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Airflow

Eawag Workflow Automation

License: MIT Python

This distributed, dockerized Apache Airflow instance runs automated workflows in support of projects from the Department Surface Waters at Eawag. Workflows are defined as DAGS (what is a DAG?) and can be found in the airflow/dags folder.

Example workflows are as follows:

  • Downloading COSMO data from MeteoSwiss airflow/dags/download_meteoswiss_cosmo.py
  • Downloading hydrodata from BAFU airflow/dags/download_bafu_hydrodata.py
  • Delft3D simulation of lakes airflow/dags/simulate_delft3dflow_operational.py simulate_delft3dflow_operational.py
  • Sencast processing of Sentinel 3 images airflow/dags/s3_sencast_operational_switzerland

A web portal for managing the workflows is available at http://eaw-alplakes2:8080 for users connected to the Eawag VPN.

Apache Airflow

Installation

1. Install docker

Follow the official install instructions here

Then run docker login to enable access to private docker images.

2. Clone repository

sudo apt-get -y update && sudo apt-get -y install git
git clone git@github.com:eawag-surface-waters-research/airflow.git
mkdir -p filesystem

3. Configure Environment

Define environmental variables

Copy the env.example file to .env and complete the required passwords.

cd airflow
cp env.example .env

Fernet key can be generated as follows:

from cryptography.fernet import Fernet
fernet_key = Fernet.generate_key()
print(fernet_key.decode())

Check content of .env contains the aforementioned variables.

cat .env

Add ssh keys

The keys folder will be mounted to the docker instance at /opt/airflow/keys.

Upload your keys to the server. There is often issues with permissions, suggested is chmod -R 777 keys, chmod 700 keys/id_rsa

DAGS requiring ssh keys:

  1. download_bafu_hydrodata.py requires keys/bafu/id_rsa

Auto update repo

If you want to auto pull any changes from the git repository. Make update.sh executable and add it to the crontab. The example below pulls the repo every 5 mins, starting on the hour (so multiple instances update at the same time).

chmod +x update.sh
crontab -e
0-59/5 * * * * /home/eawag.wroot.emp-eaw.ch/runnalja/airflow/update.sh

Additional docker images

Certain DAGS require additional docker images in order to run. They need to be pulled to the worker node before these DAGS can complete successfully.

  1. docker pull eawag/delft3d-flow:6.02.10.142612
  2. docker pull eawag/sencast:0.0.2

Sencast

Sencast requires an environment file. This needs to be manually added to the server once the first Sencast job has been run and the repository has been cloned.

4. Launch Services

Production Environment

Main

docker compose -f docker-compose.yml up -d --build

Worker

docker compose -f docker-compose-worker.yml up -d --build

The worker node defaults to looking for connections on the local docker network. If the worker node is on a different machine from the main node then the POSTGRES_ADDRESS and REDIS_ADDRESS variables need to be set in the .env file.

For the current Eawag setup this is:

POSTGRES_ADDRESS=eaw-alplakes2:5432
REDIS_ADDRESS=eaw-alplakes2:6379

The setup above is only valid for machines inside the Eawag network, in order to launch workers on machines outside the Eawag network, the ports 6379 and 5432 would need to be exposed on the main node and the variables adjusted to the new addresses.

Development Environment

docker compose -f docker-compose.yml --profile dev up --build

It's possible that there could be a port conflict when launching the development environment due to Postgres/ Redis running on the host machine. In this case it is possible to change the ports being exposed on the docker containers to avoid local conflicts by setting the following parameters in the .env file. Default values are 5432 for Postgres and 6379 for Redis.

POSTGRES_PORT=5431
REDIS_PORT=6378

Managing Instance

Terminate containers

docker compose -f docker-compose.yml down

List active containers

docker ps

Go inside the container

docker exec -it 'container-id' bash

Adding Workflows

New workflows can be added by including new python dags into the airflow/dags folder. They will then be picked up automatically by the system. Best practice for adding new DAGS is as follows:

  • Set up a local instance of airflow (see instructions above)
  • Create a new branch git checkout -b newbranchname
  • Add your new DAG and test it
  • Commit your changes and create a pull request to the master branch

Backfill jobs

In order to backfill jobs in the past the Airflow command line can be utilised.

  1. Run docker ps to get the "CONTAINER ID" of the airflow-airflow-scheduler
  2. Enter the scheduler docker exec -it 'container-id' bash
  3. Run nohup airflow dags backfill -s YYYY-MM-DD -e YYYY-MM-DD dag_id &

The nohup ... & syntax allows you close the terminal and keep the job running.