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.

param job_id

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

param job_name

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.

param json

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

param notebook_params

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

param python_params

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

param python_named_params

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

param jar_params

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

param spark_submit_params

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

param idempotency_token

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.

param databricks_conn_id

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)

param polling_period_seconds

Controls the rate which we poll for the result of this run. By default, the operator will poll every 30 seconds.

param databricks_retry_limit

Amount of times retry if the Databricks backend is unreachable. Its value must be greater than or equal to 1.

param databricks_retry_delay

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

param databricks_retry_args

An optional dictionary with arguments passed to tenacity.Retrying class.

param do_xcom_push

Whether we should push run_id and run_page_url to xcom.

param wait_for_termination

if we should wait for termination of the job run. True by default.