`airflow.providers.databricks.operators.databricks`¶

This module contains Databricks operators.

Module Contents¶

Classes¶

`DatabricksJobRunLink`	Constructs a link to monitor a Databricks Job Run.
`DatabricksSubmitRunOperator`	Submits a Spark job run to Databricks using the
`DatabricksSubmitRunDeferrableOperator`	Deferrable version of `DatabricksSubmitRunOperator`
`DatabricksRunNowOperator`	Runs an existing Spark job run to Databricks using the
`DatabricksRunNowDeferrableOperator`	Deferrable version of `DatabricksRunNowOperator`

Attributes¶

`DEFER_METHOD_NAME`
`XCOM_RUN_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_RUN_PAGE_URL_KEY = run_page_url[source]¶

class airflow.providers.databricks.operators.databricks.DatabricksJobRunLink[source]¶

Bases: airflow.models.BaseOperatorLink

Constructs a link to monitor a Databricks Job Run.

name = See Databricks Job Run[source]¶

get_link(self, operator, dttm=None, *, ti_key=None)[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

operator – airflow operator
ti_key (Optional[airflow.models.taskinstance.TaskInstanceKey]) – TaskInstance ID to return link for

Returns

link to external system

Return type

str

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, 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, **kwargs)[source]¶

Bases: airflow.models.BaseOperator

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

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/runs/submit endpoint and pass it directly to our DatabricksSubmitRunOperator through the json parameter. For example

json = {
  'new_cluster': {
    'spark_version': '2.1.0-db3-scala2.11',
    'num_workers': 2
  },
  'notebook_task': {
    'notebook_path': '/Users/airflow@example.com/PrepareData',
  },
}
notebook_run = DatabricksSubmitRunOperator(task_id='notebook_run', json=json)

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

new_cluster = {
  'spark_version': '10.1.x-scala2.12',
  'num_workers': 2
}
notebook_task = {
  'notebook_path': '/Users/airflow@example.com/PrepareData',
}
notebook_run = DatabricksSubmitRunOperator(
    task_id='notebook_run',
    new_cluster=new_cluster,
    notebook_task=notebook_task)

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 DatabricksSubmitRunOperator supports are

spark_jar_task
notebook_task
spark_python_task
spark_jar_task
spark_submit_task
pipeline_task
new_cluster
existing_cluster_id
libraries
run_name
timeout_seconds

See also

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

Parameters

json (Optional[Any]) –
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 (Optional[Dict[str, str]]) –
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 should be specified. This field will be templated.

See also

https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobssparkjartask
notebook_task (Optional[Dict[str, str]]) –
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 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 (Optional[Dict[str, Union[str, List[str]]]]) –
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 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 (Optional[Dict[str, List[str]]]) –
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 should be specified. This field will be templated.

See also

https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobssparksubmittask
pipeline_task (Optional[Dict[str, str]]) –
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 should be specified. This field will be templated.

See also

https://docs.databricks.com/dev-tools/api/2.0/jobs.html#jobspipelinetask
new_cluster (Optional[Dict[str, object]]) –
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 (Optional[str]) – 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 (Optional[List[Dict[str, str]]]) –
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 (Optional[str]) – 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 (Optional[str]) – 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 (Optional[List[Dict[str, str]]]) – 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 (Optional[int]) – 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 (Optional[Dict[Any, Any]]) – 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 (Optional[Dict[str, str]]) –
Optional specification of a remote git repository from which supported task types are retrieved.

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]¶

operator_extra_links[source]¶

execute(self, context)[source]¶

This is the main method to 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(self)[source]¶: Override this method to cleanup 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.DatabricksSubmitRunDeferrableOperator(*, json=None, tasks=None, spark_jar_task=None, notebook_task=None, spark_python_task=None, spark_submit_task=None, pipeline_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, **kwargs)[source]¶

Bases: DatabricksSubmitRunOperator

Deferrable version of DatabricksSubmitRunOperator

execute(self, context)[source]¶

This is the main method to 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(self, context, event)[source]¶

class airflow.providers.databricks.operators.databricks.DatabricksRunNowOperator(*, job_id=None, job_name=None, json=None, notebook_params=None, python_params=None, jar_params=None, spark_submit_params=None, python_named_parameters=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, **kwargs)[source]¶

Bases: airflow.models.BaseOperator

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

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,
  "notebook_params": {
    "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

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,
    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
json
notebook_params
python_params
python_named_parameters
jar_params
spark_submit_params
idempotency_token

Parameters

job_id (Optional[str]) –
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 (Optional[str]) – 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.
json (Optional[Any]) –
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
notebook_params (Optional[Dict[str, str]]) –
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 (Optional[List[str]]) –
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_parameters (Optional[Dict[str, str]]) –
A list of parameters for jobs with python wheel tasks, e.g. “python_named_parameters”: {“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 (Optional[List[str]]) –
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 (Optional[List[str]]) –
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 (Optional[str]) – 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 (Optional[Dict[Any, Any]]) – 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.

template_fields :Sequence[str] = ['json', 'databricks_conn_id'][source]¶

template_ext :Sequence[str] = ['.json-tpl'][source]¶

ui_color = #1CB1C2[source]¶

ui_fgcolor = #fff[source]¶

operator_extra_links[source]¶

execute(self, context)[source]¶

This is the main method to 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(self)[source]¶: Override this method to cleanup 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(*, job_id=None, job_name=None, json=None, notebook_params=None, python_params=None, jar_params=None, spark_submit_params=None, python_named_parameters=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, **kwargs)[source]¶

Bases: DatabricksRunNowOperator

Deferrable version of DatabricksRunNowOperator

execute(self, context)[source]¶

This is the main method to 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(self, context, event)[source]¶

airflow.providers.databricks.operators.databricks¶

Module Contents¶

Classes¶

Attributes¶

`airflow.providers.databricks.operators.databricks`¶