Benchmark: unshift vs spread

Script Preparation code:

var array = new Array(5000).fill(1).map((item, i) => ((item * i) + i * i) + 1);

Tests:

unshift
array.unshift(0);
spread
array = [0, ...array];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
unshift
spread

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 JSON and understand what is being tested.

**Benchmark Definition**

The `Benchmark Definition` JSON represents a single benchmark test case. It includes:

1. **Name**: The name of the benchmark, which in this case is "unshift vs spread".
2. **Description**: An empty description, which suggests that the benchmarker wants to compare the performance of two specific operations: `unshift(0)` and using the spread operator (`[...array]`).
3. **Script Preparation Code**: A JavaScript code snippet that creates a large array with 5,000 elements, fills it with values, and maps each element to calculate its value. This array will be used as input for both benchmark tests.
4. **Html Preparation Code**: An empty string, indicating that no HTML preparation is required.

**Individual Test Cases**

The `Test Case` JSON defines two separate test cases:

1. **"unshift"`**:
	* Benchmark Definition: `array.unshift(0);`
	* Purpose: To measure the performance of the `unshift(0)` method.
2. **"spread"`**:
	* Benchmark Definition: `array = [0, ...array];`
	* Purpose: To measure the performance of using the spread operator (`[...array]`) to create a new array.

**Library and Special Features**

Neither of these test cases uses any specific library or JavaScript feature. The tests are focused solely on comparing the performance of the `unshift(0)` method versus the spread operator.

**Pros, Cons, and Considerations**

When it comes to benchmarking performance, there are a few options to consider:

1. **Unshift Method**: This approach is simple and straightforward but may have limitations in terms of cache locality and branching.
2. **Spread Operator**: This approach uses array destructuring and can be more efficient due to its ability to optimize function calls and avoid unnecessary copies of the original array.

Pros of using the spread operator:

* Reduced branching: The spread operator can simplify the code and reduce branching, which can lead to better performance.
* Optimized function calls: The spread operator can optimize function calls by reducing the number of arguments passed to `Array.prototype.push()`.
* Cache locality: The spread operator can improve cache locality by reusing existing array elements.

Cons of using the spread operator:

* Potential overhead: Creating a new array with the spread operator may incur additional overhead due to memory allocation and deallocation.
* Limited support: Older browsers or versions of JavaScript may not support the spread operator, which could affect compatibility.

**Other Alternatives**

If you wanted to benchmark performance, you might also consider using alternative approaches, such as:

1. **Using `Array.prototype.push()`**: This approach would involve pushing elements onto an array using `push()`, followed by concatenating or assigning the resulting array.
2. **Using a loop**: A simple loop could be used to iterate over the original array and push elements onto a new array.
3. **Using a custom implementation**: Depending on your specific requirements, you might consider implementing a custom algorithm for both operations.

Keep in mind that benchmarking performance can be complex, as it depends on various factors such as hardware, software, and network conditions. It's essential to test multiple approaches and consider these factors when interpreting the results.

Related benchmarks:

unshift vs spread (version: 0)

Comparing performance of: unshift vs spread

Created: 3 years ago by: Guest

Jump to the latest result

unshift

spread

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):