Benchmark: test normal vs spread_v4

Tests:

normal
const x=[]; const y = []; const z = []; for (let i = 0; i < 1000; i++) { x.push(String(i)); y.push(String(i)); z.push(String(i)); } let a = 0; for (let i = 0; i < x.length; i++ ){ a++; } for (let i = 0; i < y.length; i++ ){ a++; } for (let i = 0; i < z.length; i++ ){ a++; }
bad
const x=[]; const y = []; const z = []; for (let i = 0; i < 1000; i++) { x.push(String(i)); y.push(String(i)); z.push(String(i)); } let a = 0; [...x,...y,...z].forEach((z) => {a++;});
different
const x=[]; const y = []; const z = []; for (let i = 0; i < 1000; i++) { x.push(String(i)); y.push(String(i)); z.push(String(i)); } let a = 0; [{r: x}, {r:y}, {r:z}].forEach((z) => { z.r.forEach((t) => {a++;}); });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
normal
bad
different

Fastest: N/A

Slowest: N/A

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/116.0.0.0 Safari/537.36

Browser/OS: Chrome 116 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
normal	4585.2 Ops/sec
bad	3677.3 Ops/sec
different	4270.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks and explore what's being tested on MeasureThat.net.

**Benchmark Overview**

The provided benchmark compares three different approaches to push elements onto arrays in JavaScript, followed by an additional test that uses object destructuring ( spread syntax).

**Test Case 1: "normal"**

This test case uses a traditional `for` loop to iterate over the range of numbers 0 to 999, pushing each number as a string into three separate arrays (`x`, `y`, and `z`). After populating the arrays, it increments a variable `a` by 1 for each element in the arrays using another `for` loop.

**Test Case 2: "bad"**

This test case is similar to the "normal" test, but with an added layer of complexity. Instead of using traditional `for` loops, it uses the spread operator (`...`) to concatenate the three arrays into a single array. The `forEach()` method is then used to iterate over the resulting array and increment the variable `a`.

**Test Case 3: "different"**

This test case uses object destructuring (spread syntax) to destructure the three arrays (`x`, `y`, and `z`) into separate variables, and then iterates over each of these variables using another `forEach()` method. The spread operator is used again to flatten the resulting array.

**Library Used: None**

There are no libraries being used in this benchmark, so we can focus on the language-specific features and syntax.

**Special JS Feature/Syntax: Spread Operator (`...`)**

The spread operator is a modern JavaScript feature introduced in ECMAScript 2015. It allows you to expand an array or object into individual elements, making it easier to work with arrays and objects. In this benchmark, the spread operator is used in two test cases (`"bad"` and `"different"`).

**Pros and Cons**

Here are some pros and cons of each approach:

* Traditional `for` loops (Test Case 1):
	+ Pros: Easy to understand and implement, no additional dependencies.
	+ Cons: Can be slow for large datasets due to the overhead of manual indexing.
* Spread Operator (`...`) (Test Cases 2 and 3):
	+ Pros: More concise and readable, can reduce code complexity. However, it may introduce additional overhead due to the creation of intermediate arrays.
	+ Cons: May not perform as well as traditional `for` loops for very large datasets.

**Other Alternatives**

In a real-world scenario, you might consider alternative approaches such as:

* Using `Array.prototype.map()` or `Array.prototype.forEach()`, which are more concise and efficient than manual indexing with `for` loops.
* Utilizing modern JavaScript features like `Symbol.iterator` or `Promise.all()`, depending on the specific use case.

Keep in mind that microbenchmarks can be highly dependent on the specific requirements of your project, so it's essential to evaluate each approach based on your specific needs and performance characteristics.

Related benchmarks:

test normal vs spread_v4 (version: 0)

Comparing performance of: normal vs bad vs different

Created: one year ago by: Guest

Jump to the latest result

normal

bad

different

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):