How a Backend System Scales from 1 User to 1 Million Users

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Most developers learn technologies like Redis, Kafka, Load Balancers, CDNs, Kubernetes, and Microservices separately.

They know what these technologies are.

But very few understand why these technologies exist in the first place.

The truth is that most scalable systems don’t start with Redis.

They don’t start with Kafka.

They don’t start with Kubernetes.

And they definitely don’t start with dozens of microservices.

Real systems evolve gradually.

They evolve because every stage of growth introduces a new bottleneck.

Once that bottleneck becomes painful enough, engineers introduce a new architectural component to solve it.

This article walks through the complete journey of how a backend system evolves from serving a handful of users to serving millions.

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Internal working of HashMap in Java 8 & Beyond

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How HashMap internally works in java is the most asked interview question. Even with just 1–2 years of experience, you’ve likely used HashMap in your code. However, many developers are unaware of how it actually works under the hood.

This article dives deep into the internal mechanics of HashMap, explaining how the get, put, operations work behind the scenes. I am going to discuss the internal working of HashMap in java with diagrams and code examples.

How HashMap works internally video tutorial

What Really Happens When You Instantiate a HashMap in Java

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Blocking Queue Implementation in Java

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In this blog, I am going to explain how to implement a thread-safe, blocking queue in Java using low-level concurrency primitives like wait(), notify() and notifyAll().

Java is multi-threaded language and in concurrent programming BlockingQueue plays a crucial role in managing producer-consumer problems. While Java provides robust implementations like ArrayBlockingQueue and LinkedBlockingQueue, understanding how to build a custom blocking queue from scratch helps you grasp core concurrency principles.

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