Why choosing the right storage matters in HLD - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Impact of Storage Choice on System Performance

Which storage type will most likely cause the highest latency for read operations in a high-traffic web application?

A Relational database with SSD storage

B Network-attached storage (NAS) over a slow network

C In-memory cache storage like Redis

D Local SSD storage on the application server

💡 Hint

Think about network delays and how storage location affects speed.

Network-attached storage over a slow network adds significant latency due to network delays, making it slower than local or in-memory storage.

❓ Architecture

intermediate

Choosing Storage for Scalability

You are designing a system expected to handle millions of user-generated images. Which storage solution best supports easy horizontal scaling?

A Distributed object storage like Amazon S3 or MinIO

B Single large relational database with RAID storage

C Local disk storage on each application server

D Network-attached storage (NAS) with limited nodes

💡 Hint

Consider how storage can grow with demand without downtime.

Distributed object storage systems are designed to scale horizontally by adding more nodes, making them ideal for large, growing datasets like images.

❓ tradeoff

advanced

Tradeoffs Between Consistency and Availability in Storage

In a distributed storage system, choosing strong consistency over eventual consistency will most likely:

A Decrease availability during network partitions

B Reduce latency for all read operations

C Increase availability but reduce data accuracy

D Allow faster writes but slower reads

💡 Hint

Think about the CAP theorem and what happens during network failures.

Strong consistency requires all nodes to agree on data before confirming writes, which can reduce availability if some nodes are unreachable during network partitions.

❓ scaling

advanced

Estimating Storage Capacity for Log Data

A system generates 500 MB of log data per hour. You want to store logs for 90 days with a replication factor of 3. How much total storage capacity is needed?

A Approximately 13.5 TB

B Approximately 32.4 TB

C Approximately 40.5 TB

D Approximately 3.24 TB

💡 Hint

Calculate total data generated, then multiply by replication factor.

500 MB/hour * 24 hours/day * 90 days = 1,080,000 MB ≈ 1.08 TB. Multiply by replication factor 3 = 3.24 TB.

❓ component

expert

Designing Storage for Mixed Workloads

You must design a storage system that supports both fast transactional queries and large-scale analytical queries. Which architecture best fits this requirement?

A Use a single relational database for both workloads

B Store all data in a distributed file system accessed by both query types

C Separate OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems with data replication

D Use in-memory cache only for all queries

💡 Hint

Consider workload types and how storage optimizes for each.

Separating OLTP and OLAP systems allows each to be optimized for its workload: OLTP for fast transactions, OLAP for complex analytics, with data replicated between them.