Benchmark: ter + map vs reduce 1235212525

Script Preparation code:

var values = []
for(i=0; i<2000000; i++){ values.push(1) }

Tests:

filter + map
values.filter(x => x % 2 === 0).map((x) => x * 2)
reduce
values.reduce((acc, x) => { if (x % 2 === 0) { acc.push(x * 2); } return acc; }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter + map
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous 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 benchmark definition, test cases, and latest results to explain what's being tested.

**Benchmark Definition**

The benchmark definition is a JSON object that describes the test case. In this case, there are two test cases:

1. "ter + map vs reduce 1235212525" (not actually used in the provided test cases)
2. "filter + map"
3. "reduce"

These test cases compare the performance of two approaches to achieve a specific goal:

* `filter` and `map`: This approach uses the `Array.prototype.filter()` method followed by `Array.prototype.map()`. The `filter()` method creates a new array with only the elements that pass the test implemented by the provided function. The `map()` method creates a new array with the results of applying the provided function to every element in this array.
* `reduce`: This approach uses the `Array.prototype.reduce()` method. In this case, it's used as an accumulator function to push elements into an array.

**Options Compared**

The two approaches compared are:

1. **filter + map**: A sequential approach where `filter` is applied first, followed by `map`.
2. **reduce**: An iterative approach that accumulates the results directly without creating a new array.

**Pros and Cons of Each Approach:**

* **filter + map**:
	+ Pros:
		- Easier to understand and maintain for developers familiar with these methods.
		- Can be more efficient if the filtering condition is computationally expensive, as it avoids unnecessary iterations over the entire dataset.
	+ Cons:
		- Creates two new arrays (one for filtering and one for mapping), which can lead to increased memory allocation and deallocation overhead.
		- May have higher CPU usage due to the sequential nature of the operations.
* **reduce**:
	+ Pros:
		- Eliminates the need for creating intermediate arrays, reducing memory allocation and deallocation overhead.
		- Can be more efficient in terms of CPU usage, as it avoids the overhead of function calls and array indexing.
	+ Cons:
		- Can be less readable and maintainable for developers unfamiliar with this method.
		- Requires proper implementation to handle edge cases (e.g., handling null or undefined values).

**Library Used**

None of the provided benchmark definitions use any external libraries. However, if a library like Lodash were used in one of these test cases, it would likely be used for utility functions that simplify common array operations, such as `filter` and `map`.

**Special JS Feature/Syntax (None)**

There are no special JavaScript features or syntax used in the provided benchmark definition.

**Other Alternatives**

In addition to the two approaches compared (`filter + map` and `reduce`), other alternatives could include:

* Using `forEach`: Instead of `map`, you could use `forEach` to iterate over the array, but this would not accumulate results like `reduce`.
* Using `Array.prototype.forEach()` with a callback function: This approach is similar to using `reduce`, but it doesn't return any value (i.e., it's not an accumulator function).
* Using a custom loop or iterative approach without relying on built-in methods.

It's worth noting that the choice of which approach to use depends on the specific requirements of your project, such as performance, readability, and maintainability.

Related benchmarks:

ter + map vs reduce 1235212525 (version: 0)

Comparing performance of: filter + map vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

filter + 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):