Benchmark: slice + map or map + slice

Script Preparation code:

var arr = [];

for (i = 0; i < 80000; i++) {
  var obj = {
     id: Math.random().toString(36).substr(2, 5),
     name: Math.random().toString(36).substr(2, 5)
  };
  arr.push(obj);
}

Tests:

slice + map
arr.slice(0, 10).map(x => x.id) arr.slice(40000, 40010).map(x => x.id) arr.slice(79000, 79010).map(x => x.id)
map + slice
arr.map(x => x.id).slice(0, 10) arr.map(x => x.id).slice(40000, 40010) arr.map(x => x.id).slice(79000, 79010)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice + map
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 what is being tested in this JavaScript microbenchmark.

**Benchmark Definition**

The benchmark is testing the performance of two approaches: `slice` followed by `map`, and `map` followed by `slice`. The input data consists of an array of 80,000 objects, each with `id` and `name` properties. This array is generated dynamically using a JavaScript script.

**Options Compared**

The benchmark compares the performance of two approaches:

1. **Approach 1: slice + map**
	* First, extract a subset of 10 elements from the original array using `slice`.
	* Then, apply a mapping function to each element in the extracted subset using `map`, extracting only the `id` property.
2. **Approach 2: map + slice**
	* Apply a mapping function to each element in the original array using `map`, extracting both the `id` and `name` properties.
	* Then, extract a subset of 10 elements from the resulting mapped array using `slice`.

**Pros and Cons**

**Slice + Map (Approach 1)**

Pros:

* This approach avoids mapping the entire array upfront, which can be memory-intensive for large datasets.
* It only maps the extracted subset, which reduces the number of iterations.

Cons:

* Since we're extracting a small subset before mapping, this approach may not take full advantage of modern CPU pipelines.
* The `slice` operation can introduce additional overhead due to its complexity.

**Map + Slice (Approach 2)**

Pros:

* This approach allows the browser's CPU pipeline to process the entire array upfront, which can lead to better performance for large datasets.
* It avoids the need to slice the array in two separate operations.

Cons:

* Mapping the entire array upfront requires more memory and computational resources.
* The `slice` operation is applied after mapping, which may reduce its effectiveness.

**Library**

In this benchmark, there doesn't seem to be any explicit library being used beyond standard JavaScript features.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in this benchmark. It solely relies on standard JavaScript functionality and optimizations available in modern browsers.

**Other Alternatives**

To further optimize these approaches, consider the following alternatives:

* Instead of using `slice`, you could use `Array.prototype.subarray()` to avoid creating a new array.
* For mapping, consider using `Array.prototype.reduce()` or `Array.prototype.forEach()` instead of `map` for potential performance benefits.
* If possible, try to use modern JavaScript features like `for...of` loops or `Promise.all()` to simplify the code and improve performance.

Keep in mind that these alternatives might require more complex modifications to the benchmark script.

Related benchmarks:

slice + map or map + slice (version: 0)

Comparing performance of: slice + map vs map + slice

Created: 3 years ago by: Registered User

Jump to the latest result

slice + map

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):