# Big O Notation Intro

``````let course ="https://specs.udemy.com/course/js-algorithms-and-data-structures-masterclass/learn/lecture/11069998#overview"

/**
* @info Big O can be understood as a label or rating for the perfomrance of the code
* Useful for discussing trade-offs between different approaches
* When your code slows down or crashes, identifying parts of the code that are inefficient can help find pain points in our apps
* Allows us to talk formally about how the runtime of an algorithm grows as the inputs grow
* It's a way of describing the relationship between the input to a function or as it grows, and how that changes the runtime of that function, the relationship between the input size and then the time relative to that input.
*/

// Write a function that calculates the sum of all numbers from 1 up t some number n

let total = 0;
for (let i = 1; i<= n; i++){
total +=i
}
return `first addUpTo \${total}`
})(1)

//     return t* (t+1)/2
// })(3)

/**
* @info performance.now to show time elapsed since function run
* different machines will record different times
* The same machine will record different times
* fast algos - speed measurements may not be precise enough
*/

/**
* instead of the method above - count the number of operations the computer has to perform
* assess whether the input will change the performance (by using for loop there's an infine amount of possibilities dependent on the input for the number of operations )

*/``````