Exercise: LinkedIn COSI Analysis

To get a better grasp on the concepts introduced in this course, it's helpful to take a look and see if you can apply them to existing software products. The nice thing about this is that you don't even need to have developed the entire system yourself - just use it as a case study for your analysis!

As an exercise, fill in the COSI worksheet for LinkedIn. What kind of architectural patterns would you need to build a platform like LinkedIn? What would the implementation in terms of infrastructure look like? Fill in the worksheet with your thoughts, and then watch the video in the next lesson to see my take on it.

Joris Jansen

# COMMUNICATION
- HTTP (API Rest)
- gRPC
- WS/SSE

The REST API communication can happen between the client and server. The gRPC will be used between different services in the backend and the WS/SSE can be used for the Chat functionality.

# ORGANISATION
- Client-Server (Frontend & Backend)
- The Backend can be built in different (micro)services, I could think of a 'main' service, a 'chat' service and a 'jobs' service (there are probably more).
- The Backend 'code' can be organized in a Clean/Onion Architecture

# STORAGE
- SQL Database, for all the structured information (user, profile, chat message)
- Object storage, for all the static files (images/videos)
- Vector Database, for all the search-related stuff

# IMPLEMENTATION
- Docker
- Kubernetes
- Self-hosted database instances
- Infrastructure as Code
- Strict release schedules with Deployment Pipelines

REPLY
Andreas [ArjanCodes Team]

Nice analysis!

REPLY
Lucas Cristovam Henriques Fonseca

For architetural patterns:

Microservices Architecture: This will allow for developing and deploying services independently of each other. Each service could correspond to a business operation like user management, post management, messaging, etc.
Event-Driven Architecture: This would be useful for real-time updates and notifications. Events can be produced by one service and consumed by one or more services.
Database Sharding and Replication: LinkedIn would require a robust data system given the huge volume of data. Database sharding and replication can help with spreading the data across many servers.
Caching: A caching layer like Redis would be needed to reduce the load on the database.
In terms of infrastructure:
Cloud Service: Infrastructure could be hosted on a cloud service like AWS, Google Cloud, or Azure to handle scalability and reliability.
Content Delivery Network : LinkedIn serves a lot of static content (profile pictures, attachments, etc.), so a CDN would be beneficial.
Load Balancers: To distribute network or application traffic across a number of servers.
Security Measures: Firewalls, encryption for data at rest and in transit, intrusion detection systems, etc.

REPLY
Andreas [ArjanCodes Team]

Good analysis! Would you say that you are familiar with the tools that are mentioned here? Is there something you would like to learn more about?

REPLY