Modern web applications have many parts. These parts, also called components, include frontend user interfaces, backend servers, APIs, and databases. When something goes wrong, it’s not always easy to know where the problem is. It could be a slow database, a broken API, or an error in the browser.
To fix problems quickly, developers use a method called self-diagnosing. This means the application can check itself and find out what is not working. One of the best ways to do this is by using embedded telemetry.
Telemetry is the process of collecting data from different parts of the app. This data helps understand performance, errors, and behavior. When this is built into the app, the components can monitor themselves, send reports, and even give early warnings when something is wrong.
If you want to learn how to build smart applications that monitor themselves, joining a full stack developer course in Bangalore is a great step. It teaches how frontend, backend, and all components work together.
Let’s learn more about embedded telemetry, self-diagnosis, and how they improve full stack apps.
What Is Embedded Telemetry?
Telemetry is like the health monitor for software. It tracks information such as:
- Response time of APIs
- Number of errors
- User activity
- CPU and memory usage
- Data transfer speed
Embedded telemetry means this tracking is built into the component itself. The code collects and sends data without needing extra tools from outside.
For example, a web page may record how long it takes to load. The backend server may track how fast it sends responses. The database may note if a query takes too long. All of these details are part of embedded telemetry.
This data is then sent to a monitoring system. Developers and support teams can use dashboards to view the status of the app in real time.
Why Self-Diagnosing Components Matter
In a full stack application, there are many points where issues can happen. Without self-diagnosing, a developer must check logs, test APIs, and ask users for details. This takes time and is not always helpful.
With self-diagnosing components:
- The app finds the problem itself
- It reports the problem with useful data
- It helps the team fix the issue faster
- In some cases, it can fix the problem automatically
For example, if a payment API starts failing, the system can detect a high number of errors. It can then alert the developer and even switch to a backup service.
This improves reliability, reduces downtime, and makes users happier.
To understand how this works in real-world apps, a full stack developer course is very useful. It teaches how to connect monitoring with backend logic and user interfaces.
How Components Collect Telemetry
Different components of the app collect different types of data. Let’s look at some examples:
Frontend (Browser)
- Page load time
- Button click tracking
- JavaScript errors
- User session data
Backend (Server)
- API response time
- Request and error count
- CPU and memory usage
- Number of active users
Database
- Slow queries
- Failed reads or writes
- Connection count
Each part sends its telemetry to a central system. This system stores the data and makes it easy to view in dashboards or alerts.
Some popular telemetry tools include:
- Prometheus
- Grafana
- Elastic Stack (ELK)
- Datadog
- New Relic
Many developers also use custom telemetry based on their app needs.
How to Build a Self-Diagnosing System
Let’s walk through the basic steps to add self-diagnosis to your app.
Step 1: Identify What to Monitor
Decide which metrics are important. These may include:
- API speed
- Database health
- Page errors
- Login success rate
Step 2: Add Telemetry Code
In your app, add small pieces of code that collect data. For example:
window.addEventListener(‘load’, () => {
const loadTime = performance.now();
sendTelemetry(‘page_load_time’, loadTime);
});
In the backend:
app.use((req, res, next) => {
const start = Date.now();
res.on(‘finish’, () => {
const timeTaken = Date.now() – start;
sendTelemetry(‘api_response_time’, timeTaken);
});
next();
});
Step 3: Store and View Data
Send the telemetry data to a central server. Use tools like Grafana or your own dashboard to view and analyze it.
Step 4: Set Alerts
Create alerts for when values go out of range. For example, alert if response time is more than 2 seconds, or if error count goes above 10 in 1 minute.
Step 5: Take Action
When alerts happen, your system can:
- Send a message to the team
- Retry a failed action
- Switch to a backup service
- Restart a service
This makes the system smart and responsive.
If you want to build such systems from start to end, a developer course can guide you through backend coding, frontend setup, and monitoring tools.
Real-Life Example
Let’s take an online store. It has a frontend, a backend API, and a database.
Here’s how self-diagnosing works:
- The frontend logs how long it takes to load the product page.
- The backend tracks how many users add products to the cart.
- The database checks how fast it retrieves product data.
Suddenly, the product page becomes slow. The telemetry shows:
- Frontend load time has increased from 1 second to 5 seconds
- Backend API is waiting too long for the database
- Database query time has doubled
Now, the system sends an alert: “Database is slow. Check the product lookup query.”
The developer sees the problem, fixes the slow query, and the app returns to normal—all within minutes.
Without telemetry, it would take hours to find and fix the issue.
Benefits of Embedded Telemetry
Here are the major benefits:
- Quick problem detection: Find problems before users report them.
- Better performance: Track speed and improve slow parts.
- Higher uptime: Fix errors faster, reduce downtime.
- Less guesswork: Real data shows what’s wrong.
- Smarter systems: Apps that fix or report their own issues.
These benefits are useful in both small and large applications.
Challenges and Tips
Adding telemetry also comes with challenges:
Too Much Data
Too much data can be hard to manage. Only track important metrics.
Performance Impact
Make sure telemetry code is light and does not slow down the app.
Privacy
Be careful not to collect personal user data. Always follow data privacy laws.
False Alerts
Set proper thresholds to avoid too many alerts for small issues.
With practice, these issues can be managed well.
To get hands-on practice, a full stack developer course helps you work with real-world tools and systems. You learn how to build, test, and monitor apps in a complete environment.
Conclusion
Self-diagnosing components are the future of smart applications. By using embedded telemetry, your app can monitor itself, detect problems, and even fix small issues. This leads to better user experience, faster support, and stronger systems.
Service outages and slow apps can damage trust. But with proper monitoring, you can respond before users even notice.
To build full stack systems that are fast, smart, and self-aware, learning both frontend and backend is important. Full stack developer course in Bangalore offers the skills to build complete apps that stay healthy and perform well.
With embedded telemetry and smart monitoring, your apps will always stay one step ahead of problems—and that’s what users love.
Business Name: ExcelR – Full Stack Developer And Business Analyst Course in Bangalore
Address: 10, 3rd floor, Safeway Plaza, 27th Main Rd, Old Madiwala, Jay Bheema Nagar, 1st Stage, BTM 1st Stage, Bengaluru, Karnataka 560068
Phone: 7353006061
Business Email: enquiry@excelr.com
