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• nomenclature #2501 : Homogénéiser le vocabulaire : « plugins », « extensions », etc.

  28 mars 2012, par tetue -

  Wah super ! Mais attention à ne pas fermer trop vite les tickets. Celui-ci concerne bien l’*homogénéisation* du vocabulaire, pas seulement le nom des répertoires, or il reste des occurrences de « @extensions@ » dans les fichiers de langue, de « verrouillés » dans l’interface, (...)

• Video encoding task not working with Django Celery Redis FFMPEG and GraphQL

  18 juin 2023, par phanio

  I'm having a hard time trying to understand how is this FFMPEG encoding works while using Django, Celery, Redis, GraphQL and Docker too.

  


  I have this video / courses platform project and want I'm trying to do using FFMPEG, Celery and Redis is to create different video resolutions so I can display them the way Youtube does inside the videoplayer ( the videoplayer is handled in frontend by Nextjs and Apollo Client ), now on the backend I've just learned that in order to use properly the FFMPEG to resize the oridinal video size, I need to use Celery and Redis to perform asyncronus tasks. I've found a few older posts here on stackoverflow and google, but is not quite enough info for someone who is using the ffmpeg and clery and redis for the first time ( I've started already step by step and created that example that adds two numbers together with celery, that works well ). Now I'm not sure what is wrong with my code, because first of all I'm not really sure where should I trigger the task from, I mean from which file, because at the end of the task I want to send the data through api using GrapQL Strawberry.

  


  This is what I've tried by now :

  


  So first things first my project structure looks like this

  


  - backend #root directory
 --- backend
    -- __init__.py
    -- celery.py
    -- settings.py
    -- urls.py
      etc..

 --- static
   -- videos

 --- video
   -- models.py
   -- schema.py
   -- tasks.py
   -- types.py
   etc..

 --- .env

 --- db.sqlite3

 --- docker-compose.yml

 --- Dockerfile

 --- manage.py

 --- requirements.txt


  


  here is my settings.py file :

  


  from pathlib import Path
import os

# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent

DEBUG = True

ALLOWED_HOSTS=["localhost", "0.0.0.0", "127.0.0.1"]

DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'


# Application definition

INSTALLED_APPS = [
    "corsheaders",
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',

    "strawberry.django",
    "video"
]

etc...

STATIC_URL = '/static/'
MEDIA_URL = '/videos/'

STATICFILES_DIRS = [
    BASE_DIR / 'static',
    # BASE_DIR / 'frontend/build/static',
]

MEDIA_ROOT = BASE_DIR / 'static/videos'

STATIC_ROOT = BASE_DIR / 'staticfiles'

STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage'

CORS_ALLOW_ALL_ORIGINS = True


CELERY_BEAT_SCHEDULER = 'django_celery_beat.schedulers:DatabaseScheduler'

# REDIS CACHE
CACHES = {
    "default": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": f"redis://127.0.0.1:6379/1",
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
        },
    }
}

# Docker
CELERY_BROKER_URL = os.environ.get("CELERY_BROKER", "redis://redis:6379/0")
CELERY_RESULT_BACKEND = os.environ.get("CELERY_BROKER", "redis://redis:6379/0")


  


  This is my main urls.py file :

  


  from django.contrib import admin
from django.conf import settings
from django.conf.urls.static import static
from django.urls import path
from django.urls.conf import include
from strawberry.django.views import GraphQLView

from video.schema import schema

urlpatterns = [
    path('admin/', admin.site.urls),
    path("graphql", GraphQLView.as_view(schema=schema)),
]

if settings.DEBUG:
    urlpatterns += static(settings.MEDIA_URL,
                          document_root=settings.MEDIA_ROOT)
    urlpatterns += static(settings.STATIC_URL,
                          document_root=settings.STATIC_ROOT)


  


  This is my celery.py file :

  


  from __future__ import absolute_import, unicode_literals
import os
from celery import Celery
from django.conf import settings

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')

backend = Celery('backend')

backend.config_from_object('django.conf:settings', namespace="CELERY")

backend.autodiscover_tasks()

@backend.task(bind=True)
def debug_task(self):
    print('Request: {0!r}'.format(self.request))


  


  This is my init.py file :

  


  from .celery import backend as celery_backend

__all__ = ('celery_backend',)


  


  This is my Dockerfile :

  


  FROM python:3
ENV PYTHONUNBUFFERED=1

WORKDIR /usr/src/backend

RUN apt-get -y update
RUN apt-get -y upgrade
RUN apt-get install -y ffmpeg

COPY requirements.txt ./
RUN pip install -r requirements.txt


  


  This is my docker-compose.yml file :

  


  version: "3.8"

services:
  django:
    build: .
    container_name: django
    command: python manage.py runserver 0.0.0.0:8000
    volumes:
      - .:/usr/src/backend/
    ports:
      - "8000:8000"
    environment:
      - DEBUG=1
      - DJANGO_ALLOWED_HOSTS=localhost 127.0.0.1 [::1]
      - CELERY_BROKER=redis://redis:6379/0
      - CELERY_BACKEND=redis://redis:6379/0
    depends_on:
      - pgdb
      - redis

  celery:
    build: .
    command: celery -A backend worker -l INFO
    volumes:
      - .:/usr/src/backend
    depends_on:
      - django
      - redis

  pgdb:
    image: postgres
    container_name: pgdb
    environment:
      - POSTGRES_DB=postgres
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
    volumes:
      - pgdata:/var/lib/postgresql/data/

  redis:
    image: "redis:alpine"

volumes:
  pgdata:


  


  And now inside my video app folder :

  


  My models.py file :

  


  

  • here I've created separated fields for all resolution sizes, from video_file_2k to video_file_144, I was thinking that maybe after the process of the encoding this will populate those fields..

  


  


  from django.db import models
from django.urls import reverse


class Video(models.Model):
    video_id = models.AutoField(primary_key=True, editable=False)
    slug = models.SlugField(max_length=255)
    title = models.CharField(max_length=150, blank=True, null=True)
    description = models.TextField(blank=True, null=True)
    video_file = models.FileField(null=False, blank=False)
    video_file_2k = models.FileField(null=True, blank=True)
    video_file_fullhd = models.FileField(null=True, blank=True)
    video_file_hd = models.FileField(null=True, blank=True)
    video_file_480 = models.FileField(null=True, blank=True)
    video_file_360 = models.FileField(null=True, blank=True)
    video_file_240 = models.FileField(null=True, blank=True)
    video_file_144 = models.FileField(null=True, blank=True)
    category = models.CharField(max_length=64, blank=False, null=False)
    created_at = models.DateTimeField(
        ("Created at"), auto_now_add=True, editable=False)
    updated_at = models.DateTimeField(("Updated at"), auto_now=True)

    class Meta:
        ordering = ("-created_at",)
        verbose_name = ("Video")
        verbose_name_plural = ("Videos")

    def get_absolute_url(self):
        return reverse("store:video_detail", args=[self.slug])

    def __str__(self):
        return self.title


  


  This is my schema.py file :

  


  import strawberry
from strawberry.file_uploads import Upload
from typing import List
from .types import VideoType
from .models import Video
from .tasks import task_video_encoding_1080p, task_video_encoding_720p


@strawberry.type
class Query:
    @strawberry.field
    def videos(self, category: str = None) -> List[VideoType]:
        if category:
            videos = Video.objects.filter(category=category)
            return videos
        return Video.objects.all()

    @strawberry.field
    def video(self, slug: str) -> VideoType:
        if slug == slug:
            video = Video.objects.get(slug=slug)
            return video

    @strawberry.field
    def video_by_id(self, video_id: int) -> VideoType:
        if video_id == video_id:
            video = Video.objects.get(pk=video_id)

          # Here I've tried to trigger my tasks, when I visited 0.0.0.0:8000/graphql url
          # and I was querying for a video by it's id , then I've got the error from celery 
            task_video_encoding_1080p.delay(video_id)
            task_video_encoding_720p.delay(video_id)

            return video


@strawberry.type
class Mutation:
    @strawberry.field
    def create_video(self, slug: str, title: str, description: str, video_file: Upload, video_file_2k: str, video_file_fullhd: str, video_file_hd: str, video_file_480: str, video_file_360: str, video_file_240: str, video_file_144: str, category: str) -> VideoType:

        video = Video(slug=slug, title=title, description=description,
                      video_file=video_file, video_file_2k=video_file_2k, video_file_fullhd=video_file_fullhd, video_file_hd=video_file_hd, video_file_480=video_file_480, video_file_360=video_file_360, video_file_240=video_file_240, video_file_144=video_file_144,category=category)
        
        video.save()
        return video

    @strawberry.field
    def update_video(self, video_id: int, slug: str, title: str, description: str, video_file: str, category: str) -> VideoType:
        video = Video.objects.get(video_id=video_id)
        video.slug = slug
        video.title = title
        video.description = description
        video.video_file = video_file
        video.category = category
        video.save()
        return video

    @strawberry.field
    def delete_video(self, video_id: int) -> bool:
        video = Video.objects.get(video_id=video_id)
        video.delete
        return True


schema = strawberry.Schema(query=Query, mutation=Mutation)


  


  This is my types.py file ( strawberry graphql related ) :

  


  import strawberry

from .models import Video


@strawberry.django.type(Video)
class VideoType:
    video_id: int
    slug: str
    title: str
    description: str
    video_file: str
    video_file_2k: str
    video_file_fullhd: str
    video_file_hd: str
    video_file_480: str
    video_file_360: str
    video_file_240: str
    video_file_144: str
    category: str


  


  And this is my tasks.py file :

  


  from __future__ import absolute_import, unicode_literals
import os, subprocess
from django.conf import settings
from django.core.exceptions import ValidationError
from celery import shared_task
from celery.utils.log import get_task_logger
from .models import Video
FFMPEG_PATH = os.environ["IMAGEIO_FFMPEG_EXE"] = "/opt/homebrew/Cellar/ffmpeg/6.0/bin/ffmpeg"

logger = get_task_logger(__name__)


# CELERY TASKS
@shared_task
def add(x,y):
    return x + y


@shared_task
def task_video_encoding_720p(video_id):
    logger.info('Video Processing started')
    try:
        video = Video.objects.get(video_id=video_id)
        input_file_path = video.video_file.path
        input_file_url = video.video_file.url
        input_file_name = video.video_file.name

        # get the filename (without extension)
        filename = os.path.basename(input_file_url)

        # path to the new file, change it according to where you want to put it
        output_file_name = os.path.join('videos', 'mp4', '{}.mp4'.format(filename))
        output_file_path = os.path.join(settings.MEDIA_ROOT, output_file_name)

        # 2-pass encoding
        for i in range(1):
           new_video_720p = subprocess.call([FFMPEG_PATH, '-i', input_file_path, '-s', '1280x720', '-vcodec', 'mpeg4', '-acodec', 'libvo_aacenc', '-b', '10000k', '-pass', i, '-r', '30', output_file_path])
        #    new_video_720p = subprocess.call([FFMPEG_PATH, '-i', input_file_path, '-s', '{}x{}'.format(height * 16/9, height), '-vcodec', 'mpeg4', '-acodec', 'libvo_aacenc', '-b', '10000k', '-pass', i, '-r', '30', output_file_path])

        if new_video_720p == 0:
            # save the new file in the database
            # video.video_file_hd.name = output_file_name
            video.save(update_fields=['video_file_hd'])
            logger.info('Video Processing Finished')
            return video

        else:
            logger.info('Proceesing Failed.') # Just for now

    except:
        raise ValidationError('Something went wrong')


@shared_task
# def task_video_encoding_1080p(video_id, height):
def task_video_encoding_1080p(video_id):
    logger.info('Video Processing started')
    try:
        video = Video.objects.get(video_id=video_id)
        input_file_path = video.video_file.url
        input_file_name = video.video_file.name

        # get the filename (without extension)
        filename = os.path.basename(input_file_path)

        # path to the new file, change it according to where you want to put it
        output_file_name = os.path.join('videos', 'mp4', '{}.mp4'.format(filename))
        output_file_path = os.path.join(settings.MEDIA_ROOT, output_file_name)

        for i in range(1):
            new_video_1080p = subprocess.call([FFMPEG_PATH, '-i', input_file_path, '-s', '1920x1080', '-vcodec', 'mpeg4', '-acodec', 'libvo_aacenc', '-b', '10000k', '-pass', i, '-r', '30', output_file_path])

        if new_video_1080p == 0:
            # save the new file in the database
            # video.video_file_hd.name = output_file_name
            video.save(update_fields=['video_file_fullhd'])
            logger.info('Video Processing Finished')
            return video
        else:
            logger.info('Proceesing Failed.') # Just for now

    except:
        raise ValidationError('Something went wrong')


  


  In my first attempt I wasn't triggering the tasks no where, then I've tried to trigger the task from the schema.py file from graphql inside the video_by_id, but there I've got this error :

  


  backend-celery-1  | django.core.exceptions.ValidationError: ['Something went wrong']
backend-celery-1  | [2023-06-18 16:38:52,859: ERROR/ForkPoolWorker-4] Task video.tasks.task_video_encoding_1080p[d33b1a42-5914-467c-ad5c-00565bc8be6f] raised unexpected: ValidationError(['Something went wrong'])
backend-celery-1  | Traceback (most recent call last):
backend-celery-1  |   File "/usr/src/backend/video/tasks.py", line 81, in task_video_encoding_1080p
backend-celery-1  |     new_video_1080p = subprocess.call([FFMPEG_PATH, '-i', input_file_path, '-s', '1920x1080', '-vcodec', 'mpeg4', '-acodec', 'libvo_aacenc', '-b', '10000k', '-pass', i, '-r', '30', output_file_path])
backend-celery-1  |                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
backend-celery-1  |   File "/usr/local/lib/python3.11/subprocess.py", line 389, in call
backend-celery-1  |     with Popen(*popenargs, **kwargs) as p:
backend-celery-1  |          ^^^^^^^^^^^^^^^^^^^^^^^^^^^
backend-celery-1  |   File "/usr/local/lib/python3.11/subprocess.py", line 1026, in __init__
backend-celery-1  |     self._execute_child(args, executable, preexec_fn, close_fds,
backend-celery-1  |   File "/usr/local/lib/python3.11/subprocess.py", line 1883, in _execute_child
backend-celery-1  |     self.pid = _fork_exec(
backend-celery-1  |                ^^^^^^^^^^^
backend-celery-1  | TypeError: expected str, bytes or os.PathLike object, not int
backend-celery-1  | 
backend-celery-1  | During handling of the above exception, another exception occurred:
backend-celery-1  | 
backend-celery-1  | Traceback (most recent call last):
backend-celery-1  |   File "/usr/local/lib/python3.11/site-packages/celery/app/trace.py", line 477, in trace_task
backend-celery-1  |     R = retval = fun(*args, **kwargs)
backend-celery-1  |                  ^^^^^^^^^^^^^^^^^^^^
backend-celery-1  |   File "/usr/local/lib/python3.11/site-packages/celery/app/trace.py", line 760, in __protected_call__
backend-celery-1  |     return self.run(*args, **kwargs)
backend-celery-1  |            ^^^^^^^^^^^^^^^^^^^^^^^^^
backend-celery-1  |   File "/usr/src/backend/video/tasks.py", line 93, in task_video_encoding_1080p
backend-celery-1  |     raise ValidationError('Something went wrong')
backend-celery-1  | django.core.exceptions.ValidationError: ['Something went wrong']


  


  If anyone has done this kind of project or something like this please any suggestion or help is much appreciated.

  


  Thank you in advance !

  


• Google Analytics 4 (GA4) vs Matomo

  7 avril 2022, par Erin

  Google announced that Universal Analytics’ days are numbered. Universal Analytics will be replaced by Google Analytics 4 (or GA4) on the 1st of July 2023. 

  If Google Analytics users want to compare year-on-year data, they have until July 2022 to get set up and start collecting data before the sun sets on Universal Analytics (or UA).

  But is upgrading to Google Analytics 4 the right move ? There’s a lot to consider, and many organisations are looking for an alternative to Google Analytics. So in this blog, we’ll compare GA4 to Matomo – the leading Google Analytics alternative. 

  In this blog, we’ll look at :

  What is Matomo ?

  Matomo is a powerful privacy-first web analytics platform that gives you 100% data ownership. First launched in 2007, Matomo is now the world’s leading open-source web analytics platform and is used by more than 1 million websites. 

  Matomo’s core values are based on ethical data collection and processing. Consistently more businesses and organisations from around the globe are adopting data-privacy-compliant web analytics solutions like Matomo. 

  Matomo offers both Cloud and On-Premise solutions (and a five-star rated WordPress plugin), making for an adaptable and flexible solution. 

  What is Google Analytics 4 ?

  Google Analytics 4 is the latest version of Google Analytics and represents a completely new approach to data-modelling than its predecessor, Universal Analytics. For an in-depth look at how GA4 and UA compare, check out this Google Analytics 4 vs Universal Analytics comparison. 

  Google Analytics 4 will soon be the only available version of analytics software from Google. So what’s the issue ? Surely, in 2022, Google makes it easy to migrate to their newest (and only) analytics platform ? Not quite.

  Google Analytics 4 vs Matomo

  Whilst the core purpose of GA4 and Matomo is similar (providing web analytics that help to optimise your website and grow your business), there are several key differences that organisations should consider before making the switch.

  Importing Historical Data from Universal Analytics

  Google Analytics 4

  Users assuming that historical data from Universal Analytics could be imported into Google Analytics 4 were faced with swift disappointment. Unfortunately, Google Analytics 4 does not have an option to import data from its predecessor, Universal Analytics. This means that businesses won’t be able to import and compare data from previous years.

  Matomo

  If you don’t want to start from scratch with your web analytics data, then Matomo is an ideal solution for data continuity. Matomo offers users the ability to import their historical Universal Analytics data. So you can keep all that valuable historical data you’ve collected over the years.

  

  User Interface

  Google Analytics 4

  GA4’s new user interface has been met with mixed reviews. Many claim that it’s overly complex and difficult to navigate. Some have even suggested that the tool has been designed specifically for enterprises with specialised analytics teams. 

  

  Matomo

  Matomo, on the other hand, is recognised for an easy to use interface, with a rating of 4.5 out of 5 stars for ease of use on Capterra. Matomo perfectly balances powerful features with a user-friendly interface so valuable insights are only a click away. There’s a reason why over 1 million websites are using Matomo. 

  

  Advanced Behavioural Analytics Features 

  Google Analytics 4

  While Google Analytics is undoubtedly robust in some areas (machine learning, for instance), what it really lacks is advanced behavioural analytics. Heatmaps, session recordings and other advanced tools can give you valuable insights into how users are engaging with your site. Well beyond pageviews and other metrics.

  Unfortunately, with this new generation of GA, Google still hasn’t introduced these features. So users have to manage subscriptions and tracking in third-party behavioural analytics tools like Hotjar or Lucky Orange, for example. This is inefficient, costly and time-consuming to manage. 

  

  Matomo 

  Meanwhile, Matomo is a one-stop shop for all of your web analytics needs. Not only do you get access to the metrics you’ve grown accustomed to with Universal Analytics, but you also get built-in behavioural analytics features like Heatmaps, Scroll Depth, Session Recordings and more. 

  Want to know if visitors are reaching your call to action at the bottom of the page ? Scroll Depth will answer that.

  Want to know why visitors aren’t clicking through to the next page ? Heatmaps will give you the insights you need.

  You get the picture – the full picture, that is. 

  

  Data Accuracy

  Google Analytics 4

  GA4 aims to make web and app analytics more privacy-centric by reducing the reliance on cookies to record certain events across platforms and devices. 

  However, when site and application visitors opt-out of cookie tracking, GA4 instead relies on machine learning to fill in the gaps. Data sampling could mean that your business is making business decisions based on inaccurate reports. 

  Matomo

  Data is the backbone of web analytics, so why make critical business decisions on sampled data ? With Matomo, you’re guaranteed 100% unsampled accurate data. So you can rest assured that any decisions you make are based on actual facts. 

  Compliance with Privacy Laws (GDPR, CCPA, etc.) 

  Google Analytics 4

  Google is making changes in an attempt to become compliant with privacy laws. However, even with GA4, users are still transferring data to the US. For this reason, both Austrian and French governments have ruled Google Analytics illegal under GDPR.

  The only possible workaround is “Privacy Shield 2.0”, but GDPR experts are still sceptical of this one. 

  Matomo

  If compliance with global privacy laws is a concern (and it should be), then Matomo is the clear winner here. 

  As an EU hosted web analytics tool, your data is stored in Europe, and no data is transferred to the US. On the other hand, if you choose to self-host, the data is stored in your country of choice.

  In addition, with cookieless tracking enabled, you can say goodbye to those pesky cookie consent screens. 

  Also, remember that under GDPR, and many other data privacy laws like CCPA and LGPD, end users have a legal right to access, amend and/or erase the personal data collected about them. 

  With Matomo you get 100% ownership of your web analytics data. This means that we don’t on-sell to third parties ; can’t claim ownership of the data ; and you can export your data at any time.

  

  Wrap up

  At the end of the day, the worst thing an organisation can do is nothing. Waiting until July 2023 to migrate to GA4 or another web analytics platform would be very disruptive and costly. Organisations need to consider their options now and start migrating in the next few months. 

  With all that said, moving to Google Analytics 4 could prove to be a costly and time-consuming operation. The global trend towards increased data privacy is a threat to platforms like Google Analytics which uses data for advertising and transfers data across borders.

  With Matomo, you get an easy to use all-in-one web analytics platform and keep your historical Universal Analytics data. Plus, you can future-proof your business by being compliant with global privacy laws and get access to advanced behavioural analytics features. 

  There’s a lot to weigh up here but fortunately, getting started with Matomo is easy. Try it free for 21-days (no credit card required) and see for yourself why over 1 million websites choose Matomo. 

  While this is the end of the road for Universal Analytics, it’s also an opportune time for organisations to find a better fit web analytics tool. 

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