Benchmark: Ocasionalmente vamos falar de um bn name

Script Preparation code:

var arr = [];
for (var i = 0; i < 12345; i++) {
  arr[i] = { "current_post": i };
}
var narr = [];

function someFn(i) {
  return (i * 3 * 8 / 1200 * 0.002 / 40 * 0.2);
}

var sumForEach = 0,
  sumReduce = 0,
  sumMap = 0,
  sumFilter = 0,
  sumFor = 0;

Tests:

map
narr = arr.map(post => post.current_post)
reduce
narr = arr.reduce(function(acc, obj) { acc.push(obj.current_post); return acc; }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Definition**

The provided JSON represents a benchmark definition, which is used to define the test cases and scripts to be executed on MeasureThat.net. The definition includes:

1. **Script Preparation Code**: This code defines an array `arr` with 12345 elements, each containing a JSON object with a single property `current_post`. Additionally, it defines a function `someFn` that performs some calculations.
2. **Html Preparation Code**: There is no HTML preparation code provided, which means the benchmark only focuses on JavaScript performance.

**Individual Test Cases**

The benchmark definition includes two test cases:

1. **map**
	* The script to be executed is: `narr = arr.map(post => post.current_post)`
	* This test case measures the performance of the `Array.prototype.map()` method, which applies a provided function to each element in an array and returns a new array with the results.
2. **reduce**
	* The script to be executed is: `narr = arr.reduce(function(acc, obj) { acc.push(obj.current_post); return acc; }, [])`
	* This test case measures the performance of the `Array.prototype.reduce()` method, which applies a provided function to each element in an array and reduces it to a single value.

**Options Compared**

The benchmark compares two different approaches for these two operations:

1. **map**
	* The original implementation (not specified) is compared against the optimized implementation.
2. **reduce**
	* The original implementation (not specified) is compared against the optimized implementation using the accumulator-based approach.

**Pros and Cons of Different Approaches**

Here are some general pros and cons of each approach:

1. **map**
	* Optimized implementation:
		+ Pros: faster execution, more readable code
		+ Cons: might require additional memory for the intermediate result array
	* Original implementation (not specified):
		+ Pros: potentially simpler to implement
		+ Cons: slower execution, less efficient use of resources
2. **reduce**
	* Optimized accumulator-based approach:
		+ Pros: faster execution, more memory-efficient
		+ Cons: might be harder to read and understand for some developers
	* Original implementation (not specified):
		+ Pros: potentially simpler to implement
		+ Cons: slower execution, less efficient use of resources

**Special JS Feature or Syntax**

There are no special JavaScript features or syntax used in this benchmark. The tests only focus on the performance of the `Array.prototype.map()` and `Array.prototype.reduce()` methods.

**Other Alternatives**

If you're looking for alternative approaches to these operations, here are a few:

1. **map**
	* Instead of using the optimized implementation, you could use the `forEach` method or the spread operator (`...`) to achieve similar results.
2. **reduce**
	* You could also use other accumulator-based methods, such as `Array.prototype.reduceRight()` or `Lodash's `reduceRight()` function.

Keep in mind that these alternatives might have different performance characteristics and may not be suitable for all use cases.

I hope this explanation helps you understand the benchmark definition and test cases!

Related benchmarks:

Ocasionalmente vamos falar de um bn name (version: 0)

Comparing performance of: map vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

map

reduce

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):