Benchmark: Javascript: reduce VS for

Javascript: reduce VS for (version: 0)

Compare the "reduce" method with the "for" iteration method. Use case: There is an array with different numbers, we will compare the performance between the two methods to find the quantity of numbers greater than zero in the array list.

Comparing performance of: Number 1: use for iterator vs Number 2: using reduce method

Created: 4 years ago by: Guest

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Script Preparation code:

var list = [1, 0, -2, -1, 3, 0, 1, 8, -10, 3, 0, -1, -3];

Tests:

Number 1: use for iterator
let countNumberGreaterThanZero = 0; for (let i = 0; i < list.length; i++) { if (list[i] > 0) { countNumberGreaterThanZero++; } }
Number 2: using reduce method
let countsNumbersGreaterThanZero = (acc, cur) => acc + (cur > 0 ? 1 : 0); list.reduce(countsNumbersGreaterThanZero, 0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Number 1: use for iterator
Number 2: using reduce method

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36 Edg/123.0.0.0

Browser/OS: Chrome 123 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Number 1: use for iterator	1190803.0 Ops/sec
Number 2: using reduce method	23131388.0 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided benchmark compares the performance of two approaches to count the numbers greater than zero in an array: using a traditional `for` loop and the `reduce()` method.

**Test Cases**

There are two test cases:

1. **Number 1: use for iterator**
This test case uses a traditional `for` loop to iterate over the array and increment a counter variable (`countNumberGreaterThanZero`) whenever it encounters a value greater than zero.
2. **Number 2: using reduce method**
This test case uses the `reduce()` method, which applies a callback function (in this case, `countsNumbersGreaterThanZero`) to each element of the array, accumulating a result.

**Options Compared**

The benchmark compares the performance of these two approaches:

* Traditional `for` loop
* `reduce()` method

**Pros and Cons**

**Traditional `for` Loop:**

Pros:

* Easy to understand and implement
* Can be optimized for specific use cases (e.g., caching, parallel execution)

Cons:

* Can be slower than other approaches due to the overhead of the loop and conditional statements
* May not scale well for large datasets

**Reduce() Method:**

Pros:

* Can be more concise and expressive than traditional loops
* Can be optimized for specific use cases (e.g., caching, parallel execution)

Cons:

* Can be less intuitive for beginners due to its functional programming nature
* May have additional overhead due to the creation of an accumulator object

**Library Used:**

The `reduce()` method uses the Array.prototype.reduce() method, which is a built-in JavaScript method that applies a callback function to each element in the array, accumulating a result.

**Special JS Feature/Syntax:**

None mentioned explicitly, but note that the use of arrow functions (`=>`) and template literals (`\r\n`) are supported by modern JavaScript engines. These features are not specific to this benchmark but are part of the broader JavaScript language.

**Other Alternatives:**

1. **Map() Method:** Instead of using `reduce()`, you could use the Array.prototype.map() method to create a new array with transformed values, and then filter or sum the resulting array.
2. **forEach() Method:** You could also use the Array.prototype.forEach() method to iterate over the array, but this would not be as concise as the `reduce()` approach.

**Benchmark Considerations:**

When interpreting benchmark results, consider factors such as:

* The specific dataset used (e.g., small vs. large arrays)
* The JavaScript engine and version used
* Any optimizations or caching applied by the benchmark code

Keep in mind that benchmarks can be influenced by various factors beyond just the algorithm being tested.

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