Benchmark: flatMap vs reduce with push

Script Preparation code:

var arr = Array.from({length: 10000}, () => Math.floor(Math.random() * 10000));

Tests:

Reduce with push
arr.reduce((acc, x) => { if (x < 5000) { acc.push(x); } return acc; }, [])
flatMap
arr.flatMap((x) => { if (x <5000) { return x; } return []; })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce with push
flatMap

Fastest: N/A

Slowest: N/A

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/111.0.0.0 Safari/537.36

Browser/OS: Chrome 111 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Reduce with push	21482.2 Ops/sec
flatMap	2004.4 Ops/sec

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

LLMs can make mistakes. Check important info.

I'd be happy to explain the benchmark and its results.

**Benchmark Description**

The benchmark is designed to compare the performance of two different approaches: using `reduce()` with `push()` and using `flatMap()`. The test case creates an array of 10,000 random integers and then applies a filtering condition to it. For the `reduce()` approach, only integers less than 5,000 are pushed onto the accumulator, while for the `flatMap()` approach, only integers less than 5,000 are returned as individual elements.

**Options Compared**

Two options are compared:

1. **`arr.reduce((acc, x) => { ... })` with `push()`:** This approach uses the `reduce()` method to accumulate a value (an array in this case). For each element in the original array, it checks if the element is less than 5,000 and pushes it onto the accumulator using `push()`. The final result is an array containing all the filtered elements.
2. **`arr.flatMap((x) => { ... })`**: This approach uses the `flatMap()` method to transform the array into a new array. For each element in the original array, it checks if the element is less than 5,000 and returns an array with that element as its only element.

**Pros and Cons**

**`arr.reduce((acc, x) => { ... })` with `push()`**

Pros:

* Can be useful when working with arrays where you need to accumulate a value over time.
* Allows for more control over the accumulation process.

Cons:

* Performance can be slower compared to other approaches, especially for large datasets.
* Requires an explicit callback function to handle each element.

**`arr.flatMap((x) => { ... })`**

Pros:

* Can be faster than `reduce()` with `push()`, especially for large datasets.
* Does not require an explicit callback function.

Cons:

* May be less intuitive for developers who are not familiar with the `flatMap()` method.
* Returns a new array, which can lead to higher memory usage.

**Library and Syntax**

The `Array.prototype.reduce()` and `Array.prototype.flatMap()` methods are built-in JavaScript methods that allow for efficient accumulation and transformation of arrays. These methods were introduced in ECMAScript 2019 (ES2020) and are supported by most modern browsers, including Chrome 111.

**Special JS Feature or Syntax**

There is no special JS feature or syntax mentioned in the benchmark code. The focus is on comparing two different approaches to achieve a common goal.

**Other Alternatives**

Other alternatives for this type of benchmark could include:

* Using `Array.prototype.forEach()` and pushing elements onto an accumulator array.
* Using a custom iterative approach with a loop and conditional statements.
* Using other libraries or frameworks that provide similar functionality, such as Lodash.

In terms of software engineers, understanding the differences between these approaches can help in making informed decisions about performance-critical code. Knowing when to use `reduce()` with `push()` versus `flatMap()` can significantly impact the efficiency and readability of code.

Related benchmarks:

flatMap vs reduce with push (version: 0)

Comparing performance of: Reduce with push vs flatMap

Created: one year ago by: Guest

Jump to the latest result

Reduce with push

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):