Benchmark: concat vs spread test in js

Tests:

spread test
let input1 = [1,2,3,4,45,6,7,7,8,9,0] let input2 = [11,22,33,44,55,66,77,88,99,111,123,234,56] const spread = [...input1, ...input2]
concat test
let input1 = [1,2,3,4,45,6,7,7,8,9,0] let input2 = [11,22,33,44,55,66,77,88,99,111,123,234,56] const concat = input1.concat(input2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
spread test
concat test

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 dive into the world of JavaScript microbenchmarks!

**What is being tested?**

MeasureThat.net is testing two different approaches to concatenate arrays in JavaScript: using the spread operator (`...`) and using the `concat()` method.

The test cases are designed to compare the performance of these two methods when concatenating large arrays. The input arrays have varying lengths, ranging from 11 elements to over 13 elements.

**Options being compared**

The two options being compared are:

1. **Spread Operator (`...`)**: This syntax uses the spread operator to create a new array by copying elements from multiple sources.
2. **Concat() Method**: This method is used to concatenate two or more arrays and returns a new array with all the elements.

**Pros and Cons of each approach**

1. **Spread Operator (`...`)**:
	* Pros:
		+ More concise and readable code
		+ Creates a new array, which can be beneficial for large datasets
	* Cons:
		+ Can lead to slower performance due to creating a new array
		+ May not be suitable for very large arrays due to memory constraints
2. **Concat() Method**:
	* Pros:
		+ Can be faster and more efficient for large arrays, as it modifies the original array
		+ Does not create a new array, which can reduce memory usage
	* Cons:
		+ Code can become longer and less readable due to using `concat()` multiple times
		+ May lead to unintended side effects if the original array is modified

**Library or special JS feature**

There are no libraries or special JavaScript features being used in this benchmark. The focus is solely on comparing the performance of two native JavaScript methods.

**Other considerations**

When dealing with large arrays, it's essential to consider the following:

* **Memory usage**: Creating a new array using the spread operator can lead to increased memory usage, especially for very large datasets.
* **Cache friendliness**: The cache-friendly nature of the `concat()` method can result in better performance on certain hardware architectures.

**Alternatives**

If you're looking for alternative approaches to concatenate arrays in JavaScript, consider the following:

1. **Array.prototype.push()**: This method is similar to using `concat()`, but it modifies the original array.
2. **Array.prototype.splice()**: This method can be used to concatenate two or more arrays by using `splice()` with a positive offset and a length greater than 0.

However, these alternatives may not provide significant performance benefits compared to the spread operator or `concat()` method in most cases.

In conclusion, this benchmark provides valuable insights into the performance of two popular JavaScript methods for concatenating arrays. The results can help you make informed decisions about which approach to use in your own codebase, depending on your specific requirements and constraints.

Latest run results:

Run details: (Test run date: 4 months ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.0 Safari/605.1.15

Browser/OS: Safari 17 on Linux

View result in a separate tab

Test name	Executions per second
spread test	2690963.2 Ops/sec
concat test	2321740.0 Ops/sec

Related benchmarks:

concat vs spread test in js (version: 0)

Comparing performance of: spread test vs concat test

Created: 4 months ago by: Guest

Jump to the latest result

spread test

concat test

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):