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Users can create and share tweets
User can track other users (follow each other)
User will be notified if someone they follow tweets
Ability to like a tweet
Largely scalable, capable of saving lots of tweets and data for each user - handle 100 million users, ~5 requests a day
Speed - quickly respond to requests < 1000ms
Availability - ensure practically 100% uptime
Security - protect data
Estimated users ~100 million users
Estimated requests ~500 million requests per day
Estimated storage ~1 KB of storage for each tweet (timestamp, user ID, likes), potentially 500 GB of data saved per day.
Data model -
Users - user info, username, email, password, profile info
Tweets - text, timestamp, user ID, likes
Followers - follower ID and followee ID
RESTful API
We can use an SQL database, lets say PostgreSQL
User table - user ID, username, email, password
Tweet table - text, timestamp, user id, likes
Follower table - follower ID, followee ID
Starting from the client, we will connect to some load balancer to distribute incoming traffic to multiple servers. We will implement caching mechanisms for things like frequently used data, like popular tweets or users.
Use a CDN to display app assets to users quickly
Can use database replication for high availability
User creates a new tweet
User requests tweet list
User requests to like a tweet
Load balancer can be managed using nginx
Caching can be managed using Redis
CDN can be managed with cloudflare
Database: PostgreSQL
Scalability vs performance: using caching
Security vs ease of use: HTTPS and OAuthentication (2 factor)
Using a relational database to query and handle a user's data and their tweets
Unexpected failures can occur with the database, load balancer or caching, which makes it important to implement multiple kinds of these load balancers, and also to replicate the database to ensure uptime
A way to search for tweets, user profile screen with replies, media and likes
A way to message each other
Tweet analytics for users to work on their engagement