Benchmark: nested reducing - MeasureThat.net

Script Preparation code:

var range = (from, to) => {
    const output = {}
    for (var x = from; x < to; x++) {
        output[x] = {
            a: x + 'a',
            b: x + 'b',
            c: x + 'c'
        }
    }
    return output
}
var newData = {
    b: 'zzzzzz'
};

Tests:

spread
const a = Object.entries(range(0, 10)).reduce((acc, [key, obj]) => { acc[key] = { ...obj, ...newData } return acc }) console.log(a)
object assign
const a = Object.entries(range(0, 10)).reduce((acc, [key, obj]) => { acc[key] = Object.assign({}, obj, newData); return acc }) console.log(a)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
spread
object assign

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.

I'll break down the provided benchmark definition and test cases to explain what's being tested, compared, and considered.

**Benchmark Definition:**

The script preparation code defines a function `range` that takes two numbers as arguments (`from` and `to`) and returns an object with numeric keys from `from` to `to`. The object has three properties for each key: `a`, `b`, and `c`, which are string concatenations of the key, `x`, and specific strings.

The `newData` object is defined outside the script preparation code. Its sole property is `b`, set to a long string `'zzzzzz'`.

**Html Preparation Code:**

There is no HTML preparation code provided in this benchmark definition.

**Individual Test Cases:**

Two test cases are provided, comparing two different approaches to process the output of the `range` function:

1. **Test Case 1: "spread"**
The benchmark definition uses `Object.entries(range(0, 10)).reduce((acc, [key, obj]) => {\r\n  acc[key] = {\r\n  \t...obj,\r\n    ...newData\r\n  }\r\n  \r\n  return acc\r\n})`

This code:

* Uses `Object.entries` to convert the output of `range` into an array of key-value pairs.
* Applies the spread operator (`...`) to merge each object with `newData`.
* Reduces the array of key-value pairs using `reduce`, accumulating the resulting objects in `acc`.

2. **Test Case 2: "object assign"**
The benchmark definition uses `Object.entries(range(0, 10)).reduce((acc, [key, obj]) => {\r\n  acc[key] = Object.assign({}, obj, newData);\r\n  \r\n  return acc\r\n})`

This code:

* Uses `Object.entries` to convert the output of `range` into an array of key-value pairs.
* Applies `Object.assign` to merge each object with `newData`.
* Reduces the array of key-value pairs using `reduce`, accumulating the resulting objects in `acc`.

**Comparison:**

The two test cases compare the performance of these two approaches:

1. **Spread operator (`...`)**: Merges objects by spreading their properties and then merging them with other objects.
2. **`Object.assign`**: Merges objects by creating a new object and assigning properties from one or more source objects.

**Pros and Cons:**

* **Spread operator (`...`)**:
	+ Pros:
		- More concise and expressive code.
		- Can be faster since it doesn't create a new object for each merge operation.
	+ Cons:
		- May be slower due to the overhead of spreading properties.
* **`Object.assign`**:
	+ Pros:
		- Can be faster since it only involves assigning properties without creating new objects.
	+ Cons:
		- Less concise and expressive code.

**Other Considerations:**

* The `newData` object is large, which may impact performance due to the overhead of merging with smaller objects.
* The benchmark definition doesn't consider other factors like array operations or memory allocation, which could affect performance.
* The use of `Object.entries` and `reduce` might be overkill for this specific task.

**Alternatives:**

Other approaches could be explored, such as:

1. Using a library like Lodash's `merge` function to merge objects.
2. Implementing a custom merge function using recursion or iteration.
3. Using a different data structure, like an array of arrays, to avoid merging objects.
4. Testing with smaller input sizes to reduce the impact of large object merges.

Keep in mind that the choice of approach depends on the specific requirements and constraints of the project.

Related benchmarks:

nested reducing (version: 1)

Comparing performance of: spread vs object assign

Created: 4 years ago by: Registered User

Jump to the latest result

spread

object assign

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):