Horizontal scaling mental model in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding horizontal scaling basics

Which statement best describes horizontal scaling in a database system like MongoDB?

A Adding more servers to distribute the load and data across multiple machines.

B Increasing the CPU and RAM of a single server to handle more requests.

C Using caching to reduce database read operations on a single server.

D Compressing data to reduce storage space on one server.

💡 Hint

Think about spreading data and load across many machines instead of upgrading one.

Horizontal scaling means adding more servers to share the workload and data, improving capacity and fault tolerance.

❓ Architecture

intermediate

MongoDB sharding architecture

In MongoDB horizontal scaling, what is the role of the config servers in a sharded cluster?

A They store metadata and configuration about the cluster’s data distribution.

B They hold the actual user data and respond to queries directly.

C They act as backup servers for data replication.

D They cache frequently accessed data to speed up reads.

💡 Hint

Think about which servers keep track of where data lives in the cluster.

Config servers store metadata about the cluster’s shards and chunk distribution, enabling routing of queries to correct shards.

❓ scaling

advanced

Scaling write operations in MongoDB

Which horizontal scaling strategy best improves write throughput in a MongoDB cluster?

A Enabling read preference to secondary nodes to balance writes.

B Sharding data across multiple shards based on a shard key to distribute writes.

C Using a single powerful server with SSD storage for faster writes.

D Adding more replicas to the replica set to increase write capacity.

💡 Hint

Consider how writes can be spread across multiple machines.

Sharding splits data and writes across multiple servers, increasing write throughput. Replica sets improve read scaling but not write capacity.

❓ tradeoff

advanced

Tradeoffs in choosing a shard key

What is a key tradeoff when selecting a shard key for horizontal scaling in MongoDB?

A Choosing a shard key increases the size of each document stored.

B Shard keys limit the number of indexes you can create on a collection.

C A poor shard key can cause uneven data distribution leading to hotspots.

D Shard keys prevent the use of replica sets for data redundancy.

💡 Hint

Think about how data is spread across servers and what happens if it’s uneven.

A bad shard key causes some shards to hold much more data or traffic, creating bottlenecks and reducing scaling benefits.

❓ estimation

expert

Estimating capacity for a sharded MongoDB cluster

You expect 1 million writes per minute and want to keep write latency low by horizontal scaling. If one shard can handle 100,000 writes per minute, how many shards do you need at minimum?

A 1 shard

B 5 shards

C 20 shards

D 10 shards

💡 Hint

Divide total writes by writes per shard to find minimum shards needed.

1,000,000 writes / 100,000 writes per shard = 10 shards minimum to handle load without latency increase.