Big O notation

𝐅𝐢𝐫𝐬𝐭𝐥𝐲, 𝐰𝐡𝐚𝐭 𝐢𝐬 𝐁𝐢𝐠 𝐎 𝐍𝐨𝐭𝐚𝐭𝐢𝐨𝐧?

Big O describes an algorithm's runtime or memory consumption without the interference of contextual variables like RAM and CPU.

It gives programmers a way to compare algorithms and identify the most efficient solution.

———

𝐁𝐢𝐠 𝐎 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 𝐨𝐧𝐞 𝐬𝐭𝐫𝐚𝐢𝐠𝐡𝐭𝐟𝐨𝐫𝐰𝐚𝐫𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧:

"How much does runtime or memory consumption grow as the size of the input increases, in the worst-case scenario?".

———

𝐓𝐨 𝐮𝐭𝐢𝐥𝐢𝐳𝐞 𝐁𝐢𝐠 𝐎, you'll need to know the possible values and how they compare with each other.

Use the attached photo for a quick reference.

𝐒𝐨, 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐩𝐩𝐥𝐲 𝐁𝐢𝐠 𝐎 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬?

Here are a few scenarios where Big O can be used:

🔸 Live coding challenges 🔸 Code walk-throughs 🔸 Discussions about projects/solutions you've built 🔸 Discussions about your approach to programming & problem-solving

When any of these scenarios come up, be sure to mention the Big O of your solution and how it compares to alternative approaches.

This is especially useful in live coding challenges where you have to compare solutions on the spot — remember to think out loud!

———

𝐓𝐢𝐩: 𝐖𝐡𝐞𝐧 𝐜𝐨𝐦𝐩𝐚𝐫𝐢𝐧𝐠 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬, 𝐩𝐚𝐲 𝐚𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦’𝐬 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬.

For example, linear complexity may be completely fine when the input can never be too large.

But if you’re dealing with big data, you’ll want to opt for something more efficient.

Of course, the goal is to get the correct Big O notation that applies to your solution.

But don't worry about getting it wrong!

Your interviewer will probably correct you when you do.

The point is to show that you are thinking about the efficiency and performance of your solution.

𝐃𝐨 𝐭𝐡𝐢𝐬 𝐚𝐧𝐝 𝐲𝐨𝐮'𝐥𝐥 𝐛𝐞 𝐚𝐛𝐥𝐞 𝐭𝐨 𝐬𝐡𝐨𝐰𝐜𝐚𝐬𝐞 𝐚𝐧 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐭𝐫𝐚𝐢𝐭 𝐭𝐡𝐚𝐭 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐡𝐢𝐫𝐢𝐧𝐠 𝐦𝐚𝐧𝐚𝐠𝐞𝐫𝐬 𝐥𝐨𝐨𝐤 𝐟𝐨𝐫:

The ability to consider a solution's viability beyond whether it works or not.

This shows maturity in your decision-making and approach to programming.