Consistent hashing in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the purpose of consistent hashing

Why is consistent hashing preferred over traditional hashing methods in distributed systems?

A Because it evenly distributes keys and minimizes reassignments when nodes change

B Because it uses less memory than traditional hashing

C Because it guarantees zero collisions between keys

D Because it requires no hashing function at all

💡 Hint

Think about what happens when a server is added or removed in a distributed cache.

Consistent hashing evenly distributes keys and minimizes the number of keys that need to be reassigned when nodes join or leave, unlike traditional hashing which can cause many keys to move.

❓ Architecture

intermediate

Designing a consistent hashing ring

You need to design a consistent hashing ring for 4 cache servers. Which approach correctly represents the ring structure?

A Use a linear list of servers and assign keys based on server index

B Assign keys randomly to any server without a ring structure

C Assign all keys to the first server and switch when it fails

D Place servers on a circular hash space and assign keys to the next clockwise server

💡 Hint

Visualize a clock face where servers are points on the circle.

Consistent hashing uses a circular hash space where each server is placed on the ring. Keys are assigned to the next server clockwise, ensuring balanced distribution and minimal movement on changes.

❓ scaling

advanced

Handling node additions in consistent hashing

When a new node is added to a consistent hashing ring, what is the impact on key distribution?

A Only keys that map between the new node and its predecessor move to the new node

B All keys are redistributed evenly among all nodes

C No keys move because the ring is static

D Keys assigned to the last node move to the new node

💡 Hint

Consider which keys fall between the new node and the previous node on the ring.

Adding a node only affects keys that fall between the new node and its predecessor on the ring, minimizing key movement and maintaining balance.

❓ tradeoff

advanced

Tradeoffs of using virtual nodes in consistent hashing

What is a key tradeoff when using virtual nodes (vnodes) in consistent hashing?

A Reduced load balancing but simpler implementation

B Improved load balancing but increased complexity in node management

C No change in load balancing but higher memory usage

D Eliminates the need for hashing functions

💡 Hint

Think about how virtual nodes affect distribution and system complexity.

Virtual nodes improve load balancing by spreading each physical node across the ring multiple times, but they add complexity in tracking and managing these virtual nodes.

❓ estimation

expert

Estimating key remapping after node failure

In a consistent hashing ring with 100 nodes and 1 million keys evenly distributed, approximately how many keys need to be remapped if one node fails?

A About 100,000 keys

B About 1,000 keys

C About 10,000 keys

D All 1 million keys

💡 Hint

Consider the fraction of the ring that one node covers.

Each node is responsible for roughly 1/100th of the keys, so when one node fails, about 1% of keys (10,000) need remapping.

Practice

1. What is the main advantage of consistent hashing in distributed systems?

easy

A. It stores data only on one server

B. It guarantees equal data size on all servers

C. It requires no hashing function

D. It minimizes data movement when servers are added or removed

Solution

1. Step 1: Understand consistent hashing purpose

   Consistent hashing is designed to reduce the amount of data that needs to be moved when servers join or leave the system.

2. Step 2: Compare options with consistent hashing benefits

   Only It minimizes data movement when servers are added or removed correctly states this key advantage. Other options are incorrect or unrelated.

3. Final Answer:

   It minimizes data movement when servers are added or removed -> Option D

4. Quick Check:

   Minimize data movement = D [OK]

Hint: Remember: consistent hashing reduces data reshuffling [OK]

Common Mistakes:

• Thinking consistent hashing balances data perfectly
• Assuming no hashing function is needed
• Believing data is stored on only one server

2. Which of the following is a correct step in implementing consistent hashing?

easy

A. Assign each server multiple points on the hash ring (virtual nodes)

B. Store all data on a single server to avoid hashing

C. Use a linear list instead of a circular ring

D. Hash data keys directly to server IP addresses without hashing servers

Solution

1. Step 1: Recall consistent hashing implementation details

   Consistent hashing uses a circular ring and assigns multiple virtual nodes per server to balance load.

2. Step 2: Evaluate each option

   Assign each server multiple points on the hash ring (virtual nodes) correctly describes virtual nodes. Options A, C, and D contradict consistent hashing principles.

3. Final Answer:

   Assign each server multiple points on the hash ring (virtual nodes) -> Option A

4. Quick Check:

   Virtual nodes = B [OK]

Hint: Virtual nodes improve load balance in consistent hashing [OK]

Common Mistakes:

• Ignoring virtual nodes and using single points
• Using linear lists instead of circular rings
• Skipping hashing of servers

3. Given a consistent hashing ring with servers at hash points 10, 30, and 50, where will the data key with hash 35 be stored?

medium

A. Server at 50

B. Server at 30

C. Server at 10

D. Server at 35

Solution

1. Step 1: Understand consistent hashing key assignment

   Data is assigned to the next server clockwise on the ring after the key's hash.

2. Step 2: Find the next server after hash 35

   Servers are at 10, 30, and 50. After 35 clockwise is 50.

3. Final Answer:

   Server at 50 -> Option A

4. Quick Check:

   Next clockwise server after 35 = 50 [OK]

Hint: Assign data to next clockwise server on ring [OK]

Common Mistakes:

• Assigning data to previous server instead of next
• Choosing server at 30 because it's closest numerically
• Assuming server at 35 exists

4. In a consistent hashing system, a developer notices that when a new server is added, almost all data moves to the new server. What is the likely cause?

medium

A. The data keys are not hashed before assignment

B. The new server has fewer virtual nodes than others

C. The hash function is not distributing servers evenly on the ring

D. The ring is implemented as a linear list

Solution

1. Step 1: Analyze the problem of data movement on server addition

   If almost all data moves to the new server, it means the new server's position dominates the ring.

2. Step 2: Identify cause related to server distribution

   This happens if the hash function places servers unevenly, clustering many keys near the new server.

3. Final Answer:

   The hash function is not distributing servers evenly on the ring -> Option C

4. Quick Check:

   Uneven server distribution = A [OK]

Hint: Check server hash distribution to avoid data skew [OK]

Common Mistakes:

• Blaming virtual nodes count without checking hash function
• Ignoring hashing of data keys
• Confusing ring structure with linear list

5. You are designing a distributed cache using consistent hashing. To handle server failures gracefully, which approach is best?

hard

A. Assign each data key to only one server without replication

B. Use virtual nodes and replicate data on the next N servers clockwise

C. Use a fixed number of servers without adding or removing nodes

D. Hash data keys to servers randomly each time

Solution

1. Step 1: Understand fault tolerance in consistent hashing

   To handle failures, data should be replicated to multiple servers to avoid data loss.

2. Step 2: Identify best practice for replication

   Using virtual nodes improves load balance, and replicating data on next N servers clockwise ensures availability.

3. Final Answer:

   Use virtual nodes and replicate data on the next N servers clockwise -> Option B

4. Quick Check:

   Replication + virtual nodes = A [OK]

Hint: Replicate data on next servers for fault tolerance [OK]

Common Mistakes:

• Not replicating data causing data loss on failure
• Avoiding virtual nodes and causing imbalance
• Randomly hashing keys each time causing inconsistency