Benchmark: Array.prototype.concat vs splice for joining lots of arrays

Tests:

immutable with concat
var a = [] var b = [ "hello", "jello" ] for (var i = 0; i < 1000; i++) { a = a.concat(b) }
direct mutation with splice + spread
var a = [] var b = [ "hello", "jello" ] for (var i = 0; i < 1000; i++) { a.splice(0, a.length, ...b) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
immutable with concat
direct mutation with splice + spread

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark measures the performance difference between two approaches for joining multiple arrays in JavaScript:

1. **Immutable approach using `concat()`**: This method creates a new array by concatenating the existing array (`a`) with another array (`b`).
2. **Mutable approach using `splice()` and spread operator**: This method modifies the existing array (`a`) by splicing new elements from array (`b`) into it.

**Options Being Compared**

The benchmark compares two options:

1. **Immutable approach using `concat()```**: This method creates a new array for each iteration, resulting in O(n) growth.
2. **Mutable approach using `splice()` and spread operator```: This method modifies the existing array in place, but requires creating an intermediate array to store the new elements.

**Pros and Cons of Each Approach**

**Immutable Approach using `concat()```**

Pros:

* Easy to understand and implement
* No risk of modifying external data structures

Cons:

* Creates a new array for each iteration, resulting in O(n) growth
* Can be slower due to the overhead of creating new arrays

**Mutable Approach using `splice()` and spread operator```

Pros:

* Modifies the existing array in place, reducing memory allocation
* Can be faster due to reduced array creation overhead

Cons:

* Requires understanding of the spread operator and `splice()` method
* Risk of modifying external data structures if not handled carefully

**Library Usage**

In this benchmark, no specific libraries are used. However, some browsers may have optimized implementations of these methods that could affect performance.

**Special JavaScript Features or Syntax**

The benchmark uses the following special features:

* The spread operator (`...`) is used to create a new array from an existing one.
* The `splice()` method is used to modify the length and content of an array.

Note that the use of these features does not significantly impact the performance analysis, as they are relatively well understood and widely supported in modern browsers.

**Other Alternatives**

Other alternatives for joining multiple arrays in JavaScript could include:

* Using a library like Lodash or Ramda, which provide optimized implementations of array operations.
* Using a streaming approach that processes arrays one element at a time, rather than creating an intermediate array.
* Using a specialized data structure like a linked list or a balanced binary search tree to reduce memory allocation.

However, these alternatives may not be relevant to this specific benchmark, as it focuses on the performance difference between two basic approaches.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.75 Safari/537.36

Browser/OS: Chrome 73 on Mac OS X 10.14.0

View result in a separate tab

Test name	Executions per second
immutable with concat	474.6 Ops/sec
direct mutation with splice + spread	4071.4 Ops/sec

Related benchmarks:

Array.prototype.concat vs splice for joining lots of arrays (version: 0)

Comparing performance of: immutable with concat vs direct mutation with splice + spread

Created: 5 years ago by: Guest

Jump to the latest result

immutable with concat

direct mutation with splice + spread

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):