Benchmark: FindIndex + splice vs filter + includes

Script Preparation code:

var arr = new Array(150000);
arr.fill({ id: 0 });
arr = arr.map((el, idx) => el.id = idx);
var foo = Math.floor(Math.random() * 150000);

Tests:

FindIndex + splice
var index = arr.indexOf(foo); var newArr = arr.splice(index, 1);
filter + includes
var include = arr.includes(foo); var index = arr.filter(el => el !== foo);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
FindIndex + splice
filter + includes

Fastest: N/A

Slowest: N/A

Latest run results:

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

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I'll break down the benchmark and explain what's being tested, compared options, pros and cons, library usage, special JS features, and alternatives.

**Benchmark Overview**

The benchmark is designed to compare the performance of two approaches:

1. **FindIndex + splice**: This approach uses `Array.indexOf()` to find the index of an element in the array, then uses `Array.splice()` to remove the element at that index.
2. **filter + includes**: This approach uses `Array.filter()` to create a new array with elements that don't match a certain condition (in this case, not equal to `foo`), and then checks if `foo` is included in the resulting array using `Array.includes()`.

**Options Compared**

The two approaches are compared in terms of their performance. The benchmark measures the number of executions per second for each approach on a large array of 150,000 elements.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

1. **FindIndex + splice**:
	* Pros: Can be efficient if you need to remove multiple elements at once.
	* Cons: Requires `Array.indexOf()` which has a linear search complexity (O(n)), and `Array.splice()` which can be slow for large arrays.
2. **filter + includes**:
	* Pros: Avoids the use of `Array.indexOf()` and `Array.splice()`, making it potentially faster for large arrays. Also, `Array.includes()` is generally faster than checking array length using `Array.length`.
	* Cons: Creates a new array with filtered elements, which can be memory-intensive.

**Library Usage**

None of the approaches explicitly use any libraries. However, some JavaScript engines (like V8) may optimize and implement certain library functions or APIs under the hood, such as `Array.includes()`.

**Special JS Features**

The benchmark uses the following special JavaScript features:

* **`var` keyword**: Used to declare variables.
* **`Math.random()`**: Used to generate a random number for indexing.
* **Template literals (`(el, idx) => el.id = idx`)**: Used to create an arrow function that modifies `el`.
* **`Array.prototype.fill()````: Used to fill the array with an initial value.

**Alternatives**

Other approaches to find an element in an array and remove it might include:

1. **Using `Array.forEach()`**: Instead of `Array.indexOf()`, you could use `Array.forEach()` to iterate over the array and check if each element matches.
2. **Using a custom search function**: If performance is critical, you could implement your own search function using techniques like binary search or hash tables.

Keep in mind that these alternatives might not be as efficient or straightforward as the approaches used in this benchmark.

**Benchmark Result Analysis**

The latest benchmark result shows that the `filter + includes` approach outperforms `FindIndex + splice`. This is likely due to the avoidance of `Array.indexOf()` and `Array.splice()`, which can be slow for large arrays. The `filter + includes` approach, on the other hand, avoids these operations altogether, making it a more efficient choice in this specific benchmark.

Please note that benchmarking results may vary depending on the JavaScript engine, platform, and other factors.

Related benchmarks:

FindIndex + splice vs filter + includes (version: 0)

Comparing performance of: FindIndex + splice vs filter + includes

Created: 3 years ago by: Guest

Jump to the latest result

FindIndex + splice

filter + includes

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):