Benchmark: REDUCE vs FOR 2018-09

Script Preparation code:

var datas = [{ean:"123456789012", hash:"SNFOsdNSF12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSFfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"ONSF12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJSBFOnoi", price:"43,7"},{ean:"123456789012", hash:"SNFONdfdfdf12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJSBFOnoinfdoinfn22fdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF1232433FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SN44FONSF12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJSBFOnoinfdoi55nfnfdin", price:"43,7"},{ean:"123456789012", hash:"SN66FONSF12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"77SNFONSF12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJSBFOnoinfdoinfn88fdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF9912324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJSB000FOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJSBFOnoi2334nfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF123267778JSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"S9ONSF12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFON0000TRTR24FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF1232YUYU4FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFOYNSF12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNIOIFONSF12324FJSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJSBFOnoinIfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324F2P2JSBFOnoinfdoinfnfdin", price:"43,7"},{ean:"123456789012", hash:"SNFONSF12324FJSBFOnoinfdoinf44nfBBBdin", price:"43,7"},{ean:"123456789012", hash:"SNFVVVONSF12324FJSB333FOnoinfdoinfnfdin", price:"43,7"}]
var array = [];

Tests:

for
for(var i = 0; i < datas.length; i++){ array[datas[i].hash] = datas[i].price; }
reduce
array = datas.reduce(function(a, product){ a[product.hash] = product.price; return a; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 6 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.81 Safari/537.36

Browser/OS: Chrome 69 on Mac OS X 10.13.6

View result in a separate tab

Test name	Executions per second
for	100468.3 Ops/sec
reduce	325841.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of MeasureThat.net and explore what's being tested in this benchmark.

**Benchmark Purpose**

The purpose of this benchmark is to compare two approaches for populating an object array from an array of data objects: the `for` loop and the `reduce()` method. The test case uses a sample dataset with 23 entries, each containing an "ean" ( presumably an identifier), a "hash", and a "price".

**Options Being Compared**

The two options being compared are:

1. **For Loop**: This approach uses a traditional `for` loop to iterate through the data array and populate the object array.
2. **Reduce() Method**: This approach uses the `reduce()` method, which applies a callback function to each element in the array, accumulating a result.

**Pros and Cons**

* **For Loop**
	+ Pros: Easy to understand and implement, suitable for small datasets.
	+ Cons: Can be slower and more memory-intensive for large datasets, as it creates a new array with intermediate results.
* **Reduce() Method**
	+ Pros: Efficient and concise way to process arrays, reducing memory usage.
	+ Cons: May be less intuitive for developers who are not familiar with the `reduce()` method.

**Library and Purpose**

The `reduce()` method is a built-in JavaScript function that applies a callback function to each element in an array, accumulating a result. In this benchmark, it's used to populate the object array by iterating through the data array and updating the corresponding values in the object array.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntaxes being tested in this benchmark. However, it's worth noting that the `reduce()` method is a modern JavaScript feature introduced in ECMAScript 2015 (ES6).

**Other Alternatives**

If you're interested in exploring alternative approaches to populating an object array from an array of data objects, some other options include:

* Using `forEach()`: This method is similar to `for` loop but provides additional benefits like support for generators and async iterations.
* Utilizing a mapping function (e.g., `Array.prototype.map()`): This approach can be more concise than using `reduce()` or a `for` loop, but may not be as efficient.
* Leveraging a third-party library (e.g., Lodash's `mapValues()` function): These libraries provide optimized and tested implementations for common array processing tasks.

I hope this explanation helps you understand the benchmark being tested on MeasureThat.net!

Related benchmarks:

REDUCE vs FOR 2018-09 (version: 0)

Comparing performance of: for vs reduce

Created: 6 years ago by: Guest

Jump to the latest result

for

reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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