DEV Community: Akarsh

How to Approach Any System Design Problem

Akarsh — Thu, 13 Aug 2026 14:35:06 +0000

System Design can feel overwhelming.

You hear words like Load Balancer, Redis, Kafka, Sharding, Replication, CDN, Microservices…

…and suddenly the question looks much harder than it actually is.

But here's the important part:

You don't need to know every technology to solve a System Design problem.

You need a repeatable way of thinking.

The Problem 🧩

Imagine you're asked:

"Design YouTube."

Your first instinct might be:

Use Microservices
Add Kafka
Use Redis
Add a Load Balancer
Use MongoDB
Add Kubernetes

But there's a problem.

You started choosing technologies before understanding the problem.

A good System Design discussion doesn't begin with:

"Which database should I use?"

It begins with:

"What exactly are we building?"

A Simple Approach 🚀

Whenever you get a System Design problem, follow this sequence:

1️⃣ Clarify Requirements

First understand what the system actually needs to do.

For example, for a URL Shortener:

Functional Requirements

User provides a long URL
System generates a short URL
Short URL redirects to the original URL

Non-Functional Requirements

High availability
Low latency
Scalability
Reliability

Don't immediately start drawing boxes.

Understand the requirements first.

2️⃣ Estimate Scale

Next, ask:

"How big does this system need to be?"

You don't always need exact numbers.

Rough estimates are enough to guide architectural decisions.

For example:

10 million users
1 million requests/day
100,000 requests/second during peak traffic
100 TB of stored data

Now the architecture starts becoming clearer.

A system handling 1,000 requests/day has very different requirements from one handling 1 million requests/second.

3️⃣ Identify the Core APIs

Now think about how clients interact with the system.

For a URL Shortener:

POST /shorten
GET /{shortCode}

For a social media application:

POST /posts
GET /feed
POST /follow

APIs help define the system's boundaries.

4️⃣ Design the High-Level Architecture

Now we can start drawing the major components.

A simple architecture might look like:

Client
   ↓
Load Balancer
   ↓
Application Servers
   ↓
Database

As the system grows, additional components might appear:

                    ┌──→ Redis
                    │
Client → Load Balancer → Application Servers → Database
                    │
                    └──→ Message Queue

The important thing isn't adding more boxes.

The important thing is understanding why each box exists.

Why This Matters 💡

System Design is less about memorizing architecture diagrams…

…and more about understanding trade-offs.

For example:

Why use a Cache?

Because repeatedly reading frequently accessed data from a database can increase latency and database load.

Client
  ↓
Application
  ↓
Cache → Data Found ✅
  ↓
Database → Cache Miss

Why use a Load Balancer?

Because sending every request to one server creates a bottleneck.

              ┌→ Server 1
Client → LB ──┼→ Server 2
              └→ Server 3

Why use a Message Queue?

Because some operations don't need to happen immediately.

For example:

User → Application
          ↓
       Message Queue
          ↓
     Background Worker
          ↓
      Send Email

This allows the main request to remain fast while background work happens separately.

Quick Tips ⚡

When solving System Design problems:

Start with requirements, not technologies.
Estimate scale before choosing architecture.
Keep the first design simple.
Introduce complexity only when there's a reason.
Think about bottlenecks.
Always discuss trade-offs.
Ask what happens when a component fails.
Think about how the system scales.

A simple architecture that you can explain is better than a complicated architecture that you cannot justify.

Common Mistake ❌

One of the biggest mistakes beginners make is:

Trying to use every technology they know.

They know Redis → add Redis.

They know Kafka → add Kafka.

They know Microservices → split everything into 20 services.

They know Kubernetes → deploy everything on Kubernetes.

But System Design isn't a technology checklist.

Every component should answer a question.

Why Redis?

Why Kafka?

Why SQL instead of NoSQL?

Why Microservices instead of a Modular Monolith?

If you can't explain the reason, you probably don't need it yet.

A Framework You Can Remember 🧠

For almost any System Design problem, remember:

R → S → A → D → B → T

R — Requirements

What are we building?

S — Scale

How many users, requests and data?

A — APIs

How will clients interact with the system?

D — Design

What are the major components?

B — Bottlenecks

What can become a problem at scale?

T — Trade-offs

Why did we choose this solution?

That's your basic System Design thinking framework.

System Design Isn’t Just About Drawing Boxes

Akarsh — Mon, 10 Aug 2026 11:40:12 +0000

You can write clean code.

You can build REST APIs.

You can work with SQL and backend.

But what happens when 10 users become 10 million users?

That's where System Design starts becoming important.

System Design is not about drawing a complicated architecture diagram.

It's about understanding how software should be designed to work reliably at scale.

The Problem

A simple application might look like this:

User
  ↓
Backend Server
  ↓
Database

For a small application, this can work perfectly.

But imagine:

1 million users
Thousands of requests per second
Large amounts of data
Users from different locations
Server failures
Database overload

Suddenly, our simple architecture starts having problems.

The real question becomes:

How do we design the system so it continues working as the number of users grows?

That's the core idea behind System Design.

A Simple Example

Imagine you're building a food delivery application.

Initially:

100 Users
    ↓
1 Server
    ↓
1 Database

Everything looks fine.

But the application becomes popular.

Now:

1 Million Users
       ↓
     ???
       ↓
   Database

One server may not be enough anymore.

The database may become overloaded.

Requests may become slow.

And if that single server crashes...

The entire application goes down.

System Design helps us solve these problems.

Why This Matters

When designing large systems, we usually care about a few important properties.

1. Scalability

Can the system handle more users and traffic?

For example:

1,000 users
     ↓
10,000 users
     ↓
1,000,000 users

A scalable system should be able to grow without requiring a complete redesign.

2. Availability

Is the system available when users need it?

If one server crashes, we don't want:

Server Down
     ↓
Application Down

Instead, we might have:

        Load Balancer
        /           \
   Server 1       Server 2
      ❌              ✅
                       ↓
                    Users

The system can continue serving requests.

3. Reliability

Does the system consistently perform its job correctly?

For example, in a payment system:

Payment Request
       ↓
   Processing
       ↓
    Success

We don't want the user to be charged twice because the system processed the same request twice.

Reliability is about designing systems that behave correctly even when things go wrong.

4. Latency

How long does it take to get a response?

For example:

Request → Server → Database → Response
              500 ms

Lower latency generally means a faster experience for users.

5. Throughput

How much work can the system handle in a given amount of time?

For example:

1000 requests / second

A system handling 10 requests per second has very different requirements from one handling 100,000 requests per second.

The Basic System Design Mindset

When you see a system design problem, don't immediately start drawing boxes.

Think about:

Users
  ↓
Requirements
  ↓
Traffic
  ↓
API
  ↓
Application
  ↓
Database
  ↓
Cache / Queue / Storage
  ↓
Monitoring

Each layer exists for a reason.

The goal isn't to add technologies just because they're popular.

The goal is to understand:

What problem does each component solve?

Quick Tips

If you're starting System Design, focus on these concepts first:

Scalability
Availability
Reliability
Latency
Throughput
Load Balancing
Caching
Database Scaling
Message Queues
API Design

These concepts will appear again and again in real-world architectures.

Common Mistake

One of the biggest mistakes beginners make is learning System Design as a collection of diagrams.

For example:

Load Balancer
Redis
Kafka
Database
CDN

and thinking:

"I know System Design now."

Not quite.

The important question is:

Why is Redis here?

Why do we need Kafka?

Why are there multiple servers?

Why can't we just use one database?

Understanding the reason behind each component is much more valuable than memorizing architectures.

Recommended Resource

A great way to learn System Design is to practice designing familiar applications.

Start with simple systems such as:

URL Shortener
Chat Application
Food Delivery Application
Notification System
E-Commerce Application

Try answering:

"What happens when the number of users becomes 100x larger?"

That question alone will teach you a lot about scalability.

Google I/O 2026 is Changing AI World

Akarsh — Thu, 28 May 2026 05:41:00 +0000

Every year, developers wait for Google I/O to see what the future of tech looks like.

But this year felt different.

Google didn’t just announce updates…
it showcased an entire AI ecosystem.

From Gemini upgrades to futuristic projects like Antigravity and Omni — here’s a simple breakdown of the biggest announcements from Google I/O 2026.

The Big Shift

Google is clearly moving towards:

An AI-first ecosystem
Multimodal computing
Assistant-driven workflows
Context-aware AI experiences

Almost every announcement had AI deeply integrated into it.

Gemini

Google upgraded Gemini heavily this year.

Key Features

Better reasoning
Faster responses
Improved coding support
More natural conversations
Strong multimodal capabilities

Gemini is now integrated into:

Android
Chrome
Gmail
Docs
Workspace
Google Search

It feels less like a chatbot now…
and more like a full AI operating layer.

Gemini Live

One of the coolest demos from the event.

Gemini Live can:

Understand voice naturally
See your screen/camera
Help in real time
Respond conversationally

This pushes AI assistants much closer to human-like interaction.

Project Astra

Project Astra looked futuristic.

It can:

Understand surroundings through camera
Remember context
Identify objects instantly
Answer live visual questions

This is probably one of the clearest looks at the future of wearable AI assistants.

Project Omni

Omni focused heavily on:

Multimodal AI
Seamless interaction
Combining text, visuals, audio, and actions

The idea is simple:

Instead of switching between apps…
AI becomes the interface itself.

Project Antigravity 2.0

Antigravity was one of the most talked-about concepts.

Google hinted at:

Futuristic interaction systems
Immersive AI experiences
Contextual computing
Spatial interfaces

Not fully consumer-ready yet…
but definitely exciting.

Android XR

Google is entering XR seriously again.

Android XR focuses on:

Smart glasses
Mixed reality
AI-assisted wearable experiences

The combination of Gemini + XR could become huge in the future.

AI Mode in Google Search

Google Search is changing fast.

Instead of only showing links:

AI summarizes answers
Gives contextual responses
Understands follow-up questions
Performs deeper reasoning

Search is slowly becoming an AI conversation engine.

Veo

Google’s AI video generation improved massively.

Veo can now:

Generate cinematic videos
Understand prompts better
Create smoother motion
Produce realistic scenes

AI video generation is evolving incredibly fast.

Imagen

Imagen received major upgrades too.

Highlights:

Better realism
Cleaner text rendering
Improved prompt understanding
More accurate image generation

The demos looked surprisingly strong.

NotebookLM

NotebookLM keeps becoming more useful for students and developers.

It now helps with:

Summarizing documents
Generating notes
Answering contextual questions
Understanding research faster

This could become a daily productivity tool for many people.

Why This Matters

The biggest takeaway from Google I/O 2026 wasn’t just new AI tools.

It was this:

AI is becoming the interface for everything.

Instead of manually using apps…
future systems may simply understand:

What you want
What you see
What you say
What you're working on

And then help automatically.

That shift is huge.

Quick Highlights

Gemini became deeply integrated everywhere
AI Search is evolving rapidly
XR + AI may become the next major platform
Real-time multimodal AI is getting practical
Google is pushing contextual computing heavily

Common Mistake

Many developers still think AI tools are only for generating code.

But the real shift is:

Workflows
Interfaces
Productivity systems
Human-computer interaction

The developers who adapt early will likely benefit the most.

Recommended Resource

This video explains the announcements really well:

https://youtu.be/3v5KAS1uuwE?si=ElP5h-3W0t4Rzr5Q

Final Thought

Google I/O 2026 felt less like a product launch…
and more like a preview of how computing itself may change.

We’re moving from:

“using software”

“collaborating with AI.”

And honestly…
that future looks closer than ever 🚀

How Communication boosts Your Confidence in Tech🚀

Akarsh — Tue, 26 May 2026 03:55:42 +0000

Many developers think confidence comes only from coding skills.
But in reality, communication is what helps your skills get noticed.

Whether you're:

Explaining a bug to your team
Presenting a project
Asking questions in meetings
Writing documentation

— strong communication makes you look more confident and professional.

A Simple Example 💡

Two developers solve the same problem.

Developer A writes great code but stays silent.
Developer B explains the approach clearly, shares updates, and helps teammates understand the solution.

Most teams naturally trust Developer B more.

That’s the power of communication in tech.

“The single biggest problem in communication is the illusion that it has taken place.”
— George Bernard Shaw

Communication Builds Confidence

The more you:

speak about your work,
ask questions,
write blogs,
collaborate with others,

the more confident you become.

Confidence is not “knowing everything.”
It’s being comfortable expressing your thoughts clearly.

“Simple can be harder than complex.”
— Steve Jobs

Quick Video Recommendation 🎥

https://www.youtube.com/embed/tt6kECewfIU

Final Thought ✨

In tech, your growth is not only about writing better code —
it’s also about explaining ideas, learning openly, and connecting with people.

Start small:

Share your learnings
Speak in discussions
Write technical posts
Help others

Your voice is part of your developer journey too.