🌐 Recap and Final Thoughts on Load Balancing Strategies
🔁 Mapping, Modulo, and the Road to Consistent Hashing
You’ve probably heard it a hundred times: “Scalability is everything in distributed systems.” But what happens when your system needs to remember who’s who and what’s where?
That’s where stateful load balancing steps into the spotlight — and trust me, it’s not always as straightforward as it sounds. In this chapter of our journey through system design, we’re diving into two major approaches for stateful load balancing, their trade-offs, and a sneak peek at the hero of our next session: consistent hashing. 🧩
🛠️ A Tale of Two Strategies
Let’s break it down — Map-based vs. Modulo-based load balancing. Each has its perks, but both come with challenges, especially when your system needs to grow or evolve.
📍 1. Map-Based Load Balancing
Imagine keeping a giant notebook mapping each user to a specific machine. Like:
User 107 → Machine A User 220 → Machine B
✅ Advantages:
Efficient routing: Just look up and send. No heavy math.
Fast resolution: Minimal computation, making it lightning-quick.
❌ Disadvantages:
Memory hog: As users grow, so does the map — massively.
Scaling headaches: Add/remove machines, and your whole map might need an overhaul.
➗ 2. Modulo-Based Load Balancing
Now think of this like dividing the user ID by the number of machines — and taking the remainder.
🧮 Machine ID = User ID mod Number of Machines
✅ Advantages:
Lightweight & simple: No need to store mappings.
Quick to compute: Great for stable environments.
❌ Disadvantages:
Total chaos during scaling: Add a machine, and suddenly everyone gets reassigned.
High data movement: Not ideal when machines come and go frequently.
🤔 Choosing the Right Tool for the Job
So when do you use each?
🗺️ Map-Based
Perfect for small-scale systems.
Great when scaling is infrequent or controlled.
Example: Mapping cities to a fixed number of weather data APIs.
🔢 Modulo-Based
Ideal for static infrastructures with few changes.
Example: Internal tools with stable traffic and rarely-changing machine pools.
🚀 Looking Ahead: The Magic of Consistent Hashing
Here’s where things get exciting. 🎩✨ The biggest pain point of modulo-based systems — everything changing when you scale — is exactly what consistent hashing is built to solve.
In our next session, we’ll unpack:
🔁 How it works: Only a fraction of users/data get reassigned when machines change.
🛠️ Real-world use: Caching, sharding, and load balancing in distributed systems.
⚖️ Trade-offs: When the added complexity is absolutely worth it.
🌟 Journey Recap: Where We've Been
Let’s zoom out for a second and see the road we’ve traveled:
🧠 High-Level Design
Clients & servers, IP address types, and how DNS works behind the scenes.
⚙️ Scaling Approaches
Vertical scaling: Beefing up one machine (with limits).
Horizontal scaling: Spreading the load — and the challenges — across multiple machines.
🌉 Load Balancers 101
Gateways vs. Load Balancers
Active-passive configurations for staying online under pressure.
🔄 Stateful vs. Stateless
Stateless: Requests can go anywhere, no memory required.
Stateful: Needs context, so we need smarter routing.
🎯 Our Current Focus
Digging into mapping and modulo for handling stateful requests.
Building toward consistent hashing — the smarter, scalable solution.
📘 What’s Next?
In the next chapter, we’re diving into the heart of consistent hashing — a game-changing technique for building systems that can scale without breaking everything.
🔍 Curious how Spotify, Amazon, and Netflix keep it all running without missing a beat? Stay tuned — the answers are coming in our next post! 💥
Would you like a visual diagram or infographic to pair with this summary for your notes or a Medium post?
Last updated