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).