subcategory
Compute

databricks_job Resource

The databricks_job resource allows you to manage Databricks Jobs to run non-interactive code in a databricks_cluster.

Example Usage

-> Note In Terraform configuration, it is recommended to define tasks in alphabetical order of their task_key arguments, so that you get consistent and readable diff. Whenever tasks are added or removed, or task_key is renamed, you'll observe a change in the majority of tasks. It's related to the fact that the current version of the provider treats task blocks as an ordered list. Alternatively, task block could have been an unordered set, though end-users would see the entire block replaced upon a change in single property of the task.

It is possible to create a Databricks job using task blocks. Single task is defined with the task block containing one of the *_task block, task_key, libraries, email_notifications, timeout_seconds, max_retries, min_retry_interval_millis, retry_on_timeout attributes and depends_on blocks to define cross-task dependencies.

resource "databricks_job" "this" {
  name = "Job with multiple tasks"

  job_cluster {
    job_cluster_key = "j"
    new_cluster {
      num_workers   = 2
      spark_version = data.databricks_spark_version.latest.id
      node_type_id  = data.databricks_node_type.smallest.id
    }
  }

  task {
    task_key = "a"

    new_cluster {
      num_workers   = 1
      spark_version = data.databricks_spark_version.latest.id
      node_type_id  = data.databricks_node_type.smallest.id
    }

    notebook_task {
      notebook_path = databricks_notebook.this.path
    }
  }

  task {
    task_key = "b"
    //this task will only run after task a
    depends_on {
      task_key = "a"
    }

    existing_cluster_id = databricks_cluster.shared.id

    spark_jar_task {
      main_class_name = "com.acme.data.Main"
    }
  }

  task {
    task_key = "c"

    job_cluster_key = "j"

    notebook_task {
      notebook_path = databricks_notebook.this.path
    }
  }
  //this task starts a Delta Live Tables pipline update
  task {
    task_key = "d"

    pipeline_task {
      pipeline_id = databricks_pipeline.this.id
    }
  }
}

Argument Reference

The resource supports the following arguments:

name - (Optional) An optional name for the job. The default value is Untitled.
job_cluster - (Optional) A list of job databricks_cluster specifications that can be shared and reused by tasks of this job. Libraries cannot be declared in a shared job cluster. You must declare dependent libraries in task settings. Multi-task syntax
always_running - (Optional) (Bool) Whenever the job is always running, like a Spark Streaming application, on every update restart the current active run or start it again, if nothing it is not running. False by default. Any job runs are started with parameters specified in spark_jar_task or spark_submit_task or spark_python_task or notebook_task blocks.
library - (Optional) (Set) An optional list of libraries to be installed on the cluster that will execute the job. Please consult libraries section for databricks_cluster resource.
retry_on_timeout - (Optional) (Bool) An optional policy to specify whether to retry a job when it times out. The default behavior is to not retry on timeout.
max_retries - (Optional) (Integer) An optional maximum number of times to retry an unsuccessful run. A run is considered to be unsuccessful if it completes with a FAILED or INTERNAL_ERROR lifecycle state. The value -1 means to retry indefinitely and the value 0 means to never retry. The default behavior is to never retry. A run can have the following lifecycle state: PENDING, RUNNING, TERMINATING, TERMINATED, SKIPPED or INTERNAL_ERROR
timeout_seconds - (Optional) (Integer) An optional timeout applied to each run of this job. The default behavior is to have no timeout.
min_retry_interval_millis - (Optional) (Integer) 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.
max_concurrent_runs - (Optional) (Integer) An optional maximum allowed number of concurrent runs of the job. Defaults to 1.
email_notifications - (Optional) (List) An optional set of email addresses notified when runs of this job begins, completes and fails. The default behavior is to not send any emails. This field is a block and is documented below.
webhook_notifications - (Optional) (List) An optional set of system destinations (for example, webhook destinations or Slack) to be notified when runs of this job begins, completes and fails. The default behavior is to not send any notifications. This field is a block and is documented below.
schedule - (Optional) (List) 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. This field is a block and is documented below.

tags Configuration Map

tags - (Optional) (Map) An optional map of the tags associated with the job. Specified tags will be used as cluster tags for job clusters.

Example

  tags = {
    environment = "dev"
    owner       = "dream-team"
  }

job_cluster Configuration Block

Shared job cluster specification. Allows multiple tasks in the same job run to reuse the cluster.

job_cluster_key - (Required) Identifier that can be referenced in task block, so that cluster is shared between tasks
new_cluster - Same set of parameters as for databricks_cluster resource.

schedule Configuration Block

quartz_cron_expression - (Required) A Cron expression using Quartz syntax that describes the schedule for a job. This field is required.
timezone_id - (Required) 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 - (Optional) Indicate whether this schedule is paused or not. Either PAUSED or UNPAUSED. When the pause_status field is omitted and a schedule is provided, the server will default to using UNPAUSED as a value for pause_status.

continuous Configuration Block

pause_status - (Optional) Indicate whether this continuous job is paused or not. Either PAUSED or UNPAUSED. When the pause_status field is omitted in the block, the server will default to using UNPAUSED as a value for pause_status.

trigger Configuration Block

pause_status - (Optional) Indicate whether this trigger is paused or not. Either PAUSED or UNPAUSED. When the pause_status field is omitted in the block, the server will default to using UNPAUSED as a value for pause_status.
file_arrival - (Required) configuration block to define a trigger for File Arrival events consisting of following attributes:
- url - (Required) string with URL under the Unity Catalog external location that will be monitored for new files. Please note that have a trailing slash character (/).
- min_time_between_trigger_seconds - (Optional) If set, the trigger starts a run only after the specified amount of time passed since the last time the trigger fired. The minimum allowed value is 60 seconds.
- wait_after_last_change_seconds - (Optional) If set, the trigger starts a run only after no file activity has occurred for the specified amount of time. This makes it possible to wait for a batch of incoming files to arrive before triggering a run. The minimum allowed value is 60 seconds.

git_source Configuration Block

This block is used to specify Git repository information & branch/tag/commit that will be used to pull source code from to execute a job. Supported options are:

url - (Required) URL of the Git repository to use.
provider - (Optional, if it's possible to detect Git provider by host name) case insensitive name of the Git provider. Following values are supported right now (could be a subject for change, consult Repos API documentation): gitHub, gitHubEnterprise, bitbucketCloud, bitbucketServer, azureDevOpsServices, gitLab, gitLabEnterpriseEdition.
branch - name of the Git branch to use. Conflicts with tag and commit.
tag - name of the Git branch to use. Conflicts with branch and commit.
commit - hash of Git commit to use. Conflicts with branch and tag.

email_notifications Configuration Block

on_start - (Optional) (List) list of emails to notify when the run starts.
on_success - (Optional) (List) list of emails to notify when the run completes successfully.
on_failure - (Optional) (List) list of emails to notify when the run fails.
no_alert_for_skipped_runs - (Optional) (Bool) don't send alert for skipped runs.

webhook_notifications Configuration Block

Each entry in webhook_notification block takes a list webhook blocks. The field is documented below.

on_start - (Optional) (List) list of notification IDs to call when the run starts. A maximum of 3 destinations can be specified.
on_success - (Optional) (List) list of notification IDs to call when the run completes successfully. A maximum of 3 destinations can be specified.
on_failure - (Optional) (List) list of notification IDs to call when the run fails. A maximum of 3 destinations can be specified.

Note that the id is not to be confused with the name of the alert destination. The id can be retrieved through the API or the URL of Databricks UI https://<workspace host>/sql/destinations/<notification id>?o=<workspace id>

Example

webhook_notifications {
  on_failure {
    id = "fb99f3dc-a0a0-11ed-a8fc-0242ac120002"
  }
}

webhook Configuration Block

id - ID of the system notification that is notified when an event defined in webhook_notifications is triggered.

-> Note The following configuration blocks can be standalone or nested inside a task block

spark_jar_task Configuration Block

parameters - (Optional) (List) Parameters passed to the main method.
main_class_name - (Optional) 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.

spark_submit_task Configuration Block

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. 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. Please use spark_jar_task, spark_python_task or notebook_task wherever possible.

parameters - (Optional) (List) Command-line parameters passed to spark submit.

spark_python_task Configuration Block

python_file - (Required) The URI of the Python file to be executed. databricks_dbfs_file, cloud file URIs (e.g. s3:/, abfss:/, gs:/), workspace paths and remote repository are supported. For Python files stored in the Databricks workspace, the path must be absolute and begin with /Repos. For files stored in a remote repository, the path must be relative. This field is required.
source - (Optional) Location type of the Python file, can only be GIT. When set to GIT, the Python file will be retrieved from a Git repository defined in git_source.
parameters - (Optional) (List) Command line parameters passed to the Python file.

notebook_task Configuration Block

notebook_path - (Required) The path of the databricks_notebook to be run in the Databricks workspace or remote repository. For notebooks stored in the Databricks workspace, the path must be absolute and begin with a slash. For notebooks stored in a remote repository, the path must be relative. This field is required.
source - (Optional) Location type of the notebook, can only be WORKSPACE or GIT. When set to WORKSPACE, the notebook will be retrieved from the local Databricks workspace. When set to GIT, the notebook will be retrieved from a Git repository defined in git_source. If the value is empty, the task will use GIT if git_source is defined and WORKSPACE otherwise.
base_parameters - (Optional) (Map) 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. 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.

pipeline_task Configuration Block

pipeline_id - (Required) The pipeline's unique ID.

-> Note The following configuration blocks are only supported inside a task block

python_wheel_task Configuration Block

entry_point - (Optional) Python function as entry point for the task
package_name - (Optional) Name of Python package
parameters - (Optional) Parameters for the task
named_parameters - (Optional) Named parameters for the task

dbt_task Configuration Block

commands - (Required) (Array) Series of dbt commands to execute in sequence. Every command must start with "dbt".
project_directory - (Optional) The relative path to the directory in the repository specified in git_source where dbt should look in for the dbt_project.yml file. If not specified, defaults to the repository's root directory. Equivalent to passing --project-dir to a dbt command.
profiles_directory - (Optional) The relative path to the directory in the repository specified by git_source where dbt should look in for the profiles.yml file. If not specified, defaults to the repository's root directory. Equivalent to passing --profile-dir to a dbt command.
catalog - (Optional) The name of the catalog to use inside Unity Catalog.
schema - (Optional) The name of the schema dbt should run in. Defaults to default.
warehouse_id - (Optional) The ID of the SQL warehouse that dbt should execute against.

You also need to include a git_source block to configure the repository that contains the dbt project.

sql_task Configuration Block

One of the query, dashboard or alert needs to be provided.

warehouse_id - (Required) ID of the (the databricks_sql_endpoint) that will be used to execute the task. Only Serverless & Pro warehouses are supported right now.
parameters - (Optional) (Map) parameters to be used for each run of this task. The SQL alert task does not support custom parameters.
query - (Optional) block consisting of single string field: query_id - identifier of the Databricks SQL Query (databricks_sql_query).
dashboard - (Optional) block consisting of single string field: dashboard_id - identifier of the Databricks SQL Dashboard databricks_sql_dashboard.
alert - (Optional) block consisting of single string field: alert_id - identifier of the Databricks SQL Alert.
file - (Optional) block consisting of single string field: path - a relative path to the file (inside the Git repository) with SQL commands to execute. Requires git_source configuration block.

Example

resource "databricks_job" "sql_aggregation_job" {
  name = "Example SQL Job"
  task {
    task_key = "run_agg_query"
    sql_task {
      warehouse_id = databricks_sql_endpoint.sql_job_warehouse.id
      query {
        query_id = databricks_sql_query.agg_query.id
      }
    }
  }
}

Exported attributes

In addition to all arguments above, the following attributes are exported:

id - ID of the job
url - URL of the job on the given workspace

Access Control

By default, all users can create and modify jobs unless an administrator enables jobs access control. With jobs access control, individual permissions determine a user’s abilities.

databricks_permissions can control which groups or individual users can Can View, Can Manage Run, and Can Manage.
databricks_cluster_policy can control which kinds of clusters users can create for jobs.

Single-task syntax (legacy)

This syntax uses Jobs API 2.0 to create a job with a single task. Only a subset of arguments above is supported (name, libraries, email_notifications, webhook_notifications, timeout_seconds, max_retries, min_retry_interval_millis, retry_on_timeout, schedule, max_concurrent_runs), and only a single block of notebook_task, spark_jar_task, spark_python_task, spark_submit_task and pipeline_task can be specified.

The job cluster is specified using either of the below argument:

new_cluster - (Optional) Same set of parameters as for databricks_cluster resource.
existing_cluster_id - (Optional) 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 strongly suggest to use new_cluster for greater reliability.

data "databricks_current_user" "me" {}
data "databricks_spark_version" "latest" {}
data "databricks_node_type" "smallest" {
  local_disk = true
}

resource "databricks_notebook" "this" {
  path     = "${data.databricks_current_user.me.home}/Terraform"
  language = "PYTHON"
  content_base64 = base64encode(<<-EOT
    # created from ${abspath(path.module)}
    display(spark.range(10))
    EOT
  )
}

resource "databricks_job" "this" {
  name = "Terraform Demo (${data.databricks_current_user.me.alphanumeric})"

  new_cluster {
    num_workers   = 1
    spark_version = data.databricks_spark_version.latest.id
    node_type_id  = data.databricks_node_type.smallest.id
  }

  notebook_task {
    notebook_path = databricks_notebook.this.path
  }
}

output "notebook_url" {
  value = databricks_notebook.this.url
}

output "job_url" {
  value = databricks_job.this.url
}

Timeouts

The timeouts block allows you to specify create and update timeouts if you have an always_running job. Please launch TF_LOG=DEBUG terraform apply whenever you observe timeout issues.

timeouts {
  create = "20m"
  update = "20m"
}

Import

The resource job can be imported using the id of the job

terraform import databricks_job.this <job-id>

Related Resources

The following resources are often used in the same context:

End to end workspace management guide.
databricks_cluster to create Databricks Clusters.
databricks_cluster_policy to create a databricks_cluster policy, which limits the ability to create clusters based on a set of rules.
databricks_current_user data to retrieve information about databricks_user or databricks_service_principal, that is calling Databricks REST API.
databricks_dbfs_file data to get file content from Databricks File System (DBFS).
databricks_dbfs_file_paths data to get list of file names from get file content from Databricks File System (DBFS).
databricks_dbfs_file to manage relatively small files on Databricks File System (DBFS).
databricks_global_init_script to manage global init scripts, which are run on all databricks_cluster and databricks_job.
databricks_instance_pool to manage instance pools to reduce cluster start and auto-scaling times by maintaining a set of idle, ready-to-use instances.
databricks_instance_profile to manage AWS EC2 instance profiles that users can launch databricks_cluster and access data, like databricks_mount.
[databricks_jobs] data to get all jobs and their names from a workspace.
databricks_library to install a library on databricks_cluster.
databricks_node_type data to get the smallest node type for databricks_cluster that fits search criteria, like amount of RAM or number of cores.
databricks_notebook to manage Databricks Notebooks.
databricks_pipeline to deploy Delta Live Tables.
databricks_repo to manage Databricks Repos.
databricks_spark_version data to get Databricks Runtime (DBR) version that could be used for spark_version parameter in databricks_cluster and other resources.
databricks_workspace_conf to manage workspace configuration for expert usage.

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job.md

job.md

databricks_job Resource

Example Usage

Argument Reference

tags Configuration Map

job_cluster Configuration Block

schedule Configuration Block

continuous Configuration Block

trigger Configuration Block

git_source Configuration Block

email_notifications Configuration Block

webhook_notifications Configuration Block

webhook Configuration Block

spark_jar_task Configuration Block

spark_submit_task Configuration Block

spark_python_task Configuration Block

notebook_task Configuration Block

pipeline_task Configuration Block

python_wheel_task Configuration Block

dbt_task Configuration Block

sql_task Configuration Block

Exported attributes

Access Control

Single-task syntax (legacy)

Timeouts

Import

Related Resources

Files

job.md

Latest commit

History

job.md

File metadata and controls

databricks_job Resource

Example Usage

Argument Reference

tags Configuration Map

job_cluster Configuration Block

schedule Configuration Block

continuous Configuration Block

trigger Configuration Block

git_source Configuration Block

email_notifications Configuration Block

webhook_notifications Configuration Block

webhook Configuration Block

spark_jar_task Configuration Block

spark_submit_task Configuration Block

spark_python_task Configuration Block

notebook_task Configuration Block

pipeline_task Configuration Block

python_wheel_task Configuration Block

dbt_task Configuration Block

sql_task Configuration Block

Exported attributes

Access Control

Single-task syntax (legacy)

Timeouts

Import

Related Resources