Benchmark: filter-map vs reduce vs reduce with destructuring 2

Script Preparation code:

a = [];
for (i = 0; i < 1000; i++) a.push(Number(i) / 1000);
var filtering = x => (x * 114514) % 1 > 0.5;
var mapping = x => x + 0.1919;
var reducing = (acc, x) => {
    if (filtering(x)) acc.push(mapping(x));
    return acc;
}
var reducingWithDestructuring = (acc, x) => {
    if (filtering(x)) {
        return [...acc, mapping(x)];
    }
    return acc;
}

Tests:

map-filter
a.filter(filtering).map(mapping);
reduce
a.reduce(reducing,[]);
reduce with desctructuring
a.reduce(reducingWithDestructuring,[]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map-filter
reduce
reduce with desctructuring

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 9 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36

Browser/OS: Chrome 141 on Mac OS X 10.15.7

View result in a separate tab

reduce

43K/s

map-filter

39K/s

reduce with desctructuring

6K/s

View exact numbers

Test name	Executions per second
✓ reduce	43,072 Ops/sec
map-filter	38,933 Ops/sec
reduce with desctructuring	6,429 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 9 months ago):

LLMs can make mistakes. Check important info.

The benchmark you provided tests three different approaches to processing an array of numbers using JavaScript. Specifically, it compares:

1. **`map-filter` approach**:
   - The code: `a.filter(filtering).map(mapping);`
   - **Description**: This utilizes the `filter` method to keep certain numbers based on a filtering function and then applies the `map` method to modify the remaining numbers through a mapping function.
   - **Pros**:
     - Readable and expressive; clear separation of filtering and mapping concerns.
     - Functional programming style, which can be easier to understand and maintain.
   - **Cons**:
     - May have performance overhead due to creating intermediate arrays, which can be substantial with large datasets.

2. **`reduce` approach**:
   - The code: `a.reduce(reducing, []);`
   - **Description**: This uses a single `reduce` method to accumulate a result while applying both filtering and mapping within a single pass.
   - **Pros**:
     - More efficient than `map-filter` since it only iterates through the array once, resulting in better performance for large arrays.
     - This reduces memory consumption by not creating intermediate arrays for filtered values.
   - **Cons**:
     - Can be less readable and may complicate understanding due to the merging of filtering and mapping logic into one function.

3. **`reduce with destructuring` approach**:
   - The code: `a.reduce(reducingWithDestructuring, []);`
   - **Description**: Similar to the above, it also combines filtering and mapping but utilizes the spread operator for constructing the accumulated array when a value meets filtering conditions.
   - **Pros**:
     - Maintains a similar structure to the regular `reduce` while leveraging modern JavaScript syntax, which can be more visually appealing.
     - Still performs in a single pass, preserving efficiency compared to `map-filter`.
   - **Cons**:
     - The use of the spread operator (`...acc`) creates a new array at each step of the reduction, which could incur performance overhead compared with the regular `reduce`, especially for large arrays.

### Benchmark Results
The benchmark results indicate the following executions per second for each method on a desktop environment with specific configurations:

- **`reduce`**: 43,072 executables/sec
- **`map-filter`**: 38,933 executables/sec
- **`reduce with destructuring`**: 6,429 executables/sec

### Considerations
- **Performance**: The `reduce` method showcased significantly better performance metrics compared to the other approaches. The `reduce with destructuring` performed noticeably worse, likely due to the array spread operation's overhead on every iteration, which negates some performance benefits associated with `reduce`.
- **Readability vs Performance**: There is a trade-off between code readability and performance in these approaches. While the `map-filter` approach is easier to understand, it is also less efficient for large datasets. The trade-offs should be considered based on project requirements and expected data sizes.
- **Alternative Solutions**: Other alternatives could include:
  - Using a traditional `for` loop to manually filter and map, potentially optimizing for performance.
  - Using libraries like Lodash which often provide utility functions for functional programming tasks that are optimized for performance, but at the cost of adding external dependencies.

The benchmark effectively illustrates the importance of choosing the right method based on performance requirements and code maintainability priorities.

filter-map vs reduce vs reduce with destructuring 2 (version: 1)

modified version of `map-filter vs reduce` that switches the order of operations

Comparing performance of: `map-filter vs reduce vs reduce with desctructuring`

Created: 9 months ago by: Guest

Go to the latest result

map-filter

reduce

reduce with desctructuring

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 9 months ago):

filter-map vs reduce vs reduce with destructuring 2 (version: 1)

modified version of `map-filter vs reduce` that switches the order of operations

Comparing performance of: map-filter vs reduce vs reduce with desctructuring

Created: 9 months ago by: Guest

Go to the latest result

map-filter

reduce

reduce with desctructuring

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 9 months ago):

Related benchmarks

Comparing performance of: `map-filter vs reduce vs reduce with desctructuring`