `$SPARK_DAEMON_JAVA_OPTS` as shown in the following example. This ensures that all default databricks managed environmental variables are included as well. Example Spark environment variables: `{"SPARK_WORKER_MEMORY": "28000m", "SPARK_LOCAL_DIRS": "/local_disk0"}` or `{"SPARK_DAEMON_JAVA_OPTS": "$SPARK_DAEMON_JAVA_OPTS -Dspark.shuffle.service.enabled=true"}`
enable_elastic_disk	`BOOL`	Autoscaling Local Storage: when enabled, this cluster dynamically acquires 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.
driver_instance_pool_id	`STRING`	The optional ID of the instance pool to use for the driver node. You must also specify `instance_pool_id`. Refer to Instance Pools API for details.
instance_pool_id	`STRING`	The optional ID of the instance pool to use for cluster nodes. If `driver_instance_pool_id` is present, `instance_pool_id` is used for worker nodes only. Otherwise, it is used for both the driver node and worker nodes. Refer to Instance Pools API for details.

NotebookOutput

Field Name	Type	Description
result	`STRING`	The value passed to dbutils.notebook.exit(). Databricks restricts this API to return the first 1 MB of the value. For a larger result, your job can store the results in a cloud storage service. This field will be absent if `dbutils.notebook.exit()` was never called.
truncated	`BOOLEAN`	Whether or not the result was truncated.

NotebookTask

All the output cells are subject to the size of 8MB. If the output of a cell has a larger size, the rest of the run will be cancelled and the run will be marked as failed. In that case, some of the content output from other cells may also be missing.

If you need help finding the cell that is beyond the limit, run the notebook against an all-purpose cluster and use this notebook autosave technique.

Field Name Type Description

notebook_path STRING The absolute path of the notebook to be run in the Databricks workspace. This path must begin with a slash. This field is required.

revision_timestamp LONG The timestamp of the revision of the notebook.

base_parameters

A map of ParamPair

Base parameters to be used for each run of this job. If the run is initiated by a call to run-now with parameters specified, the two parameters maps will be merged. If the same key is specified in base_parameters and in run-now, the value from run-now will be used.

Use Task parameter variables to set parameters containing information about job runs.

If the notebook takes a parameter that is not specified in the job’s base_parameters or the run-now override parameters, the default value from the notebook will be used.

Retrieve these parameters in a notebook using dbutils.widgets.get.

ParamPair

Name-based parameters for jobs running notebook tasks.

Important

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.

Type	Description
`STRING`	Parameter name. Pass to dbutils.widgets.get to retrieve the value.
`STRING`	Parameter value.

Run

All the information about a run except for its output. The output can be retrieved separately with the getRunOutput method.

Field Name	Type	Description
job_id	`INT64`	The canonical identifier of the job that contains this run.
run_id	`INT64`	The canonical identifier of the run. This ID is unique across all runs of all jobs.
creator_user_name	`STRING`	The creator user name. This field won’t be included in the response if the user has already been deleted.
number_in_job	`INT64`	The sequence number of this run among all runs of the job. This value starts at 1.
original_attempt_run_id	`INT64`	If this run is a retry of a prior run attempt, this field contains the run_id of the original attempt; otherwise, it is the same as the run_id.
state	RunState	The result and lifecycle states of the run.
schedule	CronSchedule	The cron schedule that triggered this run if it was triggered by the periodic scheduler.
task	JobTask	The task performed by the run, if any.
cluster_spec	ClusterSpec	A snapshot of the job’s cluster specification when this run was created.
cluster_instance	ClusterInstance	The cluster used for this run. If the run is specified to use a new cluster, this field will be set once the Jobs service has requested a cluster for the run.
overriding_parameters	RunParameters	The parameters used for this run.
start_time	`INT64`	The time at which this run was started in epoch milliseconds (milliseconds since 1/1/1970 UTC). This may not be the time when the job task starts executing, for example, if the job is scheduled to run on a new cluster, this is the time the cluster creation call is issued.
setup_duration	`INT64`	The time it took to set up the cluster in milliseconds. For runs that run on new clusters this is the cluster creation time, for runs that run on existing clusters this time should be very short.
execution_duration	`INT64`	The time in milliseconds it took to execute the commands in the JAR or notebook until they completed, failed, timed out, were cancelled, or encountered an unexpected error.
cleanup_duration	`INT64`	The time in milliseconds it took to terminate the cluster and clean up any associated artifacts. The total duration of the run is the sum of the setup_duration, the execution_duration, and the cleanup_duration.
end_time	`INT64`	The time at which this run ended in epoch milliseconds (milliseconds since 1/1/1970 UTC). This field will be set to 0 if the job is still running.
trigger	TriggerType	The type of trigger that fired this run.
run_name	`STRING`	An optional name for the run. The default value is `Untitled`. The maximum allowed length is 4096 bytes in UTF-8 encoding.
run_page_url	`STRING`	The URL to the detail page of the run.
run_type	`STRING`	The type of the run. `JOB_RUN` - Normal job run. A run created with Run now. `WORKFLOW_RUN` - Workflow run. A run created with dbutils.notebook.run. `SUBMIT_RUN` - Submit run. A run created with Run now.
attempt_number	`INT32`	The sequence number of this run attempt for a triggered job run. The initial attempt of a run has an attempt_number of 0. If the initial run attempt fails, and the job has a retry policy (`max_retries` > 0), subsequent runs are created with an `original_attempt_run_id` of the original attempt’s ID and an incrementing `attempt_number`. Runs are retried only until they succeed, and the maximum `attempt_number` is the same as the `max_retries` value for the job.

RunParameters

Parameters for this run. Only one of jar_params, python_params, or notebook_params should be specified in the run-now request, depending on the type of job task. Jobs with Spark JAR task or Python task take a list of position-based parameters, and jobs with notebook tasks take a key value map.

Field Name	Type	Description
jar_params	An array of `STRING`	A list of parameters for jobs with Spark JAR tasks, e.g. `"jar_params": ["john doe", "35"]`. The parameters will be used to invoke the main function of the main class specified in the Spark JAR task. If not specified upon `run-now`, it will default to an empty list. jar_params cannot be specified in conjunction with notebook_params. The JSON representation of this field (i.e. `{"jar_params":["john doe","35"]}`) cannot exceed 10,000 bytes. Use Task parameter variables to set parameters containing information about job runs.
notebook_params	A map of ParamPair	A map from keys to values for jobs with notebook task, e.g. `"notebook_params": {"name": "john doe", "age": "35"}`. The map is passed to the notebook and is accessible through the dbutils.widgets.get function. If not specified upon `run-now`, the triggered run uses the job’s base parameters. notebook_params cannot be specified in conjunction with jar_params. Use Task parameter variables to set parameters containing information about job runs. The JSON representation of this field (i.e. `{"notebook_params":{"name":"john doe","age":"35"}}`) cannot exceed 10,000 bytes.
python_params	An array of `STRING`	A list of parameters for jobs with Python tasks, e.g. `"python_params": ["john doe", "35"]`. The parameters are passed to Python file as command-line parameters. If specified upon `run-now`, it would overwrite the parameters specified in job setting. The JSON representation of this field (i.e. `{"python_params":["john doe","35"]}`) cannot exceed 10,000 bytes. Use Task parameter variables to set parameters containing information about job runs. Important These parameters 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.
spark_submit_params	An array of `STRING`	A list of parameters for jobs with spark submit task, e.g. `"spark_submit_params": ["--class", "org.apache.spark.examples.SparkPi"]`. The parameters are passed to spark-submit script as command-line parameters. If specified upon `run-now`, it would overwrite the parameters specified in job setting. The JSON representation of this field (i.e. `{"python_params":["john doe","35"]}`) cannot exceed 10,000 bytes. Use Task parameter variables to set parameters containing information about job runs. Important These parameters 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.

RunState

Field Name	Type	Description
life_cycle_state	RunLifeCycleState	A description of a run’s current location in the run lifecycle. This field is always available in the response.
result_state	RunResultState	The result state of a run. If it is not available, the response won’t include this field. See RunResultState for details about the availability of result_state.
state_message	`STRING`	A descriptive message for the current state. This field is unstructured, and its exact format is subject to change.

SparkJarTask

Field Name Type Description

jar_uri STRING Deprecated since 04/2016. Provide a jar through the libraries field instead. For an example, see Create.

main_class_name

STRING

The full name of the class containing the main method to be executed. This class must be contained in a JAR provided as a library.

The code should use SparkContext.getOrCreate to obtain a Spark context; otherwise, runs of the job will fail.

parameters

An array of STRING

Parameters passed to the main method.

Use Task parameter variables to set parameters containing information about job runs.

SparkPythonTask

Field Name Type Description

python_file STRING The URI of the Python file to be executed. DBFS and S3 paths are supported. This field is required.

parameters

An array of STRING

Command line parameters passed to the Python file.

Use Task parameter variables to set parameters containing information about job runs.

SparkSubmitTask

Important

You can invoke Spark submit tasks only on new clusters.
In the new_cluster specification, libraries and spark_conf are not supported. Instead, use --jars and --py-files to add Java and Python libraries and --conf to set the Spark configuration.
master, deploy-mode, and executor-cores are automatically configured by Databricks; you cannot specify them in parameters.
By default, the Spark submit job uses all available memory (excluding reserved memory for Databricks services). You can set --driver-memory, and --executor-memory to a smaller value to leave some room for off-heap usage.
The --jars, --py-files, --files arguments support DBFS and S3 paths.

For example, assuming the JAR is uploaded to DBFS, you can run SparkPi by setting the following parameters.

{
  "parameters": [
    "--class",
    "org.apache.spark.examples.SparkPi",
    "dbfs:/path/to/examples.jar",
    "10"
  ]
}

Field Name Type Description

parameters

An array of STRING

Command-line parameters passed to spark submit.

Use Task parameter variables to set parameters containing information about job runs.

ViewItem

The exported content is in HTML format. For example, if the view to export is dashboards, one HTML string is returned for every dashboard.

Field Name	Type	Description
content	`STRING`	Content of the view.
name	`STRING`	Name of the view item. In the case of code view, it would be the notebook’s name. In the case of dashboard view, it would be the dashboard’s name.
type	ViewType	Type of the view item.

RunLifeCycleState

The life cycle state of a run. Allowed state transitions are:

PENDING -> RUNNING -> TERMINATING -> TERMINATED
PENDING -> SKIPPED
PENDING -> INTERNAL_ERROR
RUNNING -> INTERNAL_ERROR
TERMINATING -> INTERNAL_ERROR

State	Description
`PENDING`	The run has been triggered. If there is not already an active run of the same job, the cluster and execution context are being prepared. If there is already an active run of the same job, the run will immediately transition into the `SKIPPED` state without preparing any resources.
`RUNNING`	The task of this run is being executed.
`TERMINATING`	The task of this run has completed, and the cluster and execution context are being cleaned up.
`TERMINATED`	The task of this run has completed, and the cluster and execution context have been cleaned up. This state is terminal.
`SKIPPED`	This run was aborted because a previous run of the same job was already active. This state is terminal.
`INTERNAL_ERROR`	An exceptional state that indicates a failure in the Jobs service, such as network failure over a long period. If a run on a new cluster ends in the `INTERNAL_ERROR` state, the Jobs service terminates the cluster as soon as possible. This state is terminal.

RunResultState

The result state of the run.

If life_cycle_state = TERMINATED: if the run had a task, the result is guaranteed to be available, and it indicates the result of the task.
If life_cycle_state = PENDING, RUNNING, or SKIPPED, the result state is not available.
If life_cycle_state = TERMINATING or lifecyclestate = INTERNAL_ERROR: the result state is available if the run had a task and managed to start it.

Once available, the result state never changes.

State	Description
SUCCESS	The task completed successfully.
FAILED	The task completed with an error.
TIMEDOUT	The run was stopped after reaching the timeout.
CANCELED	The run was canceled at user request.

TriggerType

These are the type of triggers that can fire a run.

Type	Description
PERIODIC	Schedules that periodically trigger runs, such as a cron scheduler.
ONE_TIME	One time triggers that fire a single run. This occurs you triggered a single run on demand through the UI or the API.
RETRY	Indicates a run that is triggered as a retry of a previously failed run. This occurs when you request to re-run the job in case of failures.

ViewType

Type	Description
NOTEBOOK	Notebook view item.
DASHBOARD	Dashboard view item.

ViewsToExport

View to export: either code, all dashboards, or all.

Type	Description
CODE	Code view of the notebook.
DASHBOARDS	All dashboard views of the notebook.
ALL	All views of the notebook.