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• Anomalie #3592 (En cours) : Robots.txt.html et tests de compatibilité mobile de Google

  25 novembre 2019, par Ben .

  Je rouvre pour (re)discuter du sujet je viens d’ajouter 2 captures d’écrans avec l’état actuel de contrib .

  Faut il ajouter 2 lignes au robots.txt actuel ?

  Allow :    /local/cache-gd2/
  Allow :    /local/cache-vignettes/
  

  https://git.spip.net/SPIP/dist/src/branch/master/robots.txt.html

• 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. 

  21 day free trial. No credit card required.

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I hereby accept the terms and conditions and the data processing agreeement (DPA).

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  

  » Start improving your websites now

  

• Why ffmpeg launched on a google cloud function can throw "Error : Output stream error : Maximum call stack size exceeded" ?

  15 décembre 2020, par Stepan Shilin

  I'm trying to process video files with ffmpeg running on google cloud functions. Video files are downloaded from a google file storage, processed in stream by fluent-ffmpeg and streamed to a new google storage file. It works on smaller files but throws an "Output stream error : Maximum call stack size exceeded" on larger files.

  



  I tried running the code on a normal pc, and I haven't encountered this error, even with larger files.

  



  These are the parameters I deploy the function with

  



  gcloud functions deploy $FUNCTION_NAME --runtime nodejs8 --trigger-http --timeout=180 --memory 256


  



  This is the code that processes video

  



  function cutVideo({videoUrl, startTime, duration, dist}) {
    return ffmpeg(videoUrl)
    .outputOptions('-movflags frag_keyframe+empty_moov')
    .videoCodec('copy')
    .audioCodec('copy')
    .format('mp4')
    .setStartTime(startTime)
    .setDuration(duration);
}

const sectionStream = cutVideo({
    videoUrl,
    startTime,
    duration,
    dist: tempFilePath,
});

const outputStream = bucket.file(sectionPath)
.createWriteStream({
    metadata: {
        contentType: config.contentType,
    },
    public: true,
});


  



  Actual error stack looks like this

  



    Error: Output stream error: Maximum call stack size exceeded&#xA;                                                                  at Pumpify.<anonymous> (/srv/node_modules/fluent-ffmpeg/lib/processor.js:498:34)&#xA;                                                                  at emitOne (events.js:121:20)&#xA;                                                                  at Pumpify.emit (events.js:211:7)&#xA;                                                                  at Pumpify.Duplexify._destroy (/srv/node_modules/duplexify/index.js:191:15)&#xA;                                                                  at /srv/node_modules/duplexify/index.js:182:10&#xA;                                                                  at _combinedTickCallback (internal/process/next_tick.js:132:7)&#xA;                                                                  at process._tickDomainCallback (internal/process/next_tick.js:219:9)&#xA;  RangeError: Maximum call stack size exceeded&#xA;                                                                  at replaceProjectIdToken (/srv/node_modules/@google-cloud/projectify/build/src/index.js:28:31)&#xA;                                                                  at replaceProjectIdToken (/srv/node_modules/@google-cloud/projectify/build/src/index.js:37:30)&#xA;                                                                  at replaceProjectIdToken (/srv/node_modules/@google-cloud/projectify/build/src/index.js:37:30)&#xA;                                                                  at value.map.v (/srv/node_modules/@google-cloud/projectify/build/src/index.js:30:32)&#xA;                                                                  at Array.map (<anonymous>)&#xA;                                                                  at replaceProjectIdToken (/srv/node_modules/@google-cloud/projectify/build/src/index.js:30:23)&#xA;                                                                  at replaceProjectIdToken (/srv/node_modules/@google-cloud/projectify/build/src/index.js:37:30)&#xA;                                                                  at replaceProjectIdToken (/srv/node_modules/@google-cloud/projectify/build/src/index.js:37:30)&#xA;                                                                  at value.map.v (/srv/node_modules/@google-cloud/projectify/build/src/index.js:30:32)&#xA;                                                                  at Array.map (<anonymous>)&#xA;</anonymous></anonymous></anonymous>

  



  What could cause this error on a google cloud function ?