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• Server Move For multimedia.cx

  1er août 2014, par Multimedia Mike — General

  I made a big change to multimedia.cx last week : I moved hosting from a shared web hosting plan that I had been using for 10 years to a dedicated virtual private server (VPS). In short, I now have no one to blame but myself for any server problems I experience from here on out.

  The tipping point occurred a few months ago when my game music search engine kept breaking regardless of what technology I was using. First, I had an admittedly odd C-based CGI solution which broke due to mysterious binary compatibility issues, the sort that are bound to occur when trying to make a Linux binary run on heterogeneous distributions. The second solution was an SQLite-based solution. Like the first solution, this worked great until it didn’t work anymore. Something else mysteriously broke vis-à-vis PHP and SQLite on my server. I started investigating a MySQL-based full text search solution but couldn’t make it work, and decided that I shouldn’t have to either.

  Ironically, just before I finished this entire move operation, I noticed that my SQLite-based FTS solution was working again on the old shared host. I’m not sure when that problem went away. No matter, I had already thrown the switch.

  How Hard Could It Be ?
  We all have thresholds for the type of chores we’re willing to put up with and which we’d rather pay someone else to perform. For the past 10 years, I felt that administering a website’s underlying software is something that I would rather pay someone else to worry about. To be fair, 10 years ago, I don’t think VPSs were a thing, or at least a viable thing in the consumer space, and I wouldn’t have been competent enough to properly administer one. Though I would have been a full-time Linux user for 5 years at that point, I was still the type to build all of my own packages from source (I may have still been running Linux From Scratch 10 years ago) which might not be the most tractable solution for server stability.

  These days, VPSs are a much more affordable option (easily competitive with shared web hosting). I also realized I know exactly how to install and configure all the software that runs the main components of the various multimedia.cx sites, having done it on local setups just to ensure that my automated backups would actually be useful in the event of catastrophe.

  All I needed was the will to do it.

  The Switchover Process
  Here’s the rough plan :

  • Investigate options for both VPS providers and mail hosts– I might be willing to run a web server but NOT a mail server
  • Start plotting several months in advance of my yearly shared hosting renewal date
  • Screw around for several months, playing video games and generally finding reasons to put off the move
  • Panic when realizing there are only a few days left before the yearly renewal comes due

  So that’s the planning phase. BTW, I chose Digital Ocean for VPS and Zoho for email hosting. Here’s the execution phase I did last week :

  • Register with Digital Ocean and set up DNS entries to point to the old shared host for the time being
  • Once the D-O DNS servers respond correctly using a manual ‘dig’ command, use their servers as the authoritative ones for multimedia.cx
  • Create a new Droplet (D-O VPS), install all the right software, move the databases, upload the files ; and exhaustively document each step, gotcha, and pitfall ; treat a VPS as necessarily disposable and have an eye towards iterating the process with a new VPS
  • Use /etc/hosts on a local machine to point DNS to the new server and verify that each site is working correctly
  • After everything looks all right, update the DNS records to point to the new server

  Finally, flip the switch on the MX record by pointing it to the new email provider.

  Improvements and Problems
  Hosting on Digital Ocean is quite amazing so far. Maybe it’s the SSDs. Whatever it is, all the sites are performing far better than on the old shared web host. People who edit the MultimediaWiki report that changes get saved in less than the 10 or so seconds required on the old server.

  Again, all problems are now my problems. A sore spot with the shared web host was general poor performance. The hosting company would sometimes complain that my sites were using too much CPU. I would have loved to try to optimize things. However, the cPanel interface found on many shared hosts don’t give you a great deal of data for debugging performance problems. However, same sites, same software, same load on the VPS is considerably more performant.

  Problem : I’ve already had the MySQL database die due to a spike in usage. I had to manually restart it. I was considering a cron-based solution to check if the server is running and restart it if not. In response to my analysis that my databases are mostly read and not often modified, so db crashes shouldn’t be too disastrous, a friend helpfully reminded me that, “You would not make a good sysadmin with attitudes like ‘an occasional crash is okay’.”

  To this end, I am planning to migrate the database server to a separate VPS. This is a strategy that even Digital Ocean recommends. I’m hoping that the MySQL server isn’t subject to such memory spikes, but I’ll continue to monitor it after I set it up.

  Overall, the server continues to get modest amounts of traffic. I predict it will remain that way unless Dark Shikari resurrects the x264dev blog. The biggest spike that multimedia.cx ever saw was when Steve Jobs linked to this WebM post.

  Dropped Sites
  There are a bunch of subdomains I dropped because I hadn’t done anything with them for years and I doubt anyone will notice they’re gone. One notable section that I decided to drop is the samples.mplayerhq.hu archive. It will live on, but it will be hosted by samples.ffmpeg.org, which had a full mirror anyway. The lower-end VPS instances don’t have the 53 GB necessary.

  Going Forward
  Here’s to another 10 years of multimedia.cx, even if multimedia isn’t as exciting as it was 10 years ago (personal opinion ; I’ll have another post on this later). But at least I can get working on some other projects now that this is done. For the past 4 months or so, whenever I think of doing some other project, I always remembered that this server move took priority over everything else.

• "undefined reference to av···@···"ffmpeg error，when i cross compile opencv4.5.3 which include ffmpeg lib

  11 mai 2024, par caiping Peng

  everyone,It is sorry to bother you,but i need some help.
I'm working on an embedded deployment project,doing object detection work to real-time video stream. So I have to port my c++ inference prog to RKNN1808 platform. I compile this program with CMake tool,but I cant finish my work because opencv lib cant be compiled rightly.
To FFmpeg,my configure commend is following :

  


  ./configure --enable-cross-compile --cross-prefix=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu- --target-os=linux --arch=aarch64 --prefix=/usr/local/ffmpeg  --enable-shared


  


  then I am gonna show you the ffmpeg version :

  


  libavutil      56. 70.100
libavcodec     58.134.100
libavformat    58. 76.100
libavdevice    58. 13.100
libavfilter     7.110.100
libswscale      5.  9.100
libswresample   3.  9.100
libpostproc    55.  9.100


  


  next ,I use following commend to build cmake project :

  


  cmake -D CMAKE_BUILD_TYPE=RELEASE  -D CMAKE_C_COMPILER=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-gcc -D CMAKE_CXX_COMPILER=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-g++ -D BUILD_SHARED_LIBS=ON -D CMAKE_CXX_FLAGS=-fPIC -D CMAKE_C_FLAGS=-fPIC -D CMAKE_EXE_LINKER_FLAGS=-lpthread -ldl -D ENABLE_PIC=ON -D WITH_1394=OFF -D WITH_ARAVIS=OFF -D WITH_ARITH_DEC=ON -D WITH_ARITH_ENC=ON -D WITH_CLP=OFF -D WITH_CUBLAS=OFF -D WITH_CUDA=OFF -D WITH_CUFFT=OFF -D WITH_FFMPEG=ON -D WITH_GSTREAMER=ON -D WITH_GSTREAMER_0_10=OFF -D WITH_HALIDE=OFF -D WITH_HPX=OFF -D WITH_IMGCODEC_HDR=ON -D WITH_IMGCODEC_PXM=ON -D WITH_IMGCODEC_SUNRASTER=ON -D WITH_INF_ENGINE=OFF -D WITH_IPP=OFF -D WITH_ITT=OFF -D WITH_JASPER=ON -D WITH_JPEG=ON -D WITH_LAPACK=ON -D WITH_LIBREALSENSE=OFF -D WITH_NVCUVID=OFF -D WITH_OPENCL=OFF -D WITH_OPENCLAMDBLAS=OFF -D WITH_OPENCLAMDFFT=OFF -D WITH_OPENCL_SVM=OFF -D WITH_OPENEXR=OFF -D WITH_OPENGL=OFF -D WITH_OPENMP=OFF -D WITH_OPENNNI=OFF -D WITH_OPENNNI2=OFF -D WITH_OPENVX=OFF -D WITH_PNG=OFF -D WITH_PROTOBUF=OFF -D WITH_PTHREADS_PF=ON -D WITH_PVAPI=OFF -D WITH_QT=OFF -D WITH_QUIRC=OFF  -D WITH_TBB=OFF -D WITH_TIFF=ON -D WITH_VULKAN=OFF -D WITH_WEBP=ON -D WITH_XIMEA=OFF -D CMAKE_INSTALL_PREFIX=../CrossCompileResult  -D WITH_GTK=OFF  -D BUILD_opencv_dnn=OFF ..


  


  following is the outpt about FFmpeg :

  


  --   Video I/O:
--     FFMPEG:                      YES
--       avcodec:                   YES (58.134.100)
--       avformat:                  YES (58.76.100)
--       avutil:                    YES (56.70.100)
--       swscale:                   YES (5.9.100)
--       avresample:                NO
--     GStreamer:                   NO
--     v4l/v4l2:                    YES (linux/videodev2.h)



  


  After building the cmake project,I compiled this project with comment 【make -j16】.After not so long time,I got the Error :

  


  [ 49%] Linking CXX executable ../../bin/opencv_annotation
[ 49%] Building CXX object modules/ts/CMakeFiles/opencv_ts.dir/src/ts_tags.cpp.o
[ 49%] Built target opencv_annotation
[ 49%] Linking CXX executable ../../bin/opencv_visualisation
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavcodec.so.58, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavformat.so.58, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavutil.so.56, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libswscale.so.5, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_init_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_get_riff_video_tags@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_send_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_receive_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_get_mov_video_tags@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_decoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_decoder_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_alloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_transfer_data@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_malloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avio_open@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_alloc_context@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_sub_q@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_network_init@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_free@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_flush_buffers@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_encoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_getContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_receive_frame@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_write_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_close_input@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_seek_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_freeContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_set@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_descriptor_get_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_scale@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_unref@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_parse_string@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_get_buffer@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_freep@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_find_stream_info@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_read_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_free_context@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_default_get_format@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_init@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_register_all@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_get_buffer@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_guess_sample_aspect_ratio@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_new_stream@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_constraints_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_ctx_create_derived@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_unref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_buffer_unref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_write_trailer@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_rescale_ts@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_get_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_send_frame@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_hw_config@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_buffer_ref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_get@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_free@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_is_decoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_open_input@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_lockmgr_register@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_alloc@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_create_derived@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_send_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_alloc@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_log_set_level@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_image_get_buffer_size@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_open2@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_is_encoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_guess_format@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_image_fill_arrays@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_receive_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_getCachedContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_get_tag@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_get_hwframe_constraints@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_ctx_create@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_iterate@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_log_set_callback@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_opt_set@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_get_id@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_write_header@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_parameters_copy@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_pix_fmt_to_codec_tag@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_alloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_mallocz@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_find_input_format@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_hw_frames_parameters@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_get_type_name@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avio_close@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_init@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_close@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_find_type_by_name@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_context_defaults3@LIBAVCODEC_58'
collect2: error: ld returned 1 exit status
make[2]: *** [apps/visualisation/CMakeFiles/opencv_visualisation.dir/build.make:89: bin/opencv_visualisation] Error 1
make[1]: *** [CMakeFiles/Makefile2:3357: apps/visualisation/CMakeFiles/opencv_visualisation.dir/all] Error 2
make[1]: *** Waiting for unfinished jobs....
[ 49%] Linking CXX shared library ../../lib/libopencv_calib3d.so
[ 49%] Built target opencv_calib3d
[ 50%] Linking CXX static library ../../lib/libopencv_ts.a
[ 50%] Built target opencv_ts
make: *** [Makefile:163: all] Error 2



  


  I dont know what's wrong with it,It has confused me for a few days,I real hope someone can help me solve the prob.
I promise the the ffmpeg version match the version of opencv strictly,promising the PKG_CONFIG_PATH is right.

  


  I have tried many method like changing opencv version or ffmpeg version,recompiling the ffmpeg,changing PKG_CONFIG_PATH,coping ffmpeg pc file from /usr/local/ffmpeg/lib/pkgconfig to /usr/local/lib/pkgconfig.
I hope somebody can give some idea about how to solve this problem.

  


• A Guide to App Analytics Tools that Drive Growth

  7 mars, par Daniel Crough — App Analytics

  Mobile apps are big business, generating £438 billion in global revenue between in-app purchases (38%) and ad revenue (60%). And with 96% of apps relying on in-app monetisation, the competition is fierce.

  To succeed, app developers and marketers need strong app analytics tools to understand their customers’ experiences and the effectiveness of their development efforts.

  This article discusses app analytics, how it works, the importance and benefits of mobile app analytics tools, key metrics to track, and explores five of the best app analytics tools on the market.

  What are app analytics tools ?

  Mobile app analytics tools are software solutions that provide insights into how users interact with mobile applications. They track user behaviour, engagement and in-app events to reveal what’s working well and what needs improvement.

  Insights gained from mobile app analytics help companies make more informed decisions about app development, marketing campaigns and monetisation strategies.

  What do app analytics tools do ?

  App analytics tools embed a piece of code, called a software development kit (SDK), into an app. These SDKs provide the essential infrastructure for the following functions :

  • Data collection : The SDK collects data within your app and records user actions and events, like screen views, button clicks, and in-app purchases.
  • Data filtering : SDKs often include mechanisms to filter data, ensuring that only relevant information is collected.
  • Data transmission : Once collected and filtered, the SDK securely transmits the data to an analytics server. The SDK provider can host this server (like Firebase or Amplitude), or you can host it on-premise.
  • Data processing and analysis : Servers capture, process and analyse large stores of data and turn it into useful information.
  • Visualisation and reporting : Dashboards, charts and graphs present processed data in a user-friendly format.

  

  Six ways mobile app analytics tools fuel marketing success and drive product growth

  Mobile app analytics tools are vital in driving product development, enhancing user experiences, and achieving business objectives.

  #1. Improving user understanding

  The better a business understands its customers, the more likely it is to succeed. For mobile apps, that means understanding how and why people use them.

  Mobile analytics tools provide detailed insights into user behaviours and preferences regarding apps. This knowledge helps marketing teams create more targeted messaging, detailed customer journey maps and improve user experiences.

  It also helps product teams understand the user experience and make improvements based on those insights.

  For example, ecommerce companies might discover that users in a particular area are more likely to buy certain products. This allows the company to tailor its offers and promotions to target the audience segments most likely to convert.

  #2 Optimising monetisation strategies for increased revenue and user retention

  In-app purchases and advertising make up 38% and 60% of mobile app revenue worldwide, respectively. App analytics tools provide insights companies need to optimise app monetisation by :

  • Analysing purchase patterns to identify popular products and understand pricing sensitivities.
  • Tracking in-app behaviour to identify opportunities for enhancing user engagement.

  App analytics can track key metrics like visit duration, user flow, and engagement patterns. These metrics provide critical information about user experiences and can help identify areas for improvement.

  How meaningful are the impacts ?

  Duolingo, the popular language learning app, reported revenue growth of 45% and an increase in daily active users (DAU) of 65% in its Q4 2023 financial report. The company attributed this success to its in-house app analytics platform.

  

  #3. Understanding user experiences

  Mobile app analytics tools track the performance of user interactions within your app, such as :

  • Screen views : Which screens users visit most frequently
  • User flow : How users navigate through your app
  • Session duration : How long users spend in your app
  • Interaction events : Which buttons, features, and functions users engage with most

  Knowing how users interact with your app can help refine your approach, optimise your efforts, and drive more conversions.

  #4. Personalising user experiences

  A recent McKinsey survey showed that 71% of users expect personalised app experiences. Product managers must stay on top of this since 76% of users get frustrated if they don’t receive the personalisation they expect.

  Personalisation on mobile platforms requires data capture and analysis. Mobile analytics platforms can provide the data to personalise the user onboarding process, deliver targeted messages and recommend relevant content or offers.

  Spotify is a prime example of personalisation done right. A recent case study by Pragmatic Institute attributed the company’s growth to over 500 million active daily users to its ability to capture, analyse and act on :

  • Search behaviour
  • Individual music preferences
  • Playlist data
  • Device usage
  • Geographical location

  The streaming service uses its mobile app analytics software to turn this data into personalised music recommendations for its users. Spotify also has an in-house analytics tool called Spotify Premium Analytics, which helps artists and creators better understand their audience.

  #5. Enhancing app performance

  App analytics tools can help identify performance issues that might be affecting user experience. By monitoring metrics like load time and app performance, developers can pinpoint areas that need improvement.

  Performance optimisation is crucial for user retention. According to Google research, 53% of mobile site visits are abandoned if pages take longer than three seconds to load. While this statistic refers to websites, similar principles apply to apps—users expect fast, responsive experiences.

  Analytics data can help developers prioritise performance improvements by showing which screens or features users interact with most frequently, allowing teams to focus their optimisation efforts where they’ll have the greatest impact.

  #6. Identifying growth opportunities

  App analytics tools can reveal untapped opportunities for growth by highlighting :

  • Features users engage with most
  • Underutilised app sections that might benefit from redesign
  • Common user paths that could be optimised
  • Moments where users tend to drop off

  This intelligence helps product teams make data-informed decisions about future development priorities, feature enhancements, and potential new offerings.

  For example, a streaming service might discover through analytics that users who create playlists have significantly higher retention rates. This insight could lead to development of enhanced playlist functionality to encourage more users to create them, ultimately boosting overall retention.

  Key app metrics to track

  Using mobile analytics tools, you can track dozens of key performance indicators (KPIs) that measure everything from customer engagement to app performance. This section focuses on the most important KPIs for app analytics, classified into three categories :

  • App performance KPIs
  • User engagement KPIs
  • Business impact KPIs

  While the exact metrics to track will vary based on your specific goals, these fundamental KPIs form the foundation of effective app analytics.

  

  App performance KPIs

  App performance metrics tell you whether an app is reliable and operating properly. They help product managers identify and address technical issues that may negatively impact user experiences.

  Some key metrics to assess performance include :

  • Screen load time : How quickly screens load within your app
  • App stability : How often your app crashes or experiences errors
  • Response time : How quickly your app responds to user interactions
  • Network performance : How efficiently your app handles data transfers

  User engagement KPIs

  Engagement KPIs provide insights into how users interact with an app. These metrics help you understand user behaviour and make UX improvements.

  Important engagement metrics include :

  • Returning visitors : A measure of how often users return to an app
  • Visit duration : How long users spend in your app per session
  • User flow : Visualisation of the paths users take through your app, offering insights into navigation patterns
  • Event tracking : Specific interactions users have with app elements
  • Screen views : Which screens are viewed most frequently

  Business impact KPIs

  Business impact KPIs connect app analytics to business outcomes, helping demonstrate the app’s value to the organisation.

  Key business impact metrics include :

  • Conversion events : Completion of desired actions within your app
  • Goal completions : Tracking when users complete specific objectives
  • In-app purchases : Monitoring revenue from within the app
  • Return on investment : Measuring the business value generated relative to development costs

  Privacy and app analytics : A delicate balance

  While app analytics tools can be a rich source of user data, they must be used responsibly. Tracking user in-app behaviour and collecting user data, especially without consent, can raise privacy concerns and erode user trust. It can also violate data privacy laws like the GDPR in Europe or the OCPA, FDBR and TDPSA in the US.

  With that in mind, it’s wise to choose user-tracking tools that prioritise user privacy while still collecting enough data for reliable analysis.

  Matomo is a privacy-focused web and app analytics solution that allows you to collect and analyse user data while respecting user privacy and following data protection rules like GDPR.

  The five best app analytics tools to prove marketing value

  In this section, we’ll review the five best app analytics tools based on their features, pricing and suitability for different use cases.

  Matomo — Best for privacy-compliant app analytics

  Matomo app analytics is a powerful, open-source platform that prioritises data privacy and compliance.

  

  It offers a suite of features for tracking user engagement and conversions across websites, mobile apps and intranets.

  Key features

  • Complete data ownership : Full control over your analytics data with no third-party access
  • User flow analysis : Track user journeys across different screens in your app
  • Custom event tracking : Monitor specific user interactions with customisable events
  • Ecommerce tracking : Measure purchases and product interactions
  • Goal conversion monitoring : Track completion of important user actions
  • Unified analytics : View web and app analytics in one platform for a complete digital picture

  Benefits

  • Eliminate compliance risks without sacrificing insights
  • Get accurate data with no sampling or data manipulation
  • Choose between self-hosting or cloud deployment
  • Deploy one analytics solution across your digital properties (web and app) for a single source of truth

  Pricing

  Plan	Price
  Cloud	Starts at £19/month
  On-Premise	Free

  Matomo is a smart choice for businesses that value data privacy and want complete control over their analytics data. It’s particularly well-suited for organisations in highly regulated industries, like banking.

  While Matomo’s app analytics features focus on core analytics capabilities, its privacy-first approach offers unique advantages. For organisations already using Matomo for web analytics, extending to mobile creates a unified analytics ecosystem with consistent privacy standards across all digital touchpoints, giving organisations a complete picture of the customer journey.

  Firebase — Best for Google services integration

  Firebase is the mobile app version of Google Analytics. It’s the most popular app analytics tool on the market, with over 99% of Android apps and 77% of iOS apps using Firebase.

  

  Firebase is popular because it works well with other Google services. It also has many features, like crash reporting, A/B testing and user segmentation.

  Pricing

  Plan	Price
  Spark	Free
  Blaze	Pay-as-you-go based on usage
  Custom	Bespoke pricing for high-volume enterprise users

  Adobe Analytics — Best for enterprise app analytics

  Adobe Analytics is an enterprise-grade analytics solution that provides valuable insights into user behaviour and app performance.

  

  It’s part of the Adobe Marketing Cloud and integrates easily with other Adobe products. Adobe Analytics is particularly well-suited for large organisations with complex analytics needs.

  Pricing

  Plan	Price
  Select	Pricing on quote
  Prime	Pricing on quote
  Ultimate	Pricing on quote

  While you must request a quote for pricing, Scandiweb puts Adobe Analytics at £2,000/mo–£2,500/mo for most companies, making it an expensive option.

  Apple App Analytics — Best for iOS app analysis

  Apple App Analytics is a free, built-in analytics tool for iOS app developers.

  

  This analytics platform provides basic insights into user engagement, app performance and marketing campaigns. It has fewer features than other tools on this list, but it’s a good place for iOS developers who want to learn how their apps work.

  Pricing

  Apple Analytics is free.

  Amplitude — Best for product analytics

  Amplitude is a product analytics platform that helps businesses understand user behaviour and build better products.

  

  It excels at tracking user journeys, identifying user segments and measuring the impact of product changes. Amplitude is a good choice for product managers and data analysts who want to make informed decisions about product development.

  Pricing

  Plan	Price
  Starter	Free
  Plus	From £49/mo
  Growth	Pricing on quote

  Choose Matomo’s app analytics to unlock growth

  App analytics tools help marketers and product development teams understand user experiences, improve app performance and enhance products. Some of the best app analytics tools available for 2025 include Matomo, Firebase and Amplitude.

  However, as you evaluate your options, consider taking a privacy-first approach to app data collection and analysis, especially if you’re in a highly regulated industry like banking or fintech. Matomo Analytics offers a powerful and ethical solution that allows you to gain valuable insights while respecting user privacy.

  Ready to take control of your app analytics ? Start your 21-day free trial.