Airflow s3 connection using UI
I've been trying to use Airflow to schedule a DAG. One of the DAG includes a task which loads data from s3 bucket.
For the purpose above I need to setup s3 connection. But UI provided by airflow isn't that intutive (http://pythonhosted.org/airflow/configuration.html?highlight=connection#connections). Any one succeeded setting up the s3 connection if so are there any best practices you folks follow?
Thanks.
Answer
EDIT: This answer stores your secret key in plain text which can be a security risk and is not recommended. The best way is to put access key and secret key in the login/password fields, as mentioned in other answers below. END EDIT
It's hard to find references, but after digging a bit I was able to make it work.
TLDR
Create a new connection with the following attributes:
Conn Id: my_conn_S3
Conn Type: S3
Extra:
{"aws_access_key_id":"_your_aws_access_key_id_", "aws_secret_access_key": "_your_aws_secret_access_key_"}
Long version, setting up UI connection:
- On Airflow UI, go to Admin > Connections
- Create a new connection with the following attributes:
- Conn Id:
my_conn_S3 - Conn Type:
S3 - Extra:
{"aws_access_key_id":"_your_aws_access_key_id_", "aws_secret_access_key": "_your_aws_secret_access_key_"} - Leave all the other fields (Host, Schema, Login) blank.
To use this connection, below you can find a simple S3 Sensor Test. The idea of this test is to set up a sensor that watches files in S3 (T1 task) and once below condition is satisfied it triggers a bash command (T2 task).
Testing
- Before running the DAG, ensure you've an S3 bucket named 'S3-Bucket-To-Watch'.
- Add below s3_dag_test.py to airflow dags folder (~/airflow/dags)
- Start
airflow webserver. - Go to Airflow UI (http://localhost:8383/)
- Start
airflow scheduler. - Turn on 's3_dag_test' DAG on the main DAGs view.
- Select 's3_dag_test' to show the dag details.
- On the Graph View you should be able to see it's current state.
- 'check_s3_for_file_in_s3' task should be active and running.
- Now, add a file named 'file-to-watch-1' to your 'S3-Bucket-To-Watch'.
- First tasks should have been completed, second should be started and finish.
The schedule_interval in the dag definition is set to '@once', to facilitate debugging.
To run it again, leave everything as it's, remove files in the bucket and try again by selecting the first task (in the graph view) and selecting 'Clear' all 'Past','Future','Upstream','Downstream' .... activity. This should kick off the DAG again.
Let me know how it went.
s3_dag_test.py ;
"""
S3 Sensor Connection Test
"""
from airflow import DAG
from airflow.operators import SimpleHttpOperator, HttpSensor, BashOperator, EmailOperator, S3KeySensor
from datetime import datetime, timedelta
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2016, 11, 1),
'email': ['[email protected]'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 5,
'retry_delay': timedelta(minutes=5)
}
dag = DAG('s3_dag_test', default_args=default_args, schedule_interval= '@once')
t1 = BashOperator(
task_id='bash_test',
bash_command='echo "hello, it should work" > s3_conn_test.txt',
dag=dag)
sensor = S3KeySensor(
task_id='check_s3_for_file_in_s3',
bucket_key='file-to-watch-*',
wildcard_match=True,
bucket_name='S3-Bucket-To-Watch',
s3_conn_id='my_conn_S3',
timeout=18*60*60,
poke_interval=120,
dag=dag)
t1.set_upstream(sensor)
Main References: