Benchmark: Reduce with spread operator and for

Script Preparation code:

var array = new Array(10000).fill(1)

function withSpread(acc, item, index) {
    acc[index] = {
        id: index,
        timestamp: new Date(new Date().getTime() + (index * 1000)),
        isOdd: index % 2 === 0,
        isEven: index % 2 !== 0
    }
    return {
        ...acc
    };
}

function withOutSpread(acc, item, index) {
    acc[index] = {
        id: index,
        timestamp: new Date(new Date().getTime() + (index * 1000)),
        isOdd: index % 2 === 0,
        isEven: index % 2 !== 0
    }
    return acc;
}

Tests:

reduce with spread
array.reduce(withSpread, {})
reduce without spread
array.reduce(withOutSpread, {})
for loop
var objOfIds = {} for(var index = 0; index < array.length; index++) { objOfIds[index] = { id: index, timestamp: new Date(new Date().getTime() + (index * 1000)), isOdd: index % 2 === 0, isEven: index % 2 !== 0 } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce with spread
reduce without spread
for loop

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 dive into the world of JavaScript microbenchmarks!

**Benchmark Overview**

The provided JSON represents two benchmark definitions: `Reduce with spread operator and for` and its two variants: `Reduce with spread` and `Reduce without spread`. The latter has an additional variant using a traditional `for` loop.

**Tested Options**

1. **Spread Operator (`...`)**: The spread operator is used to expand the object `acc` into a new array in the `withSpread` function.
2. **Traditional `for` Loop**: A classic `for` loop is used to iterate over the elements of the array in the `for` variant.

**Pros and Cons**

1. **Spread Operator (withSpread)**:
	* Pros: More concise, readable, and maintainable code. It also avoids the need for explicit indexing.
	* Cons: Can lead to performance issues if not optimized correctly, as it creates a new array and assigns references to each element.
2. **Traditional `for` Loop**:
	* Pros: Well-established and efficient approach, especially when working with large datasets.
	* Cons: More verbose code, which can make it harder to read and maintain.

**Library Usage**

None of the provided benchmark definitions use any external libraries.

**Special JavaScript Features/Syntax**

The spread operator (`...`) is a relatively recent addition to JavaScript (introduced in ECMAScript 2018). It's used to expand an object into a new array, allowing for more concise and expressive code. The traditional `for` loop syntax remains unchanged.

**Alternative Approaches**

1. **Array.prototype.forEach()**: This method iterates over the elements of an array without creating a new array or using indexing.
2. **Arrow Functions**: Instead of defining a separate function with the spread operator, you can use an arrow function to achieve similar results in a more concise way.

These alternative approaches might offer different performance characteristics and trade-offs compared to the original benchmark definitions.

**Benchmark Result Interpretation**

The latest benchmark result shows three execution counts for each test case:

* `Reduce without spread`: 493.9852600097656 executions per second
* `For loop`: 438.6302490234375 executions per second
* `Reduce with spread`: 86.71508026123047 executions per second

These results indicate that the traditional `for` loop approach is generally faster than both the spread operator and the original `withSpread` function.

Keep in mind that these results might vary depending on specific use cases, system configurations, and JavaScript engines.

Related benchmarks:

Reduce with spread operator and for (version: 2)

Comparing performance of: reduce with spread vs reduce without spread vs for loop

Created: 2 years ago by: Registered User

Jump to the latest result

reduce with spread

reduce without spread

for loop

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):