Benchmark: edit map vs slice no index fix

Script Preparation code:

window.arr = new Array(2000).fill({value: 'old'});
arr[1000] = {value: 'toReplace'};

Tests:

map
const editIndex = arr.findIndex(item => item.value === 'toReplace'); const newArr = arr.map((item, index) => index === editIndex ? {value: 'new'} : item);
slice
const editIndex = arr.findIndex(item => item.value === 'toReplace'); const newArr = [ ...arr.slice(0, editIndex), {value: 'new'}, ...arr.slice(editIndex + 1), ];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map
slice

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The benchmark compares two approaches to modify an array:

1. Using `Array.prototype.map()` method with a callback function that replaces a specific value in the array.
2. Using `Array.prototype.slice()` method to create a new array with the replaced value.

**Options Compared**

Both options use the same underlying data structure, but differ in how they perform the replacement operation.

* **Option 1 (Map)**: Uses `map()` method, which creates a new array by iterating over each element and applying the callback function. In this case, the callback function replaces the value of the specific item with `'new'`.
	+ Pros:
		- Efficient for large arrays since it uses a single pass through the data.
		- Can handle any type of data in the array (not just numbers or strings).
	+ Cons:
		- Creates a new array, which can be memory-intensive for very large datasets.
		- May have performance issues if the callback function is complex or computationally expensive.
* **Option 2 (Slice)**: Uses `slice()` method to create a subset of the original array. The callback function then replaces the specific value in the subset with `'new'`.
	+ Pros:
		- Does not create a new array, which can be more memory-efficient than `map()`.
		- Can be faster since it only iterates over the specific range of elements.
	+ Cons:
		- Requires specifying the exact index of the element to replace, which can lead to errors if the index is incorrect.
		- Only works for arrays with a fixed length.

**Library and Special JS Feature**

Neither option uses a library, but they do utilize JavaScript's built-in `Array.prototype` methods. The callback function in both options is also a special feature of JavaScript, allowing you to pass a function as an argument to another function.

**Benchmark Preparation Code**

The preparation code creates a large array (`arr`) with 2000 elements and fills it with objects containing the string `'old'`. One element (`arr[1000]`) is then replaced with an object containing the string `'toReplace'`.

**Latest Benchmark Result**

The benchmark results show that the `map()` method is slightly faster than the `slice()` method for this specific test case. However, the difference in execution speed is relatively small (about 4,000 executions per second).

**Other Alternatives**

There are other ways to modify an array, such as:

* Using a `for` loop with indexing
* Utilizing `Array.prototype.forEach()` method with a callback function
* Leveraging modern JavaScript features like `const arr = [...arr]; arr[1000] = {value: 'new'};`

However, these alternatives may have different trade-offs in terms of performance, memory usage, and code readability.

Overall, the benchmark highlights the efficiency differences between `map()` and `slice()` methods when performing array operations. While both approaches have their pros and cons, `map()` is generally a more versatile and efficient solution for large datasets.

Related benchmarks:

edit map vs slice no index fix (version: 0)

Which is faster?

Comparing performance of: map vs slice

Created: 6 years ago by: Guest

Jump to the latest result

map

slice

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):