Benchmark: Diferentes tipos de flatmap

Script Preparation code:

var arr = Array(10_000).fill(0)

Tests:

Reduce
arr.reduce((acc, x) => [...acc, x, x], [])
flatMap
arr.flatMap(x => [x, x])
Reduce push
arr.reduce((acc, x) => (acc.push(x, x),acc), [])
Reduce prealloc
arr.reduce((acc, x) => (acc.push(x, x),acc), Array(20_000))
Concat Apply
[].concat.apply([], [arr,arr])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce
flatMap
Reduce push
Reduce prealloc
Concat Apply

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's being tested in this benchmark.

**Benchmark Purpose**

The purpose of this benchmark is to compare the performance of different approaches for flattening an array in JavaScript. The test cases are designed to isolate the effects of specific language features and optimizations.

**Test Cases**

There are four main test cases:

1. `arr.reduce((acc, x) => [...acc, x, x], [])`: This test case uses the `reduce` method with a callback function that concatenates each element to the accumulator array using the spread operator (`[...acc, x, x]`). The accumulator is initialized as an empty array.
2. `arr.flatMap(x => [x, x])`: This test case uses the `flatMap` method, which was introduced in ECMAScript 2019 (ES9). It flattens the inner array `[x, x]` and returns a new array with the results.
3. `arr.reduce((acc, x) => (acc.push(x, x), acc))`: This test case uses the `reduce` method with a callback function that pushes each element to the accumulator array using the `push` method (`(acc.push(x, x), acc)`). The accumulator is initialized as an empty array.
4. `arr.reduce((acc, x) => (acc.push(x, x), acc), Array(20_000))`: This test case uses the `reduce` method with a callback function that pushes each element to the accumulator array using the `push` method (`(acc.push(x, x), acc)`). The accumulator is initialized as an array of 20,000 elements.

**Library and Special Features**

There are no libraries used in this benchmark. However, it's worth noting that the `flatMap` method uses a new syntax introduced in ES9, which may not be supported by older browsers or environments.

**Options Compared**

The test cases compare the performance of different approaches for flattening an array:

* Using `reduce` with concatenation (`arr.reduce((acc, x) => [...acc, x, x], [])`)
* Using `flatMap` (introduced in ES9)
* Using `reduce` with the `push` method (`arr.reduce((acc, x) => (acc.push(x, x), acc))`)
* Using `reduce` with pre-allocation of the accumulator array (`arr.reduce((acc, x) => (acc.push(x, x), acc), Array(20_000))`)

**Pros and Cons**

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

1. **Reduce with concatenation**: Pros - simple and easy to understand. Cons - can be slow due to the creation of intermediate arrays.
2. **flatMap**: Pros - efficient and concise. Cons - may not be supported by older browsers or environments.
3. **Reduce with push**: Pros - allows for pre-allocation of the accumulator array, which can improve performance. Cons - more complex and harder to understand.
4. **Reduce with pre-allocation**: Pros - improves performance by avoiding intermediate arrays. Cons - requires careful handling of edge cases.

**Other Alternatives**

Some other alternatives for flattening an array in JavaScript include:

* Using `Array.prototype.flat()` (introduced in ES6)
* Using `Array.prototype.flatMap()` (introduced in ES9)
* Using a custom function to flatten the array

It's worth noting that the choice of approach depends on the specific use case, performance requirements, and compatibility considerations.

Related benchmarks:

Diferentes tipos de flatmap (version: 0)

Comparing performance of: Reduce vs flatMap vs Reduce push vs Reduce prealloc vs Concat Apply

Created: 3 years ago by: Guest

Jump to the latest result

Reduce

flatMap

Reduce push

Reduce prealloc

Concat Apply

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):