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Amrishkhan Sheik Abdullah
Amrishkhan Sheik Abdullah

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The Myth Of Stateless Systems

#webdev #productivity #programming #javascript

One of the most common phrases in modern software architecture is:

Our service is stateless.

You'll hear it everywhere.

Cloud architecture discussions.

Microservices presentations.

System design interviews.

Kubernetes deployments.

Backend engineering meetings.

Somewhere along the way, "stateless" became synonymous with:

Scalable

Modern

Cloud Native

Good Architecture
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The problem is:

Most stateless systems aren't actually stateless.

They simply moved the state somewhere else.

And understanding that distinction can completely change how you design software.

What Does Stateless Actually Mean?

A truly stateless system does not retain information between requests.

Consider:

GET /health
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Request arrives.

Response is generated.

Request disappears.

No memory.

No history.

No persistence.

The system doesn't care whether you've called it:

1 Time

10 Times

1 Million Times
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Every request is independent.

This is the purest form of statelessness.

Why Developers Love Stateless Systems

Stateless systems are attractive because they're easy to scale.

Imagine:

Server A
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handling a request.

The next request can go to:

Server B
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or:

Server C
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because none of them need historical context.

Load balancing becomes simple.

Auto-scaling becomes simple.

Infrastructure becomes simpler.

At least on paper.

The Authentication Example

Let's examine one of the most common claims.

Suppose someone says:

We Use JWTs

Our API Is Stateless
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At first glance:

They're right.

The server doesn't store a session.

Everything needed is inside:

JWT Token
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Problem solved?

Not quite.

Consider:

User Gets Banned
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How do you invalidate the token?

Now suddenly:

Revocation Lists

Token Blacklists

User State

Permissions
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appear.

The state didn't disappear.

It moved.

Stateless Usually Means "State Elsewhere"

This is one of the most important observations in distributed systems.

When engineers say:

Stateless Service
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they often mean:

State Exists

But Not Inside This Service
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The state might live in:

  • PostgreSQL
  • Redis
  • Kafka
  • Browser Storage
  • JWT Tokens
  • External APIs

But it still exists.

And someone still has to manage it.

Databases Are State

This sounds obvious.

Yet it's easy to forget.

Imagine:

GET /users/123
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returns:

{
  "id": 123,
  "name": "John"
}
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Where did that information come from?

State.

Specifically:

Database State
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Your application may be stateless.

Your system is not.

Redis Is State

A common pattern:

API
↓
Redis Cache
↓
Database
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The API appears stateless.

Redis now contains:

Sessions

Locks

Rate Limits

Cached Data
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The state moved.

Nothing disappeared.

The Browser Is State

Frontend developers often hear:

Single Page Applications
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described as stateless clients.

Then we discover:

localStorage

sessionStorage

IndexedDB

Cookies

Memory
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The browser is carrying enormous amounts of state.

The application simply delegated responsibility.

Kubernetes Doesn't Remove State

Many developers associate containers with statelessness.

Container:

Stateless
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Database:

Stateful
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Seems straightforward.

Until:

Configuration

Secrets

Volumes

Persistent Storage

Service Discovery
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enter the picture.

Even containerized systems are surrounded by state.

The Shopping Cart Problem

Imagine an e-commerce platform.

Customer:

Adds Product To Cart
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Where is that cart?

Options:

Database

Redis

Browser

Session

Cookie
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Pick any location.

The moment a cart exists:

State Exists
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The question isn't whether the system has state.

The question is:

Where Does It Live?
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The Retry Connection

In the previous article we discussed retries.

Suppose:

Create Order
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times out.

Client retries.

How does the system know:

This Request Already Happened
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?

Because somewhere:

State Exists
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Without state:

Idempotency becomes impossible.

The Event Sourcing Perspective

Event Sourcing makes this explicit.

Instead of storing:

Current State
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it stores:

Events
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Example:

AccountCreated

MoneyDeposited

MoneyWithdrawn
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Current state is reconstructed from history.

The state still exists.

It's simply represented differently.

Stateless APIs Still Depend On State

A common architecture:

Load Balancer
↓
Stateless APIs
↓
Database
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People often focus on:

Stateless APIs
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The real challenge usually lives in:

Database Consistency
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because that's where the state actually resides.

Why State Is Hard

State creates:

Synchronization Problems

Caching Problems

Concurrency Problems

Consistency Problems

Time Problems
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Notice something.

Most articles in this series eventually return to state.

That's not an accident.

State is where complexity lives.

Real World Example: Flight Booking

Imagine:

Seat Available
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Customer begins booking.

Before payment completes:

Another Customer Reserves Same Seat
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Now the system must answer:

Who Owns The Seat?
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That's a state problem.

Not a networking problem.

Not a framework problem.

A state problem.

Real World Example: API Studio

Suppose we're building an API Studio.

Questions appear:

Which Workspace Is Active?

Which Environment Is Selected?

Which Git Branch Is Checked Out?

Which Request Was Last Opened?

Which Collection Is Cached?
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These are all forms of state.

The software's complexity comes from managing them correctly.

The Hidden Truth

Most architecture discussions focus on:

Frameworks

Containers

Cloud Platforms

Languages
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The real challenge is often:

State Management
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Because every system ultimately needs to answer:

What Is True Right Now?
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And that is fundamentally a state question.

Pros Of Stateless Services

1. Easier Horizontal Scaling

Requests can be distributed freely.

2. Simpler Deployments

No local state migration.

3. Better Fault Tolerance

Instances can be replaced easily.

4. Simpler Load Balancing

Any server can handle any request.

5. Better Cloud Compatibility

Works well with modern infrastructure.

Cons Of Chasing Statelessness

1. State Doesn't Disappear

It moves.

2. Complexity Moves Elsewhere

Usually databases or caches.

3. Debugging Becomes Harder

State is distributed.

4. Consistency Challenges Increase

Multiple systems now share responsibility.

5. Architecture Can Become Misleading

Teams may underestimate where complexity truly lives.

The Real Lesson

One of the most valuable questions in software engineering is:

Where Is The State?
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Whenever you encounter:

  • Retries
  • Authentication
  • Caching
  • Event Sourcing
  • Distributed Systems
  • Concurrency

ask that question.

Because state rarely disappears.

It simply changes location.

And many architectural mistakes happen when teams assume:

State Eliminated
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when reality is:

State Relocated
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The most successful engineers understand this difference.

Because there are very few stateless systems.

There are only systems that are honest about where the state lives.

What's Next?

In the next article we'll discuss:

Everything Is A Queue Eventually

Because after enough scale, enough traffic, and enough complexity, you'll discover a surprising truth:

Everything
Eventually
Waits In Line
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And software is no exception.

About The Author

Hi, I'm Amrish Khan.

I enjoy building developer tools, exploring software architecture, and writing about the deeper ideas behind everyday programming concepts.

I'm also building Aruvix — a growing ecosystem of local-first developer tools designed to process data directly in the browser without unnecessary uploads.

Here's a detailed blog on Aruvix:

https://dev.to/amrishkhan05/aruvix-the-ultimate-offline-first-developer-toolkit-e0i

You can follow my work and thoughts here:

Portfolio:
https://www.amrishkhan.dev

LinkedIn:
https://www.linkedin.com/in/amrishkhan

GitHub:
https://www.github.com/amrishkhan05

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