| commit | 453ac1bb34937ddd00c3bf0c1db73ac1ccc6a5d3 | [log] [tgz] |
|---|---|---|
| author | Courtney Wurtz <courtney.wurtz@gmail.com> | Fri Aug 25 12:08:37 2017 -0400 |
| committer | Mike Perry <mike@astronomer.io> | Wed Sep 06 10:20:23 2017 -0400 |
| tree | 40f7b17412e84b20fc75764be9a795f529f19e47 | |
| parent | bdae8970dbc6f17f6f80fae52123a82455f7d227 [diff] |
Fixed first session login persistance issue The first time a user logs into Airflow, it fails to persist the session. This causes the login form to silently reload, leading a user to question wtf just happened. The cause is the user_id is not populated from the auto-increment id after it is inserted into the database. I don’t have the time right now to investigate what is happening inside flask/alchemy to cause this, so I just did a quick fix of querying the database after committing the changes. Since this only happens the first time an email is used to login, the extra load is basically irrelivant. On a side note, I tried doing a “session.refresh(user)” after the “session.commit”, however this gives an error of “Instance '<User at 0x110f872b0>' is not persistent within this Session”, so it just redoes the query that would happen if a user logs in with a user that already exists in the airflow database. Resolves https://github.com/astronomerio/engineering/issues/163
NOTE: The transition from 1.8.0 (or before) to 1.8.1 (or after) requires uninstalling Airflow before installing the new version. The package name was changed from airflow to apache-airflow as of version 1.8.1.
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: