Benchmark: Shiogui - Push x Reduce Logged

Script Preparation code:

var data = [{
        calendar: [{
                id: 1,
                name: "Calendar 1",
            },
            {
                id: 2,
                name: "Calendar 2",
            }
        ]
    },
    {
        calendar: [{
            id: 3,
            name: "Calendar 3",
        }, ]
    },
    {
        calendar: [{
                id: 4,
                name: "Calendar 4",
            },
            {
                id: 5,
                name: "Calendar 5",
            }
        ]
    }
];

Tests:

Push
var calendars = []; data.forEach(d => calendars.push(...d.calendar));
Reduce
var calendars = data.reduce((acc, cur) => [...acc, ...cur.calendar], []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Push
Reduce

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided benchmark is designed to compare two approaches: `Push` and `Reduce`. The test case uses an array of objects, each with a `calendar` property that contains multiple calendar entries. The objective is to measure which approach is faster.

**Options Compared**

The two options being compared are:

1. **Push**: This method iterates through the `data` array and pushes each `calendar` element into a new array `calendars`. This requires iterating through the entire array, creating a new array for each element.
2. **Reduce**: This method uses the `reduce()` function to accumulate the elements of the `calendar` arrays into a single array `calendars`. The accumulator is initialized with an empty array and each subsequent element is added to it.

**Pros and Cons of Each Approach**

### Push

Pros:

* Easy to understand and implement
* Can be optimized for specific use cases (e.g., using `slice()` or `map()`)

Cons:

* Requires iterating through the entire array, which can be slow for large datasets
* Creates a new array for each element, which can lead to memory issues if not handled carefully

### Reduce

Pros:

* Only requires one pass through the data structure (array), reducing memory allocations and garbage collection overhead
* Can be optimized using the accumulator to minimize work done in each iteration

Cons:

* Requires understanding of the `reduce()` function and its behavior
* May be less readable for developers unfamiliar with this method

**Library: Lodash**

The `data.forEach()` function is part of the Lodash library, which provides a set of utility functions for functional programming. The `forEach()` function executes a callback function once for each element in an array.

**Special JavaScript Feature/Syntax: None**

There are no special JavaScript features or syntax used in this benchmark.

**Other Considerations**

* Both approaches have a time complexity of O(n), where n is the number of elements in the `data` array.
* The Push approach may perform better for small datasets due to its simplicity and ease of optimization, while Reduce may outperform it for large datasets due to its efficiency.
* Memory usage will be higher with the Push approach since a new array is created for each element.

**Alternative Approaches**

Other approaches that could be used to compare include:

1. **Using `concat()`**: This method would iterate through the data and concatenate each calendar element into a single array using the `concat()` function.
2. **Using `Array.prototype.reduce()` with an arrow function**: This approach would utilize the same reduce method but use an arrow function for better readability.
3. **Using `Promise.all()`**: If the calendars were being fetched from an API, this method could be used to parallelize the operation and measure performance.

Keep in mind that each of these alternative approaches would have its pros and cons, and the best approach would depend on the specific requirements and constraints of the project.

Latest run results:

Run details: (Test run date: 6 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36

Browser/OS: Chrome 124 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Push	9977612.0 Ops/sec
Reduce	9934412.0 Ops/sec

Related benchmarks:

Shiogui - Push x Reduce Logged (version: 0)

Comparing performance of: Push vs Reduce

Created: 6 months ago by: Registered User

Jump to the latest result

Push

Reduce

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):