How do you build a large scale system?
Table of Contents
How do you build a large scale system?
Here are some ways to prepare for scaling early:
- Avoid monolithic architecture. Monolithic architecture means the UI layer, logic layer, and database layer all reside in one server.
- Build systems that can scale by duplication.
- Separate functionality.
- Think ahead in the cloud.
- Consider AI early on.
- Add IoT.
How do you create a distributed system?
Building a “Simple” Distributed System – The What
- what the resource allocation library must do.
- the design of the protocol that describes the what in more formal detail.
- the formal verification of the protocol with TLA+.
- a high level view of the implementation, also known as the how.
What is large scale distributed system?
Large-scale distributed systems are the core software infrastructure underlying cloud computing. These systems consist of tens of thousands of networked computers working together to provide unprecedented performance and fault-tolerance.
How do you create a scalable distributed system?
Implement the application and database server on a single node.
- Vertical Scaling (Stronger and faster)
- Horizontal Scaling – Application Server (Delegate and Balance)
- Caching (Respond Fast)
- Horizontal Scaling and Replication – Database Server (Create copies)
- Sharding and Federation – Database Server (Divide and Conquer)
How does distributed computing work in distributed systems?
A distributed computer system consists of multiple software components that are on multiple computers, but run as a single system. The computers that are in a distributed system can be physically close together and connected by a local network, or they can be geographically distant and connected by a wide area network.
How do you scale system design?
6 Best Ways to Scale Your Systems
- Splitting services. Splitting large monolithic software projects into smaller ones is not a new concept.
- Horizontal scaling.
- Separate databases for reading and writing concerns.
- Database sharding.
- Memory caching.
- Going to the cloud.