Setting up Celery workers on separate machines involves configuring a distributed Celery setup where multiple worker processes run on different machines or nodes to process tasks asynchronously. This is a common practice for distributing and scaling task processing in a distributed system. Below are the general steps to set up Celery workers on separate machines:
Note: In this explanation, we'll assume you already have a basic understanding of Celery and have it installed on the machines where you intend to run the workers.
Install Celery:
Ensure Celery is installed on all the machines where you want to set up workers. You can use pip to install Celery:
pip install celery
Create a Celery Application:
Create a Python script that defines your Celery application. This script should configure your Celery app, including the broker (message queue) and any necessary settings. You can use various brokers like RabbitMQ, Redis, or others. Here's an example of a basic Celery application setup:
# celery_app.py
from celery import Celery
app = Celery(
'myapp',
broker='pyamqp://guest@localhost//', # Replace with your broker URL
backend='rpc://', # Replace with your result backend URL
)
Start the Celery Workers:
On each machine where you want to run Celery workers, you'll start Celery worker processes using the celery command. You need to specify the Celery application you created in the previous step and the name of the queue(s) to listen to:
celery -A celery_app worker --loglevel=info
-A celery_app: Specifies the Celery application defined in your Python script.worker: Indicates that this is a worker process.--loglevel=info: Sets the log level (you can adjust this as needed).Make sure to run this command on each machine where you want to set up workers.
Distribute Tasks:
To distribute tasks to the Celery workers on separate machines, you can use Celery's task decorators and the .delay() method. For example, define a task in your Celery application script and call it from your main application code:
# celery_app.py
from celery import Celery
app = Celery(
'myapp',
broker='pyamqp://guest@localhost//',
backend='rpc://',
)
@app.task
def my_task(arg1, arg2):
# Task logic
return arg1 + arg2
Then, from your main application code on any machine, you can use .delay() to send tasks to the Celery workers:
from celery_app import my_task result = my_task.delay(10, 20) # Send the task to Celery workers
Monitor and Manage Workers:
You can use Celery's built-in tools, such as celery -A celery_app status, celery -A celery_app inspect, and Flower (a real-time Celery monitoring tool), to monitor and manage your Celery workers running on separate machines.
By following these steps, you can set up and distribute Celery workers across separate machines to process tasks concurrently and asynchronously. Ensure that the machines have network connectivity and access to the message queue (broker) for proper communication between the Celery components.
Running Celery on Windows can be a bit trickier compared to running it on Unix-like systems, as Celery and its dependencies were primarily designed for Unix-based operating systems. However, you can still run Celery on Windows using a few workarounds.
Here's a general outline of the steps to run Celery on Windows:
Install Redis: Celery requires a message broker to manage its tasks. While RabbitMQ is often used on Unix systems, Redis is a popular choice for Windows due to better compatibility.
redis-server.exe.Install Celery: Install Celery using pip:
pip install celery
Create a Celery Configuration File: Create a celeryconfig.py file in your project directory to configure Celery. Here's a basic example:
broker_url = 'redis://localhost:6379/0' result_backend = 'redis://localhost:6379/0'
Run Celery Worker: To start a Celery worker, open a terminal and navigate to your project directory. Then run the following command:
celery -A your_module_name worker --loglevel=info
Replace your_module_name with the actual name of the Python module where your Celery tasks are defined.
Define Celery Tasks: Define your Celery tasks in the specified module. For example:
from celery import Celery
app = Celery('your_module_name')
@app.task
def add(x, y):
return x + y
Invoke Celery Tasks: In your code, you can now invoke Celery tasks using the .delay() method:
from your_module_name import add result = add.delay(4, 6)
Please note that running Celery on Windows might not be as efficient or reliable as on Unix systems due to compatibility issues and performance limitations. If you encounter difficulties or performance problems, consider using Windows Subsystem for Linux (WSL) or running Celery on a Unix-like environment, such as Docker containers or a virtual machine.
To set up email notifications in Apache Airflow, you need to configure the email settings in the Airflow configuration file and then use the EmailOperator or EmailSensor to send or monitor emails within your workflows. Here's how you can set up email notifications:
Configure Email Settings:
Open your Airflow configuration file (airflow.cfg) and locate the email section. Fill in the SMTP server details and other email-related settings:
[email] email_backend = airflow.utils.email.send_email_smtp smtp_host = smtp.example.com smtp_starttls = True smtp_ssl = False smtp_user = your_username smtp_password = your_password smtp_port = 587 smtp_mail_from = [email protected] smtp_timeout = 60
Replace the placeholders (smtp_host, smtp_user, smtp_password, etc.) with your actual email server details.
Using EmailOperator to Send Emails:
You can use the EmailOperator to send emails from within your Airflow workflows. Here's a basic example:
from airflow import DAG
from airflow.operators.email_operator import EmailOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'airflow',
'start_date': datetime(2023, 1, 1),
}
dag = DAG('email_example', default_args=default_args, schedule_interval=None)
send_email_task = EmailOperator(
task_id='send_email',
to='[email protected]',
subject='Airflow Email Example',
html_content='<p>This is an example email sent from Airflow.</p>',
dag=dag,
)
send_email_task
Using EmailSensor for Email-Based Sensing:
The EmailSensor allows you to wait for certain conditions in emails before proceeding with your workflow:
from airflow import DAG
from airflow.sensors.email_sensor import EmailSensor
from datetime import datetime, timedelta
default_args = {
'owner': 'airflow',
'start_date': datetime(2023, 1, 1),
}
dag = DAG('email_sensing_example', default_args=default_args, schedule_interval=None)
email_sensor = EmailSensor(
task_id='wait_for_email',
poke_interval=300, # Check email every 5 minutes
timeout=3600, # Stop after 1 hour
subject='Expected Subject', # Subject to match
to='[email protected]',
dag=dag,
)
email_sensor
These are basic examples to get you started with email notifications in Apache Airflow. Make sure to customize the to, subject, and other parameters according to your requirements. Additionally, you can explore more advanced email features in Airflow, such as attaching files, sending HTML emails, and using Jinja templates for email content.