Airflow task stuck in 'queued' state. Dependencies Blocking Task From Getting Scheduled

Question:

I have the following Airflow DAG which uploads a single local file into an S3 bucket.

# airflow related
from airflow import DAG
from airflow.operators.python import PythonOperator
# other packages
from datetime import datetime
import boto3

with DAG(
    dag_id='upload_to_s3',
    start_date=datetime(2020, 5, 5),
    schedule_interval='@once',
    catchup=False,
) as dag:
    pass



def file_upload():
    #Creating Session With Boto3.
    session = boto3.Session(
    aws_access_key_id='my_access_key_id',
    aws_secret_access_key='my_aws_secret_access_key'
    )

    #Creating S3 Resource From the Session.s
    s3 = session.resource('s3')
    result = s3.Bucket('flight-data-test-bucket').upload_file('/opt/airflow/dags/pricedata.xlsx', 'pricedata.xlsx')


    return result


with DAG(
    dag_id='upload_to_s3',
    start_date=datetime(2020, 5, 5),
    schedule_interval='@once',
    catchup=False,
) as dag:
    # Upload the file
    task_file_to_s3 = PythonOperator(
        task_id='upload_to_s3',
        python_callable=file_upload
    )

The DAG is imported in Airflow without any errors however when I try to force run it doesn’t do anything as can be also seen in the below screenshot:

enter image description here

When I check the Task Instance Details it says that "Dependencies Blocking Task From Getting Scheduled. DependencyReasonTask Instance StateTask is in the ‘queued’ state."

enter image description here

I assume that this might be related to something going wrong with the start_date or schedule_interval but I’m not sure. Any ideas? I have been running Airflow on Windows through Docker.

Asked By: panos

||

Answers:

It turned out that there was no problem with my DAG but something was wrong with my docker.

After removing everything(containers, images, etc.) I deleted all my airflow data and run a clean install by following this tutorial to install airflow. Now my DAG is working.

https://www.youtube.com/watch?v=itxM0MC1ZEA

Answered By: panos