Now that you have the fundamentals down, it’s time to explore how to scale horizontally.
This module focuses on the macroscopic patterns for distributing requests and controlling traffic across a server fleet. These are the mechanisms that allow a service to grow from 1 server to 1,000 servers without breaking user experience.
Key topics include:
- Load Balancing: Distributing requests across N servers using algorithms like Round Robin, Least Connections, and Weighted policies. Understanding L4 vs L7 balancing and modern data plane modes (DSR, Anycast).
- Consistent Hashing: How DynamoDB, Cassandra, and CDNs distribute data across nodes without cascading rehashing. Advanced algorithms: Maglev (Google) and Rendezvous.
- Rate Limiting & Backpressure: Protecting services from overload using Token Bucket, Leaky Bucket, and adaptive rate limiting. Propagating backpressure signals through the system.
- Service Discovery & Mesh: How services find each other in dynamic fleets. Client-side vs server-side discovery, the xDS API, and the sidecar pattern (Envoy).
This module answers the question: “How do we distribute work fairly and prevent overload?”
Module Chapters
Load Balancing 101: The Digital Concierge
Imagine a restaurant with only one waiter and 100 hungry customers. That's a server without a load balancer. Learn how to scale your system by adding more help.
Start Learning →Deep Dive: Consistent Hashing & The Ring
Why hash(key) % N fails at scale, and how the 'Ring' topology (used by DynamoDB, Cassandra, and Chord) solves it.
Start Learning →Load Balancing 101: Primitives for Scale
Deep dive into L4 vs L7, fundamental algorithms, and modern implementation patterns like Sidecars and eBPF.
Start Learning →Rate Limiting & Backpressure
Protecting your system from the 'Thundering Herd'. Implementing Rate Limiting, Load Shedding, and Backpressure.
Start Learning →Service Discovery & Mesh Control Planes
Beyond static IPs. Exploring the xDS API, sidecars, and why 'Service Discovery' is the heartbeat of a dynamic fleet.
Start Learning →