Let's design a Twitter-like service. Consider the following:

1. Core Functionality: How would you implement the ability for users to post tweets, follow other users, and view a timeline of tweets from the users they follow?
2. Data Model: What database schema would you use to store tweets, user information, and follower relationships? Consider scalability and efficiency for reads and writes.
3. Scalability: How would you handle a large number of users and tweets? Discuss strategies for sharding, caching, and load balancing.
4. Real-time Updates: How would you ensure that users see new tweets in their timeline in near real-time? Consider using technologies like WebSockets or Server-Sent Events.
5. Search: How would you implement a search feature that allows users to find tweets containing specific keywords or hashtags? Think about indexing strategies and search algorithms.
6. Rate Limiting: How would you prevent abuse and ensure fair usage of the service? Describe your approach to rate limiting API requests.
7. Example Scenario: Imagine a user, Alice, follows 1,000 other users, including Bob. Bob posts a tweet. Explain how that tweet would propagate to Alice's timeline, considering potential performance bottlenecks.
8. Technology Stack: What technologies (programming languages, frameworks, databases, etc.) would you choose and why?

Designing a Twitter-like Service

Let's outline the design of a Twitter-like service, addressing core functionality, data model, scalability, real-time updates, search, rate limiting, and technology stack.

1. Core Functionality

• Posting Tweets: Users should be able to post tweets with a character limit.
• Following Users: Users should be able to follow other users.
• Timeline: Users should see a timeline of tweets from users they follow, in reverse chronological order.

2. Data Model

We'll use a relational database (e.g., PostgreSQL, MySQL) for its strong consistency and support for relationships.

Tables:

1. Users Table

   Field	Type	Description
   user_id	BIGINT	Primary Key, Unique user identifier
   username	VARCHAR(50)	Unique username
   email	VARCHAR(100)	User's email address
   password_hash	VARCHAR(255)	Hashed password
   created_at	TIMESTAMP	Timestamp of user creation
   updated_at	TIMESTAMP	Timestamp of last user update
   profile_info	TEXT	Additional profile information (JSON)

2. Tweets Table

   Field	Type	Description
   tweet_id	BIGINT	Primary Key, Unique tweet identifier
   user_id	BIGINT	Foreign Key, User who posted the tweet
   content	VARCHAR(280)	Tweet content (limited to 280 characters)
   created_at	TIMESTAMP	Timestamp of tweet creation
   updated_at	TIMESTAMP	Timestamp of last tweet update

3. Followers Table

   Field	Type	Description
   follower_id	BIGINT	Foreign Key, User who is following
   followee_id	BIGINT	Foreign Key, User being followed
   created_at	TIMESTAMP	Timestamp when the follow relationship started

Relationships:

• One-to-many relationship between Users and Tweets (one user can post multiple tweets).
• Many-to-many relationship between Users (followers and followees) through the Followers table.

3. Scalability

• Sharding:
  • User Sharding: Shard users based on user_id into different database instances.
  • Tweet Sharding: Shard tweets based on user_id of the tweet's author.
• Caching:
  • Timeline Caching: Cache timelines in Redis. Invalidate cache on new tweets from followed users.
  • Tweet Caching: Cache individual tweets.
• Load Balancing:
  • Use load balancers (e.g., Nginx, HAProxy) to distribute traffic across multiple application servers and database instances.
• Read Replicas: Use read replicas for the database to handle read-heavy operations (e.g., timeline retrieval).

4. Real-time Updates

• WebSockets:
  • Use WebSockets for real-time push of new tweets to users' timelines.
  • Maintain WebSocket connections between users and the application server.
  • When a user posts a tweet, push it to all followers' active WebSocket connections.
• Server-Sent Events (SSE):
  • Alternative to WebSockets for unidirectional data flow.
  • Clients subscribe to a stream of updates from the server.

5. Search

• Indexing:
  • Use Elasticsearch or Solr for indexing tweet content.
  • Index fields like content, user_id, and hashtags.
• Search Algorithms:
  • Implement full-text search capabilities for keyword searches.
  • Use inverted indexes for efficient searching.
• Real-time Indexing:
  • Update the search index whenever a new tweet is posted.

6. Rate Limiting

• API Gateway:
  • Implement rate limiting at the API gateway level (e.g., using Kong, API Gateway).
• Token Bucket Algorithm:
  • Use the token bucket algorithm to limit the number of requests per user per time window.
  • Example: 100 requests per minute per user.
• Custom Headers:
  • Return rate limit information in HTTP headers (e.g., X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset).

7. Example Scenario: Alice Following Bob

1. Bob posts a tweet.
2. The tweet is stored in the Tweets table and indexed in Elasticsearch.
3. A background process retrieves all followers of Bob from the Followers table.
4. For each follower (including Alice):
   • If Alice has an active WebSocket connection, the tweet is pushed to her timeline in real-time.
   • Otherwise, the tweet will be retrieved when Alice refreshes her timeline (cached or fetched from the database).

Potential Bottlenecks & Mitigation:

• Fan-out: When a popular user (e.g., Bob) posts a tweet, a large number of followers need to be updated.
  • Solution: Use a fan-out service with message queues (e.g., Kafka, RabbitMQ) to distribute the tweet to followers asynchronously.
  • The fan-out service subscribes to a stream of new tweets and pushes them to individual follower timelines.

8. Technology Stack

• Programming Language: Python (Flask/Django) or Java (Spring Boot).
• Web Framework: Flask/Django (Python) or Spring Boot (Java).
• Database: PostgreSQL or MySQL.
• Caching: Redis.
• Search: Elasticsearch.
• Real-time Communication: WebSockets (e.g., Socket.IO) or Server-Sent Events.
• Message Queue: Kafka or RabbitMQ.
• Load Balancer: Nginx or HAProxy.
• API Gateway: Kong or AWS API Gateway.