Benchmark: Flatmap vs reduce with objects

Script Preparation code:

var a = Array(10000).fill({b: [1,2,3,4,5]})

Tests:

reduce
a.reduce((acc, curr) => [...acc, ...curr.b], [])
flatmap
a.flatMap(({b}) => b)

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:

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

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Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is comparing two JavaScript methods: `flatmap` and `reduce`, both used to process arrays of objects. The goal is to see which method performs better on this specific test case.

**Test Case 1: reduce**

In this test, the `reduce` method is used to flatten an array of objects by concatenating their inner arrays. The benchmark preparation code creates an array of 10,000 objects with a single property "b" containing an array of five numbers.

The benchmark itself calls the `reduce` method with the initial value set to an empty array (`[]`) and accumulates the results in the accumulator (`acc`). In each iteration, it takes an object from the original array and concatenates its inner array with the accumulator using the spread operator (`[...acc, ...curr.b]`).

**Pros and Cons of Reduce Approach:**

Pros:

* `reduce` is a more functional programming-style approach, which can be beneficial for code readability and maintainability.
* It allows for a clear accumulation of results in the accumulator.

Cons:

* In this specific test case, it might lead to slower performance due to the overhead of creating intermediate arrays.
* `reduce` can be less efficient when dealing with large datasets or complex data structures.

**Test Case 2: flatmap**

In contrast, the `flatmap` method is used to flatten an array of objects by mapping each object's inner array and then flattening the resulting array. The benchmark preparation code creates an array of 10,000 objects with a single property "b" containing an array of five numbers.

The benchmark itself calls the `flatMap` method directly on the array of objects, which maps each object to its inner array using the spread operator (`b`) and then flattens the resulting array using the same spread operator.

**Pros and Cons of Flatmap Approach:**

Pros:

* `flatMap` is generally faster than `reduce` when dealing with large datasets or complex data structures.
* It avoids creating intermediate arrays, which can reduce memory allocation and deallocation overhead.

Cons:

* The functional programming-style approach might make it less readable for developers familiar with imperative programming styles.

**Library Used:**

The benchmark uses the JavaScript Array.prototype methods `reduce()` and `flatMap()`. These methods are part of the ECMAScript standard and have been supported by most modern browsers since their introduction in ECMAScript 2019 (ES10).

**Special JS Feature or Syntax:**

There is no explicit use of special JavaScript features or syntax, such as async/await or generators.

**Other Alternatives:**

If you wanted to write this benchmark using alternative methods, here are a few options:

1. Using `forEach()` instead of `reduce()` and `flatMap()`: This would require manually iterating over the array and concatenating results, which could lead to slower performance.
2. Using a library like Lodash's `flatMapDeep()` or `reduce()` functions: These libraries provide optimized implementations of common functional programming utilities.
3. Writing a custom iterative algorithm using loops: This would require implementing a loop that iterates over the array, accumulates results, and flattens the inner arrays.

Keep in mind that these alternatives might not be as efficient or readable as using the built-in `reduce()` and `flatMap()` methods.

Related benchmarks:

Flatmap vs reduce with objects (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

No previous run results

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