System-Design on TutorialEdge.net

Caching in System Design

Sat, 20 Jun 2026 12:00:00 +0000

Caching trades memory and freshness for speed and reduced load. By storing a copy of frequently accessed data closer to the consumer, you serve repeated requests without touching the primary data store.

At scale, caching is one of the highest-leverage tools available. A database query that takes 20ms on a cold path takes under 1ms when served from Redis. That difference compounds across millions of requests per day.

Caches live at many layers. Client-side caches store data in the browser or mobile app. CDN caches hold static assets and rendered pages at the edge.

Consistency and the CAP Theorem: A System Design Guide

Sat, 20 Jun 2026 12:00:00 +0000

The CAP theorem is the first concept most interviewers reach for when probing your knowledge of distributed systems. It frames every data store decision you will ever make at scale.

Proposed by Eric Brewer in 2000 and later formalised as a theorem, CAP says a distributed data store can guarantee at most two of three properties: Consistency, Availability, and Partition tolerance. You cannot have all three.

This guide explains what each property actually means, what the real tradeoff looks like in practice, and how to talk about it confidently when an interviewer asks.

Database Replication Explained

Sat, 20 Jun 2026 12:00:00 +0000

Database replication is the process of keeping copies of the same data on multiple nodes. It is one of the most fundamental tools for building systems that are fast, resilient, and always on.

Replication solves three problems at once. It scales reads by spreading queries across nodes, improves availability by removing single points of failure, and improves durability so data survives the loss of any one machine.

This guide explains the core patterns, the real tradeoffs, and how to reason about them clearly in an interview.

Database Sharding and Partitioning

Sat, 20 Jun 2026 12:00:00 +0000

Vertical scaling - adding more CPU, RAM, or faster disks to one machine - has hard limits. Once you hit them, no single node can hold all your data or serve all your writes.

Sharding solves this by splitting one dataset across many nodes. Each node holds a subset of the rows, so the write load and storage are distributed rather than concentrated.

The terms are easy to confuse. Partitioning means dividing a table into logical segments; sharding means spreading those partitions across separate physical machines or nodes. Sharding is partitioning taken to a distributed level.

Message Queues in System Design

Sat, 20 Jun 2026 12:00:00 +0000

When services call each other synchronously, they are tightly coupled. If the downstream service is slow, the caller blocks. If it is down, the caller fails.

A message queue breaks that coupling by placing an asynchronous buffer between the two. The producer writes a message and moves on immediately. The consumer reads it whenever it is ready.

This decoupling is what makes queues one of the most common components in large-scale system design.