How Your App Handles 10M Users Without Breaking

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Ever wondered how large-scale apps handle thousands (or even millions) of simultaneous users without crashing? The unsung hero behind that performance is load balancing—a system that distributes incoming traffic across multiple servers to keep things fast, efficient, and available.

In today’s newsletter, we’re diving into three fundamental load balancing strategies that power everything from your favorite SaaS tools to global-scale web services:

• Round Robin

• Least Connections

• Consistent Hashing

Let’s break them down—how they work, when to use them, and what to watch out for.

Why Load Balancing Matters

Before we get into the types, let’s recap why load balancing is critical:

• Scalability: Add servers without redesigning the system.

• Redundancy: Route around failures.

• Performance: Avoid overloading any single server.

• Flexibility: Distribute across geographies or availability zones.

Whether you're building microservices, deploying APIs, or scaling a monolith—load balancers are key.

1. Round Robin – The Simplest Distribution

How it works:
Requests are distributed to servers one after another in a loop.

Use case:

• When all your backend servers have roughly equal capacity and load.

• Great for stateless applications like REST APIs or static file servers.

Pros:

• Easy to implement.

• Works well in homogenous server environments.

Cons:

• Doesn’t account for server load—might overwhelm a slow server.

• No session awareness.

Client → Load Balancer → Server 1 → Server 2 → Server 3 → Server 1 → ...

2. Least Connections – Smart Under Load

How it works:
Traffic is routed to the server with the fewest active connections.

Use case:

• For stateful applications or services with varying session durations (e.g., chat apps, video streaming).

Pros:

• Automatically adjusts to traffic load.

• Helps prevent overload on busy nodes.

Cons:

• Needs continuous monitoring of active connections.

• Slightly more complex to implement.

Client → Load Balancer → Server with least number of active connections

3. Consistent Hashing – Sticky and Scalable

How it works:
A hash function (usually based on IP address or session ID) determines which server gets the request. Servers are placed on a “hash ring,” and the same client is always routed to the same server (until a change happens).

Use case:

• When stateful sessions or caching are involved (e.g., user sessions in memory, Redis).

• Good for minimizing disruption during scaling (adding/removing servers).

Pros:

• Predictable and stable.

• Fewer cache misses when topology changes.

Cons:

• Slightly complex math (need to understand hash rings and virtual nodes).

• Doesn’t inherently balance load—some hashing schemes may result in uneven distribution.

Hash(client IP) → Server A  
Hash(client IP) → Server B (if topology changes slightly)

Which One Should You Use?

Real-World Applications

• Nginx & HAProxy: Support all three strategies.

• AWS ALB / ELB: Use advanced algorithms behind the scenes, including least outstanding requests.

• Kubernetes Ingress: Can integrate with hashing and round-robin depending on the controller.

Pro Tips

• Combine strategies: Use least connections with session stickiness for high-volume web apps.

• Always test under load: What works on dev won’t always scale in prod.

• Use health checks: Dead servers should be auto-removed from the rotation.

Try This Challenge

Spin up 3 dummy servers (using Flask or Express) and build a Python-based load balancer using:

• Round-robin logic with a request counter.

• Least connections by tracking open sockets.

• Consistent hashing using SHA-256 and a virtual node ring.

Fresh Breakthroughs and Bold Moves in Tech & AI

Stay ahead with curated updates on innovations, disruptions, and game-changing developments shaping the future of technology and artificial intelligence.

New Study Finds AI Coding Tools Slow Down Veteran Developers—Here’s What to Know. Link

• Experienced developers expected coding tools like Cursor and Copilot to cut task time by ~24%, but tasks actually took around 19% longer when AI was used. Despite this, developers believed they were faster—even after the fact.

• AI-generated suggestions were accepted less than 44% of the time, and significant time was spent reviewing, interpreting, and correcting output—which introduced delays instead of reducing effort

• When working on large, mature, and well-known codebases, developers encountered minimal actual benefit from AI tools. The tools lacked deep context, making corrections more burdensome than helpful

• While the slowdown was specific to experienced engineers, earlier studies affirm that novice or unfamiliar developers benefit more from AI assistance during prototyping or simpler tasks

• Though productivity may dip for senior devs, AI coding tools are reshaping developer workflows—shifting focus toward reviewing, modifying, and deploying AI-generated code. Many still find tools enjoyable or helpful in reducing mental friction

Until next deploy,
— The Nullpointer Club Team