List of Big-O for PHP functions

Since it doesn't seem like anyone has done this before I thought it'd be good idea to have it for reference somewhere. I've gone though and either via benchmark or code-skimming to characterize the array_* functions. I've tried to put the more interesting Big-O near the top. This list is not complete.

Note: All the Big-O where calculated assuming a hash lookup is O(1) even though it's really O(n). The coefficient of the n is so low, the ram overhead of storing a large enough array would hurt you before the characteristics of lookup Big-O would start taking effect. For example the difference between a call to array_key_exists at N=1 and N=1,000,000 is ~50% time increase.

Interesting Points:

isset/array_key_exists is much faster than in_array and array_search +(union) is a bit faster than array_merge (and looks nicer). But it does work differently so keep that in mind. shuffle is on the same Big-O tier as array_rand array_pop/array_push is faster than array_shift/array_unshift due to re-index penalty

Lookups:

array_key_exists O(n) but really close to O(1) - this is because of linear polling in collisions, but because the chance of collisions is very small, the coefficient is also very small. I find you treat hash lookups as O(1) to give a more realistic big-O. For example the different between N=1000 and N=100000 is only about 50% slow down.

isset( $array[$index] ) O(n) but really close to O(1) - it uses the same lookup as array_key_exists. Since it's language construct, will cache the lookup if the key is hardcoded, resulting in speed up in cases where the same key is used repeatedly.

in_array O(n) - this is because it does a linear search though the array until it finds the value.

array_search O(n) - it uses the same core function as in_array but returns value.

Queue functions:

array_push O(∑ var_i, for all i)

array_pop O(1)

array_shift O(n) - it has to reindex all the keys

array_unshift O(n + ∑ var_i, for all i) - it has to reindex all the keys

Array Intersection, Union, Subtraction:

array_intersect_key if intersection 100% do O(Max(param_i_size)*∑param_i_count, for all i), if intersection 0% intersect O(∑param_i_size, for all i)

array_intersect if intersection 100% do O(n^2*∑param_i_count, for all i), if intersection 0% intersect O(n^2)

array_intersect_assoc if intersection 100% do O(Max(param_i_size)*∑param_i_count, for all i), if intersection 0% intersect O(∑param_i_size, for all i)

array_diff O(π param_i_size, for all i) - That's product of all the param_sizes

array_diff_key O(∑ param_i_size, for i != 1) - this is because we don't need to iterate over the first array.

array_merge O( ∑ array_i, i != 1 ) - doesn't need to iterate over the first array

+ (union) O(n), where n is size of the 2nd array (ie array_first + array_second) - less overhead than array_merge since it doesn't have to renumber

array_replace O( ∑ array_i, for all i )

Random:

shuffle O(n)

array_rand O(n) - Requires a linear poll.

Obvious Big-O:

array_fill O(n)

array_fill_keys O(n)

range O(n)

array_splice O(offset + length)

array_slice O(offset + length) or O(n) if length = NULL

array_keys O(n)

array_values O(n)

array_reverse O(n)

array_pad O(pad_size)

array_flip O(n)

array_sum O(n)

array_product O(n)

array_reduce O(n)

array_filter O(n)

array_map O(n)

array_chunk O(n)

array_combine O(n)

I'd like to thank Eureqa for making it easy to find the Big-O of the functions. It's an amazing free program that can find the best fitting function for arbitrary data.

EDIT:

For those who doubt that PHP array lookups are O(N), I've written a benchmark to test that (they are still effectively O(1) for most realistic values).

https://i.stack.imgur.com/PgRQd.png

$tests = 1000000;
$max = 5000001;


for( $i = 1; $i <= $max; $i += 10000 ) {
    //create lookup array
    $array = array_fill( 0, $i, NULL );

    //build test indexes
    $test_indexes = array();
    for( $j = 0; $j < $tests; $j++ ) {
        $test_indexes[] = rand( 0, $i-1 );
    }

    //benchmark array lookups
    $start = microtime( TRUE );
    foreach( $test_indexes as $test_index ) {
        $value = $array[ $test_index ];
        unset( $value );
    }
    $stop = microtime( TRUE );
    unset( $array, $test_indexes, $test_index );

    printf( "%d,%1.15f\n", $i, $stop - $start ); //time per 1mil lookups
    unset( $stop, $start );
}

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The explanation for the case you specifically describe is that associative arrays are implemented as hash tables - so lookup by key (and correspondingly, array_key_exists) is O(1). However, arrays aren't indexed by value, so the only way in the general case to discover whether a value exists in the array is a linear search. There's no surprise there.

I don't think there's specific comprehensive documentation of the algorithmic complexity of PHP methods. However, if it's a big enough concern to warrant the effort, you can always look through the source code.

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You almost always want to use isset instead of array_key_exists. I'm not looking at the internals, but I'm pretty sure that array_key_exists is O(N) because it iterates over each and every key of the array, while isset tries to access the element using the same hash algorithm that is used when you access an array index. That should be O(1).

One "gotcha" to watch out for is this:

$search_array = array('first' => null, 'second' => 4);

// returns false
isset($search_array['first']);

// returns true
array_key_exists('first', $search_array);

I was curious, so I benchmarked the difference:

<?php

$bigArray = range(1,100000);

$iterations = 1000000;
$start = microtime(true);
while ($iterations--)
{
    isset($bigArray[50000]);
}

echo 'is_set:', microtime(true) - $start, ' seconds', '<br>';

$iterations = 1000000;
$start = microtime(true);
while ($iterations--)
{
    array_key_exists(50000, $bigArray);
}

echo 'array_key_exists:', microtime(true) - $start, ' seconds';
?>

is_set: 0.132308959961 seconds
array_key_exists: 2.33202195168 seconds

Of course, this doesn't show time complexity, but it does show how the 2 functions compare to each other.

To test for time complexity, compare the amount of time it takes to run one of these functions on the first key and the last key.

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If people were running into trouble in practice with key collisions, they would implement containers with a secondary hash lookup or balanced tree. The balanced tree would give O(log n) worst case behavior and O(1) avg. case (the hash itself). The overhead is not worth it in most practical in memory applications, but perhaps there are databases that implement this form of mixed strategy as their default case.

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