Latency vs Throughput

☕ Understanding Latency vs Throughput in Real-World Systems

Whenever I talk to devs diving into distributed systems or backend engineering, one of the first conceptual roadblocks is understanding latency and throughput — and more importantly, how to balance them.

So here’s how I’d explain it if we were at a coffee shop, chatting over a system design whiteboard napkin.

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♻ What is Latency?

Latency is the time it takes to complete one request.

Think of it as the delay between:

• You hitting “Submit Order” on Amazon

• And the page confirming: “Your order is placed!”

🕒 Low latency = fast response
😩 High latency = waiting too long

Unit: milliseconds (ms)

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📆 What is Throughput?

Throughput is the number of requests your system can handle per second/minute.

Think of it like how many coffee orders a café can serve per hour.

📈 High throughput = handling lots of users
📉 Low throughput = bottlenecks, queue build-up

Unit: requests/sec, events/min

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⚖️ Latency vs Throughput: Not Always Best Friends

Here’s where the balance gets tricky:

• If you optimize for latency (fast responses), you might need to reduce concurrent load = lower throughput

• If you optimize for throughput (handle more users), you may need to batch or queue = increases latency

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☕ Coffee Shop Analogy

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💻 Real-World Examples

🚀 Low Latency Systems:

• Payment confirmation

• Live chat

• Gaming servers

• High-frequency trading

📊 High Throughput Systems:

• Analytics pipelines

• Log processors

• E-commerce order exports

• Stream aggregators (Kafka consumers, Flink jobs)

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🧠 So... How Do Big Tech Companies Handle It?

They architect their systems to decouple latency-sensitive and throughput-heavy workloads.

Here’s how:

✅ Techniques Big Companies Use:

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📆 In Code Terms:

@GetMapping("/api/order-summary")
public Order getSummary() {
    // Fetch a lightweight, precomputed result (like from cache or a small DB read)
}

@PostMapping("/api/notify-user")
public void notifyUser() {
    // Send to Kafka — async — increases throughput
}

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🧪 TL;DR

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⚖️ The art of system design is figuring out where you can afford latency and where you must prioritize throughput — or vice versa.