Benchmark: filter,map,reduce 2

Tests:

filter,map
const isFeatureNode = (feature) => feature.type === 'Feature'; const newNodes=[{type:'Feature', id:1}, {type:'non', id:2}, {type:'Feature', id:3}, {type:'non', id:4}]; newNodes.filter((feature) => !isFeatureNode(feature)).map((node) => node.id);
reduce spread
const isFeatureNode = (feature) => feature.type === 'Feature'; const newNodes=[{type:'Feature', id:1}, {type:'non', id:2}, {type:'Feature', id:3}, {type:'non', id:4}]; newNodes.reduce((acc, feature) => ( !isFeatureNode(feature) ? [...acc, feature.id] : acc ), []);
reduce push
const isFeatureNode = (feature) => feature.type === 'Feature'; const newNodes=[{type:'Feature', id:1}, {type:'non', id:2}, {type:'Feature', id:3}, {type:'non', id:4}]; newNodes.reduce((acc, feature) => { if (!isFeatureNode(feature)) { acc.push(feature); } return acc; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter,map
reduce spread
reduce push

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/96.0.4664.45 Safari/537.36

Browser/OS: Chrome 96 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
filter,map	11100890.0 Ops/sec
reduce spread	6641110.5 Ops/sec
reduce push	15131393.0 Ops/sec

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 is being tested, compared, and the pros and cons of different approaches.

**Benchmark Overview**

The benchmark compares three ways to process an array of objects with two types: "Feature" and "non". The goal is to filter out the "non" elements and then perform a specific operation on the remaining "Feature" elements. The three approaches are:

1. `filter` + `map`
2. `reduce` (with spread operator)
3. `reduce` (without spread operator)

**Approach 1: `filter` + `map`**

This approach first filters out the non-"Feature" elements using `Array.prototype.filter()`. The resulting array only contains "Feature" elements. Then, it applies a mapping function to each element in the filtered array using `Array.prototype.map()`, which returns an array of ids.

**Pros:**

* Easy to understand and implement
* Fast execution time due to lazy evaluation (filtering before mapping)

**Cons:**

* Two separate methods are used, which can lead to additional overhead and memory allocation
* The resulting array is created twice (once by filtering and once by mapping), which can be inefficient in terms of memory allocation

**Approach 2: `reduce` (with spread operator)**

This approach uses `Array.prototype.reduce()` to accumulate an array of ids from the non-"Feature" elements while filtering out those that are not "Feature". The spread operator (`...`) is used to create a new array by spreading the accumulated array.

**Pros:**

* Single method call, which can reduce overhead and memory allocation
* No need to create intermediate arrays

**Cons:**

* Less intuitive for developers who are familiar with `filter` and `map`
* Can lead to more complex code due to the use of a single method for multiple operations

**Approach 3: `reduce` (without spread operator)**

This approach uses `Array.prototype.reduce()` to filter out non-"Feature" elements while accumulating an array of ids. No spread operator is used.

**Pros:**

* Similar to Approach 2, with the added advantage of being more intuitive for developers familiar with `filter` and `map`

**Cons:**

* More complex code due to the need to manually push elements to the accumulator array

**Library Usage**

The benchmark uses the following libraries:

* None explicitly mentioned in the provided benchmark definitions. However, it is likely that the test environment (e.g., Browser) includes standard library functions like `Array.prototype.filter()` and `Array.prototype.map()`, as well as `Array.prototype.reduce()`.

**Special JS Feature or Syntax**

The benchmark uses ES6+ syntax features like:

* Arrow functions (`const isFeatureNode = (feature) => feature.type === 'Feature';`)
* Spread operator (`...`)

These features are used to make the code more concise and readable, but may not be supported in older browsers.

**Other Alternatives**

Other approaches could include:

* Using `Array.prototype.forEach()` with a callback function that filters out non-"Feature" elements and accumulates an array of ids.
* Using a library like Lodash, which provides a `filter` function for filtering arrays.

However, these alternatives may not be as efficient or intuitive as the three approaches compared in this benchmark.

Related benchmarks:

filter,map,reduce 2 (version: 0)

filter.map vs reduce

Comparing performance of: filter,map vs reduce spread vs reduce push

Created: 3 years ago by: Guest

Jump to the latest result

filter,map

reduce spread

reduce push

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):