Benchmark: test reduc - MeasureThat.net

Script Preparation code:

var data = Array(10000).fill({key: 'key', value : 'value'})

Tests:

reduce
data.reduce((acc, current) => { acc[current.key] = current.value; return acc; }, {});
for
const acc = {}; for(let i=0 ; i<data.length ; i++) { acc[data[i].key] = data[i].value; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
for

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'd be happy to help explain the JavaScript microbenchmark on MeasureThat.net.

**Overview of the Benchmark**

The provided benchmark measures the performance difference between two approaches for reducing an object in JavaScript: the `Array.prototype.reduce()` method and a traditional `for` loop.

**Script Preparation Code**

The script preparation code defines an array of 10,000 objects with a single key-value pair each:
```javascript
var data = Array(10000).fill({key: 'key', value: 'value'});
```
This array will be used as input for both benchmarking approaches.

**Benchmark Definition**

There are two benchmark definitions:

1. **`data.reduce()`**: This line of code uses the `Array.prototype.reduce()` method to iterate over the `data` array and accumulate a new object with key-value pairs.
```javascript
data.reduce((acc, current) => {
  acc[current.key] = current.value;
  return acc;
}, {});
```
This approach is concise and expressive, but it may incur some overhead due to the creation of an intermediate result object.

2. **`for` loop**: This code uses a traditional `for` loop to iterate over the `data` array and accumulate a new object with key-value pairs.
```javascript
const acc = {};
for (let i = 0; i < data.length; i++) {
  acc[data[i].key] = data[i].value;
}
```
This approach is more verbose, but it can be faster since it avoids the creation of an intermediate result object.

**Options Compared**

The two benchmarking approaches differ in their use of an intermediate result object. The `data.reduce()` method creates a new object to store the accumulated key-value pairs, while the `for` loop accumulates values directly in the `acc` object.

**Pros and Cons**

* **`data.reduce()`**: Pros:
	+ Concise and expressive code
	+ Easy to read and maintain
*   Cons:
    + May incur overhead due to intermediate result object creation

*   **`for` loop**: Pros:
	+ Can be faster since it avoids intermediate result object creation
	+ Allows for direct access to the `acc` object
*   Cons:
    + More verbose code
    + Requires explicit looping and indexing

**Library and Special JS Feature**

In this benchmark, there are no external libraries used.

**Special JS Features**

There is no special JavaScript feature or syntax used in these benchmarking approaches. The code is standard ECMAScript 2022 compliant.

**Other Alternatives**

Other alternatives for reducing an object in JavaScript could include:

1. Using `Array.prototype.forEach()` with a callback function.
```javascript
data.forEach((item) => {
  acc[item.key] = item.value;
});
```
This approach has similar performance characteristics to the `for` loop, but is slightly more concise.

2. Using `Array.prototype.map()` and then using an object literal to accumulate values.
```javascript
const mappedData = data.map((item) => ({ key: item.key, value: item.value }));
const acc = {};
mappedData.forEach((item) => {
  acc[item.key] = item.value;
});
```
This approach is more concise than the `for` loop, but may incur additional overhead due to the use of `map()`.

Keep in mind that these alternatives may have different performance characteristics and trade-offs compared to the original benchmarking approaches.

Related benchmarks:

test reduc (version: 0)

Comparing performance of: reduce vs for

Created: 3 years ago by: Guest

Jump to the latest result

reduce

for

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):