Hi everyone! In this article, we are going to cover the complete end‑to‑end guide to learning system design. This is going to be one of the most in‑depth as well as crystal‑clear guide on exactly what we need to learn in high‑level design and low‑level design. Now whenever we talk about system design, it’s a very important concept—especially if we are targeting SDE‑2 or higher roles, and if we want to become a CTO and a tech lead in the future—because within system design we cover many important concepts that are highly essential for designing highly scalable, robust systems. What is System Design? To understand exactly what system design is, let’s talk in basic layman terms. System design is designing day‑to‑day tech‑based systems, like Netflix, Uber, Instagram, WhatsApp—that we use on a daily basis. In system design interviews, we are asked to design one of these systems. Designing Real-World Systems For example, the interviewer may say: “You have to design WhatsApp.” Now as a software engineer, to design WhatsApp, there are some basic tools and concepts we need to know, and these tools and concepts are what we learn in system design. We are doing a detailed discussion on which concepts we need to learn. Two Parts of System Design (HLD & LLD) We learn system design in two parts. The first is High‑Level Design (HLD), in which we design an overview of the system—for example, if we design WhatsApp, we decide which database we use, how we use message queues, how we use cache, maybe. In this way we identify different components of a system and design the overall system—here we don’t write any actual code. The second part is Low‑Level Design (LLD), in which we do actual machine‑level coding: we design APIs, draw class diagrams, define our models—our actual coding skills are tested. And we will cover both HLD and LLD concepts in detail. Prerequisite: Development Experience There is an important prerequisite before learning system design, and that is to actually have hands‑on experience in development. If we learn system design directly without having done any development, without having built any project, without having worked at least a little at SDE‑1 level in a company, many concepts will feel theoretical and we won’t have practical understanding. So it is crucial that before learning system design, we have designed some basic systems or projects ourselves so that we can relate when we talk about database or cache what their actual usages are in a real‑life system. In the first half of the article, we will cover HLD, and in the second half, LLD. Detailed discussion on HLD Suppose we are in an interview and the interviewer asks us to design a Netflix‑like system, or to design Netflix itself, which means designing a service able to serve video content to millions of users across different countries seamlessly. To understand this system well, we need to understand two things: functional and non‑functional requirements. Functional requirements: exact features in the system that users will use—for Netflix, users should be able to register and log in; users should be able to purchase a subscription; users should be able to play and pause videos.
Non‑functional requirements: system qualities—Netflix should be a secure service so only paid users can use paid content, so authentication and authorization must be strong; the system should have low latency, because if videos take too long to start, users won’t watch; the system should be scalable, so millions can watch the same movie or show concurrently. Functional requirements: exact features in the system that users will use—for Netflix, users should be able to register and log in; users should be able to purchase a subscription; users should be able to play and pause videos. Non‑functional requirements: system qualities—Netflix should be a secure service so only paid users can use paid content, so authentication and authorization must be strong; the system should have low latency, because if videos take too long to start, users won’t watch; the system should be scalable, so millions can watch the same movie or show concurrently. Once we identify these, we design the overall system. HLD step by step Fundamentals: serverless vs. serverful architectures

For example, deploying on AWS Lambda vs. AWS EC2. Lambda gives serverless facilities but can be cost‑inefficient at scale; EC2 you manage more yourself. No fixed answer—always consider trade‑offs.


Horizontal vs. vertical scaling

Vertical scaling: Increase RAM, CPU on a single server until a limit.
Horizontal scaling: Add multiple servers behind a load balancer to distribute load.


How the Internet Works

Request‑response cycle, DNS, threads, processes—basic networking knowledge.


Databases

SQL vs. NoSQL (e.g., MongoDB, Neo4j), pros and cons.
In‑memory databases, replication, migration.
Partitioning & sharding (horizontal partitioning)—common interview topic.


Consistency & Availability

CAP theorem: trade‑off between consistency and availability during network partitions.
Consistency levels: eventual, causal, linearizability, quorum.
Isolation levels: read uncommitted, read committed, repeatable read.
Choose consistency vs. availability based on use case (e.g., payments favor consistency; notifications favor availability).


Cache

Purpose: store frequently or heavily used data for low‑latency access.
Types: Redis, Memcached.
Write policies: write‑back, write‑through, write‑around.
Eviction policies: LRU, LFU, segmented LRU.


CDNs (Content Delivery Networks)

Distribute static content (video segments) close to users for fast delivery.


Networking

TCP vs. UDP, HTTP/1.x vs. HTTP/2 vs. HTTP/3, WebSockets, WebRTC for real‑time video.


Load Balancers

Stateless vs. stateful load balancing.
Algorithms: round robin, least connections, consistent hashing.
Reverse proxy, rate limiting to prevent DDoS.


Message Queues

Asynchronous processing for non‑critical tasks.
Kafka, RabbitMQ, pub‑sub model.


Monoliths vs. Microservices

When and why to split a monolith into microservices.
Single point of failure, cascading failures.
Containerization (Docker) and migration strategies.


Monitoring & Logging

Tools: AWS CloudWatch, Grafana, Prometheus.
Logs and metrics to detect issues under heavy load.
Anomaly detection.


Security

Authentication (OAuth tokens), authorization (ACLs).
Encryption at rest and in transit.


Trade‑Offs

No single right answer—explain your thought process and justify choices.
Push vs. pull, consistency vs. availability, SQL vs. NoSQL, memory vs. latency vs. throughput vs. accuracy.


Practice

The more system design problems you practice, the better you get. Fundamentals: serverless vs. serverful architectures

For example, deploying on AWS Lambda vs. AWS EC2. Lambda gives serverless facilities but can be cost‑inefficient at scale; EC2 you manage more yourself. No fixed answer—always consider trade‑offs. Fundamentals: serverless vs. serverful architectures For example, deploying on AWS Lambda vs. AWS EC2. Lambda gives serverless facilities but can be cost‑inefficient at scale; EC2 you manage more yourself. No fixed answer—always consider trade‑offs. For example, deploying on AWS Lambda vs. AWS EC2. Lambda gives serverless facilities but can be cost‑inefficient at scale; EC2 you manage more yourself. No fixed answer—always consider trade‑offs. Horizontal vs. vertical scaling

Vertical scaling: Increase RAM, CPU on a single server until a limit.
Horizontal scaling: Add multiple servers behind a load balancer to distribute load. Horizontal vs. vertical scaling Vertical scaling: Increase RAM, CPU on a single server until a limit.
Horizontal scaling: Add multiple servers behind a load balancer to distribute load. Vertical scaling: Increase RAM, CPU on a single server until a limit. Horizontal scaling: Add multiple servers behind a load balancer to distribute load. How the Internet Works

Request‑response cycle, DNS, threads, processes—basic networking knowledge. How the Internet Works Request‑response cycle, DNS, threads, processes—basic networking knowledge. Request‑response cycle, DNS, threads, processes—basic networking knowledge. Databases

SQL vs. NoSQL (e.g., MongoDB, Neo4j), pros and cons.
In‑memory databases, replication, migration.
Partitioning & sharding (horizontal partitioning)—common interview topic. Databases SQL vs. NoSQL (e.g., MongoDB, Neo4j), pros and cons.
In‑memory databases, replication, migration.
Partitioning & sharding (horizontal partitioning)—common interview topic. SQL vs. NoSQL (e.g., MongoDB, Neo4j), pros and cons. In‑memory databases, replication, migration. Partitioning & sharding (horizontal partitioning)—common interview topic. Consistency & Availability

CAP theorem: trade‑off between consistency and availability during network partitions.
Consistency levels: eventual, causal, linearizability, quorum.
Isolation levels: read uncommitted, read committed, repeatable read.
Choose consistency vs. availability based on use case (e.g., payments favor consistency; notifications favor availability). Consistency & Availability CAP theorem: trade‑off between consistency and availability during network partitions.
Consistency levels: eventual, causal, linearizability, quorum.
Isolation levels: read uncommitted, read committed, repeatable read.
Choose consistency vs. availability based on use case (e.g., payments favor consistency; notifications favor availability). CAP theorem: trade‑off between consistency and availability during network partitions. Consistency levels: eventual, causal, linearizability, quorum. Isolation levels: read uncommitted, read committed, repeatable read. Choose consistency vs. availability based on use case (e.g., payments favor consistency; notifications favor availability). Cache

Purpose: store frequently or heavily used data for low‑latency access.
Types: Redis, Memcached.
Write policies: write‑back, write‑through, write‑around.
Eviction policies: LRU, LFU, segmented LRU. Cache Purpose: store frequently or heavily used data for low‑latency access.
Types: Redis, Memcached.
Write policies: write‑back, write‑through, write‑around.
Eviction policies: LRU, LFU, segmented LRU. Purpose: store frequently or heavily used data for low‑latency access. Types: Redis, Memcached. Write policies: write‑back, write‑through, write‑around. Eviction policies: LRU, LFU, segmented LRU. CDNs (Content Delivery Networks)

Distribute static content (video segments) close to users for fast delivery. CDNs (Content Delivery Networks) Distribute static content (video segments) close to users for fast delivery. Distribute static content (video segments) close to users for fast delivery. Networking

TCP vs. UDP, HTTP/1.x vs. HTTP/2 vs. HTTP/3, WebSockets, WebRTC for real‑time video. Networking TCP vs. UDP, HTTP/1.x vs. HTTP/2 vs. HTTP/3, WebSockets, WebRTC for real‑time video. TCP vs. UDP, HTTP/1.x vs. HTTP/2 vs. HTTP/3, WebSockets, WebRTC for real‑time video. Load Balancers

Stateless vs. stateful load balancing.
Algorithms: round robin, least connections, consistent hashing.
Reverse proxy, rate limiting to prevent DDoS. Load Balancers Stateless vs. stateful load balancing.
Algorithms: round robin, least connections, consistent hashing.
Reverse proxy, rate limiting to prevent DDoS. Stateless vs. stateful load balancing. Algorithms: round robin, least connections, consistent hashing. Reverse proxy, rate limiting to prevent DDoS. Message Queues

Asynchronous processing for non‑critical tasks.
Kafka, RabbitMQ, pub‑sub model. Message Queues Asynchronous processing for non‑critical tasks.
Kafka, RabbitMQ, pub‑sub model. Asynchronous processing for non‑critical tasks. Kafka, RabbitMQ, pub‑sub model. Monoliths vs. Microservices

When and why to split a monolith into microservices.
Single point of failure, cascading failures.
Containerization (Docker) and migration strategies. Monoliths vs. Microservices When and why to split a monolith into microservices.
Single point of failure, cascading failures.
Containerization (Docker) and migration strategies. When and why to split a monolith into microservices. Single point of failure, cascading failures. Containerization (Docker) and migration strategies. Monitoring & Logging

Tools: AWS CloudWatch, Grafana, Prometheus.
Logs and metrics to detect issues under heavy load.
Anomaly detection. Monitoring & Logging Tools: AWS CloudWatch, Grafana, Prometheus.
Logs and metrics to detect issues under heavy load.
Anomaly detection. Tools: AWS CloudWatch, Grafana, Prometheus. Logs and metrics to detect issues under heavy load. Anomaly detection. Security

Authentication (OAuth tokens), authorization (ACLs).
Encryption at rest and in transit. Security Authentication (OAuth tokens), authorization (ACLs).
Encryption at rest and in transit. Authentication (OAuth tokens), authorization (ACLs). Encryption at rest and in transit. Trade‑Offs

No single right answer—explain your thought process and justify choices.
Push vs. pull, consistency vs. availability, SQL vs. NoSQL, memory vs. latency vs. throughput vs. accuracy. Trade‑Offs No single right answer—explain your thought process and justify choices.
Push vs. pull, consistency vs. availability, SQL vs. NoSQL, memory vs. latency vs. throughput vs. accuracy. No single right answer—explain your thought process and justify choices. Push vs. pull, consistency vs. availability, SQL vs. NoSQL, memory vs. latency vs. throughput vs. accuracy. Practice

The more system design problems you practice, the better you get. Practice The more system design problems you practice, the better you get. The more system design problems you practice, the better you get. Example HLD for Netflix Combination of application servers, CDN, load balancers, SQL/NoSQL databases, cache, message queues.
Aim to satisfy most users rather than delight a few—design for the common case. Combination of application servers, CDN, load balancers, SQL/NoSQL databases, cache, message queues. Aim to satisfy most users rather than delight a few—design for the common case. Low‑Level Design (LLD) OOPs & SOLID Principles

Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, Dependency Inversion.


Design Patterns

Creational, structural, behavioral patterns you use daily.


Concurrency & Thread Safety

Locks, synchronization, producer‑consumer, race conditions.


UML Diagrams

Class diagrams, component diagrams (ask your recruiter if expected).


API Design

Request/response modeling, versioning, extensibility, avoiding god classes.


Common LLD Problems

Coding a URL shortener, a notification system, a chess game, etc. OOPs & SOLID Principles

Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, Dependency Inversion. OOPs & SOLID Principles Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, Dependency Inversion. Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, Dependency Inversion. Design Patterns

Creational, structural, behavioral patterns you use daily. Design Patterns Creational, structural, behavioral patterns you use daily. Creational, structural, behavioral patterns you use daily. Concurrency & Thread Safety

Locks, synchronization, producer‑consumer, race conditions. Concurrency & Thread Safety Locks, synchronization, producer‑consumer, race conditions. Locks, synchronization, producer‑consumer, race conditions. UML Diagrams

Class diagrams, component diagrams (ask your recruiter if expected). UML Diagrams Class diagrams, component diagrams (ask your recruiter if expected). Class diagrams, component diagrams (ask your recruiter if expected). API Design

Request/response modeling, versioning, extensibility, avoiding god classes. API Design Request/response modeling, versioning, extensibility, avoiding god classes. Request/response modeling, versioning, extensibility, avoiding god classes. Common LLD Problems

Coding a URL shortener, a notification system, a chess game, etc. Common LLD Problems Coding a URL shortener, a notification system, a chess game, etc. Coding a URL shortener, a notification system, a chess game, etc. Timeline for Learning If you’re already an SDE actively preparing, 2–3 months to review concepts.
If most concepts are new, 4–6 months is realistic.
Keep practice, build POCs and personal projects. If you’re already an SDE actively preparing, 2–3 months to review concepts. If most concepts are new, 4–6 months is realistic. Keep practice, build POCs and personal projects. I hope this guide helps you positively in your career. To my knowledge, this is one of the most crystal‑clear, comprehensive system design guides available. If you have any question, let me know in the comments. If you found this guide helpful, you can also share it with your circle. That’s it, see you in the next article. My name is Usman, till then, keep learning and keep exploring! My name is Usman Let’s grow, learn, and build amazing things together! Let’s grow, learn, and build amazing things together! Don’t forget to like your heart content, save it to your list, and follow me. **Stay connected with me on my other platforms:** LinkedIn | dev.to| Bluesky | HackerNoon LinkedIn LinkedIn dev.to dev.to Bluesky | HackerNoon Bluesky HackerNoon

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