System requirements

Functional:

List functional requirements for the system (Ask the chat bot for hints if stuck.)...

1.when user give a long url, the system should encode a short url and return back to user.

2.when user give a short url, the system should decode back the original long url and return back to user.

3.when user delete a url, the system should delete the relationship between long and short url.

4.an url should have an expiration date, based on the usage of the url.

5.user should be able to register and login into our tinyurl system

Non-Functional:

1.the system should low latency

2.the system should be fault taulrent, we should avoid single point of failure

3.the system should be able to scalable, based on the input volumn

Capacity estimation

lets estimate the short to long traffic is 10 times higher than long to short. if every second there is 10000/s long to short request, then short to long request would be 100000/s

supposed each host capacity is 1000 connection, then we would need 10 host for the long to short and 100 hosts for the short to long.

suppose long to short avg string length is 100 char, which is 100 byte, and our input rate would be 100B * 10000/s which is 1000KB/s and 1MB/s. and for 1 day, it will spend 1MB * 60 * 60 * 24 = 86400 MB which is 86.4GB storage

API design

when user give a long url it should return back short url, we need one API, String longToShort(String longURL);

when user give a short url, it should return back long url, we need one API, String shortToLong(String shortURL);

when user give a short url and want to delete it, we need a API boolean deleteURL(String shortURL)

when user want to register, we need one API

boolean register(UserInfo user)

when user want to login, we need one API

boolean login(UserInfo user)

Database design

we need two db, one is for long short url mapping, another one is for user info

For the URL storage database, you mentioned four columns:

1. long URL
2. short URL
3. last time used
4. user ID

For the user database, you mentioned four columns as well:

1. username
2. password
3. email address
4. user ID

High-level design

Databases:

• One database for storing user information.
• Another database for URL storage.

Load Balancer:

• Used to distribute incoming traffic across multiple instances of services to ensure high availability and scalability.

API Gateway:

• Acts as a single entry point for all the client requests.
• Handles authentication, authorization, and rate limiting like throttling.

In this architecture:

• The client interacts with the system via the Load Balancer.
• The Load Balancer distributes the incoming requests among multiple instances.
• The API Gateway handles the client requests, including authentication, authorization, and rate limiting.
• The API Gateway forwards the requests to the User Database and URL Storage Database based on the request type.
• The Business Logic Layer sits between the API Gateway and the databases.
• The Business Logic Layer is responsible for handling tasks such as encoding long URLs into short URLs using algorithms like Base64.
• The API Gateway forwards requests to the Business Logic Layer for processing before interacting with the User Database and URL Storage Database.

for registration flow, we need to check first if user already exists or not

for the login process, if validate succeed, we will generate a bear token back to user and later everytime when user send request we will check the bear token in API gateway layer

Request flows

For the registration process:

1. User sends registration information to the system.
2. Load Balancer routes the request to an API Gateway.
3. API Gateway forwards the request to the Business Logic Layer.
4. Business Logic Layer interacts with the User Database to store user information.

For the login process:

1. User sends login information to the system.
2. Load Balancer routes the request to an API Gateway.
3. API Gateway forwards the request to the Business Logic Layer.
4. Business Logic Layer communicates with the User Database to validate user credentials.

For url logic

• The flow begins with the client sending a long URL request, which goes through the API components.
• The business logic layer handles token validation, rate limiting, and request forwarding based on load balancing.
• The host checks if the long URL already exists in the system and returns the existing short URL if found.
• If the long URL does not exist, the business logic generates a short URL using the Base64 encoding algorithm.
• The generated or existing short URL is stored in the URL storage database and returned to the client as the response.
• The client initiates the process by sending a short URL request.
• The API components validate the token, check the rate limit, and handle the request based on these factors.
• The business logic layer interacts with the URL storage database to retrieve the original long URL associated with the provided short URL.
• If the long URL exists, the original long URL is returned to the client. If not found, a message indicating that the short URL was not found is returned to the client.

Detailed component design

Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...

Using a NoSQL database like Amazon DynamoDB for the URL shortening service, especially when complex queries are not required, is a good choice due to its scalability and performance capabilities. Having separate tables for URL storage and user information simplifies the data model and enhances scalability.

To visualize the database design with DynamoDB and the scaling options, let's outline the components:

1. DynamoDB Tables:

• One table for URL storage.
• Another table for user information.

1. Scaling Options:

• Vertical scaling by adding more storage and disks to individual nodes.
• Horizontal scaling by adding more nodes to the database for increased capacity.

Let's summarize this in a high-level design diagram:

Using the Base64 encoding algorithm to convert long URLs to short URLs is a common and straightforward approach in URL shortening services. Let's incorporate this algorithm into the system design:

1. Base64 Encoding:

• Implement the Base64 encoding algorithm in the business logic layer to generate short URLs from long URLs.

By applying the Base64 encoding algorithm within the system, we can efficiently generate short URLs for the given long URLs. This algorithm simplifies the process and provides a concise representation of the original URLs.

Utilizing an API Gateway for handling authentication, authorization, and rate limiting in the system is a common practice to enforce security and control access to APIs effectively. With the API Gateway, you can centralize these functionalities and easily manage them across the system.

1. API Gateway:

• Configure the API Gateway to handle authentication, authorization, and rate limiting for incoming requests.
• Implement mechanisms such as token validation, access control, and request throttling within the API Gateway to ensure secure and controlled access to the system.

By leveraging the API Gateway for these crucial functionalities, you can enhance the security and performance of the system while simplifying the management of access controls.

Load Balancer:

• Configure the load balancer to evenly distribute incoming requests among multiple hosts or instances of services.
• Improve the system's scalability, fault tolerance, and responsiveness by balancing the traffic effectively.

Trade offs/Tech choices

Indeed, there is a trade-off between using a relational database (RDBMS) and a NoSQL database for systems like a URL shortening service. Relational databases offer the advantage of supporting complex join queries, allowing for more sophisticated data analysis, insights, and logic efficiently. Here are some considerations for using a relational database:

• Relational databases excel at supporting complex SQL queries involving joins, aggregations, and relationships between tables.
• Enables efficient data retrieval and analysis techniques for deriving insights and implementing advanced logic.

• RDBMS ensures data integrity through ACID (Atomicity, Consistency, Isolation, Durability) compliance, making it suitable for applications where data consistency is paramount.
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However, it's important to balance the need for complex queries with the scalability requirements and performance considerations of the system. NoSQL databases like DynamoDB offer scalability, flexibility, and quick access to data, making them a suitable choice for systems with high throughput and simpler query needs.

For a URL shortening service that primarily focuses on storing URLs and user information without the need for complex queries or relationships between entities, a NoSQL database would be a good fit. If the system later requires more advanced analytics or relationships between user and URL data, a relational database might be a better option.

Ultimately, the choice between a relational database and a NoSQL database depends on the specific requirements, scalability needs, and data access patterns of the system.

1. Authentication Complexity:

• Implementing authentication mechanisms, like bearer token validation, adds complexity to the system and may introduce latency through additional verification steps.
• Requires the development and maintenance of secure authentication processes to ensure data security.

1. Request Throttling:

• Authentication can be leveraged to enable granular request throttling based on attributes such as IP address and user name.
• Allows for fine-grained control over request rates to prevent abuse or overload on the system.

Considering these factors, implementing authentication in the system provides enhanced security and control over user access while enabling features like request throttling for efficient resource management. While it may add complexity and potentially increase latency, the security benefits and control mechanisms it offers can outweigh these drawbacks, especially in systems handling sensitive or critical data.

Failure scenarios/bottlenecks

Handling hot URLs, where a few URLs receive a disproportionately high volume of requests, can indeed create performance bottlenecks, especially in the context of database IO limitations. To address this issue, we can consider implementing caching mechanisms to alleviate the load on the database and improve response times for frequently accessed URLs:

Future improvements

1. Caching Layer:

• Introduce a caching layer (e.g., in-memory cache like Redis) to store frequently accessed short URLs and their corresponding long URLs.
• Cache popular URLs to reduce database load and improve response time for hot URLs.

1. Batch Job Execution:

• Schedule batch jobs to run at regular intervals to identify and delete cold URLs based on their last access time.

1. RetentionPolicy and Cleanup Logic:

• Implement a retention policy that determines how long a URL can remain in storage without being accessed.
• Identify URLs that have not been accessed within the retention period and mark them for deletion.

1. URL Cleanup Process:

• Execute the batch job to delete cold URLs from the URL storage table, freeing up space in the database.