Benchmark: filter vs reduce Birynek 2

Script Preparation code:

a=[];
for(i=0;i<1000;i++) a.push({
  valami :i,
  valami2: "Name"+i
});
var filtering=item=>(item.valami % 2) === 0;

Tests:

filter
sumFilter = a.filter(item=> (item.valami % 2) === 0).length
reduce
var sumReduce = 0; a.reduce((sumReduce, item) => ((item.valami % 2) === 0) ? sumReduce + 1 : sumReduce,0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter
reduce

Fastest: N/A

Slowest: N/A

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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!

The provided JSON represents two test cases: `filter` and `reduce`. Both tests aim to compare the performance of filtering data using two different approaches in JavaScript.

**Test Case 1: filter**

In this test case, a custom filtering function is defined:
```javascript
var filtering = item => (item.valami % 2) === 0;
```
This function takes an object `item` as input and returns `true` if the value of `valami` modulo 2 is equal to 0. If not, it returns `false`.

The test creates an array `a` with 1000 objects, each containing two properties: `valami` and `valami2`. The filtering function is then applied to this array using the `.filter()` method:
```javascript
a.filter(item => (item.valami % 2) === 0)
```
The resulting length of the filtered array is stored in the variable `sumFilter`.

**Test Case 2: reduce**

In this test case, a custom reduction function is defined:
```javascript
var sumReduce = 0;
a.reduce((sumReduce, item) => ((item.valami % 2) === 0) ? sumReduce + 1 : sumReduce, 0)
```
This function initializes a variable `sumReduce` to 0 and then iterates over the array `a`. For each object in the array, it checks if the value of `valami` modulo 2 is equal to 0. If true, it increments the `sumReduce` variable by 1; otherwise, it leaves the value unchanged.

The resulting sum of the filtering results (i.e., the number of objects with `valami` divisible by 2) is stored in the variable `sumReduce`.

**Comparison and Pros/Cons**

Both tests compare the performance of filtering data using two different approaches:

*   **Filtering**: The `.filter()` method creates a new array containing only the elements that pass the test implemented by the provided function.
*   **Reduction**: A custom reduction function is applied to the original array, incrementing a running sum whenever the condition is met.

**Pros and Cons**

**Filtering (`.filter()`):**

Pros:

*   Creates a new array with filtered elements, which can be beneficial when working with large datasets or requiring exact filtering results.
*   Less memory-intensive compared to reduction methods.

Cons:

*   Can create unnecessary copies of the original data if not needed.
*   May require more CPU cycles due to array resizing and copying.

**Reduction (Custom Function):**

Pros:

*   In-place operation, reducing memory allocation and copying overhead.
*   Suitable for cases where exact filtering results are required without creating a new array.

Cons:

*   Requires additional CPU cycles for the custom reduction function and potentially more complex logic.
*   Might lead to slower performance if not optimized correctly.

**Other Considerations:**

1.  **JavaScript Engine Optimization**: The JavaScript engine's optimization techniques, such as inlining and caching, can significantly impact benchmark results.
2.  **Array Length and Distribution**: The size and distribution of the array being tested can influence the outcome, especially when using filtering or reduction methods.

**Alternative Approaches:**

1.  **Arrow Functions**: Instead of defining a custom filtering function, you could use an arrow function (`item => (item.valami % 2) === 0`) for simplicity.
2.  **Lodash's `filter()` Method**: Lodash provides its own implementation of the `.filter()` method, which might offer better performance or features compared to the native JavaScript method.

When working with microbenchmarks like these, it's essential to consider various factors and optimize your code accordingly to ensure accurate results.

Related benchmarks:

filter vs reduce Birynek 2 (version: 0)

Comparing performance of: filter vs reduce

Created: 2 years ago by: Guest

Jump to the latest result

filter

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):