How to avoid memory overflow when using array_chunk

M66 2025-04-28

In PHP, the array_chunk function is a very practical tool for splitting a large array into smaller chunks. Although it provides convenient features, it can cause memory overflow issues when dealing with very large arrays, especially when the array is very large or too many split blocks.

1. Basic usage of array_chunk

The basic syntax of the array_chunk function is as follows:

 array_chunk(array $array, int $length, bool $preserve_keys = false): array

$array : The input array.
$length : The number of elements in each subarray.
$preserve_keys : A boolean value that determines whether to maintain the key value of the original array.

 $array = [1, 2, 3, 4, 5, 6, 7, 8, 9];
$chunks = array_chunk($array, 3);
print_r($chunks);

Output:

 Array
(
    [0] => Array ( [0] => 1 [1] => 2 [2] => 3 )
    [1] => Array ( [0] => 4 [1] => 5 [2] => 6 )
    [2] => Array ( [0] => 7 [1] => 8 [2] => 9 )
)

This code divides the array $array into 3 blocks, each block containing 3 elements.

2. Why does array_chunk cause memory overflow?

When you split a very large array, array_chunk returns a new two-dimensional array where each subarray contains part of the data from the original array. If the original array is very large and the number of blocks is very large, this will cause PHP to create a large number of subarrays in memory, which may cause memory overflow issues.

For example:

Suppose you have a very large array containing millions of elements. If you use array_chunk to split it into 1000 chunks, this may create a large number of subarrays, each of which still needs to take up memory, causing a sharp increase in memory usage, which triggers a memory overflow.

3. How to avoid memory overflow?

To avoid memory overflow when using array_chunk , you can adopt the following strategies:

3.1 Using Generators

Generators are a memory-efficient way provided by PHP, which can generate data on demand instead of loading all data into memory at once. You can use generators to process data by blocks, thus avoiding loading the entire array into memory at once.

 function chunkGenerator(array $array, $chunkSize) {
    $chunk = [];
    foreach ($array as $key => $value) {
        $chunk[] = $value;
        if (count($chunk) >= $chunkSize) {
            yield $chunk;
            $chunk = [];
        }
    }
    if (count($chunk) > 0) {
        yield $chunk;
    }
}

$array = range(1, 10000000);
foreach (chunkGenerator($array, 1000) as $chunk) {
    // Process each $chunk
}

In this example, we used the yield keyword to create a generator. Each time a block is generated, it temporarily returns the block instead of loading all blocks into memory at once. This can significantly reduce memory usage.

3.2 Step by step processing large arrays

If you can't use the generator, you can consider avoiding memory overflow by loading batches or processing data step by step. For example, if your data comes from a database or external API, you can load the data in batches instead of loading the entire large array at once.

Assuming your data is fetched from a URL, you can load the data from the URL step by step, rather than loading everything at once:

 function fetchDataInChunks($url, $chunkSize) {
    $handle = fopen($url, 'r');
    $chunk = [];
    while (($line = fgets($handle)) !== false) {
        $chunk[] = $line;
        if (count($chunk) >= $chunkSize) {
            yield $chunk;
            $chunk = [];
        }
    }
    fclose($handle);
    if (count($chunk) > 0) {
        yield $chunk;
    }
}

$url = 'https://m66.net/data.csv';
foreach (fetchDataInChunks($url, 1000) as $chunk) {
    // Process each $chunk
}

3.3 Reduce excessive block creation at one time

If your array data is very large but you don't want to use a generator, you can reduce the number of blocks created at one time by resizing each block. For example, set the block size a little larger, and process larger blocks of data each time instead of splitting them into many small blocks.

4. Summary

When splitting large arrays with array_chunk , it can indeed cause memory overflow issues, especially when the array is particularly large or the number of blocks is too large. To solve this problem, we can use the following methods:

Use generator : Reduce memory footprint by generating data on demand.
Step by step : If the data comes from an external source, you can load the data in batches.
Resize blocks : Reduce the number of blocks, increase the size of each block, thereby reducing memory usage.

These methods can effectively help you deal with memory problems when large data volumes, making the program run more efficiently.

References

array_chunk