Throughput, latency, and availability in HLD - Scalability & System Analysis

Scalability Analysis - Throughput, latency, and availability

Growth Table: Throughput, Latency, and Availability

Scale	Throughput (requests/sec)	Latency (ms)	Availability (%)	What Changes?
100 users	~50	~50	99.9	Single server handles requests easily; low latency; simple setup
10,000 users	~5,000	~100	99.95	Need load balancer; some caching; latency slightly increases
1,000,000 users	~500,000	~200	99.99	Multiple servers; database replicas; CDN; latency affected by network
100,000,000 users	~50,000,000	~300+	99.999	Global distribution; sharding; advanced caching; complex failover

First Bottleneck

At small scale, the application server CPU and memory can handle throughput and latency well.

As users grow to thousands, the database becomes the first bottleneck because it handles many read/write operations.

At millions of users, network bandwidth and latency limit performance, affecting availability.

Scaling Solutions

• Horizontal scaling: Add more servers behind load balancers to increase throughput and reduce latency.
• Caching: Use in-memory caches (like Redis) to reduce database load and improve latency.
• Database replication: Use read replicas to spread read traffic and improve availability.
• Sharding: Split database by user or data to handle large scale writes and reads.
• Content Delivery Network (CDN): Cache static content closer to users to reduce latency globally.
• Failover and redundancy: Use multiple data centers and automatic failover to improve availability.

Back-of-Envelope Cost Analysis

• At 1,000 users: ~50 requests/sec, easily handled by 1 server with 1 Gbps network.
• At 1 million users: ~500,000 requests/sec, need ~100 servers (assuming 5,000 req/sec/server).
• Database: Single instance handles ~10,000 QPS; need replicas and sharding beyond that.
• Bandwidth: 1 Gbps = 125 MB/s; high throughput requires multiple network interfaces or data centers.
• Latency: Network and disk I/O dominate; caching reduces disk reads and improves response times.

Interview Tip

Start by defining throughput, latency, and availability in simple terms.

Explain how each metric affects user experience and system design.

Discuss bottlenecks at different scales and propose targeted solutions.

Use real numbers to show understanding of system limits and scaling techniques.

Self Check

Your database handles 1,000 QPS. Traffic grows 10x to 10,000 QPS. What do you do first?

Answer: Add read replicas to distribute read load and implement caching to reduce database hits. Consider sharding if writes also increase significantly.

Key Result

Throughput, latency, and availability scale differently; databases often bottleneck first at medium scale, solved by replication and caching; network and global distribution become critical at very large scale.

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