Why distributed databases handle scale in DBMS Theory - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

How distributed databases improve scalability

Which of the following best explains why distributed databases can handle scale better than single-node databases?

A They spread data and workload across multiple machines, allowing parallel processing and storage.

B They store all data on a single powerful server to avoid network delays.

C They compress data to reduce storage size on one machine.

D They use only in-memory storage to speed up access on one node.

💡 Hint

Think about how sharing work among many computers helps handle more data and users.

Distributed databases split data and tasks across many machines. This allows them to process more data and handle more users at the same time, which improves scalability.

📋 Factual

intermediate

Key feature enabling distributed database scaling

What is the main feature that allows distributed databases to scale horizontally?

A Using faster hard drives on a single server.

B Increasing the size of the database on one machine.

C Adding more powerful CPUs to a single server.

D Adding more machines to the database cluster to share load.

💡 Hint

Horizontal scaling means adding more machines, not making one machine stronger.

Horizontal scaling means adding more machines to share the workload, which distributed databases support well.

🔍 Analysis

advanced

Impact of network latency on distributed databases

Which statement correctly describes how network latency affects distributed databases when scaling?

A Network latency can cause delays because data must travel between nodes, potentially slowing some operations.

B Network latency always improves performance as more nodes are added.

C Network latency has no effect because all data is stored locally on each node.

D Network latency is eliminated by using a single central server.

💡 Hint

Consider that nodes communicate over a network, which can add delay.

In distributed databases, nodes communicate over a network. This communication can introduce delays called network latency, which may slow down some operations.

❓ Comparison

advanced

Comparing vertical and horizontal scaling in databases

Which of the following correctly contrasts vertical scaling and horizontal scaling in the context of databases?

A Vertical scaling adds more machines; horizontal scaling upgrades a single machine's hardware.

B Both vertical and horizontal scaling add more machines to the system.

C Vertical scaling upgrades a single machine's hardware; horizontal scaling adds more machines to share the load.

D Vertical scaling reduces the number of machines; horizontal scaling reduces hardware power.

💡 Hint

Think about the difference between making one machine stronger versus adding more machines.

Vertical scaling means improving one machine's hardware (CPU, RAM). Horizontal scaling means adding more machines to share the workload.

❓ Reasoning

expert

Why distributed databases maintain availability during scaling

How do distributed databases maintain high availability when scaling to many nodes?

A By storing all data on a single node to avoid synchronization issues.

B By replicating data across multiple nodes so if one fails, others can serve requests.

C By shutting down nodes during peak load to reduce errors.

D By limiting the number of users to prevent overload.

💡 Hint

Think about how copies of data help keep the system running even if some parts fail.

Distributed databases replicate data on multiple nodes. This means if one node fails, others still have the data and can continue serving users, ensuring availability.