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• 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 Optimize vs Matomo A/B Testing : Everything You Need to Know

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular A/B testing tool marketers use to validate the performance of different marketing assets, website design elements and promotional offers. 

  But by September 2023, Google will sunset both free and paid versions of the Optimize product. 

  If you’re searching for an equally robust, but GDPR compliant, privacy-friendly alternative to Google Optimize, have a look at Matomo A/B Testing. 

  Integrated with our analytics platform and conversion rate optimisation (CRO) tools, Matomo allows you to run A/B and A/B/n tests without any usage caps or compromises in user privacy.

  Disclaimer : Please note that the information provided in this blog post is for general informational purposes only and is not intended to provide legal advice. Every situation is unique and requires a specific legal analysis. If you have any questions regarding the legal implications of any matter, please consult with your legal team or seek advice from a qualified legal professional.

  Google Optimize vs Matomo : Key Capabilities Compared 

  This guide shows how Matomo A/B testing stacks against Google Optimize in terms of features, reporting, integrations and pricing.

  Supported Platforms 

  Google Optimize supports experiments for dynamic websites and single-page mobile apps only. 

  If you want to run split tests in mobile apps, you’ll have to do so via Firebase — Google’s app development platform. It also has a free tier but paid usage-based subscription kicks in after your product(s) reaches a certain usage threshold. 

  Google Optimize also doesn’t support CRO experiments for web or desktop applications, email campaigns or paid ad campaigns.Matomo A/B Testing, in contrast, allows you to run experiments in virtually every channel. We have three installation options — using JavaScript, server-side technology, or our mobile tracking SDK. These allow you to run split tests in any type of web or mobile app (including games), a desktop product, or on your website. Also, you can do different email marketing tests (e.g., compare subject line variants).

  A/B Testing 

  A/B testing (split testing) is the core feature of both products. Marketers use A/B testing to determine which creative elements such as website microcopy, button placements and banner versions, resonate better with target audiences. 

  You can benchmark different versions against one another to determine which variation resonates more with users. Or you can test an A version against B, C, D and beyond. This is called A/B/n testing. 

  Both Matomo A/B testing and Google Optimize let you test either separate page elements or two completely different landing page designs, using redirect tests. You can show different variants to different user groups (aka apply targeting criteria). For example, activate tests only for certain device types, locations or types of on-site behaviour. 

  The advantage of Matomo is that we don’t limit the number of concurrent experiments you can run. With Google Optimize, you’re limited to 5 simultaneous experiments. Likewise, 

  Matomo lets you select an unlimited number of experiment objectives, whereas Google caps the maximum choice to 3 predefined options per experiment. 

  Objectives are criteria the underlying statistical model will use to determine the best-performing version. Typically, marketers use metrics such as page views, session duration, bounce rate or generated revenue as conversion goals. 

  

  Multivariate testing (MVT)

  Multivariate testing (MVT) allows you to “pack” several A/B tests into one active experiment. In other words : You create a stack of variants to determine which combination drives the best marketing outcomes. 

  For example, an MVT experiment can include five versions of a web page, where each has a different slogan, product image, call-to-action, etc. Visitors are then served with a different variation. The tracking code collects data on their behaviours and desired outcomes (objectives) and reports the results.

  MVT saves marketers time as it’s a great alternative to doing separate A/B tests for each variable. Both Matomo and Google Optimize support this feature. However, Google Optimize caps the number of possible combinations at 16, whereas Matomo has no limits. 

  Redirect Tests

  Redirect tests, also known as split URL tests, allow you to serve two entirely different web page versions to users and compare their performance. This option comes in handy when you’re redesigning your website or want to test a localised page version in a new market. 

  Also, redirect tests are a great way to validate the performance of bottom-of-the-funnel (BoFU) pages as a checkout page (for eCommerce websites), a pricing page (for SaaS apps) or a contact/booking form (for a B2B service businesses). 

  You can do split URL tests with Google Optimize and Matomo A/B Testing. 

  Experiment Design 

  Google Optimize provides a visual editor for making simple page changes to your website (e.g., changing button colour or adding several headline variations). You can then preview the changes before publishing an experiment. For more complex experiments (e.g., testing different page block sequences), you’ll have to codify experiments using custom JavaScript, HTML and CSS.

  In Matomo, all A/B tests are configured on the server-side (i.e., by editing your website’s raw HTML) or client-side via JavaScript. Afterwards, you use the Matomo interface to start or schedule an experiment, set objectives and view reports. 

  Experiment Configuration 

  Marketers know how complex customer journeys can be. Multiple factors — from location and device to time of the day and discount size — can impact your conversion rates. That’s why a great CRO app allows you to configure multiple tracking conditions. 

  Matomo A/B testing comes with granular controls. First of all, you can decide which percentage of total web visitors participate in any given experiment. By default, the number is set to 100%, but you can change it to any other option. 

  Likewise, you can change which percentage of traffic each variant gets in an experiment. For example, your original version can get 30% of traffic, while options A and B receive 40% each. We also allow users to specify custom parameters for experiment participation. You can only show your variants to people in specific geo-location or returning visitors only. 

  Finally, you can select any type of meaningful objective to evaluate each variant’s performance. With Matomo, you can either use standard website analytics metrics (e.g., total page views, bounce rate, CTR, visit direction, etc) or custom goals (e.g., form click, asset download, eCommerce order, etc). 

  In other words : You’re in charge of deciding on your campaign targeting criteria, duration and evaluation objectives.

  A free Google Optimize account comes with three main types of user targeting options : 

  • Geo-targeting at city, region, metro and country levels. 

  • Technology targeting  by browser, OS or device type, first-party cookie, etc. 
  • Behavioural targeting based on metrics like “time since first arrival” and “page referrer” (referral traffic source). 

  Users can also configure other types of tracking scenarios (for example to only serve tests to signed-in users), using condition-based rules. 

  Reporting 

  Both Matomo and Google Optimize use different statistical models to evaluate which variation performs best. 

  Matomo relies on statistical hypothesis testing, which we use to count unique visitors and report on conversion rates. We analyse all user data (with no data sampling applied), meaning you get accurate reporting, based on first-hand data, rather than deductions. For that reason, we ask users to avoid drawing conclusions before their experiment participation numbers reach a statistically significant result. Typically, we recommend running an experiment for at least several business cycles to get a comprehensive report. 

  Google Optimize, in turn, uses Bayesian inference — a statistical method, which relies on a random sample of users to compare the performance rates of each creative against one another. While a Bayesian model generates CRO reports faster and at a bigger scale, it’s based on inferences.

  Model developers need to have the necessary skills to translate subjective prior beliefs about the probability of a certain event into a mathematical formula. Since Google Optimize is a proprietary tool, you cannot audit the underlying model design and verify its accuracy. In other words, you trust that it was created with the right judgement. 

  In comparison, Matomo started as an open-source project, and our source code can be audited independently by anyone at any time. 

  Another reporting difference to mind is the reporting delays. Matomo Cloud generates A/B reports within 6 hours and in only 1 hour for Matomo On-Premise. Google Optimize, in turn, requires 12 hours from the first experiment setup to start reporting on results. 

  When you configure a test experiment and want to quickly verify that everything is set up correctly, this can be an inconvenience.

  User Privacy & GDPR Compliance 

  Google Optimize works in conjunction with Google Analytics, which isn’t GDPR compliant. 

  For all website traffic from the EU, you’re therefore obliged to show a cookie consent banner. The kicker, however, is that you can only show an Optimize experiment after the user gives consent to tracking. If the user doesn’t, they will only see an original page version. Considering that almost 40% of global consumers reject cookie consent banners, this can significantly affect your results.

  This renders Google Optimize mostly useless in the EU since it would only allow you to run tests with a fraction ( 60%) of EU traffic — and even less if you apply any extra targeting criteria. 

  In comparison, Matomo is fully GDPR compliant. Therefore, our users are legally exempt from displaying cookie-consent banners in most EU markets (with Germany and the UK being an exception). Since Matomo A/B testing is part of Matomo web analytics, you don’t have to worry about GDPR compliance or breaches in user privacy. 

  Digital Experience Intelligence 

  You can get comprehensive statistical data on variants’ performance with Google Optimize. But you don’t get further insights on why some tests are more successful than others. 

  Matomo enables you to collect more insights with two extra features :

  • User session recordings : Monitor how users behave on different page versions. Observe clicks, mouse movements, scrolls, page changes, and form interactions to better understand the users’ cumulative digital experience. 
  • Heatmaps : Determine which elements attract the most users’ attention to fine-tune your split tests. With a standard CRO tool, you only assume that a certain page element does matter for most users. A heatmap can help you determine for sure. 

  Both of these features are bundled into your Matomo Cloud subscription. 

  Integrations 

  Both Matomo and Google Optimize integrate with multiple other tools. 

  Google Optimize has native integrations with other products in the marketing family — GA, Google Ads, Google Tag Manager, Google BigQuery, Accelerated Mobile Pages (AMP), and Firebase. Separately, other popular marketing apps have created custom connectors for integrating Google Optimize data. 

  Matomo A/B Testing, in turn, can be combined with other web analytics and CRO features such as Funnels, Multi-Channel Attribution, Tag Manager, Form Analytics, Heatmaps, Session Recording, and more ! 

  You can also conveniently export your website analytics or CRO data using Matomo Analytics API to analyse it in another app. 

  Pricing 

  Google Optimize is a free tool but has usage caps. If you want to schedule more than 5 concurrent experiments or test more than 16 variants at once, you’ll have to upgrade to Optimize 360. Optimize 360 prices aren’t listed publicly but are said to be closer to six figures per year. 

  Matomo A/B Testing is available with every Cloud subscription (starting from €19) and Matomo On-Premise users can also get A/B Testing as a plugin (starting from €199/year). In each case, there are no caps or data limits. 

  Google Optimize vs Matomo A/B Testing : Comparison Table

  Features/capabilities	Google Optimize	Matomo A/B test
  Supported channels	Web	Web, mobile, email, digital campaigns
  A/B testing
  Multivariate testing (MVT)
  Split URL tests
  Web analytics integration	Native with UA/GA4	Native with Matomo You can also migrate historical UA (GA3) data to Matomo
  Audience segmentation	Basic	Advanced
  Geo-targeting
  Technology targeting
  Behavioural targeting	Basic	Advanced
  Reporting model	Bayesian analysis	Statistical hypothesis testing
  Report availability	Within 12 hours after setup	6 hours for Matomo Cloud 1 hour for Matomo On-Premise
  Heatmaps		Included with Matomo Cloud
  Session recordings		Included with Matomo Cloud
  GDPR compliance
  Support	Self-help desk on a free tier	Self-help guides, user forum, email
  Price	Free limited tier	From €19 for Cloud subscription From €199/year as plugin for On-Premise

  Final Thoughts : Who Benefits the Most From an A/B Testing Tool ?

  Split testing is an excellent method for validating various assumptions about your target customers. 

  With A/B testing tools you get a data-backed answer to research hypotheses such as “How different pricing affects purchases ?”, “What contact button placement generates more clicks ?”, “Which registration form performs best with new app subscribers ?” and more. 

  Such insights can be game-changing when you’re trying to improve your demand-generation efforts or conversion rates at the BoFu stage. But to get meaningful results from CRO tests, you need to select measurable, representative objectives.

  For example, split testing different pricing strategies for low-priced, frequently purchased products makes sense as you can run an experiment for a couple of weeks to get a statistically relevant sample. 

  But if you’re in a B2B SaaS product, where the average sales cycle takes weeks (or months) to finalise and things like “time-sensitive discounts” or “one-time promos” don’t really work, getting adequate CRO data will be harder. 

  To see tangible results from CRO, you’ll need to spend more time on test ideation than implementation. Your team needs to figure out : which elements to test, in what order, and why. 

  Effective CRO tests are designed for a specific part of the funnel and assume that you’re capable of effectively identifying and tracking conversions (goals) at the selected stage. This alone can be a complex task since not all customer journeys are alike. For SaaS websites, using a goal like “free trial account registration” can be a good starting point.

  A good test also produces a meaningful difference between the proposed variant and the original version. As Nima Yassini, Partner at Deloitte Digital, rightfully argues :

  “I see people experimenting with the goal of creating an uplift. There’s nothing wrong with that, but if you’re only looking to get wins you will be crushed when the first few tests fail. The industry average says that only one in five to seven tests win, so you need to be prepared to lose most of the time”.

  In many cases, CRO tests don’t provide the data you expected (e.g., people equally click the blue and green buttons). In this case, you need to start building your hypothesis from scratch. 

  At the same time, it’s easy to get caught up in optimising for “vanity metrics” — such that look good in the report, but don’t quite match your marketing objectives. For example, better email headline variations can improve your email open rates. But if users don’t proceed to engage with the email content (e.g. click-through to your website or use a provided discount code), your efforts are still falling short. 

  That’s why developing a baseline strategy is important before committing to an A/B testing tool. Google Optimize appealed to many users because it’s free and allows you to test your split test strategy cost-effectively. 

  With its upcoming depreciation, many marketers are very committed to a more expensive A/B tool (especially when they’re not fully sure about their CRO strategy and its results). 

  Matomo A/B testing is a cost-effective, GDPR-compliant alternative to Google Optimize with a low learning curve and extra competitive features. 

  Discover if Matomo A/B Testing is the ideal Google Optimize alternative for your organization with our free 21-day trial. No credit card required.

• dyld[16458] : Library not loaded : @rpath/libavcodec.framework/libavcodec while running my flutter app on iOS

  31 janvier 2023, par Stéphane de Luca

  Compilation is successful. But When I run the code, I get this.

  


  Here is the full trace.

  


  Any idea ?

  


  dyld[16458]: Library not loaded: @rpath/libavcodec.framework/libavcodec
  Referenced from: <3500F5CF-B1D2-30EC-8D7F-1C29BD45D05E> /private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Runner
  Reason: tried: '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/preboot/Cryptexes/OS@rpath/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/System/Library/Frameworks/libavcodec.framework/libavcodec' (errno=2, not in dyld cache)
Library not loaded: @rpath/libavcodec.framework/libavcodec
  Referenced from: <3500F5CF-B1D2-30EC-8D7F-1C29BD45D05E> /private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Runner
  Reason: tried: '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2, not in dyld cache), '/private/preboot/Cryptexes/OS/usr/lib/swift/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/private/var/containers/Bundle/Application/984D87E5-818C-49A9-9CB5-F0CC3160D2FF/Runner.app/Frameworks/libavcodec.framework/libavcodec' (errno=2), '/usr/lib/swif
dyld config: DYLD_LIBRARY_PATH=/usr/lib/system/introspection DYLD_INSERT_LIBRARIES=/usr/lib/libBacktraceRecording.dylib:/usr/lib/libMainThreadChecker.dylib:/usr/lib/libRPAC.dylib:/Developer/Library/PrivateFrameworks/DTDDISupport.framework/libViewDebuggerSupport.dylib
(lldb) 


  


  The pod is as follows :

  


  # Uncomment this line to define a global platform for your project
platform :ios, '14.0'

# CocoaPods analytics sends network stats synchronously affecting flutter build latency.
ENV['COCOAPODS_DISABLE_STATS'] = 'true'

project 'Runner', {
  'Debug' => :debug,
  'Profile' => :release,
  'Release' => :release,
}

def flutter_root
  generated_xcode_build_settings_path = File.expand_path(File.join('..', 'Flutter', 'Generated.xcconfig'), __FILE__)
  unless File.exist?(generated_xcode_build_settings_path)
    raise "#{generated_xcode_build_settings_path} must exist. If you're running pod install manually, make sure flutter pub get is executed first"
  end

  File.foreach(generated_xcode_build_settings_path) do |line|
    matches = line.match(/FLUTTER_ROOT\=(.*)/)
    return matches[1].strip if matches
  end
  raise "FLUTTER_ROOT not found in #{generated_xcode_build_settings_path}. Try deleting Generated.xcconfig, then run flutter pub get"
end

require File.expand_path(File.join('packages', 'flutter_tools', 'bin', 'podhelper'), flutter_root)

flutter_ios_podfile_setup

target 'Runner' do
  use_frameworks!
  use_modular_headers!

  flutter_install_all_ios_pods File.dirname(File.realpath(__FILE__))
end

post_install do |installer|
  installer.pods_project.targets.each do |target|
    flutter_additional_ios_build_settings(target)
  end
end


  


  The yaml :

  


  
environment:
  sdk: '>=2.18.2 <3.0.0'

# Dependencies specify other packages that your package needs in order to work.
# To automatically upgrade your package dependencies to the latest versions
# consider running `flutter pub upgrade --major-versions`. Alternatively,
# dependencies can be manually updated by changing the version numbers below to
# the latest version available on pub.dev. To see which dependencies have newer
# versions available, run `flutter pub outdated`.
dependencies:
  flutter:
    sdk: flutter
  flutter_localizations:
    sdk: flutter
  # The following adds the Cupertino Icons font to your application.
  # Use with the CupertinoIcons class for iOS style icons.
  cupertino_icons: ^1.0.2
  video_editor: ^1.5.2
  image_picker: ^0.8.6
  helpers: ^1.2.0
  intl: ^0.17.0
  i18n_extension: ^5.0.1
  optimized_cached_image: ^3.0.1
  youtube_player_flutter: ^8.1.1
  flutter_launcher_icons: ^0.11.0
  flutter_lorem: ^2.0.0
  advance_image_picker: ^0.1.7+1
  wechat_assets_picker: ^8.1.4
  lecle_flutter_absolute_path: ^0.0.2+1
  #ffmpeg_kit_flutter: 5.1.0-LTS
  path_provider: ^2.0.11
  video_thumbnail: ^0.5.3
  flutter_document_picker: ^5.1.0
  flutter_login: ^4.1.1
  #flutter_absolute_path: ^1.0.6
  # flutter_absolute_path:
  #     git:
  #       url: https://github.com/ValeriusGC/flutter_absolute_path.git
  uuid: ^3.0.6
  flutter_form_builder: ^7.7.0
  form_builder_validators: ^8.4.0
  state_persistence: ^0.1.0
  shared_preferences: ^2.0.15
  firebase_core: ^2.4.0
  firebase_storage: ^11.0.8
  video_compress: ^3.1.2
  connectivity_plus: ^3.0.2
  internet_connection_checker: ^1.0.0+1
  cached_video_player: ^2.0.3
  visibility_detector: ^0.3.3
  firebase_database: ^10.0.7
  firebase_auth: ^4.2.1
  firebase_dynamic_links: ^5.0.9
  cloud_firestore: ^4.2.0
  cloud_functions: ^4.0.6
  cached_network_image: ^3.2.3
  ffmpeg_kit_flutter_min_gpl: ^5.1.0
  video_player: ^2.4.10
  provider: ^6.0.5
  camera: ^0.9.8+1
  share_plus: ^6.3.0
  package_info_plus: ^3.0.2

dependency_overrides:
  ffmpeg_kit_flutter_min_gpl: ^5.1.0-LTS

dev_dependencies:
  flutter_test:
    sdk: flutter


  # The "flutter_lints" package below contains a set of recommended lints to
  # encourage good coding practices. The lint set provided by the package is
  # activated in the `analysis_options.yaml` file located at the root of your
  # package. See that file for information about deactivating specific lint
  # rules and activating additional ones.
  flutter_lints: ^2.0.0

``