Airflow DVC (1.9.9) Sponsor project

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This is an Airflow extension that adds support for DVC operations.

The basic tutorial about DVC and Airflow can be found on Deep Flare website in the News section here. The motivation for such a package was to create modern automated data science pipelines that operates on versioned data.

This package is a Deep Flare contribution to the opensource community. We try to fight the SARS-Cov-2 pandemic and develop tools to support biotech industry in this fight at the same time we care about sharing our knowledge wherever it’s possible.

You can feel invited to contribute to the project and other opensource code we distribute.

Installation

To install this package please do:

  $ python3 -m pip install "airflow-dvc==1.9.9"

Or if you are using Poetry to run Apache Airflow:

  $ poetry add apache-airflow@latest
  $ poetry add "airflow-dvc@1.9.9"

What this package provides?

The package provides the following core features:

Run examples yourself

The examples are provided in the example/ directory. Please do the following to setup quick Airflow demo:

  # Your git repo clone URL
  # Example:
  #   REPO="https://GITHUB_PERSONAL_TOKEN@github.com/OWNER/REPO.git"
  $ export REPO="<YOUR_REPO_URL>"

  # Install Airflow with Poetry
  $ mkdir airflow-dvc-test && cd airflow-dvc-test
  $ poetry init
  $ poetry add apache-airflow "airflow-dvc@1.9.9"
  
  # Configure Airflow paths
  $ export AIRFLOW_HOME=$(pwd)/airflow
  $ export AIRFLOW_CONFIG=$AIRFLOW_HOME/airflow.cfg
  $ mkdir -p $AIRFLOW_HOME > /dev/null 2> /dev/null
  
  # Init Airflow
  $ poetry run airflow db init
  $ poetry run airflow users create \
      --username admin \
      --firstname Peter \
      --lastname Parker \
      --role Admin \
      --email spiderman@superhero.org
  
  # Create example DVC DAGs
  $ poetry run airflow_dvc generate example_dags
  
  # Run Airflow
  $ poetry run airflow webserver --port 8080 &
  $ poetry run airflow scheduler &

Usage

📊 DVC Operator view

After installation, you should be able to access Browse > DVC Operators option in the Airflow menu.

The DVC Operators view allows you to display all configured DVC operators and repositories that they will push the files to/pull from.

The DVC Pushes view allows you to display all commits created by the DVC operators among all repositories:

💾 DVCUpdateOperator (Uploading)

The upload operator supports various types of data inputs that you can feed into it.

Uploading a string as a file:

    from airflow_dvc import DVCUpdateOperator, DVCStringUpload
    from datetime import datetime

    upload_task = DVCUpdateOperator(
        dvc_repo="<REPO_CLONE_URL>",
        files=[
            DVCStringUpload("data/1.txt", f"This will be saved into DVC. Current time: {datetime.now()}"),
        ],
        task_id='update_dvc',
    )

Uploading local file using its path:

    from airflow_dvc import DVCUpdateOperator, DVCPathUpload

    upload_task = DVCUpdateOperator(
        dvc_repo="<REPO_CLONE_URL>",
        files=[
            DVCPathUpload("data/1.txt", "~/local_file_path.txt"),
        ],
        task_id='update_dvc',
    )

Upload content generated by a python function:

    from airflow_dvc import DVCUpdateOperator, DVCCallbackUpload

    upload_task = DVCUpdateOperator(
        dvc_repo="<REPO_CLONE_URL>",
        files=[
            DVCCallbackUpload("data/1.txt", lambda: "Test data"),
        ],
        task_id='update_dvc',
    )

Uploading file from S3: This is specially useful when you have a workflow that uses S3Hook to temporarily save the data between tasks.

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from datetime import datetime, timedelta

from io import StringIO
import pandas as pd
import requests

from airflow_dvc import DVCUpdateOperator, DVCS3Upload

s3_conn_id = 's3-conn'
bucket = 'astro-workshop-bucket'
state = 'wa'
date = '{{ yesterday_ds_nodash }}'

def upload_to_s3(state, date):
    '''Grabs data from Covid endpoint and saves to flat file on S3
    '''
    # Connect to S3
    s3_hook = S3Hook(aws_conn_id=s3_conn_id)

    # Get data from API
    url = 'https://covidtracking.com/api/v1/states/'
    res = requests.get(url+'{0}/{1}.csv'.format(state, date))

    # Save data to CSV on S3
    s3_hook.load_string(res.text, '{0}_{1}.csv'.format(state, date), bucket_name=bucket, replace=True)

def process_data(state, date):
    '''Reads data from S3, processes, and saves to new S3 file
    '''
    # Connect to S3
    s3_hook = S3Hook(aws_conn_id=s3_conn_id)

    # Read data
    data = StringIO(s3_hook.read_key(key='{0}_{1}.csv'.format(state, date), bucket_name=bucket))
    df = pd.read_csv(data, sep=',')

    # Process data
    processed_data = df[['date', 'state', 'positive', 'negative']]

    # Save processed data to CSV on S3
    s3_hook.load_string(processed_data.to_string(), 'dvc_upload.csv', bucket_name=bucket, replace=True)

# Default settings applied to all tasks
default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=1)
}

with DAG('intermediary_data_storage_dag',
         start_date=datetime(2021, 1, 1),
         max_active_runs=1,
         schedule_interval='@daily',
         default_args=default_args,
         catchup=False
         ) as dag:

    generate_file = PythonOperator(
        task_id='generate_file_{0}'.format(state),
        python_callable=upload_to_s3,
        op_kwargs={'state': state, 'date': date}
    )

    process_data = PythonOperator(
        task_id='process_data_{0}'.format(state),
        python_callable=process_data,
        op_kwargs={'state': state, 'date': date}
    )
    
    upload_to_dvc = DVCUpdateOperator(
        dvc_repo="<REPO_CLONE_URL>",
        files=[
            DVCS3Upload("dvc_path/data.txt", s3_conn_id, bucket, 'dvc_upload.csv'),
        ],
        task_id='update_dvc',
    )

    generate_file >> process_data
    process_data >> upload_to_dvc

Uploading file from S3, but using task arguments: Instead of passing list as a files parameter you can pass function as would do in case of PythonOperator:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from datetime import datetime, timedelta

from io import StringIO
import pandas as pd
import requests

from airflow_dvc import DVCUpdateOperator, DVCS3Upload

s3_conn_id = 's3-conn'
bucket = 'astro-workshop-bucket'
state = 'wa'
date = '{{ yesterday_ds_nodash }}'

def upload_to_s3(state, date):
    '''Grabs data from Covid endpoint and saves to flat file on S3
    '''
    # Connect to S3
    s3_hook = S3Hook(aws_conn_id=s3_conn_id)

    # Get data from API
    url = 'https://covidtracking.com/api/v1/states/'
    res = requests.get(url+'{0}/{1}.csv'.format(state, date))

    # Save data to CSV on S3
    s3_hook.load_string(res.text, '{0}_{1}.csv'.format(state, date), bucket_name=bucket, replace=True)

def process_data(state, date):
    '''Reads data from S3, processes, and saves to new S3 file
    '''
    # Connect to S3
    s3_hook = S3Hook(aws_conn_id=s3_conn_id)

    # Read data
    data = StringIO(s3_hook.read_key(key='{0}_{1}.csv'.format(state, date), bucket_name=bucket))
    df = pd.read_csv(data, sep=',')

    # Process data
    processed_data = df[['date', 'state', 'positive', 'negative']]

    # Save processed data to CSV on S3
    s3_hook.load_string(processed_data.to_string(), '{0}_{1}_processed.csv'.format(state, date), bucket_name=bucket, replace=True)

def get_files_for_upload(state, date):
    return [
        DVCS3Upload("dvc_path/data.txt", s3_conn_id, bucket, '{0}_{1}_processed.csv'.format(state, date)),
    ]
    
# Default settings applied to all tasks
default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=1)
}

with DAG('intermediary_data_storage_dag',
         start_date=datetime(2021, 1, 1),
         max_active_runs=1,
         schedule_interval='@daily',
         default_args=default_args,
         catchup=False
         ) as dag:

    generate_file = PythonOperator(
        task_id='generate_file_{0}'.format(state),
        python_callable=upload_to_s3,
        op_kwargs={'state': state, 'date': date}
    )

    process_data = PythonOperator(
        task_id='process_data_{0}'.format(state),
        python_callable=process_data,
        op_kwargs={'state': state, 'date': date}
    )
    
    # Passing a function as files paramterer (it sould return a list of DVCUpload objects)
    # Also we specify op_kwargs to allow passing of parameters as in case of normal PythonOperator
    upload_to_dvc = DVCUpdateOperator(
        dvc_repo="<REPO_CLONE_URL>",
        files=get_files_for_upload,
        task_id='update_dvc',
        op_kwargs={'state': state, 'date': date}
    )

    generate_file >> process_data
    process_data >> upload_to_dvc

⬇️ DVCDownloadOperator (Downloading)

We can use VCDownloadOperator similarily to the DVCUpdateOperator. The syntax is the same:

    from airflow_dvc import DVCDownloadOperator, DVCCallbackDownload

    # Download DVC file data/1.txt and print it on the screen
    upload_task = DVCDownloadOperator(
        dvc_repo="<REPO_CLONE_URL>",
        files=[
            DVCCallbackDownload("data/1.txt", lambda content: print(content)),
        ],
        task_id='update_dvc',
    )

The DVCDownload implementations are similar to DVCUpload.

👀 DVCUpdateSensor

DVCUpdateSensor will allow you to pause the DAG run until the specified file will be updated. The sensor checks the date of the latest DAG run and compares it with timestamp of meta DVC file in the repo.

from datetime import datetime
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators.bash_operator import BashOperator

from airflow_dvc import DVCUpdateSensor


with DAG('dvc_sensor_example', description='Another tutorial DAG',
    start_date=datetime(2017, 3, 20),
    catchup=False,
) as dag:

    dummy_task = DummyOperator(task_id='dummy_task', dag=dag)

    sensor_task = DVCUpdateSensor(
        task_id='dvc_sensor_task',
        dag=dag,
        dvc_repo="<REPO_CLONE_URL>",
        files=["data/1.txt"],
    )

    task = BashOperator(
        task_id='task_triggered_by_sensor',
        bash_command='echo "OK" && ( echo $[ ( $RANDOM % 30 )  + 1 ] > meowu.txt ) && cat meowu.txt')

    dummy_task >> sensor_task >> task

👀 DVCExistenceSensor

The DVCExistenceSensor is similar to the DVCUpdateSensor but it checks if the file exists in the DVC repo:

    from airflow_dvc import DVCExistenceSensor

    # Sensor will wait till the file is present
    sensor_task = DVCExistenceSensor(
        task_id='dvc_sensor_task',
        dag=dag,
        dvc_repo="<REPO_CLONE_URL>",
        files=["some_path/some_subfolder/some_file.txt"],
    )

🤖 DVCHook

You can perform all the operation manually using DVCHook:

from airflow_dvc import DVCHook, DVCPathUpload

hook = DVCHook("<REPO_URL>")

# Hook requires dag_id so if we will run this code inside class extending any Operator
# we will be able to access self.dag_id field
# Dag IDs are used to track all pushes to your DVC repositories
hook.update([
    DVCPathUpload("data/1.txt", "~/local_file_path.txt"),
], dag_id=self.dag_id)

Development

Install the project with the following command:

    $ poetry install
  • You may want to run poetry --version to check if you have Poetry installed. If the command fails then proceed to install Poetry. The installer installs the poetry tool to Poetry’s bin directory. On Unix it is located at $HOME/.poetry/bin and on Windows at %USERPROFILE%\.poetry\bin. This directory will be automatically added to your $PATH environment variable, by appending a statement to your $HOME/.profile configuration (or equivalent files). If you do not feel comfortable with this, please pass the –no-modify-path flag to the installer and manually add the Poetry’s bin directory to your path.
    • Linux/Mac: curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -
    • Windows: Type in Powershell: (Invoke-WebRequest -Uri https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py -UseBasicParsing).Content | python
  • Install project with poetry install
  • You can now use virtual env created by poetry. Please type poetry shell

Code style

The project has configured styles in pyproject.toml. To format it please call make lint.

Versioning

To bump project version before release please use the following command (for developers):

    $ poetry run bump2version minor