LLD for a Distributed Social Network with High Availability

Ever wondered how social networks handle millions of users and still stay online? It's all in the low-level design (LLD). Today, we're breaking down the LLD for a distributed social network, keeping high availability in mind. If you’re aiming to build a social network that can withstand the test of scale and uptime, let’s jump right in.

Why High Availability Matters

Imagine your favourite social network going down during a major event. Chaos, right? High availability ensures the social network remains accessible even when parts of the system fail. It’s about building resilience into the architecture from the ground up. Think of it as designing a car that can still drive even if one tire goes flat.

Core Components

1. User Profiles

• Data Model: Store user details like name, email, profile info, followers, and followees.
• Database: Use a NoSQL database like Cassandra or DynamoDB for scalability and fault tolerance. These databases are designed to handle large amounts of data across multiple nodes.
• Sharding: Distribute user profiles across multiple shards based on user ID to manage the load. Consistent hashing can help ensure even distribution.

2. Timelines

• Data Model: Each user has a timeline consisting of posts from themselves and the people they follow.
• Storage: Use a graph database like Neo4j to efficiently retrieve posts from followers. Alternatively, you can use a denormalized approach in Cassandra, where timelines are pre-computed.
• Caching: Implement a cache (e.g., Redis or Memcached) to store frequently accessed timelines. This reduces database load and improves response times.

3. Post Storage

• Data Model: Store post content, timestamps, user IDs, and media links.
• Database: Use a distributed object storage like Amazon S3 or Google Cloud Storage for media files. Store post metadata in Cassandra or DynamoDB.
• Content Delivery Network (CDN): Use a CDN to serve media content quickly from geographically distributed servers.

4. Follower/Following Relationships

• Data Model: Represent relationships as edges in a graph. Store follower and followee user IDs.
• Database: Use Neo4j or Cassandra to manage relationships efficiently.
• Caching: Cache follower and followee lists to reduce database queries during timeline generation.

Key Design Considerations

1. Data Sharding

Sharding is crucial for distributing data across multiple nodes. Use consistent hashing to map user IDs to shards. This approach minimizes data movement when nodes are added or removed.

2. Caching Strategies

• Timeline Caching: Cache pre-computed timelines in Redis or Memcached.
• User Profile Caching: Cache frequently accessed user profiles.
• Invalidation: Implement a strategy to invalidate cache entries when data changes.

3. Fault Tolerance

• Replication: Replicate data across multiple nodes to ensure availability even if some nodes fail.
• Automatic Failover: Use a system that automatically detects node failures and redirects traffic to healthy nodes.
• Circuit Breakers: Implement circuit breakers to prevent cascading failures.

4. Load Balancing

• Distribute Traffic: Use load balancers to distribute incoming traffic across multiple servers.
• Health Checks: Implement health checks to ensure traffic is only routed to healthy servers.
• Dynamic Scaling: Automatically scale the number of servers based on traffic load.

5. Asynchronous Processing

• Message Queues: Use message queues like RabbitMQ or Amazon MQ to handle asynchronous tasks such as sending notifications or processing media uploads.
• Workers: Implement worker processes to consume messages from the queues and perform the tasks.

System Diagram (React Flow UML)

Here’s a simplified UML diagram showing the main components and their interactions:

Code Snippets

User Profile Data Model (Java)

java
public class UserProfile {
    private String userId;
    private String username;
    private String email;
    private String profileInfo;
    private List<String> followers;
    private List<String> following;

    // Getters and setters
}

Timeline Retrieval (Java)

java
public List<Post> getTimeline(String userId) {
    // Retrieve timeline from cache
    List<Post> timeline = cache.get(userId + "_timeline");
    if (timeline == null) {
        // Retrieve timeline from database
        timeline = database.getTimeline(userId);
        // Cache the timeline
        cache.put(userId + "_timeline", timeline);
    }
    return timeline;
}

FAQs

Q: How do I handle data consistency across shards? A: Use eventual consistency models and techniques like vector clocks to manage conflicts.

Q: What's the best way to handle user authentication in a distributed system? A: Implement a centralized authentication service with token-based authentication (e.g., JWT).

Q: How can I monitor the health of my distributed social network? A: Use monitoring tools like Prometheus and Grafana to track key metrics such as CPU usage, memory usage, and network latency.

Coudo AI Integration

Looking to test your LLD skills? Coudo AI has some great problems. For example, you might find the expense-sharing-application-splitwise problem helpful.

Wrapping Up

Building a highly available, distributed social network is no small feat. It requires careful consideration of data models, storage solutions, caching strategies, and fault tolerance mechanisms. By focusing on these key areas, you can design a system that scales to millions of users while remaining resilient to failures. This is how you build a social network that not only works but thrives. Now, time to put these ideas into practice and see what you can build! \n\n