Benchmark: flatMap vs reduce vs filter.map

Script Preparation code:

var arr = Array.from({length: 20}, () => Math.floor(Math.random() * 100));;

Tests:

flatMap()
arr.flatMap((e) => e > 50 ? e / 2 : []);
reduce()
arr.reduce((acc, curr) => curr > 50 ? [...acc, curr / 2] : acc, []);
filter().map()
arr.filter((e) => e > 50).map((e) => e / 2);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap()
reduce()
filter().map()

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 explain what's being tested, the different approaches compared, their pros and cons, and other considerations.

**Benchmark Overview**

The benchmark compares three different ways to process an array of numbers:

1. `flatMap()`: Returns a new array with the results of applying the provided function to each element.
2. `reduce()`: Applies a function against an accumulator and each element in the array (from left to right) to reduce it to a single output value.
3. `filter().map()` : Filters out elements from the array that don't meet the condition, then maps over the remaining elements.

**Library and Special JS Features**

There is no external library used in this benchmark. However, the test cases utilize some special JavaScript features:

* The `flatMap` method is a newer feature introduced in ECMAScript 2019.
* The `reduce` method has been part of JavaScript since its inception.
* The `filter()` and `map()` methods are also built-in, but their usage with the arrow function syntax (`(e) =>`) is more modern.

**Approach Comparison**

The three approaches have different characteristics:

1. **flatMap()**: This approach returns a new array by applying the provided function to each element. It's often faster and more efficient than the other two methods because it avoids creating intermediate arrays.
2. **reduce()**: This approach applies a function against an accumulator and each element in the array, reducing it to a single output value. It can be slower than `flatMap()` due to its more complex processing steps, but it's often used when working with aggregations or transformations that require multiple passes over the data.
3. **filter().map()**: This approach filters out elements from the original array using `filter()`, then maps over the remaining elements using `map()`. It can be slower than `flatMap()` because of the two separate operations, and it creates intermediate arrays.

**Pros and Cons**

* `flatMap()`: Pros - often faster and more efficient. Cons - only supported in ECMAScript 2019 and later.
* `reduce()`: Pros - suitable for aggregations or transformations that require multiple passes over data. Cons - can be slower due to its complexity, not supported by all browsers.
* `filter().map()` : Pros - generally easy to understand, but may be slower than `flatMap()` due to the two separate operations.

**Considerations**

When choosing an approach, consider the specific use case and performance requirements:

* Use `flatMap()` when you need to process a large array quickly and don't require aggregations or transformations.
* Use `reduce()` when working with data that requires multiple passes over, such as calculating sums or averages.
* Use `filter().map()` when simplicity is more important than raw performance, but be aware of potential slower execution times.

**Alternatives**

Other alternatives to these approaches could include:

* Using a loop instead of the built-in array methods
* Utilizing other libraries or frameworks that provide optimized implementations (e.g., Lodash)
* Experimenting with different array manipulation techniques, such as using `Array.prototype.forEach()` or `Promise.all()`

Keep in mind that the best approach depends on your specific requirements and performance constraints.

Latest run results:

Run details: (Test run date: yesterday)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
flatMap()	2737284.2 Ops/sec
reduce()	2571974.0 Ops/sec
filter().map()	1630560.4 Ops/sec

Related benchmarks:

flatMap vs reduce vs filter.map (version: 0)

Comparing performance of: flatMap() vs reduce() vs filter().map()

Created: 2 years ago by: Registered User

Jump to the latest result

flatMap()

reduce()

filter().map()

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):