Benchmark: Reduce with spread operator vs without on a small array

Script Preparation code:

var array = new Array(10).fill(1)

function withSpread(acc, item, index) {
    return {
        ...acc,
        [index]: {
            id: index,
            timestamp: new Date(new Date().getTime() + (index * 1000)),
            isOdd: index % 2 === 0,
            isEven: index % 2 !== 0
        }
    };
}

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, {})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce with spread
reduce without 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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I'll break down the provided benchmark definition and test cases, explaining what's being tested, the pros and cons of different approaches, and other considerations.

**Benchmark Definition**

The benchmark is testing two approaches to reduce an array:

1. Using the spread operator (`...`) to create a new object with updated properties.
2. Without using the spread operator, by directly assigning to the existing object (`acc[index] = ...`).

**Pros and Cons of Different Approaches**

**With Spread Operator (withSpread)**

Pros:

* Creates a new object with updated properties, which can be beneficial for performance when working with large datasets or complex objects.
* Easier to read and maintain code, as the spread operator makes it explicit that a new object is being created.

Cons:

* Can lead to unnecessary memory allocations and copying of data, especially when dealing with large arrays.
* May incur additional overhead due to the creation of a new object.

**Without Spread Operator (withOutSpread)**

Pros:

* Avoids unnecessary memory allocations and copying of data.
* Can be more efficient in terms of memory usage and performance, especially for large datasets or complex objects.

Cons:

* Code can become less readable and maintainable, as it's not immediately clear that the same object is being updated.
* May require additional logic to handle updates to existing properties.

**Other Considerations**

* The benchmark uses a small array of 10 elements, which may not accurately represent the performance characteristics of larger datasets. A more comprehensive test suite might include larger arrays or varying dataset sizes.
* The `acc` parameter is initialized as an empty object (`{}`), which means that any existing properties in the original array will be lost when using the spread operator. In a real-world scenario, `acc` would likely be populated with some initial data.
* Both functions return an updated `acc` object, but the benchmark doesn't account for this. A more accurate test might involve measuring the number of executions required to update each property.

**Library and Special JS Feature**

There is no explicit library mentioned in the benchmark definition. However, JavaScript provides several built-in features that can be used for array manipulation, including `reduce()`, object literals (`{}`), and spread operator (`...`).

The spread operator is a relatively recent addition to JavaScript (introduced in ECMAScript 2018) and is used to create new objects by copying properties from an existing source. In this benchmark, the spread operator is used to create a new object with updated properties, but it's not explicitly stated as a special feature or library.

**Test Case Analysis**

The two test cases are:

1. `array.reduce(withSpread, {})`: Tests using the spread operator to reduce the array.
2. `array.reduce(withOutSpread, {})`: Tests without using the spread operator to reduce the array.

These tests measure the performance difference between these two approaches on a small array. The results will likely show that both methods have similar performance characteristics for this specific use case, but further testing might reveal differences in performance or memory usage when dealing with larger datasets or more complex objects.

Related benchmarks:

Reduce with spread operator vs without on a small array (version: 0)

Reduce with spread operator vs without on a small array

Comparing performance of: reduce with spread vs reduce without spread

Created: 2 years ago by: Guest

Jump to the latest result

reduce with spread

reduce without 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):