daemonslayer/tests-airflow

# airflow-gcp-examples

Repository with examples and smoke tests for the GCP Airflow operators and hooks.

## Setup

This Google Cloud Examples does assume you will have a standard Airflow setup up and
running. This tutorial does work perfectly locally as in a production setup because
the only requirement is that you have a service key, that we'll explain next. But first
a quick rundown of what you need:

* Running Airflow (as of this writing you need Airflow master branch!!!)
* Create a service account (Cloud Console)
* Setup a Google Cloud Connection in Airflow
* Setup variables that the DAG's will need
* Copy the DAG's to your dags folder

### Airflow setup

* Checkout master of Airflow
* pip install google-api-python-client
* python setup.py install

Make sure you're running the LocalExecutor and have a decent database setup.

### Google Cloud Service Key

Next create a service account where you want your smoke tests and examples to run in. Go
to the console:

![console](img/console_service_account.png?raw=true)

And create a service key. Provision a JSON private key and give it Editor's rights

![console](img/create_service_account.png?raw=true)

### Airflow Connection

In Airflow you need to define the *gcp_smoke* named connection to your project:

![console](img/airflow_connection.png?raw=true)

Supply the path to the downloaded private key, supply the *project_id* and define the
minimum scope of *https://www.googleapis.com/auth/cloud-platform*

### Airflow Variables

You need to setup variables that are used in the examples. You can tweak them to suite
your environment.

![console](img/airflow_variables.png?raw=true)

variable | example value | note
--- | --- | ---
gc_project | my-project | Project where the examples will run in
gcq_dataset | airflow | BigQuery dataset for examples
gcq_tempset | airflow_temp | BiqQuery dataset with 1 day retentions
gcs_bucket | airflow-gcp-smoke | Storage bucket
gcs_root | data | Storage root path (required, no start and end with slash)