Why Throughput, latency, and availability in HLD? - Purpose & Use Cases

The Big Idea

What if your favorite app suddenly slowed down or stopped working during a big event? Understanding these concepts can prevent that!

The Scenario

Imagine running a busy coffee shop where every order is taken and served by just one person without any tools or system.

Customers line up, wait a long time, and sometimes orders get mixed up or missed.

The Problem

Handling many customers manually is slow and stressful.

Orders pile up, mistakes happen, and some customers leave unhappy because they waited too long or never got their coffee.

The Solution

By understanding throughput, latency, and availability, we can design a system that serves many customers quickly, keeps wait times low, and stays open without interruptions.

This means using multiple baristas, efficient order tracking, and backup plans for busy times or machine failures.

Before vs After

✗ Before

serve_one_customer_at_a_time()
wait_for_order()
make_coffee()
serve_customer()

✓ After

parallel_serve_customers()
optimize_order_flow()
monitor_system_health()

What It Enables

It enables building systems that handle many requests fast, respond quickly, and stay reliable even under pressure.

Real Life Example

Think of a popular online store during a sale: it must process thousands of orders per second (throughput), show pages quickly (low latency), and never crash (high availability).

Key Takeaways

Throughput measures how much work a system can do in a time.

Latency is the delay before a system responds.

Availability means the system is ready and working when needed.

Practice

1. Which metric best describes how many requests a system can handle per second?

easy

A. Throughput

B. Latency

C. Availability

D. Scalability

Solution

1. Step 1: Understand throughput meaning

   Throughput measures the amount of work or requests a system can process in a given time.

2. Step 2: Compare with other metrics

   Latency measures response time, availability measures uptime, scalability is about growth capacity.

3. Final Answer:

   Throughput -> Option A

4. Quick Check:

   Throughput = Requests per second [OK]

Hint: Throughput = how much work done per time [OK]

Common Mistakes:

• Confusing latency with throughput
• Mixing availability with throughput
• Thinking scalability equals throughput

2. Which of the following correctly defines latency in system design?

easy

A. The total number of requests processed per second

B. The time taken to respond to a single request

C. The percentage of time the system is operational

D. The ability to add more servers to handle load

Solution

1. Step 1: Define latency

   Latency is the delay or time taken for a system to respond to one request.

2. Step 2: Eliminate other options

   The total number of requests processed per second describes throughput. The ability to add more servers to handle load describes scalability. The percentage of time the system is operational describes availability.

3. Final Answer:

   The time taken to respond to a single request -> Option B

4. Quick Check:

   Latency = Response time [OK]

Hint: Latency = response time per request [OK]

Common Mistakes:

• Confusing latency with throughput
• Thinking availability is latency
• Mixing scalability with latency

3. A system processes 1000 requests in 10 seconds. Each request takes 5 milliseconds to respond. What is the system's throughput and average latency?

medium

A. Throughput: 1000 requests/sec, Latency: 5 ms

B. Throughput: 100 requests/sec, Latency: 50 ms

C. Throughput: 10 requests/sec, Latency: 5 ms

D. Throughput: 100 requests/sec, Latency: 5 ms

Solution

1. Step 1: Calculate throughput

   Throughput = total requests / total time = 1000 / 10 = 100 requests per second.

2. Step 2: Understand latency

   Latency is given as 5 milliseconds per request.

3. Final Answer:

   Throughput: 100 requests/sec, Latency: 5 ms -> Option D

4. Quick Check:

   Throughput = 100, Latency = 5 ms [OK]

Hint: Throughput = total requests ÷ time; latency given directly [OK]

Common Mistakes:

• Dividing latency by number of requests
• Confusing throughput units
• Multiplying latency by total requests

4. A system claims 99.9% availability but is down for 10 minutes in a day. What is the actual availability and is the claim correct?

medium

A. Actual availability is 99.31%, claim is incorrect

B. Actual availability is 99.93%, claim is correct

C. Actual availability is 99.93%, claim is incorrect

D. Actual availability is 99.31%, claim is correct

Solution

1. Step 1: Calculate total minutes in a day

   24 hours x 60 minutes = 1440 minutes per day.

2. Step 2: Calculate availability

   Availability = (Total time - downtime) / Total time = (1440 - 10) / 1440 = 1430 / 1440 ≈ 0.9931 or 99.31%.

3. Step 3: Compare with claim

   Claim is 99.9%, actual is 99.31%, so claim is incorrect.

4. Final Answer:

   Actual availability is 99.31%, claim is incorrect -> Option A

5. Quick Check:

   Availability = 99.31%, Claim wrong [OK]

Hint: Availability = (uptime ÷ total time) x 100% [OK]

Common Mistakes:

• Using hours instead of minutes
• Incorrectly subtracting downtime
• Misreading percentage claim

5. You design a web service that must handle 10,000 requests per second with an average latency under 100 ms and 99.99% availability. Which design choice best balances these requirements?

hard

A. Use a single powerful server with caching to reduce latency

B. Use a database with strong consistency but no replication

C. Distribute load across multiple servers with load balancers and health checks

D. Optimize only latency by increasing CPU speed on one server

Solution

1. Step 1: Analyze throughput and availability needs

   10,000 requests/sec and 99.99% availability require scaling and fault tolerance.

2. Step 2: Evaluate design options

   Using a single powerful server with caching or optimizing CPU speed on one server risks overload and downtime. Using a database with strong consistency but no replication risks availability due to lack of fault tolerance. Distributing load across multiple servers with load balancers and health checks supports scaling and high availability.

3. Final Answer:

   Distribute load across multiple servers with load balancers and health checks -> Option C

4. Quick Check:

   Scaling + availability = load balancer + health checks [OK]

Hint: Scale horizontally with load balancers for throughput and availability [OK]

Common Mistakes:

• Relying on single server for high throughput
• Ignoring replication for availability
• Focusing only on latency without scaling