Benchmark: Array .push() vs .unshift() with multiple elements

Script Preparation code:

var arr = [];
var items = [1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0];

Tests:

.unshift()
arr.length = 0; arr.unshift(...items);
.push()
arr.length = 0; arr.push(...items);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
.unshift()
.push()

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the provided benchmark and explain what is being tested, compared, and other considerations.

**Benchmark Definition**

The provided JSON defines a benchmark for measuring the performance difference between `Array.prototype.unshift()` and `Array.prototype.push()` when appending multiple elements to an array. The benchmark definition consists of two test cases:

1. `.unshift()`: Tests the performance of `arr.unshift(...items)` where `arr` is an empty array, and `items` is a large array containing 40 unique integers.
2. `.push()`: Tests the performance of `arr.push(...items)` with the same conditions as above.

**Options Compared**

The two options being compared are:

1. `Array.prototype.unshift()`: This method adds one or more elements to the beginning of an array and returns the new length of the array.
2. `Array.prototype.push()`: This method adds one or more elements to the end of an array and returns the new length of the array.

**Pros and Cons**

* **`Array.prototype.unshift()`**: Pros:
	+ Can be faster for large arrays because it modifies the original array in place, reducing memory allocation.
	+ Reduces the number of iterations over the array, which can lead to better cache locality.
* Cons:
	+ Requires more memory initially due to the need to allocate space for the new elements.
	+ May cause issues when working with sparse arrays or arrays that have a large gap in their indices.
* **`Array.prototype.push()`**: Pros:
	+ More intuitive and easier to read, as it appends elements to the end of the array.
	+ Less prone to errors due to its more predictable behavior.
* Cons:
	+ May be slower for large arrays because it creates a new array in memory, which can lead to increased memory allocation.

**Library and Special JS Features**

There are no libraries or special JavaScript features being used in this benchmark. The tests only rely on the built-in `Array.prototype` methods.

**Other Considerations**

* **Cache Locality**: Both methods have varying cache locality due to their indexing behavior. `unshift()` has better cache locality because it accesses elements at the beginning of the array, while `push()` accesses elements towards the end.
* **Memory Allocation**: The memory allocation pattern is different for each method. `unshift()` modifies the original array in place, reducing memory allocation, while `push()` creates a new array in memory.
* **Browser Variability**: The benchmark results are specific to Chrome 106 on Windows Desktop devices. It's essential to test on multiple browsers, devices, and platforms to ensure the findings generalize.

**Alternatives**

Other alternatives for appending elements to an array include:

1. Using `concat()`: This method returns a new array that contains all the elements of the original array and then adds the provided elements.
2. Using `splice()`: This method removes or replaces existing elements in an array and inserts new elements into the specified position.
3. Using `Array.prototype.set()` (in older browsers): This method sets the values of a range of elements in an array.

These alternatives may offer different trade-offs in terms of performance, memory usage, and readability, depending on the specific use case.

Related benchmarks:

Array .push() vs .unshift() with multiple elements (version: 0)

Comparing performance of: .unshift() vs .push()

Created: 5 years ago by: Guest

Jump to the latest result

.unshift()

.push()

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):