Python package to extend Airflow functionality with CWL v1.0 support
Project description
- Travis CI
- CWL conformance tests
cwl-airflow
Python package to extend Apache-Airflow 1.9.0 functionality with CWL v1.0 support.
Check it out
(assuming that you already have installed and properly configured python, pip, setuptools and docker that has access to pull images from the DockerHub; if something is missing refer to Installation instructions)
- Install cwl-airflow
$ pip install cwl-airflow --user --find-links https://michael-kotliar.github.io/cwl-airflow-wheels/
- Init configuration
$ cwl-airflow init
- Run demo
$ cwl-airflow demo --auto
- When all demo wokrflows are submitted program will provide you with the link for Airflow Webserver. It may take some time (usually less then half a minute) for Airflow Webserver to load and display all the data
Table of Contents
How It Works
Key concepts
- CWL descriptor file - YAML or JSON file to describe the workflow inputs, outputs and steps. File should be compatible with CWL v1.0 specification
- Job file - YAML or JSON file to set the values for the wokrflow inputs.
For cwl-airflow to function properly the Job file should include 3 mandatory and
one optional fields:
- workflow - mandatory field to specify the absolute path to the CWL descriptor file
- output_folder - mandatory field to specify the absolute path to the folder where all the output files should be moved after successful workflow execution
- tmp_folder - optional field to specify the absolute path to the folder for storing intermediate results. After workflow execution this folder will be deleted.
- uid - mandatory field that is used for generating DAG's unique identifier.
- DAG - directed acyclic graph that describes the workflow structure.
- Jobs folder - folder to keep all Job files scheduled for execution or the ones that have already been processed. The folder's location is set as jobs parameter of cwl section in Airflow configuration file.
To build a workflow cwl-airflow uses three basic classes:
- CWLStepOperator - executes a separate workflow step
- JobDispatcher - serializes the Job file and provides the worflow with input data
- JobCleanup - returns the calculated results to the output folder
A set of CWLStepOperators, JobDispatcher and JobCleanup are combined in CWLDAG that defines a graph to reflect the workflow steps, their relationships and dependencies. Automatically generated cwl_dag.py script is placed in the DAGs folder. When Airflow Scheduler loads DAGs from the DAGs folder, the cwl_dag.py script parses all the Job files from the Jobs folder and creates DAGs for each of them.
Installation
Requirements
Ubuntu 16.04.4 (Xenial Xerus)
- python 2.7 or 3.5 (tested on the default Python 2.7.12 and 3.5.2)
- docker
Log out and log back in so that your group membership is re-evaluated.sudo apt-get update sudo apt-get install apt-transport-https ca-certificates curl software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" sudo apt-get update sudo apt-get install docker-ce sudo groupadd docker sudo usermod -aG docker $USER
- python-dev (or python3-dev if using Python 3.5)
sudo apt-get install python-dev # python3-dev
python-dev is required in case your system needs to compile some python packages during the installation. We have built python wheels for most of such packages and provided them through --find-links argument while installing cwl-airflow. Nevertheless in case of installation problems you might still be required to install this dependency.
macOS 10.13.5 (High Sierra)
- python 2.7 or 3.6 (tested on the default Python 2.7.10 and the latest Python 3.6.5 availble from Homebrew)
- docker (follow the link to install Docker on Mac)
- Apple Command Line Tools
xcode-select --install
Click Install on the pop up when it appears, follow the instructions
Both Ubuntu and macOS
- pip
wget https://bootstrap.pypa.io/get-pip.py python get-pip.py --user
When using the on MacOS, you might need to update your PATH variable following the instruction printed on console - setuptools (should be updated to the latest)
pip install -U setuptools --user
Install cwl-airflow
$ pip install cwl-airflow --user --find-links https://michael-kotliar.github.io/cwl-airflow-wheels/
To avoid installing Xcode for macOS users and python-dev for Ubuntu users, some of the Python packages have been already compiled and put into the separate repository that is set with --find-links argument.
Using cwl-airflow
Configuration
Before using cwl-airflow it should be initialized with the default configuration by running the command
$ cwl-airflow init
Optional parameters:
Flag | Description | Default |
---|---|---|
-l | number of processed jobs kept in history, int | 10 x Running, 10 x Success, 10 x Failed |
-j | path to the folder where all the new jobs will be added, str | ~/airflow/jobs |
-t | timeout for importing all the DAGs from the DAG folder, sec | 30 |
-r | webserver workers refresh interval, sec | 30 |
-w | number of webserver workers to be refreshed at the same time, int | 1 |
-p | number of threads for Airflow Scheduler, int | 2 |
If you update Airflow configuration file manually (default location is ~/airflow/airflow.cfg), make sure to run cwl-airflow init command to apply all the changes, especially if core/dags_folder or cwl/jobs parameters from the configuration file are changed.
Submitting new job
To submit new CWL descriptor and Job files to cwl-airflow run the following command
cwl-airflow submit WORKFLOW JOB
Optional parameters:
Flag | Description | Default |
---|---|---|
-o | path to the folder where all the output files should be moved after successful workflow execution, str | current directory |
-t | path to the temporary folder for storing intermediate results, str | /tmp |
-u | ID for DAG's unique identifier generation, str | random uuid |
-r | uns submitted workflow with Airflow Scheduler, bool | False |
Arguments -o
, -t
and -u
doesn't overwrite the values from the Job file set in the fields
output_folder, tmp_folder and uid correspondingly.
The submit command will resolve all relative paths from Job file adding mandatory fields workflow, output_folder and uid (if not provided) and will copy Job file to the Jobs folder. The CWL descriptor file and all input files referenced in the Job file should not be moved or deleted while workflow is running. The submit command will not execute submitted workflow unless -r argument is provided. Otherwise, make sure that Airflow Scheduler (and optionally Airflow Webserver) is running.
To start Airflow Scheduler
airflow scheduler
To start Airflow Webserver
airflow webserver
Demo mode
To get the list of the available demo workflows to run
$ cwl-airflow demo --list
To submit demo workflow from the list (to execute submitted wokrflow Airflow Scheduler should be started separately)
$ cwl-airflow demo super-enhancer.cwl
To submit all available demo workflows (to execute submitted wokrflows Airflow Scheduler should be started separately)
$ cwl-airflow demo --manual
To execute all available demo workflows (automatically starts Airflow Scheduler and Airflow Webserver)
$ cwl-airflow demo --auto
Optional parameters:
Flag | Description | Default |
---|---|---|
-o | path to the folder where all the output files should be moved after successful workflow execution, str | current directory |
-t | path to the temporary folder for storing intermediate results, str | /tmp |
-u | ID for DAG's unique identifier generation, str | random uuid |
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