Programming

In the previous article, you learned how load balancers distribute traffic across servers. But even with many servers, your system can be slow if every request hits the database. This is where caching comes in. Caching is one of the most effective ways to speed up any system. It reduces database load, lowers latency, and saves money. What is Caching? Caching means storing a copy of data in a faster storage layer so future requests can be served quicker. Instead of fetching data from a slow source (like a database), you fetch it from a fast source (like memory). ...

In the previous article, you learned about horizontal scaling — adding more servers to handle more traffic. But when you have multiple servers, how do you distribute traffic across them? That is what a load balancer does. It is one of the most important components in any scalable system. What is a Load Balancer? A load balancer is a device or software that distributes incoming network traffic across multiple servers. Think of it as a traffic director at a busy intersection. ...

In the previous article, you learned what system design is and how to approach any design problem. Now let us talk about the most fundamental concept: scalability. Scalability is the ability of a system to handle more work by adding resources. Every system starts small. The question is: what happens when it needs to grow? What is Scalability? Imagine you own a coffee shop. On Monday, you serve 50 customers. Your single barista handles it fine. But on Saturday, 500 people show up. What do you do? ...

You know how to write code. You can build features, fix bugs, and ship apps. But when someone asks you to design a system that handles millions of users, you freeze. This is what system design is about. It is the skill of building software systems that work at scale. And it is not just for senior engineers or job interviews. Every developer needs it. This is the first article in the System Design from Zero to Senior series. We will start from the basics and build up to designing real systems like URL shorteners, chat apps, and video streaming platforms. ...

Choosing the right data structure is half the solution to any coding problem. In interviews, the first question is always: “What data structure should I use?” A wrong choice leads to slow solutions or unnecessary complexity. In this article, you will learn a decision framework for choosing the right data structure, see head-to-head comparisons, and get a complete cheat sheet you can reference any time. This article covers everything from our first 9 articles in the series. ...

Union-Find (also called Disjoint Set Union or DSU) is a data structure that tracks elements split into non-overlapping groups. It answers one question very fast: “Are these two elements in the same group?” It is the best tool for connected components problems and appears in many graph-related interview questions. In this article, you will learn how Union-Find works, why path compression makes it nearly O(1), and how to use it in coding interviews. ...

Every time you type in a search bar and see suggestions appear, a trie is probably working behind the scenes. A trie (pronounced “try”) is a tree-like data structure designed for fast string operations. It is the go-to data structure for autocomplete, spell checkers, and word search problems. In this article, you will learn how tries work, how to build one from scratch, and how to use them in coding interviews. ...

Graphs are one of the most powerful and versatile data structures in computer science. Social networks, maps, dependency systems, and the internet itself are all graphs. Graph problems appear frequently in coding interviews, especially at top tech companies. In this article, you will learn how to represent graphs, traverse them with BFS and DFS, detect cycles, and perform topological sort. We show every example in Kotlin, Python, and Go. What is a Graph? A graph is a collection of vertices (also called nodes) connected by edges. Unlike trees, graphs can have cycles, and nodes can connect to any other node. ...

A heap is a data structure that always gives you the smallest (or largest) element instantly. It powers priority queues, scheduling systems, and some of the most common interview patterns like “top K elements.” In this article, you will learn how heaps work, how to implement them, and how to use them in coding interviews. We show every example in Kotlin, Python, and Go. What is a Heap? A heap is a complete binary tree that satisfies the heap property: ...

Trees are one of the most important data structures for coding interviews. They appear in about 20-25% of all interview questions at top tech companies. If you are comfortable with trees, you have a big advantage. In this article, you will learn binary trees, tree traversals, and binary search trees (BST). We show every example in Kotlin, Python, and Go. What is a Tree? A tree is a hierarchical data structure made of nodes connected by edges. It looks like an upside-down tree — the root is at the top, and the leaves are at the bottom. ...