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• gcc "relocation R_X86_64_PC32 against symbol `ff_M24A'" error when linking statically against ffmpeg on linux

  22 janvier 2016, par YB_Evil

  I am trying to build a JNI shared library which statically links to ffmpeg.

  But at the linking stage, gcc fails with the following error :

  /usr/bin/ld: ./lib_lin64/libswscale.a(swscale.o): relocation R_X86_64_PC32 against symbol `ff_M24A' can not be used when making a shared object; recompile with -fPIC

  I am using the following commands to compile my jni library :

  gcc -I $JAVA_HOME/include -I $JAVA_HOME/include/linux -I ./include -fPIC -c *.c
  

  gcc -shared -Wl,--no-undefined -o libnv_avc_dec.so *.o -Wl,-Bstatic -L./lib_lin64 -lavcodec -lavutil -lswresample -lswscale -Wl,-Bdynamic -lm

  And I only use h264 decoding feature, so I am also building ffmpeg from source with the minimal required feature set. The ./configure command I use is :

  ./configure \
--enable-pic --prefix=ffmpeg-dist \
--disable-debug --enable-version3 --enable-gpl \
--disable-everything --enable-hwaccel=h264_vdpau --enable-hwaccel=h264_vaapi --enable-hwaccel=h264_qsv --enable-hwaccel=h264_mmal \
--enable-decoder=h264 --enable-decoder=h264_vdpau --enable-decoder=h264_crystalhd --enable-decoder=h264_mmal --enable-decoder=h264_qsv \
--disable-iconv --disable-securetransport --disable-xlib --disable-zlib --disable-lzma  --disable-bzlib  --disable-doc --disable-programs --disable-avformat --disable-avfilter --disable-postproc

  So, as I understand, the linker tells me that ffmpeg should be compiled with -fPIC flag in order to make a shared library. But I believe that I’ve already done so by specifying the —enable-pic configure flag. And I am pretty much stuck here because I am not very familiar with autotools, nor with ffmpeg build process in particular.

  If this is the issue of ffmpeg .a libs not being compiled with -fPIC flag, how can i force it ? And if this is not the case, what am i doing wrong and how can i fix this error ?

  Environment details : Ubuntu 14.04.3 64-bit in Virtualbox, gcc 4.8.5 and 5.3 (both give the same results), ffmpeg v.2.8.5

• Edit audio files on the server with Meteor

  28 décembre 2015, par Mohammed Hussein

  I am building a Meteor application to split uploaded audio files.

  I upload the audio files and store them using GridFS :

  child = Npm.require('child_process');
  
  
  
  var fs = Npm.require('fs');
  
  
  
  storagePath= fs.realpathSync(process.env.PWD+'/audio');
  
  
  
  StaticServer.add('/audio', clipsPath);

  and then using a method I split the audio file using :

  child.exec(command);

  the command is the ffmpeg command used to cut the source audio file and store it on the storagePath.

  The application worked fine locally but when I tried to deploy it to digital ocean I got errors, stating that the file /audio does not exist.
  I use mupx to deploy and the error appears after "verifying deployment".

  Here is the error :

      -----------------------------------STDERR-----------------------------------
  
      eteor-dev-bundle@0.0.0 No README data
  
      => Starting meteor app on port:80
  
  
  
      /bundle/bundle/programs/server/node_modules/fibers/future.js:245
  
                                                      throw(ex);
  
                                                            ^
  
      Error: ENOENT, no such file or directory '/bundle/bundle/audio'
  
          at Object.fs.lstatSync (fs.js:691:18)
  
          at Object.realpathSync (fs.js:1279:21)
  
          at server/startup.js:10:20
  
          at /bundle/bundle/programs/server/boot.js:249:5
  
      npm WARN package.json meteor-dev-bundle@0.0.0 No description
  
      npm WARN package.json meteor-dev-bundle@0.0.0 No repository field.
  
      npm WARN package.json meteor-dev-bundle@0.0.0 No README data
  
      => Starting meteor app on port:80
  
  
  
      /bundle/bundle/programs/server/node_modules/fibers/future.js:245
  
                                                      throw(ex);
  
                                                            ^
  
      Error: ENOENT, no such file or directory '/bundle/bundle/audio'
  
          at Object.fs.lstatSync (fs.js:691:18)
  
          at Object.realpathSync (fs.js:1279:21)
  
          at server/startup.js:10:20
  
          at /bundle/bundle/programs/server/boot.js:249:5
  
  
  
      => Redeploying previous version of the app
  
  
  
      -----------------------------------STDOUT-----------------------------------
  
  
  
      To see more logs type 'mup logs --tail=50'
  
  
  
      ----------------------------------------------------------------------------

  The main question is how to generate an output file using ffmpeg and store it in a place where I can access it and display it in the browser.

• 10 Key Google Analytics Limitations You Should Be Aware Of

  9 mai 2022, par Erin

  Google Analytics (GA) is the biggest player in the web analytics space. But is it as “universal” as its brand name suggests ?
  

  Over the years users have pointed out a number of major Google Analytics limitations. Many of these are even more visible in Google Analytics 4. 

  Introduced in 2020, Google Analytics 4 (GA4) has been sceptically received. As the sunset date of 1st, July 2023 for the current version, Google Universal Analytics (UA), approaches, the dismay grows stronger.

  To the point where people are pleading with others to intervene : 

  

  Main limitations of Google Analytics

  Google Analytics 4 is advertised as a more privacy-centred, comprehensive and “intelligent” web analytics platform. 

  According to Google, the newest version touts : 

  • Machine learning at its core provides better segmentation and fast-track access to granular insights 
  • Privacy-by-design controls, addressing restrictions on cookies and new regulatory demands 
  • More complete understanding of customer journeys across channels and devices 

  Some of these claims hold true. Others crumble upon a deeper investigation. Newly advertised Google Analytics capabilities such as ‘custom events’, ‘predictive insights’ and ‘privacy consent mode’ only have marginal improvements. 

  Complex setup, poor UI and lack of support with migration also leave many other users frustrated with GA4. 

  

  Let’s unpack all the current (and legacy) limitations of Google Analytics you should account for. 

  1. No Historical Data Imports 

  Google rushed users to migrate from Universal Analytics to Google Analytics 4. But they overlooked one important precondition — backwards compatibility. 

  You have no way to import data from Google Universal Analytics to Google Analytics 4. 

  Historical records are essential for analysing growth trends and creating benchmarks for new marketing campaigns. Effectively, you are cut short from past insights — and forced to start strategising from scratch. 

  At present, Google offers two feeble solutions : 

  • Run data collection in parallel and have separate reporting for GA4 and UA until the latter is shut down. Then your UA records are gone. 
  • For Ecommerce data, manually duplicate events from UA at a new GA4 property while trying to figure out the new event names and parameters. 

  Google’s new data collection model is the reason for migration difficulties. 

  In Google Analytics 4, all analytics hits types — page hits, social hits, app/screen view, etc. — are recorded as events. Respectively, the “‘event’ parameter in GA4 is different from one in Google Universal Analytics as the company explains : 

  

  This change makes migration tedious — and Google offers little assistance with proper events and custom dimensions set up. 

  2. Data Collection Limits 

  If you’ve wrapped your head around new GA4 events, congrats ! You did a great job, but the hassle isn’t over. 

  You still need to pay attention to new Google Analytics limits on data collection for event parameters and user properties. 

  

  These apply to :

  • Automatically collected events
  • Enhanced measurement events
  • Recommended events 
  • Custom events 

  When it comes to custom events, GA4 also has a limit of 25 custom parameters per event. Even though it seems a lot, it may not be enough for bigger websites. 

  You can get higher limits by upgrading to Google Analytics 360, but the costs are steep. 

  3. Limited GDPR Compliance 

  Google Analytics has a complex history with European GDPR compliance. 

  A 2020 ruling by the Court of Justice of the European Union (CJEU) invalidated the Privacy Shield framework Google leaned upon. This framework allowed the company to regulate EU-US data transfers of sensitive user data. 

  But after this loophole was closed, Google faced a heavy series of privacy-related fines :

  • French data protection authority, CNIL, ruled that  “the transfers to the US of personal data collected through Google Analytics are illegal” — and proceeded to fine Google for a record-setting €150 million at the beginning of 2022. 
  • Austrian regulators also deemed Google in breach of GDPR requirements and also branded the analytics as illegal. 

  Other EU-member states might soon proceed with similar rulings. These, in turn, can directly affect Google Analytics users, whose businesses could face brand damage and regulatory fines for non-compliance. In fact, companies cannot select where the collected analytics data will be stored — on European servers or abroad — nor can they obtain this information from Google.

  Getting a web analytics platform that allows you to keep data on your own servers or select specific Cloud locations is a great alternative. 

  Google also has been lax with its cookie consent policy and doesn’t properly inform consumers about data collection, storage or subsequent usage. Google Analytics 4 addresses this issue to an extent. 

  By default, GA4 relies on first-party cookies, instead of third-party ones — which is a step forward. But the user privacy controls are hard to configure without losing most of the GA4 functionality. Implementing user consent mode to different types of data collection also requires a heavy setup. 

  4. Strong Reliance on Sampled Data 

  To compensate for ditching third-party cookies, GA4 more heavily leans on sampled data and machine learning to fill the gaps in reporting. 

  In GA4 sampling automatically applies when you :

  • Perform advanced analysis such as cohort analysis, exploration, segment overlap or funnel analysis with not enough data 
  • Have over 10,000,000 data rows and generate any type of non-default report 

  Google also notes that data sampling can occur at lower thresholds when you are trying to get granular insights. If there’s not enough data or because Google thinks it’s too complex to retrieve. 

  In their words :

  

  Data sampling adds “guesswork” to your reports, meaning you can’t be 100% sure of data accuracy. The divergence from actual data depends on the size and quality of sampled data. Again, this isn’t something you can control. 

  Unlike Google Analytics 4, Matomo applies no data sampling. Your reports are always accurate and fully representative of actual user behaviours. 

  5. No Proper Data Anonymization 

  Data anonymization allows you to collect basic analytics about users — visits, clicks, page views — but without personally identifiable information (or PII) such as geo-location, assigns tracking ID or other cookie-based data. 

  This reduced your ability to :

  • Remarket 
  • Identify repeating visitors
  • Do advanced conversion attribution 

  But you still get basic data from users who ignored or declined consent to data collection. 

  By default, Google Analytics 4 anonymizes all user IP addresses — an upgrade from UA. However, it still assigned a unique user ID to each user. These count as personal data under GDPR. 

  For comparison, Matomo provides more advanced privacy controls. You can anonymize :

  • Previously tracked raw data 
  • Visitor IP addresses
  • Geo-location information
  • User IDs 

  This can ensure compliance, especially if you operate in a sensitive industry — and delight privacy-mindful users ! 

  6. No Roll-Up Reporting

  Getting a bird’s-eye view of all your data is helpful when you need hotkey access to main sites — global traffic volume, user count or percentage of returning visitors.

  With Roll-Up Reporting, you can see global-performance metrics for multiple localised properties (.co.nz, .co.uk, .com, etc,) in one screen. Then zoom in on specific localised sites when you need to. 

  

  7. Report Processing Latency 

  The average data processing latency is 24-48 hours with Google Analytics. 

  Accounts with over 200,000 daily sessions get data refreshes only once a day. So you won’t be seeing the latest data on core metrics. This can be a bummer during one-day promo events like Black Friday or Cyber Monday when real-time information can prove to be game-changing ! 

  Matomo processes data with lower latency even for high-traffic websites. Currently, we have 6-24 hour latency for cloud deployments. On-premises web analytics can be refreshed even faster — within an hour or instantly, depending on the traffic volumes. 

  8. No Native Conversion Optimisation Features

  Google Analytics users have to use third-party tools to get deeper insights like how people are interacting with your webpage or call-to-action.

  You can use the free Google Optimize tool, but it comes with limits : 

  • No segmentation is available 
  • Only 10 simultaneous running experiments allowed 

  There isn’t a native integration between Google Optimize and Google Analytics 4. Instead, you have to manually link an Optimize Container to an analytics account. Also, you can’t select experiment dimensions in Google Analytics reports.

  What’s more, Google Optimize is a basic CRO tool, best suited for split testing (A/B testing) of copy, visuals, URLs and page layouts. If you want to get more advanced data, you need to pay for extra tools. 

  Matomo comes with a native set of built-in conversion optimization features : 

  • Heatmaps 
  • User session recording 
  • Sales funnel analysis 
  • A/B testing 
  • Form submission analytics 

  

  9. Deprecated Annotations

  Annotations come in handy when you need to provide extra context to other team members. For example, point out unusual traffic spikes or highlight a leak in the sales funnel. 

  This feature was available in Universal Analytics but is now gone in Google Analytics 4. But you can still quickly capture, comment and share knowledge with your team in Matomo. 

  You can add annotations to any graph that shows statistics over time including visitor reports, funnel analysis charts or running A/B tests. 

  10. No White Label Option 

  This might be a minor limitation of Google Analytics, but a tangible one for agency owners. 

  Offering an on-brand, embedded web analytics platform can elevate your customer experience. But white label analytics were never a thing with Google Analytics, unlike Matomo. 

  Wrap Up 

  Google set a high bar for web analytics. But Google Analytics inherent limitations around privacy, reporting and deployment options prompt more users to consider Google Analytics alternatives, like Matomo. 

  With Matomo, you can easily migrate your historical data records and store customer data locally or in a designated cloud location. We operate by a 100% unsampled data principle and provide an array of privacy controls for advanced compliance. 

  Start your 21-day free trial (no credit card required) to see how Matomo compares to Google Analytics ! 

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