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.

Practice

1. Why do distributed databases handle scale better than single-server databases?

easy

A. Because they spread data and workload across multiple machines

B. Because they use only one powerful computer

C. Because they store data in a single location

D. Because they limit the number of users accessing data

Solution

1. Step 1: Understand the concept of distributed databases

   Distributed databases store data on many computers instead of just one.

2. Step 2: Recognize how spreading data helps scale

   Spreading data and workload means many machines share the work, so the system can handle more data and users.

3. Final Answer:

   Because they spread data and workload across multiple machines -> Option A

4. Quick Check:

   Distributed databases = spread data/workload = better scale [OK]

Hint: Think: More machines share work, so system handles more [OK]

Common Mistakes:

• Thinking a single powerful computer is enough
• Believing data stored in one place scales well
• Assuming limiting users improves scaling

2. Which of the following is a correct reason why distributed databases improve reliability?

easy

A. They store all data on a single server

B. They replicate data across multiple nodes

C. They delete old data regularly

D. They restrict access to one user at a time

Solution

1. Step 1: Identify how reliability is improved in distributed systems

   Reliability means data is safe and accessible even if one machine fails.

2. Step 2: Understand data replication

   Replicating data means copying it to multiple machines, so if one fails, others still have the data.

3. Final Answer:

   They replicate data across multiple nodes -> Option B

4. Quick Check:

   Replication = data copies = better reliability [OK]

Hint: Replication means copies on many machines, so safer data [OK]

Common Mistakes:

• Thinking storing data on one server improves reliability
• Confusing deleting data with reliability
• Believing restricting users improves reliability

3. Consider a distributed database system with 4 nodes. If each node can handle 1000 queries per second, what is the total query capacity of the system?

medium

A. 250 queries per second

B. 1000 queries per second

C. 4000 queries per second

D. 5000 queries per second

Solution

1. Step 1: Understand capacity per node

   Each node can handle 1000 queries per second.

2. Step 2: Calculate total capacity by adding all nodes

   4 nodes x 1000 queries = 4000 queries per second total capacity.

3. Final Answer:

   4000 queries per second -> Option C

4. Quick Check:

   4 x 1000 = 4000 queries/sec [OK]

Hint: Multiply nodes by capacity per node for total [OK]

Common Mistakes:

• Using capacity of one node as total
• Dividing instead of multiplying
• Adding extra queries beyond node capacity

4. A distributed database is not scaling well. Which of the following is a likely cause?

medium

A. The database uses multiple machines

B. Data is replicated on all nodes

C. There are too many nodes handling queries

D. Data is not evenly distributed across nodes

Solution

1. Step 1: Identify what causes poor scaling

   Poor scaling happens if some nodes have too much data or work, causing bottlenecks.

2. Step 2: Understand uneven data distribution

   If data is not spread evenly, some nodes get overloaded while others are idle, hurting performance.

3. Final Answer:

   Data is not evenly distributed across nodes -> Option D

4. Quick Check:

   Uneven data = overloaded nodes = poor scaling [OK]

Hint: Check if data is balanced across nodes for good scale [OK]

Common Mistakes:

• Thinking more nodes always cause poor scaling
• Believing replication causes poor scaling
• Assuming multiple machines hurt scaling

5. A company wants to handle a sudden increase in users without slowing down their database. Which distributed database feature should they focus on to handle this scale?

hard

A. Adding more nodes to share the workload

B. Reducing data replication to save space

C. Storing all data on a single powerful server

D. Limiting user access during peak times

Solution

1. Step 1: Understand the need to handle more users

   More users mean more queries and data requests, requiring more processing power.

2. Step 2: Identify how distributed databases handle increased load

   Adding more nodes spreads the workload, so the system can handle more users without slowing down.

3. Final Answer:

   Adding more nodes to share the workload -> Option A

4. Quick Check:

   More nodes = shared workload = better scaling [OK]

Hint: Add nodes to share work and handle more users [OK]

Common Mistakes:

• Thinking reducing replication improves scaling
• Believing one powerful server can handle all load
• Assuming limiting users is the best scaling method