Benchmark: filter map vs reduce

Script Preparation code:

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

Tests:

Reduce
var sources = arr.reduce(function(result, number) { if (number % 2 !== 0) { result.push(number); } return result; }, []);
Filter
var sources = arr.filter(function(number) { if (number % 2 !== 0) { return false; // skip } return true; }).map(function(number) { return number; })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce
Filter

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.

Let's dive into the world of JavaScript microbenchmarks and analyze the provided benchmark.

**Benchmark Overview**

The benchmark compares the performance of three approaches to process an array of numbers:

1. **Filter + Map**: This approach uses the `filter()` method to remove odd numbers from the array, followed by the `map()` method to return only the remaining numbers.
2. **Reduce**: This approach uses the `reduce()` method to accumulate the sum of all numbers in the array.

**Options Compared**

The benchmark tests two options:

* Filter + Map
* Reduce

**Pros and Cons of Each Approach**

### Filter + Map

Pros:

* Easy to understand and implement for developers familiar with JavaScript arrays.
* Can be more efficient than Reduce if the array is very large, since it only processes even numbers.

Cons:

* Requires creating an intermediate array (result of filter()) which can lead to higher memory usage.
* May have slower performance compared to Reduce due to the overhead of filtering and mapping.

### Reduce

Pros:

* Eliminates the need for an intermediate array, reducing memory usage.
* Can be more efficient than Filter + Map since it only processes numbers in a single pass.

Cons:

* Requires creating a callback function that accumulates the sum, which can lead to higher complexity and potential bugs if not implemented correctly.
* May have slower performance due to the overhead of iterating over the array.

**Library Usage**

None.

**Special JS Features or Syntax**

The benchmark uses JavaScript syntax features such as arrow functions (`=>`) and template literals (`\r\n`).

**Other Considerations**

* The benchmark prepares an array of 12345 numbers, which is a relatively large dataset.
* The `someFn()` function is not used in the benchmark but was present in the original script preparation code. It seems to be a test case for a different scenario.

**Alternatives**

Other approaches to process an array of numbers could include:

* Using a loop with conditional statements instead of JavaScript methods (e.g., `for` loop, `if-else` statements).
* Using other functional programming libraries or frameworks (e.g., Lodash) that provide optimized implementations for these operations.
* Using parallel processing techniques (e.g., Web Workers) to take advantage of multiple CPU cores.

In summary, the benchmark compares two common approaches to process an array of numbers: Filter + Map and Reduce. Each approach has its pros and cons, and the choice between them depends on the specific use case and performance requirements.

Related benchmarks:

filter map vs reduce (version: 0)

Comparing performance of: Reduce vs Filter

Created: 5 years ago by: Guest

Jump to the latest result

Reduce

Filter

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):