How I Identify the Right DSA Pattern (Before Writing Any Code)

When people ask how to get better at DSA, the advice is usually some version of:

“Practice more problems.”

That’s not wrong.
It’s just incomplete.

What actually made the biggest difference for me was learning how to identify the pattern early often before I thought about code at all.

Once I got reasonably good at that, the rest of the problem usually unfolded on its own.

I don’t start with the problem statement

This might sound odd, but I don’t mentally engage with the whole story upfront.

Instead, I look for a few very specific signals:

• What kind of input am I given?
• What’s the constraint on time and space?
• What exactly is the output asking for?

Those three things eliminate most options very quickly.

Constraints do most of the work

If I see:

• “Sorted array”
• “Find a pair/subarray”
• “O(n) expected”

I’m already thinking two pointers or sliding window.

If the problem:

• asks for ranges
• mentions cumulative values
• wants counts or sums over subarrays

My brain immediately goes to prefix sums and hashing.

If the input size is large and the answer space is monotonic, I’m thinking binary search often not over the array, but over the answer itself.

None of this feels clever anymore.
It feels mechanical.

And that’s a good thing.

Keywords matter, but not the way people think

People often memorize keyword → pattern mappings.

That helps initially, but it breaks down.

What I pay more attention to is what must stay true as I move through the input.

That’s usually the invariant.

For example:

• Am I shrinking a search space?
• Am I maintaining a valid window?
• Am I accumulating something monotonically?
• Am I eliminating options as I go?

Once I can articulate that in a sentence, the pattern usually becomes obvious.

The moment of clarity usually comes early

There’s a specific feeling I’ve learned to trust.

If, within the first couple of minutes, I can say:
“This feels like X pattern because Y must remain true”

Then I know I’m on the right track even if I don’t yet know all the edge cases.

If I can’t say that, I slow down.

That’s usually a sign I’m trying to brute-force my way into insight instead of stepping back.

This is why coding comes last for me now

Earlier, I used to jump into code quickly.

That often led to:

• messy logic
• backtracking
• unclear explanations

Now, if I can’t explain the invariant in plain English, I don’t open the editor.

Once the pattern is clear:

• the loop structure is obvious
• pointer movement decisions make sense
• complexity is almost self-evident

The code becomes an implementation detail, not the core work.

This is what I’m trying to capture

This way of thinking constraints first, invariant second, code last is what I’m slowly trying to document more systematically.

Not as rules.
Not as templates.

Just as a way to make DSA feel less random and more… structured.

I’ve been indexing patterns and the signals that point to them in one place (indexedcode.com), mostly as a reference for myself. Writing about it here helps me refine that thinking.

What I’ll write about next

In the next post, I want to dig into why difficulty labels (easy/medium/hard) are often misleading, and why they sometimes do more harm than good when you’re learning.

If pattern recognition feels fuzzy right now, that’s normal.
It sharpens with repetition the right kind of repetition.

And once it clicks, you’ll notice you’re solving problems differently than before.

Slower at first.
Much faster in the long run.