airflow.providers.databricks.operators.databricks

This module contains Databricks operators.

Module Contents

Classes

DatabricksJobRunLink

Constructs a link to monitor a Databricks Job Run.

DatabricksCreateJobsOperator

Creates (or resets) a Databricks job using the API endpoint.

DatabricksSubmitRunOperator

Submits a Spark job run to Databricks using the api/2.1/jobs/runs/submit API endpoint.

DatabricksSubmitRunDeferrableOperator

Deferrable version of DatabricksSubmitRunOperator.

DatabricksRunNowOperator

Runs an existing Spark job run to Databricks using the api/2.1/jobs/run-now API endpoint.

DatabricksRunNowDeferrableOperator

Deferrable version of DatabricksRunNowOperator.

DatabricksTaskBaseOperator

Base class for operators that are run as Databricks job tasks or tasks within a Databricks workflow.

DatabricksNotebookOperator

Runs a notebook on Databricks using an Airflow operator.

DatabricksTaskOperator

Runs a task on Databricks using an Airflow operator.

Functions

is_repair_reason_match_exist(operator, run_state)

Check if the repair reason matches the run state message.

update_job_for_repair(operator, hook, job_id, run_state)

Update job settings(partial) to repair the run with all failed tasks.

Attributes

DEFER_METHOD_NAME

XCOM_RUN_ID_KEY

XCOM_JOB_ID_KEY

XCOM_RUN_PAGE_URL_KEY

airflow.providers.databricks.operators.databricks.DEFER_METHOD_NAME = 'execute_complete'[source]
airflow.providers.databricks.operators.databricks.XCOM_RUN_ID_KEY = 'run_id'[source]
airflow.providers.databricks.operators.databricks.XCOM_JOB_ID_KEY = 'job_id'[source]
airflow.providers.databricks.operators.databricks.XCOM_RUN_PAGE_URL_KEY = 'run_page_url'[source]
airflow.providers.databricks.operators.databricks.is_repair_reason_match_exist(operator, run_state)[source]

Check if the repair reason matches the run state message.

Parameters
Returns

True if repair reason matches the run state message, False otherwise

Return type

bool

airflow.providers.databricks.operators.databricks.update_job_for_repair(operator, hook, job_id, run_state)[source]

Update job settings(partial) to repair the run with all failed tasks.

Parameters

Bases: airflow.models.BaseOperatorLink

Constructs a link to monitor a Databricks Job Run.

name = 'See Databricks Job Run'[source]

Link to external system.

Note: The old signature of this function was (self, operator, dttm: datetime). That is still supported at runtime but is deprecated.

Parameters
Returns

link to external system

Return type

str

class airflow.providers.databricks.operators.databricks.DatabricksCreateJobsOperator(*, json=None, name=None, description=None, tags=None, tasks=None, job_clusters=None, email_notifications=None, webhook_notifications=None, notification_settings=None, timeout_seconds=None, schedule=None, max_concurrent_runs=None, git_source=None, access_control_list=None, databricks_conn_id='databricks_default', polling_period_seconds=30, databricks_retry_limit=3, databricks_retry_delay=1, databricks_retry_args=None, **kwargs)[source]

Bases: airflow.models.BaseOperator

Creates (or resets) a Databricks job using the API endpoint.

See also

https://docs.databricks.com/api/workspace/jobs/create https://docs.databricks.com/api/workspace/jobs/reset

Parameters

  • json (Any | None) –

    A JSON object containing API parameters which will be passed directly to the api/2.1/jobs/create endpoint. The other named parameters (i.e. name, tags, tasks, etc.) to this operator will be merged with this json dictionary if they are provided. If there are conflicts during the merge, the named parameters will take precedence and override the top level json keys. (templated)

    See also

    For more information about templating see Jinja Templating.

  • name (str | None) – An optional name for the job.

  • description (str | None) – An optional description for the job.

  • tags (dict[str, str] | None) – A map of tags associated with the job.

  • tasks (list[dict] | None) – A list of task specifications to be executed by this job. Array of objects (JobTaskSettings).

  • job_clusters (list[dict] | None) – A list of job cluster specifications that can be shared and reused by tasks of this job. Array of objects (JobCluster).

  • email_notifications (dict | None) – Object (JobEmailNotifications).

  • webhook_notifications (dict | None) – Object (WebhookNotifications).

  • notification_settings (dict | None) – Optional notification settings.

  • timeout_seconds (int | None) – An optional timeout applied to each run of this job.

  • schedule (dict | None) – Object (CronSchedule).

  • max_concurrent_runs (int | None) – An optional maximum allowed number of concurrent runs of the job.

  • git_source (dict | None) – An optional specification for a remote repository containing the notebooks used by this job’s notebook tasks. Object (GitSource).

  • access_control_list (list[dict] | None) –

    List of permissions to set on the job. Array of object (AccessControlRequestForUser) or object (AccessControlRequestForGroup) or object (AccessControlRequestForServicePrincipal).

    See also

    This will only be used on create. In order to reset ACL consider using the Databricks UI.

  • databricks_conn_id (str) – Reference to the Databricks connection. (templated)

  • polling_period_seconds (int) – Controls the rate which we poll for the result of this run. By default the operator will poll every 30 seconds.

  • databricks_retry_limit (int) – Amount of times retry if the Databricks backend is unreachable. Its value must be greater than or equal to 1.

  • databricks_retry_delay (int) – Number of seconds to wait between retries (it might be a floating point number).

  • databricks_retry_args (dict[Any, Any] | None) – An optional dictionary with arguments passed to tenacity.Retrying class.

template_fields: Sequence[str] = ('json', 'databricks_conn_id')[source]
ui_color = '#1CB1C2'[source]
ui_fgcolor = '#fff'[source]
execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.databricks.operators.databricks.DatabricksSubmitRunOperator(*, json=None, tasks=None, spark_jar_task=None, notebook_task=None, spark_python_task=None, spark_submit_task=None, pipeline_task=None, dbt_task=None, new_cluster=None, existing_cluster_id=None, libraries=None, run_name=None, timeout_seconds=None, databricks_conn_id='databricks_default', polling_period_seconds=30, databricks_retry_limit=3, databricks_retry_delay=1, databricks_retry_args=None, do_xcom_push=True, idempotency_token=None, access_control_list=None, wait_for_termination=True, git_source=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), **kwargs)[source]

Bases: airflow.models.BaseOperator

Submits a Spark job run to Databricks using the api/2.1/jobs/runs/submit API endpoint.

See: https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit

There are three ways to instantiate this operator.

See also

For more information on how to use this operator, take a look at the guide: DatabricksSubmitRunOperator

Parameters

  • tasks (list[object] | None) –

    Array of Objects(RunSubmitTaskSettings) <= 100 items.

    See also

    https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit

  • json (Any | None) –

    A JSON object containing API parameters which will be passed directly to the api/2.1/jobs/runs/submit endpoint. The other named parameters (i.e. spark_jar_task, notebook_task..) to this operator will be merged with this json dictionary if they are provided. If there are conflicts during the merge, the named parameters will take precedence and override the top level json keys. (templated)

    See also

    For more information about templating see Jinja Templating. https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit

  • spark_jar_task (dict[str, str] | None) –

    The main class and parameters for the JAR task. Note that the actual JAR is specified in the libraries. EITHER spark_jar_task OR notebook_task OR spark_python_task OR spark_submit_task OR pipeline_task OR dbt_task should be specified. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobssparkjartask

  • notebook_task (dict[str, str] | None) –

    The notebook path and parameters for the notebook task. EITHER spark_jar_task OR notebook_task OR spark_python_task OR spark_submit_task OR pipeline_task OR dbt_task should be specified. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobsnotebooktask

  • spark_python_task (dict[str, str | list[str]] | None) –

    The python file path and parameters to run the python file with. EITHER spark_jar_task OR notebook_task OR spark_python_task OR spark_submit_task OR pipeline_task OR dbt_task should be specified. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobssparkpythontask

  • spark_submit_task (dict[str, list[str]] | None) –

    Parameters needed to run a spark-submit command. EITHER spark_jar_task OR notebook_task OR spark_python_task OR spark_submit_task OR pipeline_task OR dbt_task should be specified. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobssparksubmittask

  • pipeline_task (dict[str, str] | None) –

    Parameters needed to execute a Delta Live Tables pipeline task. The provided dictionary must contain at least pipeline_id field! EITHER spark_jar_task OR notebook_task OR spark_python_task OR spark_submit_task OR pipeline_task OR dbt_task should be specified. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobspipelinetask

  • dbt_task (dict[str, str | list[str]] | None) – Parameters needed to execute a dbt task. The provided dictionary must contain at least the commands field and the git_source parameter also needs to be set. EITHER spark_jar_task OR notebook_task OR spark_python_task OR spark_submit_task OR pipeline_task OR dbt_task should be specified. This field will be templated.

  • new_cluster (dict[str, object] | None) –

    Specs for a new cluster on which this task will be run. EITHER new_cluster OR existing_cluster_id should be specified (except when pipeline_task is used). This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobsclusterspecnewcluster

  • existing_cluster_id (str | None) – ID for existing cluster on which to run this task. EITHER new_cluster OR existing_cluster_id should be specified (except when pipeline_task is used). This field will be templated.

  • libraries (list[dict[str, Any]] | None) –

    Libraries which this run will use. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/2.0/jobs.html#managedlibrarieslibrary

  • run_name (str | None) – The run name used for this task. By default this will be set to the Airflow task_id. This task_id is a required parameter of the superclass BaseOperator. This field will be templated.

  • idempotency_token (str | None) – an optional token that can be used to guarantee the idempotency of job run requests. If a run with the provided token already exists, the request does not create a new run but returns the ID of the existing run instead. This token must have at most 64 characters.

  • access_control_list (list[dict[str, str]] | None) – optional list of dictionaries representing Access Control List (ACL) for a given job run. Each dictionary consists of following field - specific subject (user_name for users, or group_name for groups), and permission_level for that subject. See Jobs API documentation for more details.

  • wait_for_termination (bool) – if we should wait for termination of the job run. True by default.

  • timeout_seconds (int | None) – The timeout for this run. By default a value of 0 is used which means to have no timeout. This field will be templated.

  • databricks_conn_id (str) – Reference to the Databricks connection. By default and in the common case this will be databricks_default. To use token based authentication, provide the key token in the extra field for the connection and create the key host and leave the host field empty. (templated)

  • polling_period_seconds (int) – Controls the rate which we poll for the result of this run. By default the operator will poll every 30 seconds.

  • databricks_retry_limit (int) – Amount of times retry if the Databricks backend is unreachable. Its value must be greater than or equal to 1.

  • databricks_retry_delay (int) – Number of seconds to wait between retries (it might be a floating point number).

  • databricks_retry_args (dict[Any, Any] | None) – An optional dictionary with arguments passed to tenacity.Retrying class.

  • do_xcom_push (bool) – Whether we should push run_id and run_page_url to xcom.

  • git_source (dict[str, str] | None) – Optional specification of a remote git repository from which supported task types are retrieved.

  • deferrable (bool) –

    Run operator in the deferrable mode.

    See also

    https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit

template_fields: Sequence[str] = ('json', 'databricks_conn_id')[source]
template_ext: Sequence[str] = ('.json-tpl',)[source]
ui_color = '#1CB1C2'[source]
ui_fgcolor = '#fff'[source]
execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

on_kill()[source]

Override this method to clean up subprocesses when a task instance gets killed.

Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up, or it will leave ghost processes behind.

execute_complete(context, event)[source]
class airflow.providers.databricks.operators.databricks.DatabricksSubmitRunDeferrableOperator(*args, **kwargs)[source]

Bases: DatabricksSubmitRunOperator

Deferrable version of DatabricksSubmitRunOperator.

execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.databricks.operators.databricks.DatabricksRunNowOperator(*, job_id=None, job_name=None, job_parameters=None, json=None, dbt_commands=None, notebook_params=None, python_params=None, jar_params=None, spark_submit_params=None, python_named_params=None, idempotency_token=None, databricks_conn_id='databricks_default', polling_period_seconds=30, databricks_retry_limit=3, databricks_retry_delay=1, databricks_retry_args=None, do_xcom_push=True, wait_for_termination=True, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), repair_run=False, databricks_repair_reason_new_settings=None, cancel_previous_runs=False, **kwargs)[source]

Bases: airflow.models.BaseOperator

Runs an existing Spark job run to Databricks using the api/2.1/jobs/run-now API endpoint.

See: https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow

There are two ways to instantiate this operator.

In the first way, you can take the JSON payload that you typically use to call the api/2.1/jobs/run-now endpoint and pass it directly to our DatabricksRunNowOperator through the json parameter. For example

json = {
    "job_id": 42,
    "job_parameters": {"dry-run": "true", "oldest-time-to-consider": "1457570074236"},
}

notebook_run = DatabricksRunNowOperator(task_id="notebook_run", json=json)

Another way to accomplish the same thing is to use the named parameters of the DatabricksRunNowOperator directly. Note that there is exactly one named parameter for each top level parameter in the run-now endpoint. In this method, your code would look like this:

job_id = 42

dbt_commands = ["dbt deps", "dbt seed", "dbt run"]

notebook_params = {"dry-run": "true", "oldest-time-to-consider": "1457570074236"}

python_params = ["douglas adams", "42"]

jar_params = ["douglas adams", "42"]

spark_submit_params = ["--class", "org.apache.spark.examples.SparkPi"]

notebook_run = DatabricksRunNowOperator(
    job_id=job_id,
    dbt_commands=dbt_commands,
    notebook_params=notebook_params,
    python_params=python_params,
    jar_params=jar_params,
    spark_submit_params=spark_submit_params,
)

In the case where both the json parameter AND the named parameters are provided, they will be merged together. If there are conflicts during the merge, the named parameters will take precedence and override the top level json keys.

Currently the named parameters that DatabricksRunNowOperator supports are

  • job_id

  • job_name

  • job_parameters

  • json

  • dbt_commands

  • notebook_params

  • python_params

  • python_named_parameters

  • jar_params

  • spark_submit_params

  • idempotency_token

  • repair_run

  • databricks_repair_reason_new_settings

  • cancel_previous_runs

Parameters

  • job_id (str | None) –

    the job_id of the existing Databricks job. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow

  • job_name (str | None) – the name of the existing Databricks job. It must exist only one job with the specified name. job_id and job_name are mutually exclusive. This field will be templated.

  • job_parameters (dict[str, str] | None) –

    A dict from keys to values that override or augment the job’s parameters for this run. Job parameters are passed to any of the job’s tasks that accept key-value parameters. Job parameters supersede notebook_params, python_params, python_named_parameters, jar_params, spark_submit_params, and they cannot be used in combination. This field will be templated.

    See also

    https://docs.databricks.com/en/workflows/jobs/settings.html#add-parameters-for-all-job-tasks

  • json (Any | None) –

    A JSON object containing API parameters which will be passed directly to the api/2.1/jobs/run-now endpoint. The other named parameters (i.e. notebook_params, spark_submit_params..) to this operator will be merged with this json dictionary if they are provided. If there are conflicts during the merge, the named parameters will take precedence and override the top level json keys. (templated)

    See also

    For more information about templating see Jinja Templating. https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow

  • dbt_commands (list[str] | None) –

    A list containing the dbt commands to run using the dbt command line interface. This field will be templated.

    See also

    https://docs.databricks.com/en/jobs/dbt.html

  • notebook_params (dict[str, str] | None) –

    A dict 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 will be accessible through the dbutils.widgets.get function. See Widgets for more information. If not specified upon run-now, the triggered run will use the job’s base parameters. notebook_params cannot be specified 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. This field will be templated.

    See also

    https://docs.databricks.com/user-guide/notebooks/widgets.html

  • python_params (list[str] | None) –

    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. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow

  • python_named_params (dict[str, str] | None) –

    A list of named parameters for jobs with python wheel tasks, e.g. “python_named_params”: {“name”: “john doe”, “age”: “35”}. If specified upon run-now, it would overwrite the parameters specified in job setting. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow

  • jar_params (list[str] | None) –

    A list of parameters for jobs with JAR tasks, e.g. “jar_params”: [“john doe”, “35”]. The parameters will be passed to JAR 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. {“jar_params”:[“john doe”,”35”]}) cannot exceed 10,000 bytes. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow

  • spark_submit_params (list[str] | None) –

    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. This field will be templated.

    See also

    https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunNow

  • idempotency_token (str | None) – an optional token that can be used to guarantee the idempotency of job run requests. If a run with the provided token already exists, the request does not create a new run but returns the ID of the existing run instead. This token must have at most 64 characters.

  • databricks_conn_id (str) – Reference to the Databricks connection. By default and in the common case this will be databricks_default. To use token based authentication, provide the key token in the extra field for the connection and create the key host and leave the host field empty. (templated)

  • polling_period_seconds (int) – Controls the rate which we poll for the result of this run. By default, the operator will poll every 30 seconds.

  • databricks_retry_limit (int) – Amount of times retry if the Databricks backend is unreachable. Its value must be greater than or equal to 1.

  • databricks_retry_delay (int) – Number of seconds to wait between retries (it might be a floating point number).

  • databricks_retry_args (dict[Any, Any] | None) – An optional dictionary with arguments passed to tenacity.Retrying class.

  • do_xcom_push (bool) – Whether we should push run_id and run_page_url to xcom.

  • wait_for_termination (bool) – if we should wait for termination of the job run. True by default.

  • deferrable (bool) – Run operator in the deferrable mode.

  • repair_run (bool) – Repair the databricks run in case of failure.

  • databricks_repair_reason_new_settings (dict[str, Any] | None) – A dict of reason and new_settings JSON object for which to repair the run. None by default. None means to repair at all cases with existing job settings otherwise check whether RunState state_message contains reason and update job settings as per new_settings using databricks partial job update endpoint (https://docs.databricks.com/api/workspace/jobs/update). If nothing is matched, then repair will not get triggered.

  • cancel_previous_runs (bool) – Cancel all existing running jobs before submitting new one.

template_fields: Sequence[str] = ('json', 'databricks_conn_id')[source]
template_ext: Sequence[str] = ('.json-tpl',)[source]
ui_color = '#1CB1C2'[source]
ui_fgcolor = '#fff'[source]
execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event=None)[source]
on_kill()[source]

Override this method to clean up subprocesses when a task instance gets killed.

Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up, or it will leave ghost processes behind.

class airflow.providers.databricks.operators.databricks.DatabricksRunNowDeferrableOperator(*args, **kwargs)[source]

Bases: DatabricksRunNowOperator

Deferrable version of DatabricksRunNowOperator.

class airflow.providers.databricks.operators.databricks.DatabricksTaskBaseOperator(caller='DatabricksTaskBaseOperator', databricks_conn_id='databricks_default', databricks_retry_args=None, databricks_retry_delay=1, databricks_retry_limit=3, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), existing_cluster_id='', job_cluster_key='', new_cluster=None, polling_period_seconds=5, wait_for_termination=True, workflow_run_metadata=None, **kwargs)[source]

Bases: airflow.models.BaseOperator, abc.ABC

Base class for operators that are run as Databricks job tasks or tasks within a Databricks workflow.

Parameters

  • caller (str) – The name of the caller operator to be used in the logs.

  • databricks_conn_id (str) – The name of the Airflow connection to use.

  • databricks_retry_args (dict[Any, Any] | None) – An optional dictionary with arguments passed to tenacity.Retrying class.

  • databricks_retry_delay (int) – Number of seconds to wait between retries.

  • databricks_retry_limit (int) – Amount of times to retry if the Databricks backend is unreachable.

  • deferrable (bool) – Whether to run the operator in the deferrable mode.

  • existing_cluster_id (str) – ID for existing cluster on which to run this task.

  • job_cluster_key (str) – The key for the job cluster.

  • new_cluster (dict[str, Any] | None) – Specs for a new cluster on which this task will be run.

  • notebook_packages – A list of the Python libraries to be installed on the cluster running the notebook.

  • notebook_params – A dict of key-value pairs to be passed as optional params to the notebook task.

  • polling_period_seconds (int) – Controls the rate which we poll for the result of this notebook job run.

  • wait_for_termination (bool) – if we should wait for termination of the job run. True by default.

  • workflow_run_metadata (dict[str, Any] | None) – Metadata for the workflow run. This is used when the operator is used within a workflow. It is expected to be a dictionary containing the run_id and conn_id for the workflow.

monitor_databricks_job()[source]

Monitor the Databricks job.

Wait for the job to terminate. If deferrable, defer the task.

execute(context)[source]

Execute the operator. Launch the job and monitor it if wait_for_termination is set to True.

execute_complete(context, event)[source]
class airflow.providers.databricks.operators.databricks.DatabricksNotebookOperator(notebook_path, source, databricks_conn_id='databricks_default', databricks_retry_args=None, databricks_retry_delay=1, databricks_retry_limit=3, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), existing_cluster_id='', job_cluster_key='', new_cluster=None, notebook_packages=None, notebook_params=None, polling_period_seconds=5, wait_for_termination=True, workflow_run_metadata=None, **kwargs)[source]

Bases: DatabricksTaskBaseOperator

Runs a notebook on Databricks using an Airflow operator.

The DatabricksNotebookOperator allows users to launch and monitor notebook job runs on Databricks as Airflow tasks. It can be used as a part of a DatabricksWorkflowTaskGroup to take advantage of job clusters, which allows users to run their tasks on cheaper clusters that can be shared between tasks.

See also

For more information on how to use this operator, take a look at the guide: DatabricksNotebookOperator

Parameters

  • notebook_path (str) – The path to the notebook in Databricks.

  • source (str) – Optional location type of the notebook. 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. For more information please visit https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsCreate

  • databricks_conn_id (str) – The name of the Airflow connection to use.

  • databricks_retry_args (dict[Any, Any] | None) – An optional dictionary with arguments passed to tenacity.Retrying class.

  • databricks_retry_delay (int) – Number of seconds to wait between retries.

  • databricks_retry_limit (int) – Amount of times to retry if the Databricks backend is unreachable.

  • deferrable (bool) – Whether to run the operator in the deferrable mode.

  • existing_cluster_id (str) – ID for existing cluster on which to run this task.

  • job_cluster_key (str) – The key for the job cluster.

  • new_cluster (dict[str, Any] | None) – Specs for a new cluster on which this task will be run.

  • notebook_packages (list[dict[str, Any]] | None) – A list of the Python libraries to be installed on the cluster running the notebook.

  • notebook_params (dict | None) – A dict of key-value pairs to be passed as optional params to the notebook task.

  • polling_period_seconds (int) – Controls the rate which we poll for the result of this notebook job run.

  • wait_for_termination (bool) – if we should wait for termination of the job run. True by default.

  • workflow_run_metadata (dict | None) – Metadata for the workflow run. This is used when the operator is used within a workflow. It is expected to be a dictionary containing the run_id and conn_id for the workflow.

template_fields = ('notebook_params', 'workflow_run_metadata')[source]
CALLER = 'DatabricksNotebookOperator'[source]
class airflow.providers.databricks.operators.databricks.DatabricksTaskOperator(task_config, databricks_conn_id='databricks_default', databricks_retry_args=None, databricks_retry_delay=1, databricks_retry_limit=3, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), existing_cluster_id='', job_cluster_key='', new_cluster=None, polling_period_seconds=5, wait_for_termination=True, workflow_run_metadata=None, **kwargs)[source]

Bases: DatabricksTaskBaseOperator

Runs a task on Databricks using an Airflow operator.

The DatabricksTaskOperator allows users to launch and monitor task job runs on Databricks as Airflow tasks. It can be used as a part of a DatabricksWorkflowTaskGroup to take advantage of job clusters, which allows users to run their tasks on cheaper clusters that can be shared between tasks.

See also

For more information on how to use this operator, take a look at the guide: DatabricksTaskOperator

Parameters

  • task_config (dict) – The configuration of the task to be run on Databricks.

  • databricks_conn_id (str) – The name of the Airflow connection to use.

  • databricks_retry_args (dict[Any, Any] | None) – An optional dictionary with arguments passed to tenacity.Retrying class.

  • databricks_retry_delay (int) – Number of seconds to wait between retries.

  • databricks_retry_limit (int) – Amount of times to retry if the Databricks backend is unreachable.

  • deferrable (bool) – Whether to run the operator in the deferrable mode.

  • existing_cluster_id (str) – ID for existing cluster on which to run this task.

  • job_cluster_key (str) – The key for the job cluster.

  • new_cluster (dict[str, Any] | None) – Specs for a new cluster on which this task will be run.

  • polling_period_seconds (int) – Controls the rate which we poll for the result of this notebook job run.

  • wait_for_termination (bool) – if we should wait for termination of the job run. True by default.

CALLER = 'DatabricksTaskOperator'[source]
template_fields = ('workflow_run_metadata',)[source]

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