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• Six Best Amplitude Alternatives

  10 décembre 2024, par Daniel Crough

  Product analytics is big business. Gone are the days when we could only guess what customers were doing with our products or services. Now, we can track, visualise, and analyse how they interact with them and, with that, constantly improve and optimise. 

  The problem is that many product analytics tools are expensive and complicated — especially for smaller businesses. They’re also packed with functionality more attuned to the needs of massive companies. 

  Amplitude is such a tool. It’s brilliant and it has all the bells and whistles that you’ll probably never need. Fortunately, there are alternatives. In this guide, we’ll explore the best of those alternatives and, along the way, provide the insight you’ll need to select the best analytics tool for your organisation. 

  Amplitude : a brief overview

  To set the stage, it makes sense to understand exactly what Amplitude offers. It’s a real-time data analytics tool for tracking user actions and gaining insight into engagement, retention, and revenue drivers. It helps you analyse that data and find answers to questions about what happened, why it happened, and what to do next.

  However, as good as Amplitude is, it has some significant disadvantages. While it does offer data export functionality, that seems deliberately restricted. It allows data exports for specific events, but it’s not possible to export complete data sets to manipulate or format in another tool. Even pulling it into a CSV file has a 10,000-row limit. There is an API, but not many third-party integration options.

  Getting data in can also be a problem. Amplitude requires manual tags on events that must be tracked for analysis, which can leave holes in the data if every possible subsequent action isn’t tagged. That’s a time-consuming exercise, and it’s made worse because those tags will have to be updated every time the website or app is updated. 

  As good as it is, it can also be overwhelming because it’s stacked with features that can create confusion for novice or inexperienced analysts. It’s also expensive. There is a freemium plan that limits functionality and events. Still, when an organisation wants to upgrade for additional functionality or to analyse more events, the step up to the paid plan is massive.

  Lastly, Amplitude has made some strides towards being a web analytics option, but it lacks some basic functionality that may frustrate people who are trying to see the full picture from web to app.

  Snapshot of Amplitude alternatives

  So, in place of Amplitude, what product analytics tools are available that won’t break the bank and still provide the functionality needed to improve your product ? The good news is that there are literally hundreds of alternatives, and we’ve picked out six of the best.

  1. Matomo – Best privacy-focused web and mobile analytics
  2. Mixpanel – Best for product analytics
  3. Google Analytics – Best free option
  4. Adobe Analytics – Best for predictive analytics
  5. Umami – Best lightweight tool for product analytics
  6. Heap – Best for automatic user data capture

  A more detailed analysis of the Amplitude alternatives

  Now, let’s dive deeper into each of the six Amplitude alternatives. We’ll cover standout features, integrations, pricing, use cases and community critiques. By the end, you’ll know which analytics tool can help optimise website and app performance to grow your business.

  1. Matomo – Best privacy-friendly web and app analytics

  Privacy is a big concern these days, especially for organisations with a presence in the European Union (EU). Unlike other analytics tools, Matomo ensures you comply with privacy laws and regulations, like the General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act (CCPA).

  Matomo helps businesses get the insights they need without compromising user privacy. It’s also one of the few self-hosted tools, ensuring data never has to leave your site.

  Matomo is open-source, which is also rare in this class of tools. That means it’s available for anyone to adapt and customise as they wish. Everything you need to build custom APIs is there.

  

  Its most useful capabilities include visitor logs and session recordings to trace the entire customer journey, spot drop-off points, and fine-tune sales funnels. The platform also comes with heatmaps and A/B testing tools. Heatmaps provide a useful visual representation of your data, while A/B testing allows for more informed, data-driven decisions.

  Despite its range of features, many reviewers laud Matomo’s user interface for its simplicity and user-friendliness. 

  Why Matomo : Matomo is an excellent alternative because it fills in the gaps where Amplitude comes up short, like with cookieless tracking. Also, while Amplitude focuses mainly on behavioural analytics, Matomo offers both behavioural and traditional analytics, which allows more profound insight into your data. Furthermore, Matomo fully complies with the strictest privacy regulations worldwide, including GDPR, LGPD, and HIPAA.

  Standout features include multi-touch attribution, visits log, content engagement, ecommerce, customer segments, event tracking, goal tracking, custom dimensions, custom reports, automated email reports, tag manager, sessions recordings, roll-up reporting that can pull data from multiple websites or mobile apps, Google Analytics importer, Matomo tag manager, comprehensive visitor tracking, heatmaps, and more.

  Integrations with 100+ technologies, including Cloudflare, WordPress, Magento, Google Ads, Drupal, WooCommerce, Vue, SharePoint and Wix.

  Pricing is free for Matomo On-Premise and $23 per month for Matomo Cloud, which comes with a 21-day free trial (no credit card required).

  Strengths

  • Privacy focused
  • Cookieless consent banners
  • 100% accurate, unsampled data
  • Open-source code 
  • Complete data ownership (no sharing with third parties)
  • Self-hosting and cloud-based options
  • Built-in GDPR Manager
  • Custom alerts, white labelling, dashboards and reports

  Community critiques 

  • Premium features are expensive and proprietary
  • Learning curve for non-technical users

  2. Mixpanel – Best for product analytics

  Mixpanel is a dedicated product analytics tool. It tracks and analyses customer interactions with a product across different platforms and helps optimise digital products to improve the user experience. It works with real-time data and can provide answers from customer and revenue data in seconds.

  It also presents data visualisations to show how customers interact with products.

  

  Mixpanel allows you to play around filters and views to reveal and chart some useful customer trends. (Image source)

  Why Mixpanel : One of the strengths of this platform is the ability to test hypotheses. Need to test an ambitious idea ? Mixpanel data can do it with real user analytics. That allows you to make data-driven decisions to find the best path forward.

  Standout features include automatic funnel segment analysis, behavioural segmentation, cohort segmentation, collaboration support, customisable dashboards, data pipelines, filtered data views, SQL queries, warehouse connectors and a wide range of pre-built integrations.

  Integrations available include Appcues, AppsFlyer, AWS, Databox, Figma, Google Cloud, Hotjar, HubSpot, Intercom, Integromat, MailChimp, Microsoft Azure, Segment, Slack, Statsig, VWO, Userpilot, WebEngage, Zapier, ZOH) and dozens of others.

  Pricing starts with a freemium plan valid for up to 20 million events per month. The growth plan is affordable at $25 per month and adds features like no-code data transformations and data pipeline add-ons. The enterprise version runs at a monthly cost of $833 and provides the full suite of features and services and premium support.

  There’s a caveat. Those prices only allow up to 1,000 Monthly Tracked Users (MTUs), calculated based on the number of visitors that perform a qualifying event each month. Beyond that, MTU plans start at $20,000 per year.

  Strengths

  • User behaviour and interaction tracking
  • Unlimited cohort segmentation capabilities
  • Drop-off analysis showing where users get stuck
  • A/B testing capabilities

  Community critiques 

  • Expensive enterprise features
  • Extensive setup and configuration requirements

  3. Google Analytics 4 – Best free web analytics tool

  The first thing to know about Google Analytics 4 is that it’s a web analytics tool. In other words, it tracks sessions, not user behaviours in app environments. It can provide details on how people found your website and how they go there, but it doesn’t offer much detail on how people use your product. 

  There is also an enterprise version, Google Analytics 360, which is not free. We’ve broken down the differences between the two versions elsewhere.

  

  GA4’s audience overview shows visitors, sessions, session lengths, bounce rates, and user engagement data. (Image source)

   

  Why Google Analytics : It’s great for gauging the effectiveness of marketing campaigns, tracking goal completions (purchases, cart additions, etc.) and spotting trends and patterns in user engagement.

  Standout features include built-in automation, customisable conversion goals, data drill-down functionality, detailed web acquisition metrics, media spend ROI calculations and out-of-the-box web analytics reporting.

  Integrations include all major CRM platforms, CallRail, DoubleClick DCM, Facebook, Hootsuite, Marketo, Shopify, VWO, WordPress, Zapier and Zendesk, among many others.

  Pricing is free for the basic version (Google Analytics 4) and scales based on features and data volume. The advanced features (in Google Analytics 360) are pitched at enterprises, and pricing is custom.

  Strengths

  • Free to start
  • Multiple website management
  • Traffic source details
  • Up-to-date traffic data

  Community critiques 

  • Steep learning curve 
  • Data sampling

  4. Adobe Analytics – Best for predictive analytics

  A fully configured Adobe Analytics implementation is the Swiss army knife of analytics tools. It begins with web analytics, adds product analytics, and then wraps it up nicely with predictive analytics.

  Unlike all the Amplitude alternatives here, there’s no free version. Adobe Analytics has a complicated pricing matrix with options like website analytics, marketing analytics, attribution, and predictive analytics. It also has a wide range of customisation options that will appeal to large businesses. But for smaller organisations, it may all be a bit too much.

  Mixpanel allows you to play around filters and views to reveal and chart some useful customer trends. (Image source)

  

  Adobe Analytics’ cross-channel attribution ties actions from different channels into a single customer journey. (Image source)

   

  Why Adobe Analytics : For current Adobe customers, this is a logical next step. Either way, Adobe Analytics can combine, evaluate, and analyse data from any part of the customer journey. It analyses that data with predictive intelligence to provide insights to enhance customer experiences.

   

  Standout features include AI-powered prediction analysis, attribution analysis, multi-channel data collection, segmentation and detailed customer journey analytics, product analytics and web analytics.

   

  Integrations are available through the Adobe Experience Cloud Exchange. Adobe Analytics also supports data exchange with brands such as BrightEdge, Branch.io, Google Ads, Hootsuite, Invoca, Salesforce and over 200 other integrations.

   

  Pricing starts at $500 monthly, but prospective customers are encouraged to contact the company for a needs-based quotation.

   

  Strengths

  • Drag-and-drop interface
  • Flexible segmentation 
  • Easy-to-create conversion funnels
  • Threshold-based alerts and notifications

  Community critiques 

  • No free version
  • Lack of technical support
  • Steep learning curve

  5. Umami – Best lightweight tool for web analytics

  The second of our open-source analytics solutions is Umami, a favourite in the software development community. Like Matomo, it’s a powerful and privacy-focused alternative that offers complete data control and respects user privacy. It’s also available as a cloud-based freemium plan or as a self-hosted solution.

   

  

  Umami’s dashboard reveals the busiest times of day and which pages are visited when.(Image source)

   

  Why Umami : Unami has a clear and simple user interface (UI) that lets you measure important metrics such as page visits, referrers, and user agents. It also features event tracking, although some reviewers complain that it’s quite limited.

  Standout features can be summed up in five words : privacy, simplicity, lightweight, real-time, and open-source. Unami’s UI is clean, intuitive and modern, and it doesn’t slow down your website. 

  Integrations include plugins for VuePress, Gatsby, Craft CMS, Docusaurus, WordPress and Publii, and a module for Nuxt. Unami’s API communicates with Javascript, PHP Laravel and Python.

  Pricing is free for up to 100k monthly events and three websites, but with limited support and data retention restrictions. The Pro plan costs $20 a month and gives you unlimited websites and team members, a million events (plus $0.00002 for each event over that), five years of data and email support. Their Enterprise plan is priced custom.

  Strengths

  • Freemium plan
  • Open-source
  • Lightweight 

  Community critiques 

  • Limited support options
  • Data retention restrictions
  • No funnel functionality

  6. Heap – Best for automatic data capture

  Product analytics with a twist is a good description of Heap. It features event auto-capture to track user interactions across all touchpoints in the user journey. This lets you fully understand how and why customers engage with your product and website. 

  Using a single Javascript snippet, Heap automatically collects data on everything users do, including how they got to your website. It also helps identify how different cohorts engage with your product, providing the critical insights teams need to boost conversion rates.

  

  Heap’s journeys feature combines funnel and path analysis. (Image source)

   

  Why Heap : The auto-capture functionality solves a major shortcoming of many product analytics tools — manual tracking. Instead of having to set up manual tags on events, Heap automatically captures all data on user activity from the start. 

  Standout features include event auto-capture, session replay, heatmaps, segments (or cohorts) and journeys, the last of which combines the functions of funnel and path analysis tools into a single feature.

  Integrations include AWS, Google, Microsoft Azure, major CRM platforms, Snowflake and many other data manipulation platforms.

  Pricing is quote-based across all payment tiers. There is also a free plan and a 14-day free trial.

  Strengths

  • Session replay
  • Heatmaps 
  • User segmentation
  • Simple setup 
  • Event auto-capture 

  Community critiques 

  • No A/B testing functionality
  • No GDPR compliance support

  Choosing the best solution for your team

  When selecting a tool, it’s crucial to understand how product analytics and web analytics solutions differ. 

  Product analytics tools track users or accounts and record the features they use, the funnels they move through, and the cohorts they’re part of. Web analytics tools focus more on sessions than users because they’re interested in data that can help improve website usage. 

  Some tools combine product and web analytics to do both of these jobs.

  Area of focus

  Product analytics tools track user behaviour within SaaS- or app-based products. They’re helpful for analysing features, user journeys, engagement metrics, product development and iteration. 

  Web analytics tools analyse web traffic, user demographics, and traffic sources. They’re most often used for marketing and SEO insights.

  Level of detail

  Product analytics tools provide in-depth tracking and analysis of user interactions, feature usage, and cohort analysis.

  Web analytics tools provide broader data on page views, bounce rates, and conversion tracking to analyse overall site performance.

  Whatever tools you try, your first step should be to search for reviews online to see what people who’ve used them think about them. There are some great review sites you can try. See what people are saying on Capterra, G2, Gartner Peer Insights, or TrustRadius. 

  Use Matomo to power your web and app analytics

  Web and product analytics is a competitive field, and there are many other tools worth considering. This list is a small cross-section of what’s available.

  That said, if you have concerns about privacy and costs, consider choosing Matomo. Start your 21-day free trial today.

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• Lean Analytics in a Privacy-First Environment – Bootcamp with Timo Dechau

  29 novembre 2024, par Daniel Crough — Banking and Financial Services, GDPR, Marketing, Privacy, Videos, Featured Banking Content

  In a recent bootcamp, Timo Dechau walked attendees through his approach to data and measurement in privacy-focused analytics environments. He demonstrates how to shift from a chaotic, ‘track-it-all’ mentality to a focused method that prioritizes quality over quantity. This post will summarize some of his key privacy-first analytics ideas, but be sure to check out the on-demand video for more detail.

  Watch the bootcamp on demand

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  Unrestrained data collection leads to data bloat

  Marketing and the business world are experiencing a data problem. Analysts and business intelligence teams grapple with large amounts of data that aren’t always useful and are often incomplete. The idea that “more data is better” became a guiding principle in the early 2000s, encouraging companies to gather everything possible using all available data collection methods. This unrestrained pursuit often led to an unexpected problem : data bloat. Too much data, too little clarity. Digital marketers, analysts, and business leaders now try to navigate vast amounts of information that create more confusion than insight, especially when the data is incomplete due to privacy regulations.

  

  Cutting through the noise, focusing on what matters

  The “more data is better” mindset emerged when digital marketers were beginning to understand data’s potential. It seemed logical : more data should mean more opportunities to optimise, personalise, and drive results. But in practice, gathering every possible piece of data often leads to a cluttered, confusing pile of metrics that can mislead more than guide.

  This approach carries hidden costs. Excessive data collection burns resources, increases privacy concerns, and leaves teams unfocused. It’s easy to get lost trying to make sense of endless dashboards, metrics, and reports. More data doesn’t necessarily lead to better decisions ; it often just leads to more noise, hindering effective data management.

  Rethinking data management : From data overload to data mindfulness

  Data management has often prioritised comprehensive data gathering without considering the specific value of each data point. This approach has created more information, but not necessarily better insights.

  Data mindfulness is about taking a deliberate, focused approach to data collection and analysis. Instead of trying to collect everything, it emphasises gathering only what truly adds value. It’s about ensuring the data you collect serves a purpose and directly contributes to better insights and data-driven decision-making.

  Think of it like applying a “lean” methodology to data—trimming away the unnecessary and keeping only what is essential. Or consider embracing data minimalism to declutter your data warehouse, keeping only what truly sparks insight.

  

  Mindful data is ethical data

  Adopting a mindful approach to data can pay off in several ways :

  • Reduces overwhelm : When you reduce the clutter, you’re left with fewer, clearer metrics that lead to stronger decisions and actionable data insights.

  • Mitigates compliance risks : By collecting less, companies align better with privacy regulations and build trust with their customers. Privacy-first analytics and privacy-compliant analytics practices mean there’s no need for invasive tracking if it doesn’t add value—and customers will appreciate that.

  • Enhances data ethics : Focusing on the quality rather than the quantity of data collected ensures ethical data collection and management. Companies use data responsibly, respect user privacy, and minimise unnecessary data handling, strengthening customer relationships and brand integrity.

  • Improves data efficiency : Focused analytics means better use of resources. You’re spending less time managing meaningless metrics and more time working on meaningful insights. Many companies have found success by switching to a leaner, quality-first data approach, reporting sharper, more impactful results.

  Shifting towards simplicity and lean analytics

  If data mindfulness sounds appealing, here’s how you can get started :

  1. Ask the right questions. Before collecting any data, ask yourself : Why are we collecting this ? How will it drive value ? If you can’t answer these questions clearly, that data probably isn’t worth collecting. This is a key step in smart data management.

  2. Simplify metrics. Focus on the KPIs that truly matter for your business. Choose a handful of key metrics that reflect your goals rather than a sprawling list of nice-to-haves. Embracing data simplicity helps in targeting data collection effectively.

  3. Audit your current data. Review your existing data collection processes. Which metrics are you actively using to make decisions ? Eliminate any redundant or low-value metrics that create noise. Use ethical data management practices to ensure data efficiency and compliance. Understanding what is data management in this context is crucial.

  4. Implement lean analytics practices. Shift towards lean analytics by cutting down on unnecessary tracking. This can involve reducing reliance on multiple tracking scripts, simplifying your reporting, and setting up a streamlined dashboard focused on key outcomes. Embrace data reduction strategies to eliminate waste and boost effectiveness.

  Who should watch this bootcamp

  This bootcamp is perfect for data analysts, product managers, digital marketers and business leaders who are seeking a more streamlined approach to data measurement. If you’re interested in moving away from a chaotic “track-it-all” mentality and towards a focused, lean, and privacy-first analytics strategy, this workshop is for you.

  What you’ll discover

  • Practical steps : Learn actionable strategies to reduce data bloat and implement lean, privacy-first analytics in your organisation.

  • Real-life examples : Explore case studies of companies that have successfully adopted focused and privacy-first analytics.

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• Error Opening RTMP Stream through FFmpeg command when executed through exec package [closed]

  3 octobre 2024, par Akhil

  I have been trying to transcode the live stream from RTMP server running on rtmp://localhost:1936/live/test with FFmpeg in a Go application using os/exec package, But seems to not work and gives the input/output error (I have attached below). The same exact ffmpeg command when I execute on terminal, works as its supposed to. Not Sure why that is, here is my code for reproducing and analyzing the mistakes.

  


  ffmpegCmd := fmt.Sprintf("ffmpeg -nostdin -i rtmp://localhost:1936/live/%s -c:v libx264 -s %s -f %s %s/stream.mpd",
        streamKey, resolution, sp.OutputFormat, outputPath)
    log.Printf("Executing FFmpeg command: %s", ffmpegCmd)

    // Prepare the command execution with a timeout context
    ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second) // Set a 60-second timeout
    defer cancel()

    cmd := exec.CommandContext(ctx, "bash", "-c", ffmpegCmd)


  


  the ffmpeg command looks like this :
  
ffmpeg -nostdin -i rtmp://localhost:1936/live/test -c:v libx264 -s 1920x1080 -f dash output/test/1080p/stream.mpd

  


  I get the following error :

  


  Error opening input: Input/output error

Error opening input file rtmp://localhost:1936/live/test.

Error opening input files: Input/output error

Exiting normally, received signal 2.

signal: interrupt


  


  I have already tried to break the command, and then execute it. Something like :

  


  cmd := exec.CommandContext(ctx,
        "ffmpeg",
        "-nostdin",
        "-i", "rtmp://localhost:1936/live/"+streamKey,
        "-c:v", "libx264",
        "-s", resolution,
        "-f", sp.OutputFormat,
        outputPath+"/stream.mpd")


  


  After running the ffmpeg command with -loglevel debug and -report :

  


  Here is the logs and errors I get :

  


  When I run it within the go application :

  


  ffmpeg started on 2024-10-02 at 12:00:06
Report written to "ffmpeg-20241002-120006.log"
Log level: 48
Command line:
ffmpeg -loglevel debug -report -i rtmp://localhost:1936/live/test -c:v libx264 -s 1920x1080 -f dash ./output/test/1080p/stream.mpd
ffmpeg version 7.0.2 Copyright (c) 2000-2024 the FFmpeg developers
  built with Apple clang version 15.0.0 (clang-1500.3.9.4)
  configuration: --prefix=/opt/homebrew/Cellar/ffmpeg/7.0.2_1 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags='-Wl,-ld_classic' --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libaribb24 --enable-libbluray --enable-libdav1d --enable-libharfbuzz --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox --enable-audiotoolbox --enable-neon
  libavutil      59.  8.100 / 59.  8.100
  libavcodec     61.  3.100 / 61.  3.100
  libavformat    61.  1.100 / 61.  1.100
  libavdevice    61.  1.100 / 61.  1.100
  libavfilter    10.  1.100 / 10.  1.100
  libswscale      8.  1.100 /  8.  1.100
  libswresample   5.  1.100 /  5.  1.100
  libpostproc    58.  1.100 / 58.  1.100
Splitting the commandline.
Reading option '-loglevel' ... matched as option 'loglevel' (set logging level) with argument 'debug'.
Reading option '-report' ... matched as option 'report' (generate a report) with argument '1'.
Reading option '-i' ... matched as input url with argument 'rtmp://localhost:1936/live/test'.
Reading option '-c:v' ... matched as option 'c' (select encoder/decoder ('copy' to copy stream without reencoding)) with argument 'libx264'.
Reading option '-s' ... matched as option 's' (set frame size (WxH or abbreviation)) with argument '1920x1080'.
Reading option '-f' ... matched as option 'f' (force container format (auto-detected otherwise)) with argument 'dash'.
Reading option './output/test/1080p/stream.mpd' ... matched as output url.
Finished splitting the commandline.
Parsing a group of options: global .
Applying option loglevel (set logging level) with argument debug.
Applying option report (generate a report) with argument 1.
Successfully parsed a group of options.
Parsing a group of options: input url rtmp://localhost:1936/live/test.
Successfully parsed a group of options.
Opening an input file: rtmp://localhost:1936/live/test.
[AVFormatContext @ 0x13f721f90] Opening 'rtmp://localhost:1936/live/test' for reading
[rtmp @ 0x13f6040e0] No default whitelist set
[tcp @ 0x13f7223d0] No default whitelist set
[tcp @ 0x13f7223d0] Original list of addresses:
[tcp @ 0x13f7223d0] Address ::1 port 1936
[tcp @ 0x13f7223d0] Address 127.0.0.1 port 1936
[tcp @ 0x13f7223d0] Interleaved list of addresses:
[tcp @ 0x13f7223d0] Address ::1 port 1936
[tcp @ 0x13f7223d0] Address 127.0.0.1 port 1936
[tcp @ 0x13f7223d0] Starting connection attempt to ::1 port 1936
[tcp @ 0x13f7223d0] Connection attempt to ::1 port 1936 failed: Connection refused
[tcp @ 0x13f7223d0] Starting connection attempt to 127.0.0.1 port 1936
[tcp @ 0x13f7223d0] Successfully connected to 127.0.0.1 port 1936
[rtmp @ 0x13f6040e0] Handshaking...
[rtmp @ 0x13f6040e0] Type answer 3
[rtmp @ 0x13f6040e0] Server version 13.14.10.13
[rtmp @ 0x13f6040e0] Proto = rtmp, path = /live/test, app = live, fname = test
[rtmp @ 0x13f6040e0] Window acknowledgement size = 5000000
[rtmp @ 0x13f6040e0] Max sent, unacked = 5000000
[rtmp @ 0x13f6040e0] New incoming chunk size = 4096
[rtmp @ 0x13f6040e0] Creating stream...
[rtmp @ 0x13f6040e0] Sending play command for 'test'
[rtmp @ 0x13f6040e0] Deleting stream...
[in#0 @ 0x13f721d40] Error opening input: Input/output error
Error opening input file rtmp://localhost:1936/live/test.
Error opening input files: Input/output error
Exiting normally, received signal 2.


  


  This is what i get when i run the same command on terminal :

  


  <same as="as" but="but" please="please" scroll="scroll" further="further">&#xA;&#xA;[rtmp @ 0x1437144c0] No default whitelist set&#xA;[tcp @ 0x143604f20] No default whitelist set&#xA;[tcp @ 0x143604f20] Original list of addresses:&#xA;[tcp @ 0x143604f20] Address ::1 port 1936&#xA;[tcp @ 0x143604f20] Address 127.0.0.1 port 1936&#xA;[tcp @ 0x143604f20] Interleaved list of addresses:&#xA;[tcp @ 0x143604f20] Address ::1 port 1936&#xA;[tcp @ 0x143604f20] Address 127.0.0.1 port 1936&#xA;[tcp @ 0x143604f20] Starting connection attempt to ::1 port 1936&#xA;[tcp @ 0x143604f20] Connection attempt to ::1 port 1936 failed: Connection refused&#xA;[tcp @ 0x143604f20] Starting connection attempt to 127.0.0.1 port 1936&#xA;[tcp @ 0x143604f20] Successfully connected to 127.0.0.1 port 1936&#xA;[rtmp @ 0x1437144c0] Handshaking...&#xA;[rtmp @ 0x1437144c0] Type answer 3&#xA;[rtmp @ 0x1437144c0] Server version 13.14.10.13&#xA;[rtmp @ 0x1437144c0] Proto = rtmp, path = /live/test, app = live, fname = test&#xA;[rtmp @ 0x1437144c0] Window acknowledgement size = 5000000&#xA;[rtmp @ 0x1437144c0] Max sent, unacked = 5000000&#xA;[rtmp @ 0x1437144c0] New incoming chunk size = 4096&#xA;[rtmp @ 0x1437144c0] Creating stream...&#xA;[rtmp @ 0x1437144c0] Sending play command for &#x27;test&#x27;&#xA;[flv @ 0x143604b30] Format flv probed with size=2048 and score=100&#xA;[flv @ 0x143604b30] Before avformat_find_stream_info() pos: 13 bytes read:2263 seeks:0 nb_streams:0&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 64, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft32_ns_float_neon - type: fft_float, len: 32, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 64, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft32_ns_float_neon - type: fft_float, len: 32, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_pfa_15xM_inv_float_c - type: mdct_float, len: 120, factors[2]: [15, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft4_fwd_float_neon - type: fft_float, len: 4, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 128, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft_sr_ns_float_neon - type: fft_float, len: 64, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_pfa_15xM_inv_float_c - type: mdct_float, len: 480, factors[2]: [15, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft16_ns_float_neon - type: fft_float, len: 16, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 512, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft_sr_ns_float_neon - type: fft_float, len: 256, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_pfa_15xM_inv_float_c - type: mdct_float, len: 960, factors[2]: [15, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft32_ns_float_neon - type: fft_float, len: 32, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 1024, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft_sr_ns_float_neon - type: fft_float, len: 512, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_fwd_float_c - type: mdct_float, len: 1024, factors[2]: [2, any], flags: [unaligned, out_of_place, fwd_only]&#xA;        fft_sr_ns_float_neon - type: fft_float, len: 512, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;[NULL @ 0x144124920] nal_unit_type: 7(SPS), nal_ref_idc: 3&#xA;[NULL @ 0x144124920] Decoding VUI&#xA;[NULL @ 0x144124920] nal_unit_type: 8(PPS), nal_ref_idc: 3&#xA;[NULL @ 0x144124920] Decoding VUI&#xA;[h264 @ 0x144124920] nal_unit_type: 7(SPS), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] Decoding VUI&#xA;[h264 @ 0x144124920] nal_unit_type: 8(PPS), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] nal_unit_type: 7(SPS), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] nal_unit_type: 8(PPS), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] nal_unit_type: 5(IDR), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] Decoding VUI&#xA;[h264 @ 0x144124920] Format yuv420p chosen by get_format().&#xA;[h264 @ 0x144124920] Reinit context to 1280x720, pix_fmt: yuv420p&#xA;[h264 @ 0x144124920] no picture &#xA;[flv @ 0x143604b30] All info found&#xA;[flv @ 0x143604b30] rfps: 29.666667 0.016552&#xA;[flv @ 0x143604b30] rfps: 29.750000 0.009347&#xA;[flv @ 0x143604b30] rfps: 29.750000 0.009347&#xA;[flv @ 0x143604b30] rfps: 29.833333 0.004197&#xA;[flv @ 0x143604b30] rfps: 29.916667 0.001104&#xA;[flv @ 0x143604b30] rfps: 29.916667 0.001104&#xA;[flv @ 0x143604b30] rfps: 30.000000 0.000067&#xA;[flv @ 0x143604b30] rfps: 30.000000 0.000067&#xA;[flv @ 0x143604b30] rfps: 60.000000 0.000270&#xA;[flv @ 0x143604b30] rfps: 60.000000 0.000270&#xA;[flv @ 0x143604b30] rfps: 120.000000 0.001079&#xA;[flv @ 0x143604b30] rfps: 120.000000 0.001079&#xA;[flv @ 0x143604b30] rfps: 240.000000 0.004316&#xA;[flv @ 0x143604b30] rfps: 240.000000 0.004316&#xA;[flv @ 0x143604b30] rfps: 29.970030 0.000204&#xA;[flv @ 0x143604b30] rfps: 29.970030 0.000204&#xA;[flv @ 0x143604b30] rfps: 59.940060 0.000814&#xA;[flv @ 0x143604b30] rfps: 59.940060 0.000814&#xA;[flv @ 0x143604b30] After avformat_find_stream_info() pos: 496783 bytes read:496783 seeks:0 frames:179&#xA;Input #0, flv, from &#x27;rtmp://localhost:1936/live/test&#x27;:&#xA;  Metadata:&#xA;    |RtmpSampleAccess: true&#xA;    Server          : NGINX RTMP (github.com/arut/nginx-rtmp-module)&#xA;    displayWidth    : 1280&#xA;    displayHeight   : 720&#xA;    fps             : 30&#xA;    profile         : &#xA;    level           : &#xA;  Duration: 00:00:00.00, start: 6.742000, bitrate: N/A&#xA;  Stream #0:0, 138, 1/1000: Audio: aac (LC), 48000 Hz, stereo, fltp, 163 kb/s&#xA;  Stream #0:1, 41, 1/1000: Video: h264 (High), 1 reference frame, yuv420p(tv, bt709, progressive, left), 1280x720 [SAR 1:1 DAR 16:9], 0/1, 2560 kb/s, 30 fps, 30 tbr, 1k tbn&#xA;Successfully opened the file.&#xA;Parsing a group of options: output url ./output/test/1080p/stream.mpd.&#xA;Applying option c:v (select encoder/decoder (&#x27;copy&#x27; to copy stream without reencoding)) with argument libx264.&#xA;Applying option s (set frame size (WxH or abbreviation)) with argument 1920x1080.&#xA;Applying option f (force container format (auto-detected otherwise)) with argument dash.&#xA;Successfully parsed a group of options.&#xA;Opening an output file: ./output/test/1080p/stream.mpd.&#xA;[out#0/dash @ 0x123707480] No explicit maps, mapping streams automatically...&#xA;[vost#0:0/libx264 @ 0x123707d60] Created video stream from input stream 0:1&#xA;detected 10 logical cores&#xA;[h264 @ 0x123607b70] nal_unit_type: 7(SPS), nal_ref_idc: 3&#xA;[h264 @ 0x123607b70] Decoding VUI&#xA;[h264 @ 0x123607b70] nal_unit_type: 8(PPS), nal_ref_idc: 3&#xA;[aost#0:1/aac @ 0x144028080] Created audio stream from input stream 0:0&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: md&#xA;&#xA;<it simply="simply" starts="starts" working="working">&#xA;</it></same>

  


  I am not sure if there is something to do with Permissions.