Benchmark: Math.max() vs Array.reduce()2

Script Preparation code:

var values = new Array(5000);

for (let i = 0; i < values.length; ++i) {
    values[i] = i % 2;
}

Tests:

Reduce
const lines = { odd: [], even: [], }; values.reduce((_, curr) => { if (curr % 2 === 0) { if (!lines.even) { lines.even = [curr] } else { lines.even.push(curr); } } else { if (!lines.odd) { lines.odd = [curr] } else { lines.odd.push(curr); } } });
for loop
const obj = { odd: [], even: [], }; for (let i = 0; i < values.length; i++) { if (values[i] % 2 === 0) { obj.even.push(values[i]); } else { obj.odd.push(values[i]); } } return obj;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce
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 break down the provided benchmark and its options.

**Benchmark Definition**

The benchmark is designed to compare the speed of two approaches:

1. **Array.reduce()**: This method is used to reduce an array to a single value, but in this case, it's being used to create a new object with two properties: `odd` and `even`, where each property contains values from the original array that meet certain conditions (i.e., odd or even indices, respectively).
2. **For loop**: This is a traditional, iterative approach that uses a loop to iterate over the values in the array, pushing them into separate arrays for odd and even indices.

**Options Compared**

The two options being compared are:

* **Array.reduce()**: This method reduces an array to a single value or an object with multiple properties. In this case, it's used to create a new object.
* **For loop**: This is a traditional, iterative approach that uses a loop to iterate over the values in the array.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Array.reduce()**:
	+ Pros:
		- More concise and expressive code
		- Can be faster for large datasets (since it avoids explicit loops)
		- Reduces boilerplate code
	+ Cons:
		- Less intuitive for developers without experience with this method
		- May have performance issues if not implemented correctly
* **For loop**:
	+ Pros:
		- More familiar and intuitive for many developers
		- Allows for easier debugging and optimization (e.g., using break or return statements)
	+ Cons:
		- More verbose code compared to Array.reduce()
		- May be slower for large datasets due to the explicit loop

**Library and Special JS Features**

In this benchmark, there is no specific library being used. However, some special JavaScript features are employed:

* **Template literals**: Used in the `for` loop's HTML preparation code to create a more readable string.
* **Arrow functions**: Used in both the Array.reduce() method definition and the for loop (though not explicitly stated).

**Other Considerations**

When choosing between these two approaches, consider the following factors:

* Code readability and maintainability: If you prioritize concise and expressive code, Array.reduce() might be a better choice. However, if readability is more important, the for loop might be preferable.
* Performance: For large datasets, Array.reduce() can provide faster performance due to its optimized implementation. However, if you're working with smaller datasets or need more control over iteration, the for loop might be a better fit.

**Alternative Approaches**

If neither of these options suits your needs, consider other approaches:

* **Map**: You could use the `map()` method in combination with Object.create() to achieve similar results.
* **Filter and reduce**: Another approach is to use the `filter()` method to create separate arrays for odd and even values, then use Array.reduce() to combine these arrays into a single object.

Keep in mind that each of these alternatives has its own trade-offs and may not offer significant performance improvements over the original options.

Related benchmarks:

Math.max() vs Array.reduce()2 (version: 0)

Compare speed of Math.max() vs Array.reduce().

Comparing performance of: Reduce vs for loop

Created: 2 years ago by: Guest

Jump to the latest result

Reduce

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):