From Working Code to Efficient Code: Big O in Practice with C#
Many times, we write code that "works" and solves the immediate problem. But is it efficient?
Efficiency is not just about running fast: it involves scalability, memory usage, and how the system behaves as the amount of data grows. That's where Big O comes in. A way to understand how an algorithm's performance changes as input size increases.
Understanding the Basics of Big O
Big O describes the complexity of an algorithm in relation to the input size n.
Some classic examples:
O(1) - Constant: execution time doesn't change with input size. Example: accessing an array element by index.
O(n) - Linear: time grows proportionally with input size. Example: iterating through a list.
O(n²) - Quadratic: grows with the square of the input size. Example: nested loops.
O(log n) - Logarithmic: grows much slower than O(n). Example: binary search on a sorted array.
O(n log n) - Linearithmic: common in efficient sorting algorithms, like MergeSort or QuickSort.
The goal isn't to measure milliseconds but to understand growth trends: will your algorithm keep performing well, or will it break down as your data grows?
The Price of Poorly Optimized Software
When we talk about optimization, many people think it's just about "making the system faster." But the impact is much bigger: poorly optimized software brings financial, technical, and even reputational costs.
Direct Financial Costs
More infrastructure: inefficient code demands extra CPU, memory, and servers.
Higher cloud bills: in Azure or AWS, every extra second of execution has a price.
Expensive scalability: an O(n²) algorithm may force a company to scale sooner than expected.
Indirect Costs That Slow Teams Down
Blocked development: the team wastes time fighting performance bottlenecks instead of delivering new features.
Growing complexity: optimizing too late often means risky, large refactors.
Rework: sometimes the only option is rewriting entire modules. Always more expensive than planning right from the start.
User and Business Impact
Bad experience: slow screens kill user satisfaction.
Customer loss: in e-commerce, even a few extra seconds of load time can reduce conversions.
Reputation: sluggish software signals lack of quality.
Example
Imagine a daily report that takes 30 minutes to run because it uses List.Contains (O(n)) across millions of records.
By switching to HashSet.Contains (O(1)), execution time drops to 30 seconds.
Less waiting, lower CPU usage, and reduced infrastructure costs.
How to Apply Big O in Legacy Systems
With legacy systems, you can't just rewrite everything from scratch. The key is to be surgical: find where performance really matters and focus there.
Do I Need to Analyze the Entire Codebase?
Definitely not.
Reviewing every single line is impractical. The better approach is evidence-driven: measure, identify real bottlenecks, then analyze complexity.
And here's where the Pareto Principle (80/20) comes in:
👉 Usually, 80% of execution time is spent in just 20% of the code.
In other words, start with the critical parts, the ones that really move the needle.
New Projects: Avoiding Performance Traps
Starting a project from scratch gives you the advantage of planning properly from day one. That doesn't mean over-engineering early, but being aware of potential risks.
Preventive Mindset from the Architecture Onward
Think about scalability early: today it's 100 records, tomorrow it could be millions.
Question your data structures: need key-value lookups? Use Dictionary. Need fast lookups? Use HashSet.
Don't overcomplicate too soon: keep it simple, but avoid obvious pitfalls like quadratic loops in contexts that can grow.
Best Practices to Start Right
Use LINQ carefully: long chains can cause multiple hidden iterations.
Pick the right structures: HashSet for fast lookups, SortedDictionary when you need automatic ordering, and so on.
Database matters: often the bottleneck isn't in C#, but in unindexed or unpaginated SQL queries.
Measure early: logging execution times, memory usage, and simple metrics helps avoid surprises in production.
Refactoring Smartly: Organizing the Team and Using the Right Tools
Refactoring and optimization aren't just technical work. they require team organization and the right tooling.
Identify and Prioritize the Real Bottlenecks
Slow reports, screens that lag, nightly jobs that never finish.
Collect data: profiling, logs, and user feedback.
Prioritize by impact: sometimes an O(n²) method isn't the problem, an O(n) method running over millions of records can be far worse.
Free and Accessible Tools That Make a Difference
Visual Studio Diagnostic Tools (Free) - included in Community Edition.
PerfView (Free) - open source from Microsoft itself.
dotTrace (JetBrains) - paid, but free for open source.
Roslyn Analyzers (Free) - flags inefficient patterns in code.
SonarQube Community (Free) - highlights code smells and complexity.
BenchmarkDotNet (Free) - perfect for comparing implementations.
These tools help replace guesswork with hard data.
Turn Bottlenecks Into a Performance Backlog
Each identified issue should become a backlog item with description, evidence, and a proposed fix. That way, the team works transparently and systematically.
Refactor in Small, Measurable Steps
No "big bang" rewrites. Optimize, measure, validate. Small gains accumulate safely.
Involve the Entire Team (It's Not Just the Dev's Job)
Developers refactor.
QA ensures nothing breaks.
DevOps monitors production.
Product Owners help prioritize.
Establish a Continuous Performance Cycle
Make performance part of the Definition of Done.
Monitor actively.
Create internal guidelines so bad practices don't creep back in.
A piece of code that "just works" may be costing you more than you think - in infrastructure, team time, and user experience.
Big O isn't an academic detail; it's a mindset that helps you make smarter decisions in both legacy and new projects.
In the end, the key is simple: measure, prioritize, and optimize what truly matters.
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