Benchmark: push vs spread 11031

Script Preparation code:

arr = Array.from({length: 1000}, () => Math.floor(Math.random() * 40));

Tests:

push
arr.reduce((acc,cur)=>{ const key = cur%10; acc[key]? acc[key].push(cur) : acc[key] = [cur] return acc; },{})
spread
arr.reduce((acc,cur)=>{ const key = cur%10; acc[key] = acc[key]? [...acc[key],cur] : [cur] return acc; },{})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
push
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):

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

The benchmark is designed to compare the performance of two approaches: using the `push` method and using the spread operator (`...`) when reducing an array in JavaScript. The test case creates an array with 1000 random elements, reduces it by grouping elements based on their remainder when divided by 10, and then pushes or spreads the result into another object.

**Script Preparation Code**

The script preparation code generates a random array `arr` with 1000 elements:
```javascript
arr = Array.from({length: 1000}, () => Math.floor(Math.random() * 40));
```
This creates an array where each element is a random integer between 0 and 39.

**Html Preparation Code**

There is no HTML preparation code, which means that the benchmark only tests the JavaScript code.

**Benchmark Definition**

The benchmark definition consists of two test cases:

1. **push**: This test case uses the `push` method to add elements to an object:
```javascript
arr.reduce((acc, cur) => {
  const key = cur % 10;
  acc[key] ? acc[key].push(cur) : acc[key] = [cur];
  return acc;
}, {});
```
2. **spread**: This test case uses the spread operator (`...`) to add elements to an object:
```javascript
arr.reduce((acc, cur) => {
  const key = cur % 10;
  acc[key] = acc[key]? [...acc[key], cur] : [cur];
  return acc;
}, {});
```
**Pros and Cons**

Using the `push` method has a few advantages:

* It is more concise and easier to read.
* It may be faster since it only requires a single operation.

However, using the `push` method also has some drawbacks:

* It can be slower than using the spread operator in certain cases, especially when dealing with large arrays or objects.
* It can lead to performance issues if the object being pushed into is very large.

Using the spread operator (`...`) has its own set of advantages and disadvantages:

* It is more modern and efficient, since it only requires a single operation like `push`.
* However, it may be slower than using the `push` method in some cases, especially when dealing with large arrays or objects.
* It can lead to memory issues if the object being spread into is very large.

**Other Considerations**

One important consideration is that both approaches assume that the key of the resulting object will always exist. If this is not the case, using `push` may lead to performance issues or errors, while using the spread operator may be more reliable.

**Libraries and Special Features**

There are no libraries used in this benchmark. However, it's worth noting that some JavaScript engines may have special features or optimizations that affect the performance of these tests.

**Alternatives**

If you want to create a similar benchmark, you can use other approaches like:

* Using `Object.assign()` instead of `push` and spread operator.
* Using `Map` or `Set` data structures instead of objects for storing results.
* Using different sorting algorithms or randomization techniques to change the behavior of the test.

Keep in mind that these alternatives may affect the accuracy or reliability of the benchmark.

Related benchmarks:

push vs spread 11031 (version: 0)

Comparing performance of: push vs spread

Created: 3 years ago by: Guest

Jump to the latest result

push

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):