Benchmark: flatmap vs for of vs custom flatMap (version 2)

Script Preparation code:

var n = 1000
var arr = [...Array(n)];

function flatMap(arr, fn) {
  const acc = [];
  for (let i = 0; i < arr.length; i++) {
    acc.push(...fn(arr[i]));
  }
  return acc;
}
var fn = (x,i) => [i + 1, i + 2]

Tests:

flatmap
arr.flatMap(fn)
for of
let res = [], i=0; for(const i of arr) res.push(...[i + 1, i + 2]);
custom flatMap
flatMap(arr, fn)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatmap
for of
custom flatMap

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is comparing three approaches to flatten an array: `flatMap` (native JavaScript method), `for...of` loop, and a custom implementation of `flatMap`. The goal is to determine which approach is the fastest.

**Options Compared**

1. **`flatMap`**: This is a native JavaScript method that flattens an array by mapping each element to an array and then concatenating them.
2. **`for...of` loop**: This is a traditional loop construct in JavaScript that iterates over an array using a for...of loop.
3. **Custom `flatMap`**: This is a custom implementation of the `flatMap` method, which maps each element to an array and then pushes the result into another array.

**Pros and Cons**

1. **`flatMap`**:
	* Pros: Native JavaScript method, well-optimized by browser engines.
	* Cons: May not be as flexible or customizable as other approaches.
2. **`for...of` loop**:
	* Pros: Can be customized to fit specific use cases, easy to understand and implement.
	* Cons: Slower than native methods due to the overhead of a loop.
3. **Custom `flatMap`**:
	* Pros: Highly customizable, can be optimized for specific use cases.
	* Cons: Requires manual implementation and optimization, may not be as efficient as native methods.

**Libraries and Special Features**

In this benchmark, there are no libraries used. However, it's worth noting that the custom `flatMap` implementation uses a variable `fn` to represent the mapping function, which is a common pattern in functional programming languages.

**Other Considerations**

When implementing benchmarks like this, several other factors come into play:

* **Cache locality**: The browser engine may optimize for cache locality when executing the benchmark. For example, if the input array is stored in contiguous memory locations.
* **Garbage collection**: Garbage collection may occur during the execution of the benchmark, which can impact performance.
* **Compiler optimizations**: Compiler optimizations like loop unrolling or dead code elimination may affect the results.

**Alternatives**

Other alternatives to measure flatMap performance could include:

* Using a different programming language or framework that provides an optimized `flatMap` method.
* Measuring the performance of the custom `flatMap` implementation in a specific use case or scenario.
* Comparing the performance of different browsers, versions, or platforms.

In summary, this benchmark compares three approaches to flatten an array: native JavaScript methods, traditional loops, and custom implementations. The results will help determine which approach is the fastest and most efficient for a given use case.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36

Browser/OS: Chrome 116 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
flatmap	7765.6 Ops/sec
for of	167675.6 Ops/sec
custom flatMap	160789.9 Ops/sec

Related benchmarks:

flatmap vs for of vs custom flatMap (version 2) (version: 1)

Comparing performance of: flatmap vs for of vs custom flatMap

Created: one year ago by: Registered User

Jump to the latest result

flatmap

for of

custom flatMap

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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