🚀 Top 5 Proven Ways to Improve API Performance

[Inspired by BYTEBYTE GO]
Design for speed, scale, and reliability.

In today's fast-paced world of digital applications, API performance isn't just a backend concern — it's a user experience mandate. A sluggish API can mean the difference between a happy user and a frustrated one.

Whether you're building a public REST API or internal microservices, improving API performance is a game-changer for scalability, developer productivity, and user trust.

Here are 5 proven techniques that consistently deliver results:

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1️⃣ Result Pagination — Keep It Small, Keep It Fast

🧠 The Problem:

APIs that return large datasets (e.g., thousands of users or products) can:

• Overload the database
• Cause timeouts or memory issues
• Freeze the frontend trying to render everything at once

✅ The Solution:

Paginate your results. Return small, manageable chunks.

🔧 Implementation:

Use LIMIT/OFFSET or cursor-based pagination depending on your use case.

SELECT * FROM users
ORDER BY id
LIMIT 50 OFFSET 100;

Or for cursor-based (better for infinite scroll):

GET /users?after=1050&limit=50

💡 Bonus:

Always return metadata:

{
  "data": [...],
  "page": 3,
  "per_page": 50,
  "total": 1240
}

🚨 Caution:

Avoid OFFSET in huge datasets — use indexed cursors to prevent performance hits.

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2️⃣ Asynchronous Logging — Log Without Blocking

🧠 The Problem:

Logging every request synchronously can:

• Block the event loop (in Node.js or Python)
• Cause slowdowns under high load
• Add unnecessary I/O overhead

✅ The Solution:

Use non-blocking, buffered, or asynchronous logging techniques.

🔧 Implementation:

Node.js + Pino (with async transport):

import pino from 'pino';
const logger = pino({
  transport: {
    target: 'pino-pretty',
  }
});

Or with a queue-based system:

• Push logs to Redis or Kafka
• Flush them in batches to log storage (Elasticsearch, Loki, etc.)

🧠 Tip:

Decouple logging entirely using background workers — let your API focus on its job.

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3️⃣ Data Caching — Query Less, Serve Faster

🧠 The Problem:

Every API hit that goes straight to the database:

• Increases response time
• Adds load to your DB (especially under spikes)
• Repeats identical queries

✅ The Solution:

Cache frequent data in memory (e.g., Redis, in-process LRU cache).

🔧 Implementation:

const cached = await redis.get(key);
if (cached) return JSON.parse(cached);

const data = await db.query(...);
await redis.set(key, JSON.stringify(data), { EX: 60 }); // TTL 60 seconds
return data;

🧠 Good candidates for caching:

• Homepage/product listings
• User preferences
• Configuration and static data

🚨 Caution:

Stale data issues are real — use cache invalidation or time-based TTLs wisely.

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4️⃣ Payload Compression — Shrink It Before You Ship It

🧠 The Problem:

Large JSON responses are heavy on bandwidth and time-to-first-byte.

✅ The Solution:

Compress responses using gzip or brotli — especially helpful over slow networks.

🔧 Server-side Setup:

Express.js

import compression from 'compression';
app.use(compression());

Nginx

gzip on;
gzip_types application/json text/plain;

⚠️ Don't forget:

• Compression adds CPU overhead — benchmark before enabling for tiny payloads
• Let clients opt-in using Accept-Encoding: gzip

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5️⃣ Connection Pooling — Reuse, Don’t Rebuild

🧠 The Problem:

Creating a new DB connection for every API call is:

• Expensive
• Slow
• Dangerous (can exhaust DB limits)

✅ The Solution:

Use a connection pool to maintain and reuse a set of DB connections.

🔧 Examples:

PostgreSQL with pg-pool:

const pool = new Pool({ max: 10 });
const client = await pool.connect();

NestJS with TypeORM (default pooling via configuration):

TypeOrmModule.forRoot({
  type: 'postgres',
  host: 'localhost',
  username: 'user',
  password: 'pass',
  poolSize: 10,
});

💡 Bonus:

Connection pools can be monitored and tuned — keep an eye on idle/used connections in production.

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🎯 Final Thoughts

Performance tuning isn’t about magic bullets — it’s about stacking smart decisions like these:

• Paginate results to keep responses lightweight ✅
• Log asynchronously to avoid blocking the app ✅
• Cache intelligently to reduce repeated DB hits ✅
• Compress payloads for faster transmission ✅
• Reuse DB connections to avoid overhead ✅

Together, they transform your API into a fast, reliable, and scalable system.

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🙌 Your Turn

Have you used any of these techniques in production? Gotchas you learned the hard way?
Let’s trade war stories in the comments 💬

Or if you're starting out — try implementing just one of these in your current project and benchmark the result. You'll be surprised at the gains 🚀