Another alternative is KMP (Knuth–Morris–Pratt).
The KMP algorithm searches for a length-m substring in a length-n string in worst-case O(n+m) time, compared to a worst-case of O(n⋅m) for the naive algorithm, so using KMP may be reasonable if you care about worst-case time complexity.
Here s a JavaScript implementation by Project Nayuki, taken from https://www.nayuki.io/res/knuth-morris-pratt-string-matching/kmp-string-matcher.js:
// Searches for the given pattern string in the given text string using the Knuth-Morris-Pratt string matching algorithm.
// If the pattern is found, this returns the index of the start of the earliest match in text . Otherwise -1 is returned.
function kmpSearch(pattern, text) {
if (pattern.length == 0)
return 0; // Immediate match
// Compute longest suffix-prefix table
var lsp = [0]; // Base case
for (var i = 1; i < pattern.length; i++) {
var j = lsp[i - 1]; // Start by assuming we re extending the previous LSP
while (j > 0 && pattern[i] !== pattern[j])
j = lsp[j - 1];
if (pattern[i] === pattern[j])
j++;
lsp.push(j);
}
// Walk through text string
var j = 0; // Number of chars matched in pattern
for (var i = 0; i < text.length; i++) {
while (j > 0 && text[i] != pattern[j])
j = lsp[j - 1]; // Fall back in the pattern
if (text[i] == pattern[j]) {
j++; // Next char matched, increment position
if (j == pattern.length)
return i - (j - 1);
}
}
return -1; // Not found
}
console.log(kmpSearch( ays , haystack ) != -1) // true
console.log(kmpSearch( asdf , haystack ) != -1) // false