Advanced Nodejs Interview Questions

High traffic handle karne ke liye main:

• Load balancing use karta hoon (Nginx / AWS ELB)
• Clustering (PM2) se multiple instances run karta hoon
• Caching (Redis) use karta hoon taaki DB load kam ho
• Database optimize karta hoon (indexes, pooling)
• Async/background jobs (queues like RabbitMQ) use karta hoon
• Rate limiting apply karta hoon

Short Answer :
High traffic handle karne ke liye load balancing, clustering, caching aur async processing use karke system ko scalable banaya jata hai.

Sabse pehle main logs check karta hoon — PM2 logs, application logs aur system logs (jaise /var/log) — taaki exact error message aur stack trace mil sake.

Uske baad main crash pattern analyze karta hoon — kya ye specific API hit par ho raha hai, ya high traffic par. Agar memory related issue lagta hai to main memory usage aur CPU spikes monitor karta hoon (top, htop, PM2 metrics) taaki memory leak ya infinite loop identify ho sake.

Phir main recent deployments ya code changes review karta hoon, kyunki zyadatar crashes kisi recent change ki wajah se hote hain. Saath hi main unhandled exceptions aur promise rejections check karta hoon, kyunki Node.js me agar ye handle na ho to process crash ho sakta hai.

Agar DB ya external service involved hai to main slow queries ya timeout issues bhi check karta hoon. Zarurat pade to main replicate karne ki koshish karta hoon staging ya local me, taaki exact scenario samajh aaye.

End me, fix apply karne ke baad main monitoring tools (PM2, New Relic, CloudWatch) lagata hoon taaki future me issue proactively detect ho sake.

Production me main logging ke liye structured logging use karta hoon (jaise Winston ya Pino), jisme logs ko proper format (JSON) me store karte hain aur alag-alag levels (info, error, debug) maintain karte hain.

Logs ko centralize karne ke liye unhe ELK stack (Elasticsearch, Logstash, Kibana) ya cloud services (AWS CloudWatch) me bhejte hain, jisse easily search aur analyze ho sake.

Monitoring ke liye main PM2, New Relic ya Datadog use karta hoon jisse CPU, memory, response time aur error rate track hota hai, aur alerts setup karte hain taaki issue aate hi notify ho jaye.

Connection pooling me hum multiple reusable DB connections ka pool create karte hain taaki har request par naya connection create na karna pade, isse performance improve hoti hai.

Express me main ORM ya drivers (jaise Sequelize / MySQL driver) ke through pool size, max/min connections aur timeout configure karta hoon. Har request pool se connection leta hai aur kaam ke baad release kar deta hai.

Saath hi main ensure karta hoon ki connections properly close/release ho, warna connection leak ho sakta hai aur app slow ya crash ho sakta hai.

Timeout implement karne ke liye main HTTP client (jaise Axios) me timeout set karta hoon, taki agar API fixed time me response na de to request fail ho jaye.

Retry ke liye main retry logic lagata hoon — ya to manually (loop/recursive) ya libraries (axios-retry) se — jisme limited attempts aur delay (exponential backoff) set karta hoon, taki system overload na ho.

Production me main ensure karta hoon ki retry sirf safe operations (GET) par ho aur proper logging ho.

Short Answer :
Timeout ke liye request time limit set karte hain aur retry ke liye limited attempts with delay (exponential backoff) use karte hain.

Large payload handle karne ke liye main streaming approach use karta hoon, taki data chunk-wise process ho aur memory overload na ho.

File uploads ke case me main Multer ya direct streams (S3 upload) use karta hoon, aur unnecessary large body ko block karne ke liye request size limit set karta hoon (limit in body parser).

Saath hi main compression enable karta hoon aur agar possible ho to large data ko pagination ya chunking me break kar deta hoon.

Express me middleware do type ke hote hain — synchronous aur asynchronous — aur inka main difference execution aur handling ka hota hai.

Synchronous middleware:
Ye immediately execute hota hai aur blocking nature ka hota hai. Isme koi async operation nahi hota, aur ye direct next() call karke aage badh jata hai.
Example: logging middleware

app.use((req, res, next) => {
  console.log(req.url);
  next();
});

Asynchronous middleware:
Isme async operations hote hain jaise DB call, API call, file read, etc. Ye non-blocking hota hai aur jab async task complete hota hai tab next() call hota hai.

Example: user authentication (DB se check)

app.use(async (req, res, next) => {
  const user = await User.findById(req.headers.id);
  if (!user) return res.status(401).send("Unauthorized");
  next();
});

Real-world example:
Maan lo ek e-commerce app hai:

• Synchronous middleware: har request ka log print karna
• Asynchronous middleware: user authentication ya order fetch karna DB se

Key diff:
Synchronous middleware turant execute hota hai, jabki asynchronous middleware me async tasks complete hone ke baad execution aage badhta hai.

Memory leak tab hota hai jab app memory allocate karta rehta hai lekin properly release nahi karta, jisse time ke saath memory continuously badhti rehti hai aur eventually app crash ho sakta hai.

Detect karne ke liye sabse pehle main memory usage monitor karta hoon — jaise process.memoryUsage(), PM2 metrics, ya tools like New Relic. Agar memory continuously increase ho rahi hai bina drop hue, to ye leak ka sign hai.

Uske baad main heap snapshots aur profiling tools use karta hoon (Chrome DevTools / Node inspector) taaki pata chale kaunse objects memory me stuck hain. Isse exact source identify karna easy ho jata hai.

Phir main code review karta hoon aur common issues check karta hoon:

• Global variables ya caches jo clear nahi ho rahe
• Event listeners jo remove nahi ho rahe
• Unclosed DB connections ya file handles
• Large objects jo memory me hold ho rahe hain

Real-world Example:
Ek project me maine dekha ki API hit hone par memory dheere-dheere badh rahi thi. Investigation me pata chala ki humne ek in-memory cache banaya tha (object me data store kar rahe the) lekin uska cleanup ya TTL nahi tha. Har request me data add ho raha tha aur kabhi remove nahi ho raha tha.
Fix me maine Redis with TTL use kiya aur unnecessary data cleanup implement kiya, jisse memory stable ho gayi.