Why distributed databases handle scale in DBMS Theory - Quick Recap

Recall & Review

beginner

What is a distributed database?

A distributed database is a collection of data spread across multiple computers or servers that work together as one system.

beginner

How do distributed databases improve scalability?

They spread data and workload across many machines, so they can handle more users and data by adding more servers.

beginner

What is horizontal scaling in distributed databases?

Horizontal scaling means adding more machines to share the load instead of making one machine more powerful.

intermediate

Why is fault tolerance important in distributed databases?

Because data is copied on multiple machines, if one fails, others can keep working without losing data or service.

intermediate

What role does data partitioning play in scaling distributed databases?

Data partitioning splits data into parts stored on different servers, so each server handles less data and works faster.

What does horizontal scaling mean in distributed databases?

A Making one server more powerful

B Storing all data on a single server

C Reducing the number of servers

D Adding more servers to share the workload

Horizontal scaling means adding more servers to distribute the workload, improving capacity and performance.

Why do distributed databases handle more users better than single servers?

A Because they store data only in one place

B Because they use one big server

C Because they spread data and requests across many servers

D Because they limit the number of users

Distributed databases spread data and requests across multiple servers, allowing them to handle more users efficiently.

What is fault tolerance in distributed databases?

A Ability to recover from server failures without losing data

B Ability to store data on one server only

C Ability to slow down when overloaded

D Ability to delete data automatically

Fault tolerance means the system keeps working and does not lose data even if some servers fail.

What is data partitioning?

A Splitting data into parts stored on different servers

B Copying all data to every server

C Deleting old data regularly

D Storing data in one big file

Data partitioning divides data into smaller parts so different servers can handle different parts, improving speed and scale.

Which is NOT a reason distributed databases handle scale well?

A They add more servers to share load

B They store all data on a single server

C They split data into partitions

D They copy data to multiple servers

Storing all data on a single server limits scale; distributed databases avoid this by spreading data across servers.

Explain how distributed databases use multiple servers to handle more data and users.

Think about how adding more machines helps the system grow.

Describe the benefits of data partitioning and fault tolerance in distributed databases.

Consider how dividing data and copying it helps keep the system fast and safe.