Benchmark: red test - MeasureThat.net

Script Preparation code:

var arr = [1, 2, 3, 4];

Tests:

flatmap
arr.flatMap(x => [x, x * 2]);
concat
arr.reduce((acc, x) => acc.concat([x, x * 2]), []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatmap
concat

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and individual test cases.

**Benchmark Definition JSON**

The `Script Preparation Code` is used to prepare the JavaScript environment for the benchmark. In this case, it creates an array `arr` with four elements: `[1, 2, 3, 4]`. This code is executed once before each benchmark to ensure a consistent starting point.

**Individual Test Cases**

The two test cases are:

1. **flatmap**: The benchmark definition uses the `flatMap()` method on the `arr` array, which returns a new array with the results of calling the provided callback function for each element in the original array. In this case, the callback function takes an element `x` and returns `[x, x * 2]`. This creates two elements: `[1, 2]` and `[3, 6]`.

Pros:

* Easy to understand and use
* Fast execution

Cons:

* May have performance issues due to overhead of creating new arrays
* Not suitable for large datasets or complex transformations

2. **concat**: The benchmark definition uses the `reduce()` method on the `arr` array with an initial value of an empty array `[]`. The callback function takes two arguments: `acc` (the accumulator) and `x` (the current element). It concatenates `[x, x * 2]` to the accumulator. This creates a single-element array for each iteration.

Pros:

* Suitable for large datasets or complex transformations
* Can be more efficient than flatMap due to fewer overheads

Cons:

* More verbose and harder to read compared to flatMap
* May have performance issues due to concatenating arrays

**Library Usage**

There is no library used in the provided benchmark definition. The `flatMap()` and `reduce()` methods are built-in JavaScript methods.

**Special JS Feature or Syntax**

The benchmark definition uses arrow functions, which are a concise way to define small anonymous functions. They are also more memory-efficient than traditional function declarations.

Other alternatives for the `flatMap` method could be:

* `map()`: Creates a new array with the results of calling the provided callback function for each element in the original array. However, this would require more iterations and overhead compared to flatMap.
* Loops: A simple loop can achieve the same result as flatMap, but it's more verbose and harder to read.

For the `concat` method:

* Other methods like `push()` or `splice()` could be used instead of reducing concatenating arrays. However, these methods may not provide the same level of performance and readability as reduce().

**Other Alternatives**

If you wanted to write similar benchmarks for other JavaScript features or libraries, here are some alternatives:

* Instead of `flatMap()`, consider using `map()` with a loop that accumulates the results.
* Instead of `reduce()`, consider using a loop or the spread operator (`...`) to concatenate arrays.
* For libraries like Lodash, you could use their equivalent functions (e.g., `lodash.flatMap()` instead of `arr.flatMap(x => [x, x * 2])`).
* Consider benchmarking other JavaScript features, such as closures, promises, or async/await.

Keep in mind that the best approach will depend on the specific requirements and constraints of your project.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 11_0_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36

Browser/OS: Chrome 87 on Mac OS X 11.0.1

View result in a separate tab

Test name	Executions per second
flatmap	617611.6 Ops/sec
concat	1581002.0 Ops/sec

Related benchmarks:

red test (version: 0)

Comparing performance of: flatmap vs concat

Created: 3 years ago by: Registered User

Jump to the latest result

flatmap

concat

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):