Benchmark: Slice vs Map for alloc

Tests:

slice
let src_array = [] let obj1 = {} let obj2 = {} for (let i = 0; i < 10000; ++i) { src_array.push(obj1) } var array2 = src_array.slice() for (let i = 0; i < 10000; ++i) { array2[i] = obj2 }
map
let src_array = [] let obj1 = {} let obj2 = {} for (let i = 0; i < 10000; ++i) { src_array.push(obj1) } var array2 = src_array.map(x => null) for (let i = 0; i < 10000; ++i) { array2[i] = obj2 }
push
let src_array = [] let obj1 = {} let obj2 = {} for (let i = 0; i < 10000; ++i) { src_array.push(obj1) } let array2 = [] for (let i = 0; i < 10000; ++i) { array2.push(obj2) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice
map
push

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 dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition and Purpose**

The provided benchmark definition compares three approaches to allocate an array with the right allocated size: `slice`, `map`, and `push`. The goal is to determine which method is the fastest.

**Options Compared**

There are three options being compared:

1. **Slice**: This approach creates a copy of the original array using the `slice()` method, and then iterates over the copied array to assign values.
2. **Map**: This approach uses the `map()` method to create a new array with the same length as the original array, and then assigns values to each element in the new array.
3. **Push**: This approach creates an empty array, pushes the assigned value onto it 10,000 times, and then assigns values to each of the 10,000 elements.

**Pros and Cons**

* **Slice**:
	+ Pros: Efficient use of existing memory, as it only allocates a copy of the original array.
	+ Cons: Can be slower than `map` or `push` due to the overhead of creating a copy of the array.
* **Map**:
	+ Pros: Creates a new array with the same length as the original array, which can reduce memory usage. Also, it's often faster than `slice`.
	+ Cons: Can be slower than `push` for very large arrays due to the overhead of creating a new array.
* **Push**:
	+ Pros: Can be the fastest approach for very large arrays, as it only requires pushing values onto an existing array. However, it can consume more memory if the array grows too large.
	+ Cons: Requires more memory than `slice` or `map`, especially when dealing with large arrays.

**Library and Special JS Feature**

The `map()` method uses the **Array.prototype.map()** library, which is a built-in JavaScript method that creates a new array with the results of applying a provided function on every element in this array. The purpose of this library is to provide a convenient way to create new arrays by transforming existing ones.

There are no special JS features used in this benchmark, just standard JavaScript syntax and methods.

**Other Alternatives**

Some alternative approaches could be:

* Using `Array.from()` instead of `map()`, as it creates an array from an iterable object.
* Using `Array.prototype.fill()` instead of pushing values onto an existing array.
* Using a different data structure, such as a linked list or a tree, to store the allocated elements.

However, these alternatives might not be suitable for this specific benchmark, and may require additional modifications to the code.

Overall, the choice of which approach is fastest depends on the specific use case and requirements. MeasureThat.net's benchmark can help determine which method is generally faster, but it's essential to consider factors like memory usage and performance in different scenarios.

Related benchmarks:

Slice vs Map for alloc (version: 0)

to allocate a array with the right allocated size, which one is the fastest ?

Comparing performance of: slice vs map vs push

Created: 2 years ago by: Guest

Jump to the latest result

slice

map

push

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):