kacpermuda opened a new pull request, #45326:
URL: https://github.com/apache/airflow/pull/45326
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Similar to #44477 , this PR introduces a new feature to OpenLineage
integration. **It will NOT impact users that are not using OpenLineage or have
not explicitly enabled this feature (False by default).**
## TLDR;
When explicitly enabled by the user for supported operators, we will
automatically inject transport information into the Spark job properties. For
example, when submitting a Spark job using the DataprocSubmitJobOperator, we
will configure Spark/OpenLineage integration to use the same transport
configuration that Airflow integration uses.
## Why ?
Currently, this process requires manual configuration by the user, as
described
[here](https://openlineage.io/docs/integrations/spark/configuration/airflow/).
E.g.:
```
DataprocSubmitJobOperator(
task_id="my_task",
# ...
job={
# ...
"spark.openlineage.transport.type": "http",
"spark.openlineage.transport.url": openlineage_url,
"spark.openlineage.transport.compression": "gzip",
"spark.openlineage.transport.auth.apiKey": api_key,
"spark.openlineage.transport.auth.type": "apiKey",
}
)
```
Understanding how various Airflow operators configure Spark allows us to
automatically inject transport information.
## Controlling the Behavior
We provide users with a flexible control mechanism to manage this injection,
combining per-operator enablement with a global fallback configuration. This
design is inspired by the `deferrable` argument in Airflow.
```python
ol_inject_transport_info: bool = conf.getboolean(
"openlineage", "spark_inject_transport_info", fallback=False
)
```
Each supported operator will include an argument like
`ol_inject_transport_info`, which defaults to the global configuration value of
`openlineage.spark_inject_transport_info`. This approach allows users to:
1. Control behavior on a per-job basis by explicitly setting the argument.
2. Rely on a consistent default configuration for all jobs if the argument
is not set.
This design ensures both flexibility and ease of use, enabling users to
fine-tune their workflows while minimizing repetitive configuration. I am aware
that adding an OpenLineage-related argument to the operator will affect all
users, even those not using OpenLineage, but since it defaults to False and can
be ignored, I hope this will not pose any issues.
## How?
The implementation is divided into three parts for better organization and
clarity:
1. **Operator's Code (including the `execute` method):**
Contains minimal logic to avoid overwhelming users who are not actively
working with OpenLineage.
2. **Google's Provider OpenLineage Utils File:**
Handles the logic for accessing Spark properties specific to a given
operator or job.
3. **OpenLineage Provider's Utils:**
Responsible for creating / extracting all necessary information in a
format compatible with the OpenLineage Spark integration. We are also
performing modifications to the Spark properties here.
For some operators parts 1 and 2 may be in the operator's code. In general,
the specific operator / provider will know how to get the spark properties and
the OL will know what to inject and do the injection itself.
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