| commit | ed4390138de7889cb4e9b15eb0e0908e4584db79 | [log] [tgz] |
|---|---|---|
| author | Ash Berlin-Taylor <ash_github@firemirror.com> | Sat Nov 18 14:07:38 2017 +0100 |
| committer | Bolke de Bruin <bolke@xs4all.nl> | Sat Nov 18 14:08:25 2017 +0100 |
| tree | cfe67fd27c62ec74cddf2cf700ef4490259d3683 | |
| parent | f87d8aca93cf2c6df21bea7b13b6703d91f09865 [diff] |
[AIRFLOW-1795] Correctly call S3Hook after migration to boto3 In the migration of S3Hook to boto3 the connection ID parameter changed to `aws_conn_id`. This fixes the uses of `s3_conn_id` in the code base and adds a note to UPDATING.md about the change. In correcting the tests for S3ToHiveTransfer I noticed that S3Hook.get_key was returning a dictionary, rather then the S3.Object as mentioned in it's doc string. The important thing that was missing was ability to get the key name from the return a call to get_wildcard_key. Closes #2795 from ashb/AIRFLOW-1795-s3hook_boto3_fixes (cherry picked from commit 98df0d6e3b2e2b439ab46d6c9ba736777202414a) Signed-off-by: Bolke de Bruin <bolke@xs4all.nl>
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: