Forem: CodeWithIshwar

#The Most Expensive Line of Code Is the One You Didn't Delete

CodeWithIshwar — Mon, 25 May 2026 16:06:43 +0000

As developers, we're often praised for writing more code.

But some of the best engineering work I've seen involved writing less.

A few examples:

✅ Replacing 500 lines with 50 simpler lines

✅ Removing a feature nobody used

✅ Deleting duplicate business logic

✅ Eliminating an unnecessary database query

✅ Reusing an existing service instead of creating a new one

Every Line of Code Has a Cost

Every line you write adds:

Maintenance
Testing
Debugging
Documentation
Future complexity

The larger a codebase becomes, the more expensive unnecessary code gets.

The Productivity Trap

Many developers unconsciously measure productivity by:

Number of commits
Number of files changed
Lines of code written

But software engineering isn't a writing competition.

The goal is not:

❌ Write more code

The goal is:

✅ Solve problems with the simplest maintainable solution

Users don't care whether a feature required 50 lines or 5,000.

They care that it works.

Real Engineering Is Simplification

Some of the highest-impact improvements I've seen came from:

Removing dead code
Eliminating duplicate logic
Deleting unused features
Simplifying complex implementations
Reducing unnecessary dependencies

Less code often means:

✔️ Fewer bugs

✔️ Easier maintenance

✔️ Faster onboarding

✔️ Better readability

✔️ Lower technical debt

My Favorite Commit Message

Sometimes the most valuable commit you'll ever make is:


text
Removed 2,000 lines of code.

The Most Expensive Line of Code Is the One You Didn't Delete

CodeWithIshwar — Mon, 25 May 2026 16:05:06 +0000

Software engineers spend countless hours learning how to write better code.

But one of the most valuable engineering skills isn't writing code.

It's knowing when to remove it.

Over the years, I've seen teams spend weeks discussing architecture, design patterns, and optimization strategies while ignoring the simplest improvement available:

Deleting unnecessary code.

Why Every Line of Code Is a Liability

Every line you add becomes your responsibility.

Today it looks harmless.

A year later, it becomes:

Technical debt
Maintenance overhead
Additional test cases
Documentation burden
Debugging complexity

The code that solves a problem today may become the code that slows down development tomorrow.

That's why experienced engineers often view code differently:

Code is not an asset.

Code is a liability that happens to deliver value.

The less liability required to solve a problem, the better.

Example #1: Replacing 500 Lines with 50

Imagine a feature implemented through:

Multiple helper classes
Several abstraction layers
Complex conditional logic

After revisiting the requirements, the same behavior can be achieved with a much simpler implementation.

Before:

// Hundreds of lines spread across multiple classes

After:

public function process(array $items): array
{
    return array_filter($items, fn($item) => $item->isValid());
}

The functionality remains identical.

The maintenance burden drops dramatically.

Example #2: Removing Unused Features

Many systems contain features nobody uses anymore.

Yet teams continue:

Fixing bugs
Supporting edge cases
Updating dependencies
Writing regression tests

If a feature provides no business value, removing it creates immediate benefits:

✅ Smaller codebase

✅ Fewer bugs

✅ Faster development

✅ Easier onboarding

Sometimes deleting a feature delivers more value than building a new one.

Example #3: Eliminating Duplicate Logic

Consider this scenario:

public function calculateDiscount(float $price): float
{
    return $price * 0.10;
}

Months later:

public function getDiscount(float $price): float
{
    return $price * 0.15;
}

Both functions solve the same problem.

Now the system behaves inconsistently.

Removing duplication isn't just about cleaner code.

It's about preventing future bugs.

Example #4: The Fastest Query Is the One You Never Execute

Performance discussions often focus on:

Caching
Indexing
Database tuning
Distributed systems

But sometimes the best optimization is simply:

DELETE unnecessary_query;

Removing redundant work usually beats optimizing redundant work.

The Productivity Trap

One mistake many developers make is measuring productivity by output.

More commits.

More files.

More lines of code.

But software engineering isn't a writing contest.

The goal is not:

❌ Write more code.

The goal is:

✅ Solve problems effectively.

The best solution is often the simplest solution that meets the requirements.

My Favorite Commit Message

I've seen many impressive commit messages over the years.

But one remains my favorite:

Removed 2,000 lines of code.

No new framework.

No revolutionary architecture.

Just less complexity.

And often, that's exactly what a system needs.

Key Takeaways

Every line of code has a maintenance cost.
Simpler solutions are usually easier to evolve.
Unused features should be removed aggressively.
Duplicate logic creates future bugs.
The fastest code is often the code that doesn't run.
Great engineers simplify systems rather than constantly adding complexity.

Final Thoughts

The next time you're about to add another abstraction, helper class, or service layer, pause for a moment and ask:

Can I solve this by removing something instead?

Sometimes the most valuable code you'll write is the code you'll never have to maintain.

💬 What's the most useful piece of code you've ever deleted?

Tags: programming softwareengineering cleancode webdev

#CodeWithIshwar 🚀

We Don't Learn Software Engineering by Writing Code

CodeWithIshwar — Fri, 22 May 2026 16:10:27 +0000

Most of us start our journey believing that software engineering is about writing code.

Learn a programming language.
Learn a framework.
Build a project.

Repeat.

But after spending enough time working on real systems, I realized something surprising:

Writing code is only a small part of software engineering.

The real work begins after the code is written.

The Tutorial World vs The Real World

In tutorials:

Requirements are clear
Data is clean
APIs always work
Users behave as expected

In production:

Requirements change halfway through implementation
Data arrives in formats nobody anticipated
External services fail at the worst possible time
Users discover edge cases within minutes

The gap between these two worlds is where engineering happens.

The Lessons Nobody Teaches Explicitly

The most valuable lessons in my career didn't come from courses or documentation.

They came from situations like:

Debugging an issue that couldn't be reproduced locally
Tracing a performance bottleneck across multiple services
Reading code written years ago and trying to understand the original intent
Realizing a perfectly working solution couldn't scale
Discovering that the root cause wasn't the code, but an incorrect assumption

These experiences changed how I think about software development.

Instead of asking:

"How do I build this?"

I started asking:

How will this fail?
How will this scale?
How easy will this be to maintain?
What assumptions am I making?
What trade-offs am I accepting?

Experience Is Pattern Recognition

One thing I've noticed about experienced engineers:

They don't necessarily know every technology.

They've simply seen enough systems succeed and fail to recognize patterns.

A production outage today often resembles one they investigated years ago.

A performance issue reminds them of a previous bottleneck.

A design discussion echoes trade-offs they've already experienced.

Experience is not memorizing solutions.

It's recognizing patterns.

My Friday Learning

This week reinforced a lesson I keep relearning:

Software engineering isn't the art of writing code.

It's the art of understanding systems, assumptions, and trade-offs.

The code is visible.

The thinking behind it is where the real value lies.

What's one lesson that real-world projects taught you that tutorials never did?

programming #softwareengineering #backend #java #systemdesign #career #learning #webdev #opensource #codewithishwar

The Biggest Difference Between Junior and Senior Developers Isn't Coding

CodeWithIshwar — Fri, 22 May 2026 16:08:50 +0000

When I started programming, I thought becoming a better developer meant writing more code.

Learn Java.
Learn Spring Boot.
Learn Design Patterns.
Learn System Design.

Keep learning, and eventually you'll become a senior engineer.

At least, that's what I believed.

Over time, I realized something interesting:

The biggest difference between junior and senior developers isn't the amount of code they can write.

It's the questions they ask before writing it.

A junior developer might ask:

"How do I implement this feature?"

A senior developer is more likely to ask:

What happens if this service fails?
What are the edge cases?
How will this behave under load?
How difficult will this be to maintain?
What trade-offs are we making?

The implementation matters.

But the thinking behind the implementation matters even more.
A Lesson From Real Projects

Most tutorials present problems in a controlled environment.

Requirements are clear.

Data is clean.

Dependencies work.

Users behave exactly as expected.

Real projects are different.

Requirements change.

Data is messy.

Systems fail.

Users do things nobody anticipated.

That's why many developers feel confident after completing a course but overwhelmed when working on a real application.

The challenge isn't writing code.

The challenge is dealing with uncertainty.

What Actually Improved My Engineering Skills

The biggest improvements in my career didn't come from another tutorial.

They came from:

✅ Debugging production issues

✅ Reading existing codebases

✅ Refactoring poorly designed code

✅ Investigating performance bottlenecks

✅ Learning from mistakes

Each of these experiences forced me to think beyond syntax and frameworks.

They taught me how systems behave in the real world.

This Week's Learning

The more experience I gain, the more I believe:

Software engineering is not about knowing the right answers.

It's about asking the right questions.

The code is usually the easy part.

Understanding the problem is where the real work begins.

What lesson changed the way you think about software engineering?

webdev #java #backend #programming #softwareengineering #devops #career #beginners #discuss #codewithishwar

SOLID Principles - The Software Design Lesson Most Developers Learn Late

CodeWithIshwar — Wed, 20 May 2026 17:14:02 +0000

Most developers start by learning frameworks and syntax.

But as projects grow, one thing becomes clear:

Good software is not just about making features work.
It’s about building systems that remain maintainable over time.

That’s where SOLID Principles become essential 🚀

S — Single Responsibility Principle

One class should have one responsibility.

Avoid “God classes” that handle authentication, logging, database access, and business logic together.

O — Open/Closed Principle

Software should be open for extension but closed for modification.

The best systems allow new functionality without constantly changing stable code.

L — Liskov Substitution Principle

Child classes should correctly replace parent classes.

If replacing an object breaks functionality, the abstraction needs improvement.

I — Interface Segregation Principle

Keep interfaces focused and minimal.

Classes should never be forced to implement unnecessary methods.

D — Dependency Inversion Principle

Depend on abstractions, not concrete implementations.

This creates flexible, testable, and scalable architectures.

The biggest mindset shift SOLID introduces is this:

You stop writing code just for today.
You start designing systems that survive growth, scale, and future changes.

What’s your opinion on SOLID Principles — essential engineering practice or sometimes overused?

— @codewithishwar

codewithishwar #forem #SOLID #CleanCode #SoftwareEngineering #Programming #Java #BackendDevelopment #SystemDesign #Architecture #Developers #Coding #DesignPatterns #WebDevelopment #FullStackDeveloper

SOLID Principles Explained Simply

CodeWithIshwar — Wed, 20 May 2026 17:12:12 +0000

Nobody teaches this deeply in college, but every experienced developer eventually understands why it matters.

Clean architecture is not about writing “fancy” code.
It’s about building software that remains maintainable as complexity grows.

Here’s SOLID in the simplest way possible:

S — Single Responsibility Principle

One class should have one responsibility.

Avoid creating massive “God classes” that handle everything.

O — Open/Closed Principle

Software should be open for extension, closed for modification.

Add new features without constantly changing stable code.

L — Liskov Substitution Principle

Child classes should properly replace parent classes.

If a subclass breaks expected behavior, the design needs improvement.

I — Interface Segregation Principle

Keep interfaces small and focused.

Classes should not implement methods they never use.

D — Dependency Inversion Principle

Depend on abstractions, not concrete implementations.

This makes systems more scalable, testable, and flexible.

The real value of SOLID isn’t theory.
It’s reducing chaos when projects become large and teams start scaling.

What’s your opinion on SOLID Principles?
Essential engineering practice or sometimes overused?

— @codewithishwar

codewithishwar #SOLID #CleanCode #SoftwareEngineering #Programming #Java #BackendDevelopment #SystemDesign #Architecture #Developers #Coding #DesignPatterns #WebDevelopment #FullStackDeveloper

Synchronization in Node.js: Why Single-Threaded Does Not Mean Concurrency-Safe

CodeWithIshwar — Mon, 18 May 2026 16:42:44 +0000

One of the biggest misconceptions in backend engineering is:

“Node.js is single-threaded, so synchronization problems don’t exist.”

This is one of those statements that sounds correct until systems start scaling.

In reality, synchronization is still a major concern in Node.js applications.

Understanding the Node.js Execution Model

Node.js runs JavaScript on a single thread using the event loop.

This architecture provides:

Non-blocking I/O
High concurrency
Efficient request handling
Excellent scalability for network applications

Because JavaScript execution itself is single-threaded, many developers assume operations happen one at a time in a completely safe manner.

But that is not how modern backend systems behave.

Where Concurrency Actually Appears

Even though JavaScript execution uses a single thread, asynchronous operations allow multiple workflows to progress concurrently.

Examples:

Database queries
API calls
Cache operations
File system access
Background jobs
Message queue consumers

When multiple operations interact with the same shared resource, synchronization problems can occur.

Common Concurrency Problems

1. Race Conditions

Two operations read and modify the same data simultaneously, producing incorrect results.

Example:
Two payment requests updating the same wallet balance at the same time.

2. Lost Updates

One request overwrites another request’s changes because updates happen concurrently.

3. Duplicate Processing

In distributed systems, the same event may accidentally execute multiple times.

4. Cache Inconsistency

Concurrent updates may leave cache and database values out of sync.

Example Scenario

Imagine this workflow:

Current balance = ₹1000

Two requests arrive simultaneously:

Request A deducts ₹300
Request B deducts ₹500

If both requests:

Read the same balance
Modify independently
Save the result

…the final value may become incorrect.

This is a classic race condition.

The issue is not multiple JavaScript threads.

The issue is concurrent asynchronous workflows interacting with shared state.

How Synchronization Is Achieved in Node.js

Database Transactions

Transactions ensure operations execute atomically.

Common in:

Payment systems
Banking applications
Order processing systems

Atomic Operations

Modern databases provide atomic update capabilities.

Examples:

MongoDB $inc
PostgreSQL row locking
Optimistic concurrency control

These reduce race conditions significantly.

Redis Distributed Locks

Distributed systems often run across multiple servers.

Redis locks help ensure:

Only one worker processes a task
Duplicate execution is prevented

Very common in:

Job schedulers
Queue workers
Payment processing

Mutexes

Mutexes protect critical sections of code.

Only one async operation can access the protected resource at a time.

Message Queues

Queues serialize workloads and improve reliability under concurrency.

Important Insight

The biggest backend engineering challenges are rarely about writing APIs.

They are usually about:

Correctness under load
Data consistency
Distributed coordination
Concurrency handling
Reliability at scale

That is where synchronization becomes essential.

Final Takeaway

Node.js being single-threaded does NOT automatically make applications concurrency-safe.

As systems grow, synchronization becomes critical for:

Scalable backend systems
Real-time applications
Financial platforms
Distributed microservices
High-traffic APIs

Single-threaded ≠ free from concurrency problems.

NodeJS #JavaScript #BackendDevelopment #SystemDesign #Concurrency #DistributedSystems #Microservices #SoftwareEngineering #Programming #WebDevelopment #Tech #codewithishwar

Synchronization in Node.js: Why Single-Threaded Does Not Mean Safe From Concurrency Problems

CodeWithIshwar — Mon, 18 May 2026 16:41:53 +0000

One of the most common misconceptions about Node.js is:

“Node.js is single-threaded, so synchronization problems cannot happen.”

This is only partially true.

Yes, JavaScript execution in Node.js runs on a single thread using the event loop. But modern backend applications deal with asynchronous operations, external services, databases, queues, and distributed systems — all of which introduce concurrency challenges.

Understanding synchronization in Node.js is essential if you want to build scalable and reliable backend systems.

What Is Synchronization?

Synchronization is the process of controlling access to shared resources when multiple operations happen at the same time.

The goal is to prevent:

Race conditions
Data inconsistency
Duplicate processing
Lost updates
Unexpected behavior

Why Synchronization Still Matters in Node.js

Node.js applications commonly handle:

Multiple API requests simultaneously
Concurrent database updates
Shared cache access
Async file operations
Background job processing

Even though JavaScript itself runs on one thread, async operations can overlap in execution timing.

This creates situations where multiple operations interact with the same resource concurrently.

Example: Race Condition

Imagine a wallet service:

Initial balance = ₹1000

Two requests arrive:

Request A deducts ₹300
Request B deducts ₹500

If both requests:

Read the same balance
Update independently
Save the result

…the final balance may become incorrect.

This is called a race condition.

Common Synchronization Techniques in Node.js

1. Database Transactions

Transactions ensure operations execute safely as a single unit.

Useful for:

Payment systems
Banking workflows
Order processing

2. Atomic Operations

Databases provide atomic update mechanisms.

Examples:

MongoDB $inc
PostgreSQL row locking
Optimistic locking

These reduce concurrency conflicts.

3. Redis Distributed Locks

In distributed systems, Redis locks help ensure only one worker processes a task at a time.

Commonly used in:

Payment handling
Cron jobs
Distributed workers

4. Mutexes

Mutexes restrict access to critical sections of code.

Only one async operation can enter at a time.

5. Message Queues

Queues serialize workloads and reduce concurrency problems.

Popular tools:

BullMQ
RabbitMQ
Kafka

Important Takeaway

Single-threaded does NOT mean concurrency-safe.

As applications scale, synchronization becomes critical for:

High-traffic APIs
Financial systems
Real-time platforms
Distributed architectures

The real complexity in backend engineering often comes from handling concurrency correctly.

Conclusion

Node.js provides excellent performance through asynchronous and non-blocking architecture.

But scalable backend systems still require proper synchronization strategies to maintain correctness and reliability.

Understanding concurrency is what transforms developers into backend engineers capable of designing production-grade systems.

NodeJS #JavaScript #BackendDevelopment #SystemDesign #Concurrency #SoftwareEngineering #Programming #DistributedSystems #WebDevelopment #Tech #codewithishwar

Why Mercurial Still Matters in 2026 ?

CodeWithIshwar — Wed, 13 May 2026 17:39:06 +0000

Most developers today know Git.

But before Git became the industry standard, another Distributed Version Control System (DVCS) was competing closely with it:

Mercurial (Hg)

Interestingly, companies like Meta and Mozilla trusted Mercurial for large-scale engineering workflows involving massive repositories and thousands of developers.

That raises an interesting question:

If Git became the default standard, why did some elite engineering teams still choose Mercurial?

The answer reveals important lessons about:

developer experience
scalability
engineering workflows
ecosystem adoption
distributed systems thinking

What is Mercurial?

Mercurial is a Distributed Version Control System created in 2005.

Like Git, it allows developers to:

track code changes
collaborate across teams
create branches
merge changes
maintain project history

But Mercurial had a different philosophy:

prioritize simplicity and usability.

Why Developers Loved Mercurial

One thing many engineers appreciated about Mercurial was its cleaner workflow.

Example:


bash
hg status
hg commit
hg pull
hg update

Why Mercurial Still Matters in 2026

CodeWithIshwar — Wed, 13 May 2026 17:37:18 +0000

Why Mercurial Still Matters in 2026

Most developers today know Git.

But before Git became the industry standard, another Distributed Version Control System (DVCS) was competing closely with it:

Mercurial (Hg)

Interestingly, companies like Meta and Mozilla trusted Mercurial for large-scale engineering workflows involving massive repositories and thousands of developers.

This made me curious:

If Git became the default standard, why did some elite engineering teams still choose Mercurial?

The answer reveals important lessons about:

developer experience
scalability
engineering workflows
ecosystem adoption
distributed systems thinking

What is Mercurial?

Mercurial is a Distributed Version Control System created in 2005.

Like Git, it allows developers to:

track code changes
collaborate across teams
create branches
merge changes
maintain project history

But Mercurial had a different philosophy:

prioritize simplicity and usability.

Why Developers Loved Mercurial

One thing many engineers appreciated about Mercurial was its cleaner workflow.

Example:

hg status
hg commit
hg pull
hg update

Commands were:

easier to remember
more predictable
less confusing for beginners

Mercurial also separated actions clearly.
For example:

hg pull downloaded changes
hg update applied them locally

This explicit behavior reduced confusion for many teams.

Git vs Mercurial

Both Git and Mercurial are powerful DVCS tools.

But they focused on different priorities.

Git focused on:
flexibility
low-level control
performance
advanced workflows
Mercurial focused on:
simplicity
consistency
developer-friendly UX
predictable workflows

Many developers who used both systems felt Mercurial had a cleaner mental model.

Why Git Became the Standard

The biggest reason was ecosystem adoption.

Platforms like GitHub changed software collaboration forever.

Git quickly gained:

massive open-source support
enterprise adoption
CI/CD integrations
educational momentum
developer tooling ecosystem

Eventually Git became impossible to ignore.

This is an important engineering lesson:

sometimes ecosystem wins over elegance.

Why Meta Used Mercurial

One of the most interesting examples is Meta.

Meta scaled Mercurial internally for:

huge monorepositories
thousands of engineers
massive commit histories

This proved that Mercurial could scale extremely well in enterprise environments.

It also showed that engineering decisions at scale are based on:

workflow efficiency
scalability
operational reliability
developer productivity

—not simply popularity.

The Bigger Lesson

Mercurial teaches something valuable about software engineering.

The best tools are not always:

the loudest
the trendiest
the most widely adopted

Sometimes the best tools are the ones that:

reduce friction
simplify workflows
quietly scale behind the scenes

And those lessons still matter in 2026.

Final Thoughts

Even if you never use Mercurial professionally, learning about it helps developers better understand:

distributed systems
version control evolution
engineering tradeoffs
scalable developer workflows

Git won the ecosystem battle.

But Mercurial earned long-term respect from many engineers because of its simplicity and clean design.

What are your thoughts on Mercurial?

Did you ever use it in production?
Would you still choose it for certain large-scale systems today?

Mercurial #Git #SoftwareEngineering #BackendDevelopment #SystemDesign #DeveloperTools #OpenSource #Programming #DevOps #ScalableSystems #CodeWithIshwar

JavaScript Is Single-Threaded… So Why Do Race Conditions Exist....?

CodeWithIshwar — Mon, 11 May 2026 18:12:37 +0000

Just published a deep dive on one of the most misunderstood topics in JavaScript:

“JavaScript Is Single-Threaded… So Why Do Race Conditions Exist?”

Most developers assume:
single-threaded = no concurrency problems.

But modern JavaScript systems constantly deal with:

race conditions
stale state
async timing bugs
API overwrites
websocket ordering issues
synchronization failures

Because JavaScript concurrency is really about:
⚡ execution order
⚡ async scheduling
⚡ overlapping operations

—not multiple JavaScript threads.

The article explores:

Event Loop internals
Microtasks vs Macrotasks
Promise scheduling
async/await execution flow
Node.js Event Loop phases
libuv & thread pools
process.nextTick()
Worker Threads
real production synchronization bugs

One realization changed how I think about software engineering:

Large-scale systems fail more from coordination problems than syntax problems.

The deeper I study JavaScript internals,
the more I realize senior engineering is mostly about:

timing
scheduling
consistency
synchronization
execution flow

Would genuinely love feedback from other developers here.

What async JavaScript or Node.js concept took you the longest to truly understand?

JavaScript Is Single-Threaded… So Why Do Race Conditions Exist?

CodeWithIshwar — Mon, 11 May 2026 18:11:23 +0000

I just published one of the deepest articles I’ve written on JavaScript internals.

Topic:

“JavaScript Is Single-Threaded… So Why Do Race Conditions Exist?”

Most developers hear:

“JavaScript is single-threaded.”

Then naturally assume:

no concurrency
no synchronization issues
no race conditions

But modern JavaScript systems constantly suffer from:

stale React state
async timing bugs
API overwrites
websocket ordering issues
cache corruption
inconsistent backend state

Why?

Because JavaScript concurrency is not really about threads.

It’s about asynchronous operations overlapping across time.

And once you begin understanding:

Event Loop internals
Promise scheduling
Microtasks vs Macrotasks
async/await execution flow
Node.js Event Loop phases
libuv architecture
process.nextTick()
Worker Threads

JavaScript suddenly stops feeling “magical.”

It starts feeling predictable.

One realization changed how I think about engineering:

Senior software engineering is mostly about execution order and coordination.

Not syntax.

Not frameworks.

Coordination.

Scheduling.

Consistency.

Timing.

That’s the real challenge behind large-scale systems.

This article is a deep dive into:

synchronization
concurrency
async execution
Node.js internals
production race conditions
backend coordination patterns

If you enjoy low-level JavaScript internals and system behavior,
you’ll probably enjoy this one.

Would genuinely love feedback from other engineers.

What async JavaScript or Node.js concept took you the longest to fully understand?