cicconewk

const nemo = ["nemo"];

function findNemo(array) {
  let t0 = performance.now();
  for (let i = 0; i < array.lenght; i++) {
    if (array[i] === "nemo") {
      console.log("Found NEMO!");
    }
  }
  let t1 = performance.now();

cicconewk

Big Os

Check out the original source at ZeroToMastery

O(1) Constant – no loops. O(log N) Logarithmic – usually searching algorithms have log n if they are sorted (Binary Search). O(n) Linear – for loops, while loops through n items. O(n log(n)) Log Linear – usually sorting operations. O(n^2) Quadratic – every element in a collection needs to be compared to ever other element. Two nested loops. O(2^n) Exponential – recursive algorithms that solves a problem of size N. O(n!) Factorial – you are adding a loop for every element.

cicconewk

Schema.post('save', function (error, doc, next) {
  if (error.name === "MongoError" && error.code === 11000) {
    const result = {
      message: "ValidationError",
      errors: {},
    };

    Object.keys(this._doc).forEach(path => {
      const isDuplicate = error.errmsg.includes(`$${path}_1`);
      if (isDuplicate) {

cicconewk

function reverseStrings (strings_array) {
  if (!Array.isArray(strings_array)) {
    return -1
  }
  const reversed_strings = strings_array.map(str => str.split('').reverse().join(''))

  return reversed_strings
}

cicconewk

function reverseString (str) {
  return str.split('').reverse().join('')
}

function palindromeIndex (palindrome_str) {
  if (reverseString(palindrome_str) === palindrome_str) {
    return -2
  }

  const strlen = palindrome_str.length