1. Write Clean & Efficient Code 🧹

✅ Use the Right Data Structures – Choose hashmaps, sets, or tries over lists when searching is frequent.
✅ Avoid Redundant Computations – Cache results instead of recalculating values.
✅ Optimize Loops – Minimize nested loops and unnecessary iterations.

💡 Example: Instead of:

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for i in range(len(arr)):     for j in range(len(arr)):         if arr[i] == arr[j]:             print("Duplicate found") 

Use:

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seen = set() for num in arr:     if num in seen:         print("Duplicate found")     seen.add(num) 

🔹 Time Complexity improved from O(n²) → O(n)

2. Optimize Database Queries 📊

✅ Use Indexing – Speed up searches by indexing frequently queried columns.
✅ Avoid N+1 Query Problem – Use JOINs instead of multiple queries.
✅ Cache Frequently Accessed Data – Use Redis or Memcached.
✅ Limit Query Results – Fetch only necessary columns (SELECT id, name instead of SELECT *).

💡 Pro Tip: Use ORMs with caution—they can generate inefficient queries.

3. Reduce Memory Usage 🧠

✅ Use Generators Instead of Lists – Avoid loading large datasets into memory.
✅ Free Unused Objects – Use del in Python or garbage collection.
✅ Use Memory-Efficient Data Types – Example: NumPy arrays are faster than Python lists.

💡 Example: Using a generator in Python to save memory:

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def large_numbers():     for i in range(1, 10**6):         yield i nums = large_numbers()  # Doesn't store all values in memory 

4. Optimize Frontend Performance 🚀

✅ Minify & Compress Files – Use Gzip, Brotli for assets (CSS, JS, images).
✅ Lazy Load Images & Scripts – Load only what’s needed (loading="lazy" in images).
✅ Reduce DOM Manipulations – Batch updates instead of modifying elements one by one.
✅ Use Content Delivery Network (CDN) – Serve assets from a CDN to reduce load times.

💡 Pro Tip: Preload critical assets for faster rendering.

5. Optimize Backend & API Performance 🌐

✅ Reduce API Calls – Combine requests when possible.
✅ Use Asynchronous Processing – Handle time-consuming tasks in the background.
✅ Enable Gzip Compression – Compress API responses to reduce payload size.
✅ Rate Limit & Cache Responses – Use tools like Redis or Cloudflare.

💡 Example: Asynchronous processing in Node.js

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async function fetchData() {   const [users, posts] = await Promise.all([     fetch('/api/users'),     fetch('/api/posts')   ]); } 

🔹 Improves API response time by running requests in parallel.

6. Profile & Benchmark Your Code 📉

✅ Use Profiling Tools:

• Python → cProfile, memory_profiler
• JavaScript → Chrome DevTools (Performance tab)
• Java → JProfiler
  ✅ Benchmark Different Approaches – Compare different implementations to find the fastest.

💡 Example: Measure function execution time in Python

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import time start = time.time() my_function() print("Execution time:", time.time() - start) 

7. Scale Efficiently 🏗️

✅ Load Balancing – Distribute traffic across multiple servers.
✅ Database Sharding – Split large databases into smaller chunks.
✅ Use Asynchronous Queues – For processing background jobs (Celery, RabbitMQ, Kafka).
✅ Optimize Server Response Time – Reduce TTFB (Time to First Byte) with optimized queries & caching.