Jason Thomas

Jason Thomas


Getting Started With Celery Python

Everyone in the Python community has heard about Celery at least once, and maybe even already worked with it. Basically, it’s a handy tool that helps run postponed or dedicated code in a separate process or even on a separate computer or server. This saves time and effort on many levels.

An Introduction to the Celery Python Guide

Celery decreases performance load by running part of the functionality as postponed tasks either on the same server as other tasks, or on a different server. Most commonly, developers use it for sending emails. However, Celery has a lot more to offer. In this article, I’ll show you some Celery basics, as well as a couple of Python-Celery best practices.

Celery Basics

If you have worked with Celery before, feel free to skip this chapter. But if Celery is new to you, here you will learn how to enable Celeryin your project, and participate in a separate tutorial on using Celery with Django. Basically, you need to create a Celery instance and use it to mark Python functions as tasks.

It’s better to create the instance in a separate file, as it will be necessary to run Celery the same way it works with WSGI in Django. For example, if you create two instances, Flask and Celery, in one file in a Flask application and run it, you’ll have two instances, but use only one. It’s the same when you run Celery.

Primary Python Celery Examples

As I mentioned before, the go-to case of using Celery is sending email. I will use this example to show you the basics of using Celery. Here’s a quick Celery Python tutorial:

from django.conf import settings
from django.core.mail import send_mail
from django.template import Engine, Context

from myproject.celery import app

def render_template(template, context):
    engine = Engine.get_default()

    tmpl = engine.get_template(template)

    return tmpl.render(Context(context))

def send_mail_task(recipients, subject, template, context):
        message=render_template(f'{template}.txt', context),
        html_message=render_template(f'{template}.html', context)

This code uses Django, as it’s our main framework for web applications. By using Celery, we reduce the time of response to customer, as we separate the sending process from the main code responsible for returning the response.

The simplest way to execute this task is to call delay method of function that is provided by app.task decorator.

send_mail_task.delay(('noreply@example.com', ), 'Celery cookbook test', 'test', {})

Not only this — Celery provides more benefits. For example, we could set up retries upon failing.

@celery_app.task(bind=True, default_retry_delay=10 * 60)
def send_mail_task(self, recipients, subject, template, context):
    message = render_template(f'{template}.txt', context)
    html_message = render_template(f'{template}.html', context)
    except smtplib.SMTPException as ex:

Now the task will be restarted after ten minutes if sending fails. Also, you’ll be able to set the number of retries.

Some of you may wonder why I moved the template rendering outside of the send_mail call. It’s because we wrap the call of send_mail into try/except, and it’s better to have as little code in try/except as possible.

Celery for Advanced UsersCelery Django Scheduled Tasks

Celery makes it possible to run tasks by schedulers like crontab in Linux.

First of all, if you want to use periodic tasks, you have to run the Celery worker with –beat flag, otherwise Celery will ignore the scheduler. Your next step would be to create a config that says what task should be executed and when. Here’s an example:

from celery.schedules import crontab

    'monday-statistics-email': {
        'task': 'myproject.apps.statistics.tasks.monday_email',
        'schedule': crontab(day_of_week=1, hour=7),

*if you don’t use Django, you should use celery_app.conf.beat_schedule instead of CELERY_BEAT_SCHEDULE

What we have in this configuration is only one task that will be executed every Monday at 7 a.m… The root key is a name or a cronjob, not a task.

You can add arguments to tasks and choose what should be done in case the same task should run at different times with different arguments. The crontab method supports the syntax of the system crontab – such as crontab(minute=’*/15’)– to run the task every 15 minutes.

Postponed Task Execution In Celery

You can also set tasks in a Python Celery queue with a timeout before execution. (For example, when you need to send a notification after an action.) To do this, use the apply_async method with an etaor countdown argument.

Let’s look at what it might look like in code:

from datetime import datetime

    (('noreply@example.com', ), 'Celery cookbook test', 'test', {}),
    countdown=15 * 60

    (('noreply@example.com', ), 'Celery cookbook test', 'test', {}),
    eta=datetime(2019, 5, 20, 7, 0)

In the first example, the email will be sent in 15 minutes, while in the second it will be sent at 7 a.m. on May 20.

Setting Up Python Celery Queues

Celery can be distributed when you have several workers on different servers that use one message queue for task planning. You can configure an additional queue for your task/worker. For example, sending emails is a critical part of your system and you don’t want any other tasks to affect the sending. Then you can add a new queue, let’s call it mail, and use this queue for sending emails.

    'myproject.apps.mail.tasks.send_mail_task': {'queue': 'mail', },

*if you don’t use Django, use celery_app.conf.task_routesinstead of CELERY_TASK_ROUTES

Run two separate celery workers for the default queue and the new queue:

celery -A myproject worker -l info -Q celery
celery -A myproject worker -l info -Q mail

The first line will run the worker for the default queue called celery, and the second line will run the worker for the mailqueue. You can use the first worker without the -Q argument, then this worker will use all configured queues.

Python Celery Long-Running Tasks

Sometimes, I have to deal with tasks written to go through database records and perform some operations. Quite often, developers forget about data growth, which can lead to a very long task running time. It’s always better to write tasks like these in a way that allows working with data chunks. The easiest way is to add an offset and limit parameters to a task. This will allow you to indicate the size of the chunk, and the cursor to get a new chunk of data.

def send_good_morning_mail_task(offset=0, limit=100):
    users = User.objects.filter(is_active=True).order_by('id')[offset:offset + limit]
    for user in users:

    if len(users) >= limit:
        send_good_morning_mail_task.delay(offset + limit, limit)

This is a very simple example of how a task like this can be implemented. At the end of the task, we check how many users we found in the database. If the number equals the limit, then we’ve probably got new users to process. So we run the task again, with a new offset. If the user count is less than the limit, it means it’s the last chunk and we don’t have to continue. Beware, though: this task implementation needs to have the same ordering for records every time.

Celery: Getting Task Results

Most developers don’t record the results they get after running the task. Imagine that you can take a part of code, assign it to a task and execute this task independently as soon as you receive a user request. When we need the results of the task, we either get the results right away (if the task is completed), or wait for it to complete. Then we include the result to the general response. Using this approach, you can decrease response time, which is very good for your users and site rank.

We use this feature to run simultaneous operations. In one of our projects, we have a lot of user data and a lot of service providers. To find the best service provider, we do heavy calculations and checks. To do it faster, we create tasks for user with each service provider, run them and collect results to show to the user. It’s very easy to do with Celery task groups.

from celery import group

def calculate_service_provider_task(user_id, provider_id):
    user = User.objects.get(pk=user_id)
    provider = ServiceProvider.objects.get(pk=provider_id)

    return calculate_service_provider(user, provider)

def find_best_service_provider_for_user(user_id):
    user = User.objects.get(pk=user_id)
    providers = ServiceProvider.objects.related_to_user(user)

    calc_group = group([
        calculate_service_provider_task.s(user.pk, provider.pk)
        for provider in providers

    return calc_group

First, why do we even run two tasks? We use the second task to form calculation task groups, launch and return them. On top of that, the second task is where you can assign project filtration — like service providers that need to be calculated for a given user. All this can be done while Celery is doing other work. When the task group returns, the result of the first task is actually the calculation we are interested in.

Here’s an example of how to use this approach in code:

def view(request):
    find_job = find_best_service_provider_for_user.delay(request.user.pk)

    # do other stuff

    calculations_results = find_job.get().join()

    # process calculations_results and send response

Here, we run calculations as soon as possible, wait for the results at the end of the method, then prepare the response and send it to the user.

Useful TipsTiny Data

I’ve probably already mentioned that I use database record IDs as task arguments instead of full objects. This is a good way to decrease the message queue size. But what’s more important is that when a task is executed, the data in the database can be changed. And when you have only IDs, you will get fresh data as opposed to outdated data you get when passing objects.


Sometimes, issues may arise when an executed task can’t find an object in a database. Why does this happen? In Django, for instance, you want to run tasks after a user is registered, like sending a greeting email, and your Django settings wrap all requests into a transaction. In Celery, however, tasks are executed fast, before the transaction is even finished. So if you use Celery when working in Django, you might see that the user doesn’t exist in the database (yet).

To deal with this, you can Google “task transaction implementation”. In general, it’s an overwritten apply_async method in task, a class that sets up a task in transaction.on_commit signal instead of doing it immediately.


As you see, Celery has a lot more uses than just sending emails. You can run different tasks simultaneously using the main process, and while you do your job, Celery will complete the smaller tasks at hand. You can set up queues, work with data chunks on long-running tasks, and set up times for your tasks to be executed. This will allow you to better plan your work progress, plan development time more efficiently, and spend your precious time working on the bigger things while Celery task groups work their magic.

#python #web-development #django

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Getting Started With Celery Python
Ray  Patel

Ray Patel


top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind


How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.


In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips