What methods can you use to collect data on software application performance?

Most of the social media data rich companies have built their tech fortunes on tracking the user and understanding user behaviour and using it to optimize the user experience. A product mindset to building what I call "user shadowing" data collection tools can help you create the data driven organization closest to users. Some common use cases I have seen in India in the online food ordering business is - tracking real time the app usage data from item selection, add to cart & ordering. The data collected showed how users switch between apps to compare the discounts and deals across competitor apps.

This is a valuable source of qualitative data on user experience and perceived performance. Tools like surveys, interviews, and focus groups can help you understand user pain points, satisfaction levels, and expectations.

To collect data on software application performance, you can use methods like application performance monitoring (APM) tools, profiling, logging, benchmarking, and utilizing cloud-native tools provided by platforms like AWS, Azure, and GCP. These methods offer insights into performance metrics and potential bottlenecks.

User feedback, while valuable, has limitations for assessing software performance. Users may not accurately pinpoint technical issues, and their perceptions can be subjective. For instance, complaints of 'slowness' may lack specifics about the underlying cause, like server response or UI issues. Additionally, feedback can be biased towards the vocal minority, possibly overlooking the silent majority's experience. Hence, it's crucial to complement user feedback with empirical performance data for a more accurate and comprehensive understanding.

Gather insights directly from users about their experiences with the application. Surveys, interviews, and feedback forms can provide valuable qualitative data on performance issues or user satisfaction.

Key Performance Indicators (KPIs) can be used to know more about the performance of the software application. Logs and metrics that give insights about the time of usage, interactions with the application reveals alot of significant details. DAU, MAU are great measures to access performance

To get a grasp of the performance of our application, this must be monitored closely, and record every event for future analyses. Software monitoring is mostly accomplished via some of these methods: logs, metrics, or a combination of both. There are many metrics that can be used to monitor a software application, basic metrics such as response times, or transaction throughput; as well as more sophisticated use cases such as new users per month, or clicks per second.

Utilise system logs and performance metrics. These can include error logs, server response times, memory usage, and CPU load. Tools like log aggregators and performance monitoring solutions are useful here.

Logging database queries can also help understand the performance of the application. Especially the slow queries that run in background cam give great insights. Apart from this, we can aslo understand the indexing of the database done from the logged queries. Moreover, logging the functionality of the lambda functions can help understand how well the code works and how much load does it put on CPU.

Implementing logging mechanisms in the software code can capture detailed information about application events, errors, and performance metrics. Utilizing dedicated monitoring tools, such as APM (Application Performance Monitoring) solutions, enables real-time tracking of key metrics like response times and resource utilization, facilitating proactive performance management.

OpenTelemetry is a powerful observability framework that provides instrumentation libraries for various languages to instrument, generate, collect, and export telemetry data like metrics, logs, and traces to analyze software performance and behavior. It can be used for APM and works by providing APIs and SDKs to capture and manage telemetry data from services and applications.OpenTelemetry allows sending telemetry data to multiple backends, with different backends used for each signal. Elastic APM Server natively supports the OpenTelemetry protocol, so trace data and metrics collected can be sent to the Elastic APM Server. Some of the recommended datastores for OpenTelemetry include: - Prometheus - Uptrace - Grafana

Granular Insights Traces capture intricate details like function calls Comprehensive Understanding Profiles summarize time and space complexity, including CPU time, memory allocation, and disk I/O Optimization Opportunities Tools and frameworks point to specific areas for optimization Refactoring Guidance Profiles guide code refactoring by identifying inefficient segments Tool Integration Specialized tools streamline the entire process, integrating instrumentation, collection, analysis, and visualization for efficient performance management. Proactive Performance Management Utilizing traces and profiles identifies potential bottlenecks before impacting the user experience.

Implement application tracing and profiling to get a detailed view of application performance. This includes understanding how long different parts of your application take to execute and which resources they use.

Tracing involves instrumenting code to record the flow of transactions, helping identify bottlenecks. Profiling tools analyze the execution of a program, providing insights into resource utilization and allowing developers to optimize specific areas affecting software performance.

Tests and experiments are good options of measures to assess and improve software performance. Through procedures like unit tests, integration tests, and regression tests, we can understand functionality, quality, and reliability. You can also go with experiments, such as A/B tests or randomized controlled trials, to compare performance under different variables. These methodologies provide a structured approach to validate, verify, and improve performance. It's also important to leverage tools and frameworks, we can design, execute, and analyse these tests and experiments, to make sure we have a data-oriented optimization process that aligns with both user expectations and our business objectives.

Conduct performance tests like load testing, stress testing, and capacity testing. These tests simulate various conditions and loads to evaluate how the application performs.

Conducting performance testing, such as load testing or stress testing, simulates various user scenarios to assess application behavior under different conditions. Experiments involving A/B testing can also help measure performance improvements by comparing different versions of the software and analyzing user feedback and system metrics.

Analyse user interaction with the application through tools like heatmaps or session recordings. This helps in understanding real-world usage patterns and potential performance bottlenecks.

Implementing user analytics tools, like heatmaps and session recordings, allows for tracking user interactions within the application, unveiling usage patterns and identifying potential pain points. User feedback forms and surveys specifically focused on user experience can provide qualitative insights into how users perceive and navigate the software, aiding in understanding and improving overall performance.

Watching how people use software helps us know what they like. We look at where they spend the most time in the app, kind of like making a map that shows their favourite spots. It helps us see what parts they love and what areas might need some work.

To collect data on software application performance: 1.Monitoring Tools: Use specialized software like New Relic, Datadog, or Prometheus to track application metrics in real-time. 2.Logs Analysis: Review server and application logs to identify errors, bottlenecks, or performance issues. 3.User Feedback: Gather feedback from end-users through surveys, reviews, or feedback forms to understand user experience and performance issues. 4.Performance Testing: Conduct load testing, stress testing, and other performance tests using tools like JMeter or LoadRunner to simulate user activity and measure application response times. 5. Analytics Platforms: Integrate with analytics platforms like Google Analytics .e.t.c

If the product has variance in geographic locations of users and that makes a significance difference of making business decisions, then we should analyze user's location data as well. If we analyze this data, then we get insights on which user locations to focus with which strategy, and how feasible a location is that which the resources we are engaging in that region.

Automated Monitoring Tools: Automated tools can continuously monitor and report on application performance. Cross-Platform Performance: Consider performance across different devices and platforms. Data Privacy and Security: Ensure that data collection complies with privacy laws and doesn’t compromise user security. Real-Time vs Historical Analysis: Balance real-time monitoring with historical data analysis for comprehensive insights. Custom Metrics: Depending on the application's nature, custom metrics might be necessary to fully understand performance.

As the applications are increasingly distributed, methods for feedback, logging, tracing, profiling, and experiments in the distributed environments are becoming vital.

Collect data on software performance using profiling tools (e.g., MATLAB Profiler, YourKit), logging for key metrics, benchmarking with tools like JMH, monitoring tools (e.g., Prometheus, Grafana), code reviews, profiling APIs, user feedback and analytics, load testing (e.g., Apache JMeter), A/B testing, and heap dump analysis (e.g., Eclipse MAT).