How to scale a website to support millions of users? We will explain this step-by-step.
The diagram below illustrates the evolution of a simplified eCommerce website. It goes from a monolithic design on one single server, to a service-oriented/microservice architecture.
Suppose we have two services: inventory service (handles product descriptions and inventory management) and user service (handles user information, registration, login, etc.).
Step 1 - With the growth of the user base, one single application server cannot handle the traffic anymore. We put the application server and the database server into two separate servers.
Step 2 - The business continues to grow, and a single application server is no longer enough. So we deploy a cluster of application servers.
Step 3 - Now the incoming requests have to be routed to multiple application servers, how can we ensure each application server gets an even load? The load balancer handles this nicely.
Step 4 - With the business continuing to grow, the database might become the bottleneck. To mitigate this, we separate reads and writes in a way that frequent read queries go to read replicas. With this setup, the throughput for the database writes can be greatly increased.
Step 5 - Suppose the business continues to grow. One single database cannot handle the load on both the inventory table and user table. We have a few options:
1. Vertical partition. Adding more power (CPU, RAM, etc.) to the database server. It has a hard limit. 2. Horizontal partition by adding more database servers. 3. Adding a caching layer to offload read requests.
Step 6 - Now we can modularize the functions into different services. The architecture becomes service-oriented / microservice.
Question: what else do we need to support an e-commerce website at Amazon’s scale?
• • •
Missing some Tweet in this thread? You can try to
force a refresh
Model Context Protocol (MCP) is a new system introduced by Anthropic to make AI models more powerful.
It is an open standard (also being run as an open-source project) that allows AI models (like Claude) to connect to databases, APIs, file systems, and other tools without needing custom code for each new integration.
MCP follows a client-server model with 3 key components:
1 - Host: AI applications like Claude that provide the environment for AI interactions so that different tools and data sources can be accessed. The host runs the MCP Client.
Kubernetes (K8S) is an open-source container orchestration platform originally developed by Google and now maintained by CNCF.
Here’s how developers interact with Kubernetes:
1 - Developers create manifest files describing the application.
2 - Kubernetes takes these manifest files, validates them, and deploys the applications across its cluster of worker nodes.
3 - Kubernetes manages the entire lifecycle of the application.
Kubernetes is made up of two main components:
1 - Control Plane: It is like the brain of Kubernetes and consists of the following parts:
- API Server: It receives all incoming requests from users or CLI.
1 - Collaboration Tools
Software development is a social activity. Learn to use collaboration tools like Jira, Confluence, Slack, MS Teams, Zoom, etc.
2 - Programming Languages
Pick and master one or two programming languages. Choose from options like Java, Python, JavaScript, C#, Go, etc.
3 - API Development
Learn the ins and outs of API Development approaches such as REST, GraphQL, and gRPC.
4 - Web Servers and Hosting
Know about web servers as well as cloud platforms like AWS, Azure, GCP, and Kubernetes
5 - Authentication and Testing
Learn how to secure your applications with authentication techniques such as JWTs, OAuth2, etc. Also, master testing techniques like TDD, E2E Testing, and Performance Testing
6 - Databases
Learn to work with relational (Postgres, MySQL, and SQLite) and non-relational databases (MongoDB, Cassandra, and Redis).
Twitter has enforced very strict rate limiting. Some people cannot even see their own tweets.
Rate limiting is a very important yet often overlooked topic. Let's use this opportunity to take a look at what it is and the most popular algorithms.
A thread.
#RateLimitExceeded
What is rate limiting? Rate limiting controls the rate at which users or services can access a resource. Here are some examples:
- A user can send a message no more than 2 per second
- One can create a maximum of 10 accounts per day from the same IP address
Fixed Window Counter
The algorithm divides the timeline into fixed-size time windows and assigns a counter for each window. Each request increments the counter by some value. Once the counter reaches the threshold, subsequent requests are blocked until the new time window begins