Read-heavy vs write-heavy systems in HLD - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identifying system type based on workload

You have a system where 90% of operations are data retrievals and only 10% are data updates. What type of system is this?

A Write-heavy system

B Batch processing system

C Balanced system

D Read-heavy system

💡 Hint

Think about which operation is more frequent: reading or writing data.

A read-heavy system is one where most operations are reads (data retrievals). Since 90% are reads, this is a read-heavy system.

❓ Architecture

intermediate

Choosing caching strategy for read-heavy systems

Which caching strategy best suits a read-heavy system to improve performance?

A Cache frequently read data with a long expiration time

B Cache only data that changes frequently

C Cache write operations only

D Disable caching to avoid stale data

💡 Hint

Think about what data is accessed most often and how caching helps.

In read-heavy systems, caching frequently read data with a long expiration reduces database load and improves response time.

❓ scaling

advanced

Scaling write-heavy systems

Which approach is most effective to scale a write-heavy system that experiences frequent data updates?

A Use read replicas to handle write requests

B Implement sharding to distribute write load across multiple nodes

C Increase cache expiration time to reduce writes

D Use master-slave replication with slaves handling writes

💡 Hint

Consider how to distribute write operations to avoid bottlenecks.

Sharding splits data across nodes, allowing writes to be distributed and handled in parallel, which is effective for write-heavy systems.

❓ tradeoff

advanced

Tradeoffs in consistency for read-heavy vs write-heavy systems

Which statement correctly describes a tradeoff between consistency and availability in read-heavy and write-heavy systems?

A Read-heavy systems must sacrifice availability to maintain consistency

B Write-heavy systems always require strong consistency and cannot use eventual consistency

C Read-heavy systems can tolerate eventual consistency better than write-heavy systems

D Write-heavy systems prioritize availability over consistency in all cases

💡 Hint

Think about how reads and writes affect user experience with stale data.

Read-heavy systems often tolerate eventual consistency because stale reads are less critical, while write-heavy systems may require stronger consistency to avoid conflicts.

❓ estimation

expert

Estimating capacity for a mixed workload system

A system receives 1 million requests per hour. 70% are reads and 30% are writes. Each read requires 5ms processing time, and each write requires 20ms. How many processing seconds are needed per hour to handle all requests?

A 19,000 seconds

B 23,500 seconds

C 17,500 seconds

D 21,000 seconds

💡 Hint

Calculate total time for reads and writes separately, then add.

Reads: 700,000 * 5ms = 3,500,000 ms = 3,500 seconds
Writes: 300,000 * 20ms = 6,000,000 ms = 6,000 seconds
Total = 3,500 + 6,000 = 9,500 seconds.
Since options are higher, the question likely expects total CPU time considering concurrency or overhead.
Assuming 2 cores, total CPU time = 9,500 * 2 = 19,000 seconds (option A).

Practice

1. Which statement best describes a read-heavy system?

easy

A. It performs many more data updates than retrievals.

B. It performs many more data retrievals than updates.

C. It balances reads and writes equally.

D. It only processes write operations.

Solution

1. Step 1: Understand read-heavy system definition

   A read-heavy system is one where the number of read operations is much higher than write operations.

2. Step 2: Match the description to options

   It performs many more data retrievals than updates. states more data retrievals than updates, which matches the definition.

3. Final Answer:

   It performs many more data retrievals than updates. -> Option B

4. Quick Check:

   Read-heavy = more reads than writes [OK]

Hint: Read-heavy means more reads than writes [OK]

Common Mistakes:

• Confusing read-heavy with write-heavy
• Thinking read-heavy means equal reads and writes
• Assuming read-heavy means only reads

2. Which design choice is best suited for a write-heavy system?

easy

A. Use caching aggressively to speed up reads.

B. Avoid sharding to keep data centralized.

C. Optimize database for fast write operations.

D. Use read replicas to distribute read load.

Solution

1. Step 1: Identify write-heavy system needs

   Write-heavy systems require fast and efficient handling of many write operations.

2. Step 2: Match design choices to write-heavy needs

   Optimize database for fast write operations. focuses on optimizing database for fast writes, which fits write-heavy systems best.

3. Final Answer:

   Optimize database for fast write operations. -> Option C

4. Quick Check:

   Write-heavy = optimize writes [OK]

Hint: Write-heavy needs fast writes, not just fast reads [OK]

Common Mistakes:

• Choosing caching which helps reads, not writes
• Selecting read replicas which help read scaling
• Avoiding sharding which can help write scaling

3. Consider a system with 90% reads and 10% writes. Which approach improves performance most?

medium

A. Use caching layers to serve read requests.

B. Add more write nodes to handle updates.

C. Disable read replicas to reduce complexity.

D. Increase write consistency to slow writes.

Solution

1. Step 1: Analyze read-write ratio

   With 90% reads, the system is read-heavy and benefits from read optimizations.

2. Step 2: Evaluate options for read-heavy systems

   Caching (Use caching layers to serve read requests.) reduces load on main database by serving frequent reads quickly.

3. Final Answer:

   Use caching layers to serve read requests. -> Option A

4. Quick Check:

   Read-heavy + caching = better performance [OK]

Hint: High reads? Use caching to speed up reads [OK]

Common Mistakes:

• Adding write nodes when writes are low
• Disabling read replicas which help reads
• Increasing write consistency unnecessarily

4. A write-heavy system is experiencing slow writes. Which fix is most appropriate?

medium

A. Implement asynchronous writes with queues.

B. Add more read replicas to distribute load.

C. Increase cache size for read operations.

D. Reduce write consistency to eventual consistency.

Solution

1. Step 1: Identify problem and system type

   Slow writes in a write-heavy system indicate write bottlenecks.

2. Step 2: Choose fix that improves write throughput

   Asynchronous writes with queues (Implement asynchronous writes with queues.) decouple write requests, improving write speed.

3. Final Answer:

   Implement asynchronous writes with queues. -> Option A

4. Quick Check:

   Write-heavy + async writes = faster writes [OK]

Hint: Use async writes to speed up write-heavy systems [OK]

Common Mistakes:

• Adding read replicas which help reads, not writes
• Increasing cache size which helps reads
• Reducing consistency without understanding impact

5. You design a system with 70% writes and 30% reads. Which architecture best fits this write-heavy workload?

hard

A. Use a single master database with multiple read replicas.

B. Rely heavily on caching to reduce database writes.

C. Use eventual consistency with delayed writes only.

D. Implement sharding to distribute write load across nodes.

Solution

1. Step 1: Understand workload distribution

   70% writes means the system is write-heavy and needs to handle many updates efficiently.

2. Step 2: Evaluate architecture options for write-heavy systems

   Sharding (Implement sharding to distribute write load across nodes.) splits data and write load across multiple nodes, improving write scalability.

3. Step 3: Analyze other options

   Use a single master database with multiple read replicas. focuses on read scaling, not write. Rely heavily on caching to reduce database writes. reduces writes which is not possible here. Use eventual consistency with delayed writes only. may cause data staleness and is not always suitable.

4. Final Answer:

   Implement sharding to distribute write load across nodes. -> Option D

5. Quick Check:

   Write-heavy + sharding = scalable writes [OK]

Hint: Write-heavy? Use sharding to scale writes [OK]

Common Mistakes:

• Choosing read replicas which help reads
• Relying on caching to reduce writes
• Ignoring consistency trade-offs