| commit | 10ee62233056402bbe5bb84fbf42084b852a57c3 | [log] [tgz] |
|---|---|---|
| author | Jeremiah Lowin <jlowin@users.noreply.github.com> | Tue Apr 05 04:04:55 2016 -0400 |
| committer | bolkedebruin <bolkedebruin@users.noreply.github.com> | Tue Apr 05 10:04:55 2016 +0200 |
| tree | d0702b28d53ec971c46341204aa210212528b522 | |
| parent | 86f34633a1e229f5c957824bc981c72c2c9c095e [diff] |
Deprecate *args and **kwargs in BaseOperator BaseOperator silently accepts any arguments. This deprecates the behavior with a warning that says it will be forbidden in Airflow 2.0. This PR also turns on DeprecationWarnings by default, which in turn revealed that inspect.getargspec is deprecated. Here it is replaced by `inspect.signature` (Python 3) or `funcsigs.signature` (Python 2). Lastly, this brought to attention that example_http_operator was passing an illegal argument.
Airflow is a platform to programmatically author, schedule and monitor workflows.
When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
Please visit the Airflow Platform documentation for help with installing Airflow, getting a quick start, or a more complete tutorial.
For further information, please visit the Airflow Wiki.
Airflow is not a data streaming solution. Tasks do not move data from one to the other (though tasks can exchange metadata!). Airflow is not in the Spark Streaming or Storm space, it is more comparable to Oozie or Azkaban.
Workflows are expected to be mostly static or slowly changing. You can think of the structure of the tasks in your workflow as slightly more dynamic than a database structure would be. Airflow workflows are expected to look similar from a run to the next, this allows for clarity around unit of work and continuity.
DAGs: Overview of all DAGs in your environment. 
Tree View: Tree representation of a DAG that spans across time. 
Graph View: Visualization of a DAG's dependencies and their current status for a specific run. 
Task Duration: Total time spent on different tasks over time. 
Gantt View: Duration and overlap of a DAG. 
Code View: Quick way to view source code of a DAG. 
As the Airflow community grows, we'd like to keep track of who is using the platform. Please send a PR with your company name and @githubhandle if you may.
Committers:
Currently officially using Airflow: