Benchmark: speed test date-fns.parse

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/date-fns/1.30.1/date_fns.js'></script>

Script Preparation code:

var n3 = 1000;
var n4 = 10000;
var array3 = [];
var array4 = [];
for(let i = 0; i < n3; i++) {
 array3.push('200' + Math.round(Math.random()*9) + '-0' + Math.ceil(Math.random()*9) + '-0' + Math.ceil(Math.random()*9)); 
}
for(let i = 0; i < n4; i++) {
 array4.push('200' + Math.round(Math.random()*9) + '-0' + Math.ceil(Math.random()*9) + '-0' + Math.ceil(Math.random()*9)); 
}

function iterateLodash(arr) { 
	for (const item of arr) {
      dateFns.parse(item, 'yyyy-MM-dd');
    }
}

Tests:

iterate lodash n = 1000
iterateLodash(array3);
iterate lodash n = 10000
iterateLodash(array4);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
iterate lodash n = 1000
iterate lodash n = 10000

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its components.

**Benchmark Definition JSON**

The benchmark is defined as a JSON object that contains metadata about the test, such as its name, description, script preparation code, and HTML preparation code for loading external libraries.

In this case, we have two benchmark definitions:

1. The "Script Preparation Code" section defines two arrays `array3` and `array4`, each with 1000 and 10,000 elements respectively. Each element is a string in the format `200 + Math.random() * 9 - 0 + Math.ceil(Math.random() * 9) - 0 + Math.ceil(Math.random() * 9)`. This code creates arrays that will be used to test the performance of the `dateFns.parse()` function.
2. The "Html Preparation Code" section loads an external library, `date-fns.js`, which provides the `dateFns.parse()` function that is being tested.

**Individual Test Cases**

We have two individual test cases:

1. "iterate lodash n = 1000": This test case uses the `array3` array and calls the `iterateLodash()` function with it as an argument.
2. "iterate lodash n = 10000": This test case uses the `array4` array and calls the same `iterateLodash()` function with it as an argument.

**Options Compared**

The two test cases differ in the size of the input array: one has 1000 elements, while the other has 10,000 elements. The test cases are designed to measure the performance of the `dateFns.parse()` function for different input sizes.

**Pros and Cons**

The choice of testing both small and large arrays can provide valuable insights into how the `dateFns.parse()` function scales with input size. However, it's worth noting that:

* For very small inputs (e.g., 1000 elements), the overhead of calling a function and processing each element might dominate the time spent parsing the dates.
* For very large inputs (e.g., 10,000 elements), the focus is on measuring the performance of the `dateFns.parse()` function itself.

**Library: date-fns**

The `date-fns` library provides the `parse()` function that is being tested. The `parse()` function takes two arguments: a string to parse and an optional format string (in this case, `'yyyy-MM-dd'`). The function returns a Date object or throws an error if the input string cannot be parsed.

**Special JavaScript Feature or Syntax**

There doesn't seem to be any special JavaScript features or syntax being used in this benchmark. However, it's worth noting that some browsers might have specific optimization flags or settings that could affect the performance of the `dateFns.parse()` function.

**Other Alternatives**

If you wanted to measure the performance of a different date parsing library or implementation, you could modify the benchmark definition and script preparation code accordingly. For example, you could use a custom implementation of the `parse()` function instead of using an external library.

Keep in mind that measuring performance is often dependent on specific use cases and requirements. You might want to consider factors like:

* Input data distribution (e.g., are dates evenly distributed or concentrated in certain ranges?)
* Performance metrics (e.g., parsing time, memory usage, or both?)
* Specific browser or platform optimizations
* Desired level of detail for the benchmark results

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36

Browser/OS: Chrome 113 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
iterate lodash n = 1000	559.8 Ops/sec
iterate lodash n = 10000	56.6 Ops/sec

Related benchmarks:

speed test date-fns.parse (version: 0)

Comparing performance of: iterate lodash n = 1000 vs iterate lodash n = 10000

Created: one year ago by: Guest

Jump to the latest result

iterate lodash n = 1000

iterate lodash n = 10000

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):