Adding Google Analytics To All Pages Using Flask

Google Analytics gives a detailed insight about your website including how many people visited, time, demography, how many returning visitors and all such information. It’s a real important tool to have. All you have to do is create a Universal Analytics Tracking code and use it in a javascript code. The only problem is this code needs to be present in all the pages that you wants the analytics data for. So changing any part of the javascript code anytime, needs to be changed in all .html files.

However, there is a better way of doing it in flask. Create a file base.html and write the code:

<script>
(function(i,s,o,g,r,a,m){i[‘GoogleAnalyticsObject’]=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,’script’,’https://www.google-analytics.com/analytics.js’,’ga’);

ga(‘create’, ‘<track-code>’, ‘auto’);
ga(‘send’, ‘pageview’);

</script>

Then using the property of jinja2 template extend this file in all the html files, i.e. {% extends ‘gentelella/admin/base.html’ %}. Thus now when you make some change in the above mentioned javascrpt code, you need to change it only in one place and it is changed in all other places.

R14 – Memory Quota Exceeded

We, like many other organisations, are using heroku as the deployment server for our project open event organizer server. Things are pretty simple and awesome when your project is in its beginning phase and things run pretty smoothly. But as your project grows, there comes some server problem. And one of the biggest problems as your project grows is memory. Now since various packages have a different amount of memory assigned to you in case of hosting in generic servers such as heroku, so it might result in memory quota exceeded. Recently, we faced such a problem. R14 – Memory Quota Exceeded. Took us quite some time to understand what and why and how this occurred. So let me share a few things I found about this error.

Continue reading R14 – Memory Quota Exceeded

Python code examples

I’ve met many weird examples of  behaviour in python language while working on Open Event project. Today I’d like to share some examples with you. I think this knowledge is necessary, if you’d like to increase a  bit your knowledge in python area.

Simple adding one element to python list:

def foo(value, x=[]):
  x.append(value)
  return x

>>> print(foo(1))
>>> print(foo(2))
>>> print(foo(3, []))
>>> print(foo(4))

OUTPUT

[1] 
[1, 2] 
[3]
[1, 2, 4]

First output is obvious, but second not exactly. Let me explain it, It happens because x(empty list) argument is only evaluated once, So on every call foo(), we modify that list, appending a value to it. Finally we have [1,2, 4] output. I recommend to avoid mutable params as default.

Another example:

Do you know which type it is?

>>> print(type([ el for el in range(10)]))
>>> print(type({ el for el in range(10)}))
>>> print(type(( el for el in range(10))))

Again first and second type are obvious <class ‘list’>, <class ‘set’>. You may  think that last one should return type tuple but it returns a generator <class ‘generator’>.

Example:

Do you think that below code returns an exception?

list= [1,2,3,4,5]
>>> print(list [8:])

If you think that above expression returns index error you’re wrong. It returns empty list [].

Example funny boolean operators

>>> 'c' == ('c' or 'b')
True
>>> 'd' == ('a' or 'd')
False
>>> 'c' == ('c' and 'b')
False 
>>> 'd' == ('a' and 'd')
True

You can think that that OR and AND operators are broken.

You have to know how python interpreter behaves while looking for OR and AND operators.

So OR Expression takes the first statement and checks if it is true. If the first statement is true, then Python returns object’s value without checking second value. If first statement is false interpreter checks second value and returns that value.

AND operator checks if first statement is false, the whole statement has to be false. So it returns first value, but if first statement is true it checks second statement and returns second value.

Below i will show you how it works

>>> 'c' == ('c' or 'b')
>>> 'c' == 'c'
True
>>> 'd' == ('a' or 'd')
>>> 'd' == 'a'
False
>>> 'c' == ('c' and 'b')
>>> 'c' == 'b'
False 
>>> 'd' == ('a' and 'd')
>>> 'd' == 'd'
True

I hope that i have explained you how the python interpreter checks OR and AND operators. So know above examples should be more understandable.

GET and POST requests

If you wonder how to get or update page resource, you have to read this article.

It’s trivial if you have basic knowledge about HTTP protocol. I’d like to get you little involved to this subject.

So GET and POST are most useful methods in HTTP protocol.

What is HTTP?

Hypertext transfer protocol – allow us to communicate between client and server side. In Open Event project we use web browser as client and for now we use Heroku for server side.

Difference between GET and POST methods

GET – it allows to get data from specified resources

POST – it allows to submit new data to specified resources for example by html form.

GET samples:

For example we use it to get details about event

curl http://open-event-dev.herokuapp.com/api/v2/events/95

Response from server:

Of course you can use this for another needs, If you are a poker player I suppose that you’d like to know how many percentage you have on hand.

curl http://www.propokertools.com/simulations/show?g=he&s=generic&b&d&h1=AA&h2=KK&h3&h4&h5&h6&_

POST samples:

curl -X POST https://example.com/resource.cgi

You can often find this action in a contact page or in a login page.

How does request look in python?

We use Requests library to communication between client and server side. It’s very readable for developers. You can find great documentation  and a lot of code samples on their website. It’s very important to see how it works.

>>> r = requests.get('https://api.github.com/user', auth=('user', 'pass'))
>>> r.status_code
200

I know that samples are very important, but take a look how Requests library fulfils our requirements in 100%. We have decided to use it because we would like to communicate between android app generator and orga server application. We have needed to send request with params(email, app_name, and api of event url) by post method to android generator resource. It executes the process of sending an email – a package of android application to a provided email address.

data = {
    "email": login.current_user.email,
    "app_name": self.app_name,
    "endpoint": request.url_root + "api/v2/events/" + str(self.event.id)
}
r = requests.post(self.app_link, json=data)

 

Dynamically marshalling output in Flask Restplus

Do you use Flask-Restplus ? Have you felt the need of dynamically modifying API output according to condition. If yes, then this post is for you.

In this post, I will show how to use decorators to restrict GET API output. So let’s start.

This is the basic code to create an API. Here we have created a get_speaker API to get a single item from Speaker model.

from flask_restplus import Resource, Model, fields, Namespace
from models import Speaker

api = Namespace('speakers', description='Speakers', path='/')

SPEAKER = Model('Name', {
	'id': fields.Integer(),
	'name': fields.String(),
	'phone': fields.String()
})

class DAO:
	def get(speaker_id):
		return Speaker.query.get(speaker_id)

@api.route('/speakers/<int:speaker_id>')
class Speaker(Resource):
    @api.doc('get_speaker')
    @api.marshal_with(SPEAKER)
    def get(self, speaker_id):
        """Fetch a speaker given its id"""
        return DAO.get(speaker_id)

Now our need is to change the returned API data according to some condition. Like if user is authenticated then only return phone field of the SPEAKER model. One way to do this is to create condition statements in get method that marshals the output according to the situation. But if there are lots of methods which require this, then this is not a good way.

So let’s create a decorator which can change the marshal decorator at runtime. It will accept parameters as which models to marshal in case of authenticated and non-authenticated cases.

from flask_login import current_user
from flask_restplus import marshal_with

def selective_marshal_with(fields, fields_private):
    """
    Selective response marshalling. Doesn't update apidoc.
    """
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            if current_user.is_authenticated:
                model = fields
            else:
                model = fields_private
            func2 = marshal_with(model)(func)
            return func2(*args, **kwargs)
        return wrapper
    return decorator

The above code adds a wrapper over the API function which checks if the user is authenticated. If the user is authenticated, fields model is used for marshalling else fields_private is used for marshalling.

So let’s create the private model for SPEAKER. We will call it SPEAKER_PRIVATE.

from flask_restplus import Model, fields

SPEAKER_PRIVATE = Model('NamePrivate', {
	'id': fields.Integer(),
	'name': fields.String()
})

The final step is attaching the selective_marshal_with decorator to the get() method.

@api.route('/speakers/<int:speaker_id>')
class Speaker(Resource):
    @api.doc('get_speaker', model=SPEAKER)
    @selective_marshal_with(SPEAKER, SPEAKER_PRIVATE)
    def get(self, speaker_id):
        """Fetch a speaker given its id"""
        return DAO.get(speaker_id)

You will notice that I removed @api.marshal_with(SPEAKER). This was to disable automatic marshalling of output by flask-restplus. To compensate for this, I have added model=SPEAKER inapi.doc. It will not auto-marshal the output but will still show the swagger documentation.

That concludes this. The get method will now switch marshal field w.r.t to the authentication level of the user. As you may notice, the selective_marhsal_with function is generic and can be used with other models and APIs too.

 

{{ Repost from my personal blog http://aviaryan.in/blog/gsoc/dynamic-marshal-restplus.html }}

Flask-SocketIO Notifications

In the previous post I explained about configuring Flask-SocketIO, Nginx and Gunicorn. This post includes integrating Flask-SocketIO library to display notifications to users in real time.

Flask Config

For development we use the default web server that ships with Flask. For this, Flask-SocketIO fallsback to long-polling as its transport mechanism, instead of WebSockets. So to properly test SocketIO I wanted to work directly with Gunicorn (hence the previous post about configuring development environment). Also, not everyone needs to be bothered with the changes required to run it.

class DevelopmentConfig(Config):
    DEVELOPMENT = True
    DEBUG = True

    # If Env Var `INTEGRATE_SOCKETIO` is set to 'true', then integrate SocketIO
    socketio_integration = os.environ.get('INTEGRATE_SOCKETIO')
    if socketio_integration == 'true':
        INTEGRATE_SOCKETIO = True
    else:
        INTEGRATE_SOCKETIO = False

    # Other stuff

SocketIO is integrated (in development env) if the developer has set the INTEGRATE_SOCKETIO environment variable to “true”. In Production, our application runs on Gunicorn, and SocketIO integration must always be there.

Flow

To send message to a particular connection (or a set of connections) Flask-SocketIO provides Rooms. The connections are made to join a room and the message is sent in the room. So to send message to a particular user we need him to join a room, and then send the message in that room. The room name needs to be unique and related to just one user. The User database Ids could be used. I decided to keep user_{id} as the room name for a user with id {id}. This information (room name) would be needed when making the user join a room, so I stored it for every user that logged in.

@expose('/login/', methods=('GET', 'POST'))
    def login_view(self):
        if request.method == 'GET':
            # Render template
        if request.method == 'POST':
            # Take email and password from form and check if 
            # user exists. If he does, log him in.
            login.login_user(user)

            # Store user_id in session for socketio use
            session['user_id'] = login.current_user.id

            # Redirect

After the user logs in, a connection request from the client is sent to the server. With this connection request the connection handler at server makes the user join a room (based on the user_id stored previously).

@socketio.on('connect', namespace='/notifs')
def connect_handler():
    if current_user.is_authenticated():
        user_room = 'user_{}'.format(session['user_id'])
        join_room(user_room)
        emit('response', {'meta': 'WS connected'})

The client side is somewhat similar to this:

<script src="{{ url_for('static', filename='path/to/socket.io-client/socket.io.js') }}"></script>
<script type="text/javascript">
$(document).ready(function() {
    var namespace = '/notifs';

    var socket = io.connect(location.protocol + "//" + location.host + namespace, {reconnection: false});

    socket.on('response', function(msg) {
        console.log(msg.meta);
        // If `msg` is a notification, display it to the user.
    });
});
</script>

Namespaces helps when making multiple connections over the same socket.

So now that the user has joined a room we can send him notifications. The notification data sent to the client should be standard, so the message always has the same format. I defined a get_unread_notifs method for the User class that fetches unread notifications.

class User(db.Model):
    # Other stuff

    def get_unread_notifs(self, reverse=False):
        """Get unread notifications with titles, humanized receiving time
        and Mark-as-read links.
        """
        notifs = []
        unread_notifs = Notification.query.filter_by(user=self, has_read=False)
        for notif in unread_notifs:
            notifs.append({
                'title': notif.title,
                'received_at': humanize.naturaltime(datetime.now() - notif.received_at),
                'mark_read': url_for('profile.mark_notification_as_read', notification_id=notif.id)
            })

        if reverse:
            return list(reversed(notifs))
        else:
            return notifs

This class method is used when a notification is added in the database and has to be pushed into the user SocketIO room.

def create_user_notification(user, action, title, message):
    """
    Create a User Notification
    :param user: User object to send the notification to
    :param action: Action being performed
    :param title: The message title
    :param message: Message
    """
    notification = Notification(user=user,
                                action=action,
                                title=title,
                                message=message,
                                received_at=datetime.now())
    saved = save_to_db(notification, 'User notification saved')

    if saved:
        push_user_notification(user)

def push_user_notification(user):
    """
    Push user notification to user socket connection.
    """
    user_room = 'user_{}'.format(user.id)
    emit('response',
         {'meta': 'New notifications',
          'notif_count': user.get_unread_notif_count(),
          'notifs': user.get_unread_notifs()},
         room=user_room,
         namespace='/notifs')

Unit Testing

There are many stories about unit testing. Developers sometimes say that they don’t write tests because they write a good quality code. Does it make sense, if no one is infallible?.

At studies only a  few teachers talk about unit testing, but they only show basic examples of unit testing. They require to write a few tests to finish final project, but nobody really  teaches us the importance of unit testing.

I have also always wondered what benefits can it bring. As time is a really important factor in our work it often happens that we simply resign of this part of process development to get “more time” rather than spend time on writing stupid tests. But now I know that it is a vicious circle.

Customers requierments does not help us. They put a high pressure to see visible results not a few statistics about coverage status. None of them cares about some strange numbers. So, as I mentioned above, we usually focuses on building new features and get riid of tests. It may seem to save time, but it doesn’t.

In reality tests save us a lot of time because we can identify and fix bugs very quickly. If a bug ocurrs because someone’s change we don’t have to spend long hours trying to figure out wgat is going out. That’s why we need tests.  

It is especially visible in huge open source projects. FOSSASIA organization has about 200 contributors. In OpenEvent project we have about 20 active developers, who generate many lines of code every single day. Many of them change over and over again as well as interfere  with each other.

Let me provide you with a simple example. In our team we have about 7 pull requests per day. As I mentioned above we want to make our code high quality and free of bugs, but without testing identifying if pull request causes a bug is very difficult task. But fortunately this boring job makes Travis CI for us. It is a great tool which uses our tests and runs them on every PR  to check if bugs occur. It helps us to quickly notice bugs and maintain our project very well.

What is unit testing?

Unit testing is a software development method in which the smallest testable parts of an application are tested

Why do we need writing unit tests?

Let me point all arguments why unit testing is really important while developing a project.

  • To prove that our code works properly

If developer adds another condition, test checks if method returns correct results. You simply don’t need to wonder if something is wrong with you code.

  • To reduce amount of bugs

It let you to know what inputs params’ function should get and what results should be returned. You simply don’t  write unused code

  • To save development time

Developers don’t waste time on checking every code’s change if his code works correctly

  • Unit tests help to understand software design
  • To provide quick feedback about method which you are testing
  • To help document a code

How to write unit test in Python

In my work I write use tests in Python. I am going to share my sample code  with you now

  • Import module unittest
  • Choose function to test
  • Write unit test

Example OpenEvent test in Python

class TestPagesUrls(OpenEventTestCase):

   def setUp(self):

       self.app = Setup.create_app()

   def test_if_urls_exist(self):

       """Test all urls via GET method"""

       with app.test_request_context():

           for rule in app.url_map.iter_rules():

               if excluded_paths(rule):

                   status_code = self.app.get(request.url[:-1] + str(rule).replace('//', '/'),        follow_redirects=True).status_code

                   self.assertTrue(status_code in [200, 302, 401])

 

I want to check if all views exist but it required a lot of time. That’s why I wonder I how to avoid writing similar tests. Finally, based  on our list of routes I am able to write test which checks code’s status  on every page.

If some of them response returns status_code different than 200, 302 or 401, test fails.This results means that somethings is wrong. Simple, isn’t it ?  Try to test it manually…. This one short test cover about 40 use cases…

This example shows an incredible value of unit tests! If developer makes a bug in response he receives an error that something is wrong with a view. Travis CI allows to reject all  wrong pull requests and merge only these which fulfill our quality requirements.   

Fixing  error is one part but finding a bug is even harder task. But an ability to detect bug on early stage of process development reduces cost of software.

 

Update Fields with Array Input

Screenshot from 2016-07-15 17:57:58.png

There are certain fields in form where instead of a single value, we need an array of values to be stored. This are fields under the same category but having multiple elements, e.g., Tracks, Session Types, Microlocations, Sponsors, Social Links and similar such fields. Now as we know the way of doing this using simple html is provide the <input> tag with property “name” as “<field_name>[]”. So suppose we want to save the names of the mutliple tracks, we will have something like this
Screenshot from 2016-07-15 18:02:09.png

But the problem begins when you want to update name of a particular element (in this case Track). How to do it? Should we delete and create entries again? That doesn’t sound too good, does it? So what should we do? Let’s see….

Continue reading Update Fields with Array Input

Setting up Celery with Flask

In this article, I will explain how to use Celery with a Flask application. Celery requires a broker to run. The most famous of the brokers is Redis. So to start using Celery with Flask, first we will have to setup the Redis broker.

Redis can be downloaded from their site http://redis.io. I wrote a script that simplifies downloading, building and running the redis server.

#!/bin/bash
# This script downloads and runs redis-server.
# If redis has been already downloaded, it just runs it
if [ ! -d redis-3.2.1/src ]; then
    wget http://download.redis.io/releases/redis-3.2.1.tar.gz
    tar xzf redis-3.2.1.tar.gz
    rm redis-3.2.1.tar.gz
    cd redis-3.2.1
    make
else
    cd redis-3.2.1
fi
src/redis-server

When the above script is ran from the first time, the redis folder doesn’t exist so it downloads the same, builds it and then runs it. In subsequent runs, it will skip the downloading and building part and just run the server.

Now that the redis server is running, we will have to install its Python counterpart.

pip install redis

After the redis broker is set, now its time to setup the celery extension. First install celery by using pip install celery. Then we need to setup celery in the flask app definition.

# in app.py
def make_celery(app):
	# set redis url vars
	app.config['CELERY_BROKER_URL'] = environ.get('REDIS_URL', 'redis://localhost:6379/0')
    app.config['CELERY_RESULT_BACKEND'] = app.config['CELERY_BROKER_URL']
    # create context tasks in celery
    celery = Celery(app.import_name, broker=app.config['CELERY_BROKER_URL'])
    celery.conf.update(app.config)
    TaskBase = celery.Task
    class ContextTask(TaskBase):
        abstract = True
        def __call__(self, *args, **kwargs):
            with app.app_context():
                return TaskBase.__call__(self, *args, **kwargs)
    celery.Task = ContextTask
    return celery

celery = make_celery(current_app)

Now that Celery is setup on our project, let’s define a sample task.

@app.route('/task')
def view():
	background_task.delay(*args, **kwargs)
	return 'OK'

@celery.task
def background_task(*args, **kwargs):
	# code
	# more code

Now to run the celery workers, execute

celery worker -A app.celery

That should be all. Now to run our little project, we can execute the following script.

bash run_redis.sh &  # to run redis
celery worker -A app.celery &  # to run celery workers
python app.py

If you are wondering how to run the same on Heroku, just use the free heroku-redis extension. It will start the redis server on heroku. Then to run the workers and app, set the Procfile as –

web: sh heroku.sh

Then set the heroku.sh as –

#!/bin/bash
celery worker -A app.celery &
gunicorn app:app

That’s a basic guide on how to run a Flask app with Celery and Redis. If you want more information on this topic, please see my post Ideas on Using Celery in Flask for background tasks.

Ideas on using Celery with Flask for background tasks

Simply put, Celery is a background task runner. It can run time-intensive tasks in the background so that your application can focus on the stuff that matters the most. In context of a Flask application, the stuff that matters the most is listening to HTTP requests and returning response.

By default, Flask runs on a single-thread. Now if a request is executed that takes several seconds to run, then it will block all other incoming requests as it is single-threaded. This will be a very bad-experience for the user who is using the product. So here we can use Celery to move time-hogging part of that request to the background.

I would like to let you know that by “background”, Celery means another process. Celery starts worker processes for the running application and these workers receive work from the main application. Celery requires a broker to be used. Broker is nothing but a database that stores results of a celery task and provides a shared interface between main process and worker processes. The output of the work done by the workers is stored in the Broker. The main application can then access these results from the Broker.

Using Celery to set background tasks in your application is as simple as follows –

@celery.task
def background_task(*args, **kwargs):
    # do stuff
    # more stuff

Now the function background_task becomes function-able as a background task. To execute it as a background task, run –

task = background_task.delay(*args, **kwargs)
print task.state  # task current state (PENDING, SUCCESS, FAILURE)

Till now this may look nice and easy but it can cause lots of problems. This is because the background tasks run in different processes than the main application. So the state of the worker application differs from the real application.

One common problem because of this is the lack of request context. Since a celery task runs in a different process, so the request context is not available. Therefore the request headers, cookies and everything else is not available when the task actually runs. I too faced this problem and solved it using an excellent snippet I found on the Internet.

"""
Celery task wrapper to set request context vars and global
vars when a task is executed
Based on http://xion.io/post/code/celery-include-flask-request-context.html
"""
from celery import Task
from flask import has_request_context, make_response, request, g

from app import app  # the flask app


__all__ = ['RequestContextTask']


class RequestContextTask(Task):
    """Base class for tasks that originate from Flask request handlers
    and carry over most of the request context data.
    This has an advantage of being able to access all the usual information
    that the HTTP request has and use them within the task. Pontential
    use cases include e.g. formatting URLs for external use in emails sent
    by tasks.
    """
    abstract = True

    #: Name of the additional parameter passed to tasks
    #: that contains information about the original Flask request context.
    CONTEXT_ARG_NAME = '_flask_request_context'
    GLOBALS_ARG_NAME = '_flask_global_proxy'
    GLOBAL_KEYS = ['user']

    def __call__(self, *args, **kwargs):
        """Execute task code with given arguments."""
        call = lambda: super(RequestContextTask, self).__call__(*args, **kwargs)

        # set context
        context = kwargs.pop(self.CONTEXT_ARG_NAME, None)
        gl = kwargs.pop(self.GLOBALS_ARG_NAME, {})

        if context is None or has_request_context():
            return call()

        with app.test_request_context(**context):
            # set globals
            for i in gl:
                setattr(g, i, gl[i])
            # call
            result = call()
            # process a fake "Response" so that
            # [email protected]_request`` hooks are executed
            # app.process_response(make_response(result or ''))

        return result

    def apply_async(self, args=None, kwargs=None, **rest):
        self._include_request_context(kwargs)
        self._include_global(kwargs)
        return super(RequestContextTask, self).apply_async(args, kwargs, **rest)

    def apply(self, args=None, kwargs=None, **rest):
        self._include_request_context(kwargs)
        self._include_global(kwargs)
        return super(RequestContextTask, self).apply(args, kwargs, **rest)

    def retry(self, args=None, kwargs=None, **rest):
        self._include_request_context(kwargs)
        self._include_global(kwargs)
        return super(RequestContextTask, self).retry(args, kwargs, **rest)

    def _include_request_context(self, kwargs):
        """Includes all the information about current Flask request context
        as an additional argument to the task.
        """
        if not has_request_context():
            return

        # keys correspond to arguments of :meth:`Flask.test_request_context`
        context = {
            'path': request.path,
            'base_url': request.url_root,
            'method': request.method,
            'headers': dict(request.headers),
        }
        if '?' in request.url:
            context['query_string'] = request.url[(request.url.find('?') + 1):]

        kwargs[self.CONTEXT_ARG_NAME] = context

    def _include_global(self, kwargs):
        d = {}
        for z in self.GLOBAL_KEYS:
            if hasattr(g, z):
                d[z] = getattr(g, z)
        kwargs[self.GLOBALS_ARG_NAME] = d

To run a task in Request context mode, do –

@celery.task(base=RequestContextTask, bind=True)
def background_task(self, *args, **kwargs):
    # do stuff
    # more stuff

If you are wondering what the RequestContextTask class does, it simply stores all request context vars and global vars when a background task is called (task.delay()) and then unpacks those values to their proper places when the task is about to be run. The above snippet can be easily extended to store any value.

Another challenge that some people may face is the occasional Parsing/Serialization error. This happens because the data being sent to/from a function that is to be background executed is too complex.

Serialization is the process of converting complex data structures and objects into a plain string. Serialization of data is necessary because the background tasks and the main thread run in different processes. Now think how will the main thread communicate the celery thread to do some task. This is done using serialization of the concerned data. So to avoid serialization errors, it is recommended that you make background tasks such that they require only simple arguments to run and they return only simple data.

So basically keeping small and simple tasks is recommended when using Celery. Follow this golden rule and you will not run into any problems.

 

{{ Repost from my personal blog | http://aviaryan.in/blog/gsoc/celery-flask-good-ideas.html }}