**Big O notation** describes the performance or complexity of an algorithm. It shows the worst-case scenario and the execution time required or the storage space used by an algorithm too.

Hopefully, this article will help you understand the basic of Big O notation with JavaScript sample code.

## 🍮 `O(1)`

`O(1)`

shows the execution time(or space) of an algorithm is always same regardless of the size of the input data set.

function isFirstElementNull(elements) { |

## 🐹 `O(N)`

`O(N)`

illustrates an algorithm’s performance will grow linearly and in direct proportion to the size of the input data set. The example is as follows:

function findValue(elements, expect) { |

In Big O notation, we should always assume the upper limit where the algorithm will perform the maximum number of iterations. So, in that case, the worst case is `return false`

after finishing the loop.

## 🏀 `O(N**2)`

`O(N**2)`

(N squared) describes an algorithm whose performance is directly proportional to the square of the size of the input data set. This is common with algorithms that involve nested iterations. O Notation of deeper nested iteration will be `O(N**3)`

, `O(N**4)`

or etc.

function hasDuplicatedValue(elements) { |

## 🐝 `O(2**N)`

`O(2**N)`

represents an algorithm whose growth doubles with each addition to the input data set and it’s exponential.

function fibonacci(number) { |

## 🚌 `O(log N)`

Big O notation of **Binary search** like following example is `O(log N)`

.

function binarySearch(elements, expect) { |

`O(log N)`

algorithm like the binary search denotes produces a growth curve that peaks at the beginning and slowly flattens out as the size of the data sets increase. This makes algorithms extremely efficient when processing with large data sets.

## 😎 Special Thanks

I wrote this article referencing **A beginner’s guide to Big O notation - Rob Bell**.

In addition, I could understand it with the following articles:

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