Benchmark: reduce vs flatMap, conditional

Script Preparation code:

var xs = [{
    text: "a",
    sort: false
}, {
    text: "b",
    sort: true
}, {
    text: "c",
    sort: false
}, {
    text: "d",
    sort: true
}, {
    text: "e",
    sort: false
}]

Tests:

reduce
xs.reduce((acc, x) => { if (x.sort) { acc.push(x.text); } return acc; }, ["z"])
flatMap
xs.flatMap((x) => x.sort ? [x.text] : []);

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 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36

Browser/OS: Chrome 110 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce	18423534.0 Ops/sec
flatMap	2579714.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

The provided JSON represents two benchmark test cases: `reduce` and `flatMap`. These test cases are designed to compare the performance of these two Array methods in JavaScript.

**What is tested?**

The `reduce` method applies a function against an accumulator and each element in the array (from left-to-right) to reduce it to a single output value. In this benchmark, we're testing how fast it can push elements into an accumulator array based on certain conditions (`x.sort`).

On the other hand, the `flatMap()` method returns a new array with the results of applying a provided function to every element in the original array. Here, we're testing how fast it can filter out unwanted elements and create a new array.

**Options compared**

The two benchmark test cases compare different approaches:

1. **Reduce**: uses `reduce()` method with an initial accumulator value (`[ "z" ]`) and pushes elements into the accumulator array based on the condition `x.sort`.
2. **FlatMap**: uses `flatMap()` method, which filters out unwanted elements (`x.sort` is false) and creates a new array.

**Pros and Cons**

Here's a brief overview of each approach:

* **Reduce**:
	+ Pros:
		- More explicit and controlled iteration over the array.
		- Can be more efficient when dealing with small arrays or arrays where the condition is simple.
	+ Cons:
		- May have higher overhead due to the initial accumulator value and the need to manually reset it.
		- Less concise than `flatMap()`.
* **FlatMap**:
	+ Pros:
		- More concise and expressive, making it easier to read and maintain code.
		- Can be more efficient when dealing with large arrays or arrays where the condition is simple.
	+ Cons:
		- May have higher overhead due to the creation of a new array.

**Other considerations**

When choosing between `reduce` and `flatMap`, consider the following factors:

* Array size: For small arrays, `reduce()` might be more efficient. For large arrays, `flatMap()` might be more suitable.
* Condition complexity: If the condition is simple, `flatMap()` might be a better choice. If the condition is complex, `reduce()` might be more readable and maintainable.

**Library usage**

None of the provided benchmark test cases use any external libraries. However, if you're using libraries like Lodash or Ramda for utility functions, they can impact performance and should be taken into account when benchmarking.

**Special JS features/syntax**

There are no special JavaScript features or syntax mentioned in this benchmark. The focus is on comparing the performance of `reduce` and `flatMap`.

**Alternatives**

Other alternatives to consider when benchmarking array methods include:

* **Filter()**: similar to `flatMap()`, but returns a new array with elements that pass the test.
* **Map()**: applies a function to every element in an array, returning a new array with the results.
* **Array.prototype.some()**: checks if at least one element in an array meets a condition and returns a boolean value.

Keep in mind that each of these alternatives has its own strengths and weaknesses, and choosing the right one depends on your specific use case and performance requirements.

Related benchmarks:

reduce vs flatMap, conditional (version: 0)

Comparing performance of: reduce vs flatMap

Created: 3 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):