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

Practice - 5 Tasks

1fill in blank

easy

Complete the sentence to explain why distributed databases handle scale.

DBMS Theory

Distributed databases handle scale by spreading data across multiple [1].

Drag options to blanks, or click blank then click option'

Aservers

Btables

Cindexes

Dqueries

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'tables' because they are part of databases but not the main scaling factor.

Selecting 'queries' which are commands, not storage units.

Distributed databases spread data across multiple servers to manage large amounts of data and traffic efficiently.

2fill in blank

medium

Complete the sentence to describe how distributed databases improve performance.

DBMS Theory

By distributing data, databases can handle more [1] simultaneously.

Drag options to blanks, or click blank then click option'

Aerrors

Brequests

Cindexes

Dbackups

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'errors' thinking about problems rather than workload.

Selecting 'backups' which relate to data safety, not performance.

Distributed databases improve performance by handling more requests at the same time across different servers.

3fill in blank

hard

Fix the error in the explanation about distributed databases.

DBMS Theory

Distributed databases improve scale by storing all data on a single [1].

Drag options to blanks, or click blank then click option'

Aclient

Bnetwork

Cnode

Dserver

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'server' which implies a single machine, not distributed.

Selecting 'client' which is a user device, not a storage point.

The statement is incorrect because distributed databases store data across multiple nodes, not just one server.

4fill in blank

hard

Fill both blanks to explain how distributed databases handle large data volumes.

DBMS Theory

Data is [1] across many [2] to balance load and improve access speed.

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Asharded

Bcached

Cservers

Dbacked up

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'cached' which relates to temporary storage, not distribution.

Selecting 'backed up' which is about data safety, not distribution.

Distributed databases shard data across multiple servers to balance load and speed up access.

5fill in blank

hard

Fill all three blanks to describe key benefits of distributed databases.

DBMS Theory

Distributed databases provide [1] by avoiding single points of failure, [2] by adding more nodes, and [3] by locating data closer to users.

Drag options to blanks, or click blank then click option'

Ascalability

Bredundancy

Clatency reduction

Dcomplexity

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up scalability and redundancy terms.

Choosing 'complexity' which is a challenge, not a benefit.

Distributed databases offer redundancy to prevent failures, scalability by adding nodes, and latency reduction by placing data near users.

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