Let's design a system for URL shortening, like TinyURL. Consider the following:

1. Functional Requirements:

   • Users should be able to enter a long URL and get a shorter URL.
   • Users should be able to access the original URL by visiting the shortened URL.
   • The system should be highly available and handle a large number of requests.
   • Analytics: Track the number of times a shortened URL is accessed.

2. Non-Functional Requirements:

   • The system should have low latency for both URL shortening and redirection.
   • The shortened URLs should be relatively short.
   • The system should be reliable and fault-tolerant.

3. Considerations:

   • How would you handle collisions (when two different long URLs map to the same short URL)?
   • How would you design the data store to efficiently handle a large number of URLs?
   • How would you scale the system to handle a large number of requests?
   • What algorithms could be used for generating the short URLs? Discuss the trade-offs.

Walk me through the architectural components, data model, and scaling strategies for this URL shortening service. For instance, if a user inputs https://www.example.com/very/long/path/to/resource, the system might generate a short URL like https://short.url/xyz123. When a user visits https://short.url/xyz123, they should be redirected to the original URL.

URL Shortening System Design

1. Requirements

• Functional Requirements:

  • Users can enter a long URL and get a shorter URL.
  • Users can access the original URL using the shortened URL.
  • High availability and handling of a large number of requests.
  • Analytics to track the number of accesses for each shortened URL.

• Non-Functional Requirements:

  • Low latency for both URL shortening and redirection.
  • Shortened URLs should be relatively short.
  • Reliability and fault tolerance.

2. High-Level Design

The system consists of the following components:

1. User Interface: Allows users to input long URLs.
2. API Service: Receives long URLs, generates short URLs, and handles redirection requests.
3. Data Store: Stores the mapping between short URLs and original URLs.
4. Analytics Service: Tracks access counts for short URLs.

Architecture Diagram

+------------------+
|    User          |
+------------------+
        |
        |
+------------------+
|   Web Browser    |
+------------------+
        |
        |  (Long URL)
        v
+------------------+
|   Load Balancer  |
+------------------+
        |
        v
+------------------+
|   API Service    |  (Short URL Generation, Redirection)
+------------------+
        |
    +---|---+
    |       |
    v       v
+------------------+   +------------------+
|   Data Store     |   | Analytics Service|
+------------------+   +------------------+
    |                    (Access Count Update)
    +-------------------->

3. Data Model

We can use a relational database like MySQL or PostgreSQL or a NoSQL database like Cassandra.

Table: url_mapping

Field	Type	Description
id	BIGINT	Unique identifier (primary key)
short_url	VARCHAR(255)	Shortened URL identifier
original_url	TEXT	Original URL
creation_date	TIMESTAMP	Timestamp of when the short URL was created
access_count	BIGINT	Number of times the short URL has been accessed

4. Endpoints

4.1. Shorten URL

• Endpoint: POST /api/shorten

• Request:

  {
    "long_url": "https://www.example.com/very/long/path/to/resource"
  }
  

• Response:

  {
    "short_url": "https://short.url/xyz123"
  }
  

4.2. Redirect

• Endpoint: GET /xyz123
• Response:
  • HTTP 302 Redirect to the original URL.

5. Tradeoffs

Component	Approach	Pros	Cons
Short URL Generation	Base62 Encoding	Simple, efficient, and generates relatively short URLs.	Can have collisions, but easily handled.
Data Store	Relational DB (MySQL/PostgreSQL)	ACID compliance, easy to manage, supports complex queries.	May not scale as well as NoSQL for very large datasets.
Data Store	NoSQL DB (Cassandra)	Highly scalable, fault-tolerant.	Eventual consistency, more complex to manage.
Caching	Redis	Extremely fast, reduces database load.	Requires additional infrastructure.
Scaling	Horizontal Scaling	Easy to add more servers as needed.	Requires load balancing and potentially database sharding.
Analytics	Asynchronous Processing (Kafka, Message Queues)	Decouples analytics from the main service, ensuring minimal impact on latency.	Adds complexity to the system.

6. Other Approaches

6.1. Short URL Generation Algorithms

• UUID: Generates a universally unique identifier. Pro: Simple. Con: Long URLs.
• Hashing (MD5, SHA-256): Generates a hash of the long URL. Pro: Deterministic. Con: Longer URLs, collision-prone.
• Counter-based: Use an auto-incrementing counter. Pro: Simple and avoids collisions. Con: Predictable, sequential URLs.

6.2. Data Store Options

• In-Memory Cache (Redis/Memcached): For frequently accessed URLs. Pro: Very fast. Con: Volatile, requires a fallback data store.
• Key-Value Store (DynamoDB): Scalable and fast for simple lookups. Pro: High availability. Con: Limited querying capabilities.

7. Edge Cases

1. Collision Handling:
   • If a collision occurs during short URL generation, retry with a different random seed or increment a counter.
2. URL Validation:
   • Validate the long URL to ensure it is a valid URL before shortening.
3. Redirect Loops:
   • Detect and prevent redirect loops.
4. Malicious URLs:
   • Implement a blacklist to prevent shortening of malicious URLs.
5. Handling High Traffic:
   • Implement caching and load balancing to handle high traffic.

8. Future Considerations

1. Custom Short URLs:
   • Allow users to create custom short URLs.
2. URL Expiration:
   • Implement URL expiration after a certain period.
3. Advanced Analytics:
   • Provide more detailed analytics, such as geographic location, device type, etc.
4. API Rate Limiting:
   • Implement rate limiting to prevent abuse.
5. Integration with Other Services:
   • Integrate with social media platforms for easy sharing.

Scaling Strategies

• Horizontal Scaling: Add more API service instances behind a load balancer.
• Database Sharding: Partition the database based on a hash of the short URL.
• Caching: Use Redis or Memcached to cache frequently accessed URLs.
• Content Delivery Network (CDN): Serve static assets (e.g., tracking pixels) from a CDN.
• Asynchronous Processing: Use message queues (e.g., Kafka, RabbitMQ) for analytics processing to avoid impacting the API's response time.