Benchmark: arr.slice(-1)[0] vs arr[arr.length - 1]

Script Preparation code:

var arr = Array(100000).fill(1);

Tests:

arr.slice(-1)[0]
arr.slice(-1)[0];
arr[arr.length - 1]
arr[arr.length - 1];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
arr.slice(-1)[0]
arr[arr.length - 1]

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided JSON benchmark definitions and explain what's being tested, compared, and some pros/cons of different approaches.

**Benchmark Definition**

The first part is a single "Benchmark Definition" JSON object that describes the overall benchmark:
```json
{
    "Name": "arr.slice(-1)[0] vs arr[arr.length - 1]",
    "Description": null,
    "Script Preparation Code": "var arr = Array(100000).fill(1);",
    "Html Preparation Code": null
}
```
Here, we have:

* `Name`: A descriptive name for the benchmark.
* `Description`: An empty description (usually not used).
* `Script Preparation Code`: A JavaScript code snippet that sets up a test array with 100,000 elements filled with the value 1. This is a common pattern to ensure reproducibility and consistency in benchmarks.
* `Html Preparation Code`: An empty HTML code snippet, indicating that no specific HTML setup is required for this benchmark.

**Individual Test Cases**

The second part contains an array of individual test cases:
```json
[
    {
        "Benchmark Definition": "arr.slice(-1)[0];",
        "Test Name": "arr.slice(-1)[0]"
    },
    {
        "Benchmark Definition": "arr[arr.length - 1];",
        "Test Name": "arr[arr.length - 1]"
    }
]
```
Here, we have:

* `Benchmark Definition`: A specific JavaScript expression being tested.
* `Test Name`: A descriptive name for the test case.

**What's Being Tested**

In essence, these benchmarks are comparing two different ways of accessing an element at the end of a large array in JavaScript:

1. `arr.slice(-1)[0]`
2. `arr[arr.length - 1]`

The first approach uses the `slice()` method to create a new array with a single element (the last one), and then accesses that element using `[0]`. The second approach simply accesses the last element directly.

**Options Compared**

Here, we're comparing two different approaches:

* **Approach 1: `arr.slice(-1)[0]`**
	+ Creates a new array with the last element.
	+ May incur additional memory allocation and copying overhead.
	+ Pros:
		- Can be more predictable and consistent in terms of performance.
		- May provide better cache locality.
	Cons:
		- Can result in higher memory usage due to the extra array creation.
	+ Note: The `slice()` method creates a new array, which may not be optimal for large datasets.

* **Approach 2: `arr[arr.length - 1]`**
	+ Directly accesses the last element without creating an intermediate array.
	+ May incur additional overhead due to the indexing operation.
	Pros:
		- Typically results in lower memory usage and faster execution times.
	Cons:
		- Can be less predictable and more sensitive to caching and array layout.

**Other Considerations**

* **Array Spawning**: The `fill()` method used in the script preparation code is called "array spawning". This can have performance implications, as it creates a new array without copying the elements. However, modern JavaScript engines often optimize this pattern.
* **Cache Locality**: Both approaches can benefit from cache locality, where the last element of the array is located in a contiguous block of memory, allowing for faster access.

**Library and Special JS Features**

There are no libraries or special JavaScript features explicitly mentioned in the benchmark definition. However, some modern browsers may provide additional optimization or caching mechanisms that could affect the performance of these benchmarks.

**Alternatives**

Some alternative approaches to accessing an element at the end of a large array include:

* **Using a direct indexing approach**: `arr[-1]` (note: using a negative index can be slower due to bounds checking).
* **Using a custom iterator or generator function**: Can provide more control over iteration and caching.
* **Using a specialized library or data structure**: Such as NumJS or ArrayFire, which provide optimized arrays for performance-critical applications.

Keep in mind that the best approach will depend on the specific use case, performance requirements, and target audience.

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:123.0) Gecko/20100101 Firefox/123.0

Browser/OS: Firefox 123 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
arr.slice(-1)[0]	41266820.0 Ops/sec
arr[arr.length - 1]	849750784.0 Ops/sec

Related benchmarks:

arr.slice(-1)[0] vs arr[arr.length - 1] (version: 0)

Comparing performance of: arr.slice(-1)[0] vs arr[arr.length - 1]

Created: 3 years ago by: Guest

Jump to the latest result

arr.slice(-1)[0]

arr[arr.length - 1]

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):