Social graph storage in HLD - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to identify the main data structure used for storing user connections in a social graph.

HLD

The social graph is typically stored as a [1] where nodes represent users and edges represent connections.

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

Agraph

Barray

Cstack

Dqueue

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing array because it stores lists but doesn't represent connections well.

Choosing stack or queue which are linear and don't model relationships.

The social graph is best represented as a graph data structure because it naturally models users (nodes) and their connections (edges).

2fill in blank

medium

Complete the code to describe the storage method for a social graph with millions of users.

HLD

For scalability, social graphs are often stored in a [1] database that supports relationships efficiently.

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

Akey-value

Bgraph

Crelational

Ddocument

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing relational database which can be slow for complex graph queries.

Choosing key-value or document databases which don't natively support relationships.

Graph databases are designed to efficiently store and query relationships, making them ideal for large social graphs.

3fill in blank

hard

Fix the error in describing how to represent user connections in adjacency form.

HLD

An adjacency list stores connections as a dictionary where keys are user IDs and values are lists of [1] user IDs.

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

Aconnected

Bunconnected

Crandom

Dinactive

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing unconnected or inactive which do not represent actual connections.

Choosing random which is unrelated.

Adjacency lists store the IDs of users directly connected to each user, so the correct term is connected user IDs.

4fill in blank

hard

Fill both blanks to complete the description of a common query on social graphs.

HLD

To find mutual friends between two users, we compute the intersection of their [1] lists and then filter by [2].

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

Afriend

Bfollowers

Cactive

Dconnections

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'followers' which is a one-way relationship, not mutual.

Using 'friend' which is singular and less precise than 'connections'.

We find mutual friends by intersecting the connections lists of two users and then filtering by active status to ensure relevance.

5fill in blank

hard

Fill all three blanks to complete the code snippet for storing social graph data efficiently.

HLD

storage = [1].partition_by([2]).replicate([3])

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

Agraph_db

Buser_id

C3

Drelational_db

Attempts:

3 left

💡 Hint

Common Mistakes

Using relational_db which is less efficient for graph data.

Incorrect replication factor or partition key.

Graph databases (graph_db) partition data by user_id for load balancing and replicate data 3 times for fault tolerance.

Practice

1. What is the primary purpose of social graph storage in system design?

easy

A. To handle user authentication and authorization

B. To store only user profile data without connections

C. To manage database backups efficiently

D. To store users as nodes and their relationships as edges

Solution

1. Step 1: Understand social graph components

   Social graph storage models users as nodes and their relationships as edges.

2. Step 2: Identify the main function

   The main function is to represent and query user connections, not just user data or security.

3. Final Answer:

   To store users as nodes and their relationships as edges -> Option D

4. Quick Check:

   Social graph = nodes + edges [OK]

Hint: Remember: social graph = users + connections [OK]

Common Mistakes:

• Confusing social graph with user profile storage
• Thinking it handles authentication
• Assuming it manages backups

2. Which data structure is most suitable to represent a social graph for efficient traversal?

easy

A. Stack

B. Array

C. Adjacency list

D. Queue

Solution

1. Step 1: Review data structures for graph representation

   Adjacency lists store each node with a list of connected nodes, ideal for sparse graphs like social networks.

2. Step 2: Compare with other options

   Arrays don't efficiently represent connections; stacks and queues are traversal helpers, not storage.

3. Final Answer:

   Adjacency list -> Option C

4. Quick Check:

   Efficient graph storage = adjacency list [OK]

Hint: Use adjacency list for sparse graph storage [OK]

Common Mistakes:

• Choosing arrays which waste space
• Confusing traversal structures with storage
• Ignoring graph sparsity

3. Given a social graph stored as an adjacency list: {'Alice': ['Bob', 'Carol'], 'Bob': ['Alice'], 'Carol': ['Alice']}, what is the output of querying Alice's friends?

medium

A. ['Bob']

B. ['Bob', 'Carol']

C. ['Alice']

D. []

Solution

1. Step 1: Locate Alice in adjacency list

   Alice's entry shows connections to Bob and Carol.

2. Step 2: Return Alice's friends list

   The list associated with Alice is ['Bob', 'Carol'].

3. Final Answer:

   ['Bob', 'Carol'] -> Option B

4. Quick Check:

   Alice's friends = ['Bob', 'Carol'] [OK]

Hint: Check adjacency list key for user connections [OK]

Common Mistakes:

• Returning the user name instead of friends
• Confusing direction of edges
• Returning empty list by mistake

4. In a social graph system, a developer tries to add a friendship edge between two users but the system crashes. Which is the most likely cause?

medium

A. The users do not exist in the graph nodes

B. The graph uses an adjacency list

C. The system uses directed edges

D. The graph is stored in a relational database

Solution

1. Step 1: Analyze the crash cause

   Adding an edge requires both users to exist as nodes; missing nodes cause errors.

2. Step 2: Evaluate other options

   Adjacency list, directed edges, or relational storage do not inherently cause crashes when adding edges.

3. Final Answer:

   The users do not exist in the graph nodes -> Option A

4. Quick Check:

   Missing nodes cause edge addition failure [OK]

Hint: Ensure both users exist before adding edges [OK]

Common Mistakes:

• Blaming data structure choice for crash
• Ignoring node existence before edge creation
• Assuming direction causes crash

5. You need to design a social graph storage system that supports millions of users and fast friend-of-friend queries. Which approach is best?

hard

A. Use a distributed graph database with adjacency lists and caching

B. Store all connections in a single relational table with indexes

C. Use flat files to store user connections sequentially

D. Keep all data in memory without persistence

Solution

1. Step 1: Consider scalability and query needs

   Millions of users require distributed storage and efficient traversal for friend-of-friend queries.

2. Step 2: Evaluate options for performance and scalability

   Distributed graph databases with adjacency lists and caching optimize query speed and handle scale; relational tables or flat files are less efficient; in-memory only lacks persistence.

3. Final Answer:

   Use a distributed graph database with adjacency lists and caching -> Option A

4. Quick Check:

   Scale + fast queries = distributed graph DB + caching [OK]

Hint: Combine distribution, adjacency lists, and caching for scale [OK]

Common Mistakes:

• Choosing relational tables for large graph queries
• Using flat files which are slow
• Ignoring persistence by using memory only