Benchmark: redcue vs flatMap

Script Preparation code:

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

Tests:

Reduce
arr.reduce((acc, x) => [...acc, x], [])
flatMap
arr.flatMap(x => x)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce
flatMap

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:120.0) Gecko/20100101 Firefox/120.0

Browser/OS: Firefox 120 on Windows

View result in a separate tab

Test name	Executions per second
Reduce	2.9 Ops/sec
flatMap	13583.1 Ops/sec

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.

**Overview**

The benchmark compares the performance of two approaches: `reduce` and `flatMap`. Both methods are used to transform an array, but they differ in how they handle the accumulation of values.

**Options compared**

Two options are compared:

1. **`arr.reduce((acc, x) => [...acc, x], [])`**: This method applies a callback function to each element in the array, accumulating the results in an accumulator (`acc`). The callback returns a new array by spreading the current accumulator (`[...acc, x]`) and appending the current element (`x`). Finally, the original array is returned.
2. **`arr.flatMap(x => x)`**: This method applies a callback function to each element in the array, returning a new array with the results of the callback applied to each element.

**Pros and Cons**

1. **`reduce`**:
	* Pros: More memory-efficient, as it only accumulates values in an accumulator.
	* Cons: Can be slower for large arrays due to the overhead of the callback function and accumulator updates.
2. **`flatMap`**:
	* Pros: Faster for large arrays, as it avoids the need for accumulator updates and can utilize hardware acceleration (e.g., SIMD instructions).
	* Cons: More memory-intensive, as it creates a new array with all intermediate results.

**Library**

None of the test cases use any external libraries. The benchmarks only rely on built-in JavaScript features.

**Special JS feature or syntax**

The `flatMap` method uses a special syntax called "arrow functions" (e.g., `(x) => x`). This is a shorthand way to define small, single-expression functions in JavaScript. Arrow functions are supported by most modern browsers and Node.js versions.

**Other alternatives**

For transforming arrays, other options include:

1. **`map()`**: Similar to `flatMap`, but returns an array of transformed values without accumulating intermediate results.
2. **`forEach()`**: Executes a callback function for each element in the array, but does not return any value (i.e., it's more like a side effect).
3. **`for...of` loops**: Can be used to iterate over arrays and accumulate values in variables.

Note that these alternatives may have different performance characteristics compared to `reduce` and `flatMap`. The choice of which method to use depends on the specific use case and performance requirements.

Related benchmarks:

redcue vs flatMap (version: 0)

Comparing performance of: Reduce vs flatMap

Created: 2 years ago by: Guest

Jump to the latest result

Reduce

flatMap

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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