Benchmark: My messy array test_3

Script Preparation code:

var k1_rand = new Array(1000); 
for (i = 0; i < k1_rand.length; i++) {
  k1_rand[i] = { date: (Math.random() * (new Date(2012, 0, 1).getTime())), value: i }
}

var k5_rand = new Array(5000);
for (i = 0; i < k5_rand.length; i++) {
  k5_rand[i] = { date: (Math.random() * (new Date(2012, 0, 1).getTime())), value: i }
}

var k1_straight = new Array(1000); 
for (i = 0; i < k1_straight.length; i++) {
  k1_straight[i] = { date: (new Date(2012, 0, 1).getTime() + i), value: i }
}

var k5_straight = new Array(5000);
for (i = 0; i < k5_straight.length; i++) {
  k5_straight[i] = { date: (new Date(2012, 0, 1).getTime() + i), value: i }
}

Tests:

k1 rand sort
k1_rand.sort((a,b) => (a.date > b.date) ? 1 : ((b.date > a.date) ? -1 : 0))
k5 rand sort
k5_rand.sort((a,b) => (a.date > b.date) ? 1 : ((b.date > a.date) ? -1 : 0))
k1 straight sort
k1_straight.sort((a,b) => (a.date > b.date) ? 1 : ((b.date > a.date) ? -1 : 0))
k5 straight sort
k5_straight.sort((a,b) => (a.date > b.date) ? 1 : ((b.date > a.date) ? -1 : 0))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
k1 rand sort
k5 rand sort
k1 straight sort
k5 straight sort

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 JSON and explain what is being tested.

**Benchmark Definition**

The benchmark definition is a JavaScript function that sorts an array of objects based on a specific date field. The sorting function uses a custom comparison logic to determine the order of the elements in the array.

**Test Cases**

There are four test cases, each with its own benchmark definition:

1. `k1_rand.sort((a,b) => (a.date > b.date) ? 1 : ((b.date > a.date) ? -1 : 0))`
	* This test case creates an array of 1000 random objects with a `date` field and sorts them in ascending order based on the `date`.
2. `k5_rand.sort((a,b) => (a.date > b.date) ? 1 : ((b.date > a.date) ? -1 : 0))`
	* This test case creates an array of 5000 random objects with a `date` field and sorts them in ascending order based on the `date`.
3. `k1_straight.sort((a,b) => (a.date > b.date) ? 1 : ((b.date > a.date) ? -1 : 0))`
	* This test case creates an array of 1000 objects with fixed dates and sorts them in ascending order based on the date.
4. `k5_straight.sort((a,b) => (a.date > b.date) ? 1 : ((b.date > a.date) ? -1 : 0))`
	* This test case creates an array of 5000 objects with fixed dates and sorts them in ascending order based on the date.

**Library**

The `Date` object is used throughout the benchmark definitions. The `Date` object provides methods for working with dates, including creating new dates, adding or subtracting time components, and comparing dates.

**Special JS Features/Syntax**

None of the benchmark definitions use any special JavaScript features or syntax that are specific to a particular browser or implementation.

**Options Compared**

The different test cases compare the performance of sorting arrays in two ways:

1. **Randomized data**: The `k1_rand` and `k5_rand` test cases sort arrays with randomized data, which means the dates will be different each time the benchmark is run. This allows for a more realistic representation of how the sorting algorithm would perform in real-world scenarios.
2. **Fixed data**: The `k1_straight` and `k5_straight` test cases sort arrays with fixed data, where the dates are known beforehand. This helps to isolate the effect of the sorting algorithm itself, rather than any external factors that might affect performance.

**Pros and Cons**

The pros and cons of each approach are:

* **Randomized data**: Pros:
	+ Provides a more realistic representation of real-world scenarios.
	+ Can help identify issues with the sorting algorithm's ability to handle varying input data.
* Cons:
	+ Requires more computational resources, as the benchmark must generate random data for each test case.
	+ May not be suitable for identifying specific performance bottlenecks or edge cases.
* **Fixed data**: Pros:
	+ Isolation of the sorting algorithm's effect on performance.
	+ Can help identify issues with the algorithm's ability to handle predictable input data.
* Cons:
	+ May not provide a realistic representation of real-world scenarios.
	+ Can make it more difficult to identify issues related to handling varying input data.

**Other Alternatives**

Some alternative approaches that could be used to test sorting algorithms include:

1. **Pre-sorted data**: Sort the arrays in advance and compare the performance of different sorting algorithms on pre-sorted data.
2. **Edge cases**: Test specific edge cases, such as sorting arrays with duplicate elements or arrays with a single element.
3. **Real-world datasets**: Use real-world datasets to test the performance of sorting algorithms in more realistic scenarios.

However, these alternative approaches may not provide the same level of control and isolation as the randomized data approach used in the benchmark definition.

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.181 Safari/537.36

Browser/OS: Chrome 66 on Windows

View result in a separate tab

Test name	Executions per second
k1 rand sort	5943.1 Ops/sec
k5 rand sort	879.4 Ops/sec
k1 straight sort	5932.6 Ops/sec
k5 straight sort	923.1 Ops/sec

Related benchmarks:

My messy array test_3 (version: 5)

Comparing performance of: k1 rand sort vs k5 rand sort vs k1 straight sort vs k5 straight sort

Created: 5 years ago by: Registered User

Jump to the latest result

k1 rand sort

k5 rand sort

k1 straight sort

k5 straight sort

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):