Vertical scaling vs horizontal scaling in HLD - Scaling Approaches Compared

Scalability Analysis - Vertical scaling vs horizontal scaling

Growth Table: Vertical vs Horizontal Scaling

Users/Traffic	Vertical Scaling	Horizontal Scaling
100 users	Single server with moderate CPU/RAM handles load easily	Single server; no need for multiple servers
10,000 users	Upgrade server CPU, RAM, and storage to handle more load	Add a few servers behind a load balancer to share traffic
1,000,000 users	Server hardware limits reached; expensive and risky upgrades	Many servers distributed; load balancer and data partitioning needed
100,000,000 users	Vertical scaling impractical; single server cannot handle load	Large cluster of servers; complex orchestration and sharding required

First Bottleneck

With vertical scaling, the first bottleneck is the physical limits of a single server's CPU, memory, and storage capacity. Once maxed out, no further upgrades are possible without downtime or huge cost.

With horizontal scaling, the first bottleneck is often the coordination layer, such as load balancers or database consistency, which can become complex as servers increase.

Scaling Solutions

• Vertical Scaling: Upgrade server hardware (CPU, RAM, SSD). Simple but limited by max hardware specs and cost.
• Horizontal Scaling: Add more servers to distribute load. Requires load balancers, data partitioning (sharding), and stateless design.
• Caching: Use caches (like Redis) to reduce load on servers and databases in both scaling types.
• Database Scaling: Vertical scaling by bigger DB server; horizontal scaling by read replicas and sharding.
• Network: Use CDNs to offload static content and reduce server load in horizontal scaling.

Back-of-Envelope Cost Analysis

Assuming 1,000 requests per second (RPS) per server:

• Vertical scaling: Upgrading server to handle 5,000 RPS costs 3x more hardware but limited by max specs.
• Horizontal scaling: 5 servers at 1,000 RPS each; cost scales linearly but adds complexity.
• Storage: Vertical scaling requires bigger disks; horizontal scaling distributes data across servers.
• Bandwidth: 1 Gbps network supports ~125 MB/s; multiple servers share bandwidth load.

Interview Tip

Start by explaining vertical scaling as upgrading a single machine's resources. Then contrast with horizontal scaling by adding more machines. Discuss pros and cons, bottlenecks, and when to choose each. Use simple examples like upgrading a car engine vs buying more cars to share the load.

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 queries and reduce load on the primary database before considering vertical upgrades or sharding.

Key Result

Vertical scaling upgrades a single server's resources but hits physical limits quickly; horizontal scaling adds more servers to share load and scales better for large traffic but requires more complex coordination.

Practice

1. Which of the following best describes vertical scaling in system design?

easy

A. Using caching to speed up data access

B. Adding more machines to distribute the workload

C. Splitting data across multiple databases

D. Adding more resources like CPU or RAM to a single machine

Solution

1. Step 1: Understand vertical scaling definition

   Vertical scaling means improving one machine's capacity by adding resources like CPU, RAM, or storage.

2. Step 2: Compare with other options

   Options B and C describe horizontal scaling or data partitioning, and D is about caching, not scaling.

3. Final Answer:

   Adding more resources like CPU or RAM to a single machine -> Option D

4. Quick Check:

   Vertical scaling = adding resources to one machine [OK]

Hint: Vertical scaling = beef up one machine, not add machines [OK]

Common Mistakes:

• Confusing vertical scaling with horizontal scaling
• Thinking adding machines is vertical scaling
• Mixing caching with scaling

2. Which option correctly shows horizontal scaling in a system architecture?

easy

A. Adding multiple servers behind a load balancer

B. Upgrading the CPU and RAM of a single server

C. Increasing the disk size of one database server

D. Using a faster network switch

Solution

1. Step 1: Identify horizontal scaling characteristics

   Horizontal scaling means adding more machines to share the workload, often managed by a load balancer.

2. Step 2: Eliminate other options

   Options B and C describe vertical scaling (upgrading one machine), and D is about network hardware, not scaling.

3. Final Answer:

   Adding multiple servers behind a load balancer -> Option A

4. Quick Check:

   Horizontal scaling = add machines behind load balancer [OK]

Hint: Horizontal scaling = add servers, vertical = upgrade one [OK]

Common Mistakes:

• Choosing upgrading one server as horizontal scaling
• Confusing network upgrades with scaling
• Ignoring load balancer role

3. Consider a web application initially running on a single server with 8 CPU cores and 16GB RAM. If the traffic doubles, which scaling approach will likely double the capacity most efficiently?

medium

A. Upgrade the server to 16 CPU cores and 32GB RAM (vertical scaling)

B. Add one more identical server and use a load balancer (horizontal scaling)

C. Add caching layer without changing servers

D. Increase database storage size

Solution

1. Step 1: Analyze capacity needs for doubled traffic

   Doubling traffic usually requires doubling processing power or distributing load.

2. Step 2: Compare vertical vs horizontal scaling efficiency

   Vertical scaling (Upgrade the server to 16 CPU cores and 32GB RAM (vertical scaling)) can be costly and limited by hardware max. Horizontal scaling (Add one more identical server and use a load balancer (horizontal scaling)) adds another server to share load, often more efficient and scalable.

3. Final Answer:

   Add one more identical server and use a load balancer (horizontal scaling) -> Option B

4. Quick Check:

   Doubling capacity = add server (horizontal) better than upgrade [OK]

Hint: Doubling load? Add servers horizontally, not just upgrade one [OK]

Common Mistakes:

• Assuming vertical scaling always doubles capacity easily
• Ignoring load balancer role in horizontal scaling
• Confusing storage increase with processing capacity

4. A system uses vertical scaling by upgrading a server's CPU and RAM. After some time, the server crashes under load. What is the most likely cause?

medium

A. The server reached hardware limits and cannot scale further vertically

B. The load balancer is misconfigured

C. The database storage is full

D. The network bandwidth is too high

Solution

1. Step 1: Understand vertical scaling limits

   Vertical scaling improves one machine but has physical hardware limits beyond which it cannot handle more load.

2. Step 2: Analyze crash cause

   Since the server crashes under load after upgrades, it likely hit max capacity. Other options relate to load balancer, storage, or network, which are less likely causes here.

3. Final Answer:

   The server reached hardware limits and cannot scale further vertically -> Option A

4. Quick Check:

   Vertical scaling hits hardware limits causing crashes [OK]

Hint: Vertical scaling hits max hardware limits causing failures [OK]

Common Mistakes:

• Blaming load balancer when none is used
• Confusing storage issues with CPU/RAM limits
• Ignoring hardware capacity limits

5. A startup expects rapid user growth and unpredictable traffic spikes. They want a scalable system with minimal downtime and cost efficiency. Which scaling strategy should they prioritize?

hard

A. Vertical scaling by upgrading a powerful single server

B. Relying on manual server upgrades during peak times

C. Horizontal scaling with multiple servers and auto-scaling

D. Using a single server with a large database

Solution

1. Step 1: Identify startup needs

   Rapid growth and unpredictable spikes require flexible, scalable solutions that can adjust resources quickly without downtime.

2. Step 2: Evaluate scaling options for flexibility and cost

   Vertical scaling (Vertical scaling by upgrading a powerful single server) is limited and costly for spikes. Manual upgrades (Relying on manual server upgrades during peak times) cause downtime and delay. Single server (Using a single server with a large database) risks failure. Horizontal scaling with auto-scaling (Horizontal scaling with multiple servers and auto-scaling) adds/removes servers dynamically, balancing cost and uptime.

3. Final Answer:

   Horizontal scaling with multiple servers and auto-scaling -> Option C

4. Quick Check:

   Rapid growth needs horizontal auto-scaling for cost and uptime [OK]

Hint: Rapid growth? Use horizontal auto-scaling for flexibility [OK]

Common Mistakes:

• Choosing vertical scaling for unpredictable spikes
• Ignoring downtime caused by manual upgrades
• Relying on single server risking failure