September 4, 2026

Foundations of System Design: Learn It the Way I Wish Someone Taught Me

A practical, problem-driven introduction to system design. Start with one request and add concepts only when your system actually needs them.

#System Design#Architecture#Backend#Scalability#Distributed Systems

When I first started learning system design, I felt overwhelmed.

Load balancers. Caches. Message queues. Replication. Sharding. Distributed systems.

There are a lot of terms. And most guides throw them at you like a vocabulary test.

But here is the thing: I don't think the best way to learn system design is to memorize diagrams or vocabulary.

I prefer starting with something much simpler:

Start with one request. Then introduce a new concept only when the system actually needs it.

That is what we are going to do in this guide.

We will start with a very small application and gradually make it capable of handling more traffic and more failures. Every new concept we introduce will be because the system actually broke and needed it.

If you have ever felt like system design is "too advanced" or "not for you" yet, I promise you it is. You just need the right entry point.

Let's build it together.

What we will cover

What system design actually means (and why it is not just for senior engineers)
How a browser request travels to your server
Where the database fits and why relational databases are useful
Transactions and why they matter
Scaling: vertical and horizontal
Load balancers and how they distribute traffic
Health checks and detecting failed servers
Redundancy and why it matters
The architecture we built and the problems we have not solved yet
The mental model for approaching any system design problem

Let's start.

What is System Design?

In simple terms, system design is about deciding how the different parts of a software system should work together as the system grows.

When your application has 10 users, almost any reasonable architecture can work.

When it has 100,000 users, things start getting interesting.

Your server has limited CPU and memory.

Your database has limits.

Networks fail.

Servers crash.

Requests take longer than expected.

Two users can try to change the same data at the same time.

So system design is not just:

"How do I make this application work?"

It becomes:

"How do I make this application continue working as traffic, data, and failures increase?"

If that question excites you, you are in the right place.

A Request From the Browser

Imagine you have a frontend application running in the browser.

The browser needs a list of products.

It sends a request:

code

Browser
   |
   | GET /products
   ↓
api.example.com

But api.example.com is not the server's IP address.

It is a domain name.

The browser needs to find the IP address associated with that domain.

This is where DNS (Domain Name System) comes in.

Very roughly:

code

Browser
   |
   | "Where is api.example.com?"
   ↓
DNS
   |
   | "It is available at this IP"
   ↓
IP address
   |
   ↓
Server

DNS is basically the system that helps translate human-readable domain names into network addresses.

You do not need to remember the entire DNS process yet.

For now, just remember:

Domain name → DNS resolution → IP address → server

The Server Processes the Request

Now the request reaches our server.

The server receives:

code

GET /products

It needs to retrieve the products from the database.

So our system currently looks like this:

code

Client
   |
   | Request
   ↓
Server
   |
   | Query
   ↓
Database

The database returns the data:

code

Database
   |
   | Products
   ↓
Server
   |
   | JSON response
   ↓
Client

The complete flow is:

code

Client
   ↓
Server
   ↓
Database
   ↓
Server
   ↓
Client

At this point, everything looks simple.

And that is exactly what we want.

Do not introduce a load balancer, Redis, Kafka, Kubernetes and 15 other things when one server is perfectly capable of handling the application.

Where Does the Database Fit?

Let us say our application has these entities:

code

Users
Products
Orders
Accounts
Sessions

This data has structure.

A user has an ID, name and email.

A product has an ID, name and price.

An order belongs to a user and contains products.

This is where a relational database can be a very good fit.

Examples include:

PostgreSQL
MySQL
Oracle
SQLite

Relational databases organize data into tables, which contain rows and columns.

For example:

code

users
 
id | name | email
---|------|----------------
1  | Noor | noor@example.com
2  | Ali  | ali@example.com

And:

code

products
 
id | name       | price
---|------------|------
1  | Keyboard   | 100
2  | Mouse      | 50

The important thing is not memorizing the names of databases.

The important question is:

What kind of data and guarantees does my application need?

Why Relational Databases Are Useful

One major advantage of relational databases is that they let us model relationships between data.

Suppose Noor buys a keyboard.

We might have:

code

users
  ↓
orders
  ↓
order_items
  ↓
products

The database can represent those relationships explicitly.

We can then query information such as:

Which products did this user purchase?

This is where SQL and operations such as JOIN become useful.

For example, conceptually:

code

SELECT users.name, products.name
FROM users
JOIN orders ON orders.user_id = users.id
JOIN order_items ON order_items.order_id = orders.id
JOIN products ON products.id = order_items.product_id;

You do not need to memorize this query.

The important idea is that relational databases are very good when your data has clear relationships and you need strong consistency around those relationships.

Transactions

Now we have reached our first really important system-design problem.

Imagine a user places an order.

Two things need to happen:

code

1. Create the order
2. Deduct the user's balance

What happens if step 1 succeeds but step 2 fails?

We could end up with:

code

Order created
Money not deducted

That is an invalid state for many applications.

This is where transactions become important.

A transaction allows us to group multiple database operations into one logical operation.

Conceptually:

code

BEGIN TRANSACTION
 
Create order
Deduct balance
 
COMMIT

If something goes wrong:

code

BEGIN TRANSACTION
 
Create order
Deduct balance ← FAIL
 
ROLLBACK

The database can roll back the changes made by the transaction.

The simple mental model is:

Either the required operations succeed together, or the transaction does not commit their changes.

Transactions are built around the ACID properties:

code

A → Atomicity
C → Consistency
I → Isolation
D → Durability

You do not need to master ACID right now.

Just understand why transactions exist:

They help us keep related database operations in a valid state when something fails or when multiple operations happen concurrently.

Our First Bottleneck

So far we have:

code

                ┌──────────┐
Client ────────→│  Server  │
                └────┬─────┘
                     │
                     ↓
                ┌──────────┐
                │ Database │
                └──────────┘

Now imagine our application becomes popular.

We start getting:

code

10 requests/second

Then:

code

100 requests/second

Then:

code

1,000 requests/second

Our server has limited resources.

It has:

code

CPU
RAM
Network
Disk

At some point, one machine may no longer be enough.

Now we have to think about scaling.

This is where system design starts becoming interesting.

Vertical Scaling

The first thing we can do is make the existing server stronger.

Maybe we currently have:

code

4 CPU cores
8 GB RAM

We could upgrade it to:

code

16 CPU cores
64 GB RAM

This is called vertical scaling.

In simple terms:

Make the machine bigger.

It is easy to understand and often the simplest solution.

But it has limits.

Problem 1: Hardware has limits

You cannot infinitely increase the CPU and RAM of one machine.

Eventually you reach the limits of the available hardware or the cost becomes unreasonable.

Problem 2: Single point of failure

We still have one server.

If that server goes down:

code

Server crashes
   ↓
Application unavailable

This is a single point of failure.

A single point of failure is a component whose failure can bring down an important part of the system.

So we have solved one problem but created another question:

Can we run multiple servers?

Horizontal Scaling

Instead of making one server bigger, we can add more servers.

For example:

code

        Server 1
       /
Client ── Server 2
       \
        Server 3

This is called horizontal scaling.

Instead of:

code

1 very powerful server

we have:

code

3 servers

Now if one server fails, the other servers may still be able to handle requests.

But we have created a new problem.

How does the client know which server to talk to?

We do not want clients manually choosing:

code

server-1.example.com
server-2.example.com
server-3.example.com

We need something in front of them.

Load Balancer

This is where a load balancer comes in.

A load balancer receives incoming requests and decides which server should handle each request.

Our architecture becomes:

code

                    ┌──────────┐
                    │ Server 1 │
                   /
Client → Load Balancer → Server 2
                   \
                    └──────────┘
                         Server 3

The client does not need to know about all the application servers.

It talks to the load balancer.

The load balancer distributes traffic between available servers.

Now we have:

code

Client
   ↓
Load Balancer
   ↓
┌────────┬────────┬────────┐
│Server 1│Server 2│Server 3│
└────────┴────────┴────────┘

This gives us more capacity and can improve availability.

But now we need to decide:

How does the load balancer choose a server?

Load-Balancing Algorithms

There is not one universal algorithm.

Different algorithms work better for different workloads.

Round Robin

The load balancer sends requests in sequence:

code

Request 1 → Server 1
Request 2 → Server 2
Request 3 → Server 3
Request 4 → Server 1

Simple and useful when servers have similar capacity and requests have roughly similar cost.

Weighted Round Robin

Maybe our servers are not equally powerful.

code

Server 1 → weight 1
Server 2 → weight 2
Server 3 → weight 3

The stronger server receives more traffic.

Least Connections

The load balancer sends a new request to the server currently handling the fewest active connections.

code

Server 1 → 30 connections
Server 2 → 12 connections
Server 3 → 21 connections
 
New request → Server 2

This can be useful when requests have different durations.

IP Hash

The load balancer hashes the client's IP address and uses the result to choose a server.

Conceptually:

code

Client IP
   ↓
Hash
   ↓
Server selection

This can provide a form of request affinity, although relying on IP alone has limitations.

Random

The load balancer randomly chooses an available server.

Simple, but whether it is appropriate depends on the workload and implementation.

Least Response Time

The load balancer can consider response times and direct traffic toward servers that are responding faster.

This can be useful when server load is not evenly distributed.

Consistent Hashing

Consistent hashing is useful in systems where we want related keys to map predictably to servers while minimizing how much mapping changes when servers are added or removed.

It becomes especially useful in distributed caches and partitioned systems.

Do not worry if that sounds complicated.

We will come back to it when we actually need it.

But What If a Server Is Dead?

Imagine we have:

code

Server 1 → healthy
Server 2 → healthy
Server 3 → crashed

We do not want the load balancer to continue sending traffic to Server 3.

This is where health checks come in.

The load balancer periodically checks whether a server is healthy.

For example:

code

GET /health

The server might respond:

code

{
  "status": "ok"
}

If the server stops responding or reports that it is not ready, the load balancer can stop sending new traffic to it.

So now:

code

             Load Balancer
              /         \
             ↓           ↓
        Server 1      Server 2
        healthy       healthy
 
        Server 3
        unhealthy
           X

This is much better than blindly trusting that every server is alive.

If you want a ready-made health check implementation you can drop into an Express, Fastify or Hono app, I built one for Blockend. It handles liveness and readiness checks out of the box:

https://blockend.noorulhassan.com/docs/02-blocks/07-health-check

Health Checks Are More Than "Is the Process Running?"

There is an important distinction here.

A server process can be running while the application is not actually ready to serve traffic.

For example:

code

Node process → running
Database     → unavailable

The process itself is alive.

But the application might not be able to perform useful work.

This is why production systems often distinguish between things such as:

code

Liveness
Readiness

Liveness asks something like:

Is the application process alive?

Readiness asks:

Is the application ready to receive traffic?

This distinction becomes important when deploying applications, restarting services, and handling failures.

Redundancy

We have now introduced another important idea:

Redundancy.

Instead of depending on one component:

code

Server

we have:

code

Server 1
Server 2
Server 3

If one fails, others can continue serving traffic.

The same principle can apply to other parts of the system.

For example:

code

Multiple application servers
Multiple database replicas
Multiple cache nodes
Multiple availability zones

The goal is to avoid unnecessary single points of failure.

But redundancy creates another problem.

If we have three application servers, where is the user's session stored?

And if all three servers need the same data, how do they share it?

This is where our simple architecture starts becoming a distributed system.

The Architecture We Have Built

Let us stop here and look at how far we have come.

We started with:

code

Client → Server → Database

Then traffic increased.

We added vertical scaling.

Then we reached the limits of one machine.

We added horizontal scaling.

Then we needed something to distribute traffic.

We added a load balancer.

Then we needed to detect failed servers.

We added health checks.

Our architecture now looks roughly like:

code

                         ┌───────────┐
                         │ Database  │
                         └─────▲─────┘
                               │
                               │
                         ┌─────┴─────┐
                         │   Load    │
Client ─────────────────→│ Balancer  │
                         └─────┬─────┘
                         ┌─────┼─────┐
                         ↓     ↓     ↓
                      Server Server Server
                         1     2     3

Notice something important.

We did not start with all of these components.

We earned each component by encountering a problem.

That is how I think system design should be learned.

The Problems We Have Not Solved Yet

Our system is better, but we are not finished.

We still have several problems.

Database bottleneck

All servers are talking to one database.

What happens when database traffic becomes too high?

We might need:

indexes
query optimization
connection pooling
caching
read replicas
partitioning
sharding

But we should not add these just because they exist.

We add them when the problem requires them.

Repeated expensive requests

Suppose thousands of users request the same product:

code

GET /products/123

Every request goes:

code

Server → Database

Why query the database 10,000 times if the data does not change frequently?

This is where caching becomes useful.

Background work

Suppose creating an account also requires:

code

Send email
Generate report
Process image
Notify another service

Do we really want the HTTP request to wait for all of that?

Probably not.

This leads us toward:

code

Queues
Background workers
Asynchronous processing

Service failures

What happens if another service is temporarily unavailable?

Do we retry?

How many times?

How quickly?

What if retries make the problem worse?

This leads us toward:

code

Timeouts
Retries
Backoff
Circuit breakers
Idempotency

Data growth

What happens when our database grows from:

code

1 GB

to:

code

1 TB

Eventually we may need to think about:

code

Partitioning
Archiving
Replication
Sharding

And this is where system design starts becoming much deeper.

The Mental Model I Want You to Keep

If you are learning system design, do not try to memorize diagrams.

Instead, keep asking:

What is the bottleneck?

Then:

What failure can happen here?

Then:

What happens when traffic increases?

Then:

What happens when this component goes down?

Then:

What consistency does the application actually need?

Then:

What is the simplest solution that solves the problem?

That last question is important.

A system with:

code

Client
 ↓
Server
 ↓
PostgreSQL

is not a bad architecture simply because it does not have Kafka, Redis, Kubernetes and 20 microservices.

If that system handles the workload reliably, it is a good system.

Good system design is not about adding more components.

It is about making the right tradeoffs for the problem you actually have.

Where We Go From Here

We started with one request:

code

Client
   ↓
Server
   ↓
Database

Then we introduced concepts only when we needed them:

code

More traffic
   ↓
Scaling
   ↓
Horizontal scaling
   ↓
Load balancer
   ↓
Health checks
   ↓
Redundancy

And this is only the beginning.

From here, we can start introducing:

code

Caching
    ↓
Database scaling
    ↓
Read replicas
    ↓
Queues
    ↓
Background workers
    ↓
Retries and backoff
    ↓
Idempotency
    ↓
Consistency
    ↓
Replication
    ↓
Partitioning
    ↓
Sharding
    ↓
Distributed systems

We will introduce each one when there is a real problem that requires it.

Because that is the way I wish someone had taught me system design.

Not:

"Here are 50 components. Memorize them."

But:

"Here is a system. Let's see what breaks when we push it."

That is where system design actually starts.

What You Learned

Let us take a step back.

You started with a single browser request and ended up with:

A server that processes requests
A relational database that stores structured data
Transactions that keep related operations consistent
Vertical scaling to handle more load
Horizontal scaling to handle even more
A load balancer that distributes traffic across servers
Health checks that detect failed servers
Redundancy that avoids single points of failure

And you did not just memorize these concepts.

You encountered each problem and solved it with the right tool.

That is the mental model. That is what matters.

The next time someone says "you need a load balancer," you will not just know what it is.

You will know why it exists.

And that makes all the difference.

September 4, 2026

Foundations of System Design: Learn It the Way I Wish Someone Taught Me

A practical, problem-driven introduction to system design. Start with one request and add concepts only when your system actually needs them.

#System Design#Architecture#Backend#Scalability#Distributed Systems

When I first started learning system design, I felt overwhelmed.

Load balancers. Caches. Message queues. Replication. Sharding. Distributed systems.

There are a lot of terms. And most guides throw them at you like a vocabulary test.

But here is the thing: I don't think the best way to learn system design is to memorize diagrams or vocabulary.

I prefer starting with something much simpler:

Start with one request. Then introduce a new concept only when the system actually needs it.

That is what we are going to do in this guide.

If you have ever felt like system design is "too advanced" or "not for you" yet, I promise you it is. You just need the right entry point.

Let's build it together.

What we will cover

What system design actually means (and why it is not just for senior engineers)
How a browser request travels to your server
Where the database fits and why relational databases are useful
Transactions and why they matter
Scaling: vertical and horizontal
Load balancers and how they distribute traffic
Health checks and detecting failed servers
Redundancy and why it matters
The architecture we built and the problems we have not solved yet
The mental model for approaching any system design problem

Let's start.

What is System Design?

In simple terms, system design is about deciding how the different parts of a software system should work together as the system grows.

When your application has 10 users, almost any reasonable architecture can work.

When it has 100,000 users, things start getting interesting.

Your server has limited CPU and memory.

Your database has limits.

Networks fail.

Servers crash.

Requests take longer than expected.

Two users can try to change the same data at the same time.

So system design is not just:

"How do I make this application work?"

It becomes:

"How do I make this application continue working as traffic, data, and failures increase?"

If that question excites you, you are in the right place.

A Request From the Browser

Imagine you have a frontend application running in the browser.

The browser needs a list of products.

It sends a request:

code

Browser
   |
   | GET /products
   ↓
api.example.com

But api.example.com is not the server's IP address.

It is a domain name.

The browser needs to find the IP address associated with that domain.

This is where DNS (Domain Name System) comes in.

Very roughly:

code

Browser
   |
   | "Where is api.example.com?"
   ↓
DNS
   |
   | "It is available at this IP"
   ↓
IP address
   |
   ↓
Server

DNS is basically the system that helps translate human-readable domain names into network addresses.

You do not need to remember the entire DNS process yet.

For now, just remember:

Domain name → DNS resolution → IP address → server

The Server Processes the Request

Now the request reaches our server.

The server receives:

code

GET /products

It needs to retrieve the products from the database.

So our system currently looks like this:

code

Client
   |
   | Request
   ↓
Server
   |
   | Query
   ↓
Database

The database returns the data:

code

Database
   |
   | Products
   ↓
Server
   |
   | JSON response
   ↓
Client

The complete flow is:

code

Client
   ↓
Server
   ↓
Database
   ↓
Server
   ↓
Client

At this point, everything looks simple.

And that is exactly what we want.

Do not introduce a load balancer, Redis, Kafka, Kubernetes and 15 other things when one server is perfectly capable of handling the application.

Where Does the Database Fit?

Let us say our application has these entities:

code

Users
Products
Orders
Accounts
Sessions

This data has structure.

A user has an ID, name and email.

A product has an ID, name and price.

An order belongs to a user and contains products.

This is where a relational database can be a very good fit.

Examples include:

PostgreSQL
MySQL
Oracle
SQLite

Relational databases organize data into tables, which contain rows and columns.

For example:

code

users
 
id | name | email
---|------|----------------
1  | Noor | noor@example.com
2  | Ali  | ali@example.com

And:

code

products
 
id | name       | price
---|------------|------
1  | Keyboard   | 100
2  | Mouse      | 50

The important thing is not memorizing the names of databases.

The important question is:

What kind of data and guarantees does my application need?

Why Relational Databases Are Useful

One major advantage of relational databases is that they let us model relationships between data.

Suppose Noor buys a keyboard.

We might have:

code

users
  ↓
orders
  ↓
order_items
  ↓
products

The database can represent those relationships explicitly.

We can then query information such as:

Which products did this user purchase?

This is where SQL and operations such as JOIN become useful.

For example, conceptually:

code

SELECT users.name, products.name
FROM users
JOIN orders ON orders.user_id = users.id
JOIN order_items ON order_items.order_id = orders.id
JOIN products ON products.id = order_items.product_id;

You do not need to memorize this query.

The important idea is that relational databases are very good when your data has clear relationships and you need strong consistency around those relationships.

Transactions

Now we have reached our first really important system-design problem.

Imagine a user places an order.

Two things need to happen:

code

1. Create the order
2. Deduct the user's balance

What happens if step 1 succeeds but step 2 fails?

We could end up with:

code

Order created
Money not deducted

That is an invalid state for many applications.

This is where transactions become important.

A transaction allows us to group multiple database operations into one logical operation.

Conceptually:

code

BEGIN TRANSACTION
 
Create order
Deduct balance
 
COMMIT

If something goes wrong:

code

BEGIN TRANSACTION
 
Create order
Deduct balance ← FAIL
 
ROLLBACK

The database can roll back the changes made by the transaction.

The simple mental model is:

Either the required operations succeed together, or the transaction does not commit their changes.

Transactions are built around the ACID properties:

code

A → Atomicity
C → Consistency
I → Isolation
D → Durability

You do not need to master ACID right now.

Just understand why transactions exist:

They help us keep related database operations in a valid state when something fails or when multiple operations happen concurrently.

Our First Bottleneck

So far we have:

code

                ┌──────────┐
Client ────────→│  Server  │
                └────┬─────┘
                     │
                     ↓
                ┌──────────┐
                │ Database │
                └──────────┘

Now imagine our application becomes popular.

We start getting:

code

10 requests/second

Then:

code

100 requests/second

Then:

code

1,000 requests/second

Our server has limited resources.

It has:

code

CPU
RAM
Network
Disk

At some point, one machine may no longer be enough.

Now we have to think about scaling.

This is where system design starts becoming interesting.

Vertical Scaling

The first thing we can do is make the existing server stronger.

Maybe we currently have:

code

4 CPU cores
8 GB RAM

We could upgrade it to:

code

16 CPU cores
64 GB RAM

This is called vertical scaling.

In simple terms:

Make the machine bigger.

It is easy to understand and often the simplest solution.

But it has limits.

Problem 1: Hardware has limits

You cannot infinitely increase the CPU and RAM of one machine.

Eventually you reach the limits of the available hardware or the cost becomes unreasonable.

Problem 2: Single point of failure

We still have one server.

If that server goes down:

code

Server crashes
   ↓
Application unavailable

This is a single point of failure.

A single point of failure is a component whose failure can bring down an important part of the system.

So we have solved one problem but created another question:

Can we run multiple servers?

Horizontal Scaling

Instead of making one server bigger, we can add more servers.

For example:

code

        Server 1
       /
Client ── Server 2
       \
        Server 3

This is called horizontal scaling.

Instead of:

code

1 very powerful server

we have:

code

3 servers

Now if one server fails, the other servers may still be able to handle requests.

But we have created a new problem.

How does the client know which server to talk to?

We do not want clients manually choosing:

code

server-1.example.com
server-2.example.com
server-3.example.com

We need something in front of them.

Load Balancer

This is where a load balancer comes in.

A load balancer receives incoming requests and decides which server should handle each request.

Our architecture becomes:

code

                    ┌──────────┐
                    │ Server 1 │
                   /
Client → Load Balancer → Server 2
                   \
                    └──────────┘
                         Server 3

The client does not need to know about all the application servers.

It talks to the load balancer.

The load balancer distributes traffic between available servers.

Now we have:

code

Client
   ↓
Load Balancer
   ↓
┌────────┬────────┬────────┐
│Server 1│Server 2│Server 3│
└────────┴────────┴────────┘

This gives us more capacity and can improve availability.

But now we need to decide:

How does the load balancer choose a server?

Load-Balancing Algorithms

There is not one universal algorithm.

Different algorithms work better for different workloads.

Round Robin

The load balancer sends requests in sequence:

code

Request 1 → Server 1
Request 2 → Server 2
Request 3 → Server 3
Request 4 → Server 1

Simple and useful when servers have similar capacity and requests have roughly similar cost.

Weighted Round Robin

Maybe our servers are not equally powerful.

code

Server 1 → weight 1
Server 2 → weight 2
Server 3 → weight 3

The stronger server receives more traffic.

Least Connections

The load balancer sends a new request to the server currently handling the fewest active connections.

code

Server 1 → 30 connections
Server 2 → 12 connections
Server 3 → 21 connections
 
New request → Server 2

This can be useful when requests have different durations.

IP Hash

The load balancer hashes the client's IP address and uses the result to choose a server.

Conceptually:

code

Client IP
   ↓
Hash
   ↓
Server selection

This can provide a form of request affinity, although relying on IP alone has limitations.

Random

The load balancer randomly chooses an available server.

Simple, but whether it is appropriate depends on the workload and implementation.

Least Response Time

The load balancer can consider response times and direct traffic toward servers that are responding faster.

This can be useful when server load is not evenly distributed.

Consistent Hashing

Consistent hashing is useful in systems where we want related keys to map predictably to servers while minimizing how much mapping changes when servers are added or removed.

It becomes especially useful in distributed caches and partitioned systems.

Do not worry if that sounds complicated.

We will come back to it when we actually need it.

But What If a Server Is Dead?

Imagine we have:

code

Server 1 → healthy
Server 2 → healthy
Server 3 → crashed

We do not want the load balancer to continue sending traffic to Server 3.

This is where health checks come in.

The load balancer periodically checks whether a server is healthy.

For example:

code

GET /health

The server might respond:

code

{
  "status": "ok"
}

If the server stops responding or reports that it is not ready, the load balancer can stop sending new traffic to it.

So now:

code

             Load Balancer
              /         \
             ↓           ↓
        Server 1      Server 2
        healthy       healthy
 
        Server 3
        unhealthy
           X

This is much better than blindly trusting that every server is alive.

If you want a ready-made health check implementation you can drop into an Express, Fastify or Hono app, I built one for Blockend. It handles liveness and readiness checks out of the box:

https://blockend.noorulhassan.com/docs/02-blocks/07-health-check

Health Checks Are More Than "Is the Process Running?"

There is an important distinction here.

A server process can be running while the application is not actually ready to serve traffic.

For example:

code

Node process → running
Database     → unavailable

The process itself is alive.

But the application might not be able to perform useful work.

This is why production systems often distinguish between things such as:

code

Liveness
Readiness

Liveness asks something like:

Is the application process alive?

Readiness asks:

Is the application ready to receive traffic?

This distinction becomes important when deploying applications, restarting services, and handling failures.

Redundancy

We have now introduced another important idea:

Redundancy.

Instead of depending on one component:

code

Server

we have:

code

Server 1
Server 2
Server 3

If one fails, others can continue serving traffic.

The same principle can apply to other parts of the system.

For example:

code

Multiple application servers
Multiple database replicas
Multiple cache nodes
Multiple availability zones

The goal is to avoid unnecessary single points of failure.

But redundancy creates another problem.

If we have three application servers, where is the user's session stored?

And if all three servers need the same data, how do they share it?

This is where our simple architecture starts becoming a distributed system.

The Architecture We Have Built

Let us stop here and look at how far we have come.

We started with:

code

Client → Server → Database

Then traffic increased.

We added vertical scaling.

Then we reached the limits of one machine.

We added horizontal scaling.

Then we needed something to distribute traffic.

We added a load balancer.

Then we needed to detect failed servers.

We added health checks.

Our architecture now looks roughly like:

code

                         ┌───────────┐
                         │ Database  │
                         └─────▲─────┘
                               │
                               │
                         ┌─────┴─────┐
                         │   Load    │
Client ─────────────────→│ Balancer  │
                         └─────┬─────┘
                         ┌─────┼─────┐
                         ↓     ↓     ↓
                      Server Server Server
                         1     2     3

Notice something important.

We did not start with all of these components.

We earned each component by encountering a problem.

That is how I think system design should be learned.

The Problems We Have Not Solved Yet

Our system is better, but we are not finished.

We still have several problems.

Database bottleneck

All servers are talking to one database.

What happens when database traffic becomes too high?

We might need:

indexes
query optimization
connection pooling
caching
read replicas
partitioning
sharding

But we should not add these just because they exist.

We add them when the problem requires them.

Repeated expensive requests

Suppose thousands of users request the same product:

code

GET /products/123

Every request goes:

code

Server → Database

Why query the database 10,000 times if the data does not change frequently?

This is where caching becomes useful.

Background work

Suppose creating an account also requires:

code

Send email
Generate report
Process image
Notify another service

Do we really want the HTTP request to wait for all of that?

Probably not.

This leads us toward:

code

Queues
Background workers
Asynchronous processing

Service failures

What happens if another service is temporarily unavailable?

Do we retry?

How many times?

How quickly?

What if retries make the problem worse?

This leads us toward:

code

Timeouts
Retries
Backoff
Circuit breakers
Idempotency

Data growth

What happens when our database grows from:

code

1 GB

to:

code

1 TB

Eventually we may need to think about:

code

Partitioning
Archiving
Replication
Sharding

And this is where system design starts becoming much deeper.

The Mental Model I Want You to Keep

If you are learning system design, do not try to memorize diagrams.

Instead, keep asking:

What is the bottleneck?

Then:

What failure can happen here?

Then:

What happens when traffic increases?

Then:

What happens when this component goes down?

Then:

What consistency does the application actually need?

Then:

What is the simplest solution that solves the problem?

That last question is important.

A system with:

code

Client
 ↓
Server
 ↓
PostgreSQL

is not a bad architecture simply because it does not have Kafka, Redis, Kubernetes and 20 microservices.

If that system handles the workload reliably, it is a good system.

Good system design is not about adding more components.

It is about making the right tradeoffs for the problem you actually have.

Where We Go From Here

We started with one request:

code

Client
   ↓
Server
   ↓
Database

Then we introduced concepts only when we needed them:

code

More traffic
   ↓
Scaling
   ↓
Horizontal scaling
   ↓
Load balancer
   ↓
Health checks
   ↓
Redundancy

And this is only the beginning.

From here, we can start introducing:

code

Caching
    ↓
Database scaling
    ↓
Read replicas
    ↓
Queues
    ↓
Background workers
    ↓
Retries and backoff
    ↓
Idempotency
    ↓
Consistency
    ↓
Replication
    ↓
Partitioning
    ↓
Sharding
    ↓
Distributed systems

We will introduce each one when there is a real problem that requires it.

Because that is the way I wish someone had taught me system design.

Not:

"Here are 50 components. Memorize them."

But:

"Here is a system. Let's see what breaks when we push it."

That is where system design actually starts.

What You Learned

Let us take a step back.

You started with a single browser request and ended up with:

A server that processes requests
A relational database that stores structured data
Transactions that keep related operations consistent
Vertical scaling to handle more load
Horizontal scaling to handle even more
A load balancer that distributes traffic across servers
Health checks that detect failed servers
Redundancy that avoids single points of failure

And you did not just memorize these concepts.

You encountered each problem and solved it with the right tool.

That is the mental model. That is what matters.

The next time someone says "you need a load balancer," you will not just know what it is.

You will know why it exists.

And that makes all the difference.