Benchmark: fight to the death - for loop vs chained methods1

Script Preparation code:

const randomStDate = () => {
    const randD = new Date(new Date("2021-12-05").getTime() + Math.random() * (new Date("2026-12-05").getTime() - new Date("2023-12-05").getTime()))
    let month = '' + (randD.getMonth() + 1);
    let day = '' + randD.getDate();
    let year = randD.getFullYear();
    if (month.length < 2) month = '0' + month;
    if (day.length < 2) day = '0' + day;
    return [year, month, day].join('-');

};

const rndId = Math.floor(Math.random() * (100000000 - 1 + 1) + 1)

const createArr = () => {
    let workArr = []
    for (let i = 0; i < 10; i++) {
        const pl = {
            tisId: rndId,
            startDate: randomStDate(),
            endDate: "2026-12-05",
            site: "Liverpool",
        }
        workArr.push(pl)

    }
    return workArr
}

var workArr = createArr()
var today =  new Date().toISOString().slice(0,10)

Tests:

for loop
let pastArr = []; let futureArr = []; for (let i = 0; i < workArr.length; i++) { const pl = workArr[i]; switch(true){ case pl.startDate < today : pastArr.push(pl); break; case futureArr.length < 1 && pl.startDate >= today : futureArr = [pl]; break; case futureArr.length > 0 && pl.startDate < futureArr[0].startDate : futureArr = [pl]; break; case futureArr.length > 0 && pl.startDate === futureArr[0].startDate : futureArr.push(pl); break; } } return [...pastArr, ...futureArr].sort((a, b) => (a.startDate > b.startDate ? 1 : -1));
chained arr
const today = new Date().toISOString().slice(0,10) const firstFutureWorks = workArr .filter(w => w.startDate > today) .sort((a, b) => (a.startDate > b.startDate ? 1 : -1)); const nextFutureDate = firstFutureWorks[0] ? firstFutureWorks[0].startDate : today; return workArr.filter(w => w.startDate <= nextFutureDate);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for loop
chained arr

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**Benchmark Definition and Test Cases**

The benchmark is designed to compare two approaches for sorting an array of objects based on their `startDate` property. The test cases are "for loop" and "chained arr".

**For Loop Approach**

The first test case, "for loop", uses a traditional `for` loop to iterate over the `workArr` array and perform the following steps:

1. Push each object into an empty array `pastArr` if its `startDate` is less than the current date.
2. If `futureArr` is empty or the current object's `startDate` is greater than or equal to the current date, create a new array with the current object and assign it to `futureArr`.
3. If the current object's `startDate` is less than the `startDate` of the first element in `futureArr`, replace the first element in `futureArr` with the current object.
4. Return a sorted array by concatenating `pastArr` and `futureArr` and sorting it based on the `startDate`.

**Chained Arr Approach**

The second test case, "chained arr", uses chained method calls to filter and sort the `workArr` array:

1. Filter out objects with `startDate` greater than the current date.
2. Sort the remaining objects by their `startDate`.
3. If the first object's `startDate` is not null or undefined, use it as the next future date; otherwise, use the current date.

**Pros and Cons**

Both approaches have their pros and cons:

* **For Loop Approach**:
	+ Pros: Easy to understand and implement.
	+ Cons: Can be slower due to the overhead of the loop and the use of `push` and `sort`.
* **Chained Arr Approach**:
	+ Pros: Often faster and more efficient, as it uses optimized method calls and avoids the need for loops and array manipulation.
	+ Cons: May require more expertise to understand and implement correctly.

**Other Considerations**

The benchmark measures the performance of these two approaches in terms of executions per second. This metric provides a general idea of how well each approach performs, but keep in mind that it's not necessarily a direct measure of code quality or maintainability.

**Library Usage**

In this benchmark, there is no explicit library usage mentioned. However, some modern JavaScript environments and browsers use various libraries and engines that can impact performance, such as WebAssembly, Just-In-Time (JIT) compilation, or GPU acceleration.

**Special JS Features or Syntax**

This benchmark does not explicitly test any special JavaScript features or syntax, but it's essential to consider the implications of using certain features, such as async/await, closures, or arrow functions, on performance and code readability.

In conclusion, this benchmark provides a useful comparison of two common approaches for sorting arrays in JavaScript. While both methods have their pros and cons, the chained arr approach often appears to be faster and more efficient. However, it's essential to consider other factors, such as code maintainability and expertise requirements, when choosing an approach.

**Other Alternatives**

For benchmarking JavaScript performance, you may also want to explore other tools and approaches, such as:

* V8 Benchmark Suite: A set of benchmarks for the V8 JavaScript engine.
* JSPerf: A tool for measuring JavaScript performance in a headless browser environment.
* Google's High Performance Browser Benchmarks (HPBB): A benchmarking framework for measuring web page loading times.

These tools can provide more comprehensive and accurate measurements, but they often require more expertise to set up and interpret.

Related benchmarks:

fight to the death - for loop vs chained methods1 (version: 0)

array length 10

Comparing performance of: for loop vs chained arr

Created: 3 years ago by: Registered User

Jump to the latest result

for loop

chained arr

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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