Design a key-value store in HLD - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a key-value store?

A key-value store is a simple database that stores data as pairs of keys and values. Each key is unique and is used to retrieve its associated value quickly.

intermediate

Why is partitioning important in a key-value store?

Partitioning splits data across multiple servers or storage units. It helps the system handle more data and requests by distributing the load, making the store scalable and faster.

advanced

What is consistent hashing and why is it used?

Consistent hashing is a technique to distribute keys evenly across servers. It minimizes data movement when servers are added or removed, helping maintain balance and availability.

intermediate

Explain the role of replication in a key-value store.

Replication means copying data to multiple servers. It improves reliability and availability, so if one server fails, data can still be accessed from others.

advanced

What is eventual consistency in distributed key-value stores?

Eventual consistency means that after some time, all copies of data will become the same. It allows faster writes but may show old data temporarily.

What does a key-value store primarily use to retrieve data?

A Unique keys

B Indexes

C SQL queries

D File paths

Key-value stores use unique keys to quickly find the associated values.

Which technique helps distribute keys evenly across servers in a key-value store?

A Caching

B Consistent hashing

C Replication

D Sharding by range

Consistent hashing evenly distributes keys and reduces data movement when servers change.

What is the main benefit of replication in a key-value store?

A Reduces storage needs

B Speeds up writes

C Improves fault tolerance

D Simplifies data model

Replication copies data to multiple servers to keep data safe if one server fails.

Which consistency model allows temporary differences in data copies but guarantees eventual agreement?

A Eventual consistency

B Strong consistency

C Immediate consistency

D Read-your-writes consistency

Eventual consistency allows temporary differences but ensures all copies match eventually.

Partitioning in a key-value store is mainly used to:

A Backup data

B Encrypt data

C Compress values

D Distribute data across servers

Partitioning splits data to spread load and improve scalability.

Describe the main components and flow of a key-value store system.

Think about how data is stored, found, and kept safe.

Explain how consistent hashing helps in scaling a key-value store.

Imagine how to keep data balanced when servers join or leave.

Practice

1. What is the primary purpose of a key-value store in system design?

easy

A. To perform complex relational queries

B. To store large binary files efficiently

C. To save data as pairs for quick lookup

D. To manage user authentication and sessions

Solution

1. Step 1: Understand key-value store basics

   A key-value store saves data as pairs where each key maps to a value for fast retrieval.

2. Step 2: Compare with other storage types

   Unlike relational databases, key-value stores do not support complex queries or file storage.

3. Final Answer:

   To save data as pairs for quick lookup -> Option C

4. Quick Check:

   Key-value store = data pairs [OK]

Hint: Key-value stores focus on pairs, not complex queries [OK]

Common Mistakes:

• Confusing key-value store with relational database
• Thinking it handles large files natively
• Assuming it manages user sessions directly

2. Which of the following is the correct operation to add or update a value in a key-value store?

easy

A. exists(key)

B. put(key, value)

C. delete(key)

D. get(key)

Solution

1. Step 1: Identify operation purpose

   Adding or updating a value requires an operation that sets the value for a key.

2. Step 2: Match operation names

   "put" is commonly used to insert or update key-value pairs; "get" retrieves, "delete" removes, "exists" checks presence.

3. Final Answer:

   put(key, value) -> Option B

4. Quick Check:

   Put = add/update [OK]

Hint: Put means add or update a key-value pair [OK]

Common Mistakes:

• Using get to add data
• Confusing delete with update
• Using exists to insert values

3. Given this pseudo-code for a key-value store:

store = {}
store.put('a', 1)
store.put('b', 2)
store.put('a', 3)
value = store.get('a')

What is the value of value after these operations?

medium

A. 3

B. 2

C. 1

D. None

Solution

1. Step 1: Track put operations

   First, key 'a' is set to 1, then 'b' to 2, then 'a' is updated to 3, overwriting previous value.

2. Step 2: Retrieve the value for 'a'

   The last value assigned to 'a' is 3, so store.get('a') returns 3.

3. Final Answer:

   3 -> Option A

4. Quick Check:

   Last put for 'a' = 3 [OK]

Hint: Last put for a key overwrites previous value [OK]

Common Mistakes:

• Assuming first value stays after update
• Confusing keys 'a' and 'b'
• Thinking get returns None if key exists

4. Consider this code snippet for a key-value store:

store = {}
def get_value(key):
    if key in store:
        return store[key]
    else:
        return None

store.put('x', 10)
print(get_value('x'))

What is the main issue preventing this code from working correctly?

medium

A. The put method is not defined for the dictionary

B. The get_value function returns None incorrectly

C. The key 'x' is not added to the store

D. The print statement syntax is wrong

Solution

1. Step 1: Check dictionary operations

   Python dictionaries do not have a put method; they use assignment like store[key] = value.

2. Step 2: Identify error cause

   Calling store.put('x', 10) will cause an AttributeError because put is undefined.

3. Final Answer:

   The put method is not defined for the dictionary -> Option A

4. Quick Check:

   Dicts use assignment, not put [OK]

Hint: Dictionaries use assignment, not put() method [OK]

Common Mistakes:

• Assuming put exists on dict
• Ignoring error from undefined method
• Thinking get_value logic is faulty

5. You want to design a scalable key-value store that handles millions of requests per second. Which design choice best supports this goal?

hard

A. Use a single in-memory dictionary on one server

B. Store all data on a single disk-based database

C. Use a relational database with complex joins

D. Partition data across multiple servers using consistent hashing

Solution

1. Step 1: Understand scalability needs

   Handling millions of requests requires distributing load and data to avoid bottlenecks.

2. Step 2: Evaluate design options

   A single in-memory dictionary or disk-based DB limits capacity; relational DB with joins is slow for key-value access. Consistent hashing partitions data evenly across servers, enabling horizontal scaling.

3. Final Answer:

   Partition data across multiple servers using consistent hashing -> Option D

4. Quick Check:

   Consistent hashing = scalable partitioning [OK]

Hint: Distribute data with consistent hashing for scalability [OK]

Common Mistakes:

• Relying on single server limits throughput
• Using disk-based DB slows access
• Choosing relational DB for simple key-value