Benchmark: filter-map vs reduce PSOW

Script Preparation code:

a=[];
for(i=0;i<1000;i++) a.push(Number(i) * Math.random() * 1000);


var filtering = x => x > 5000;
var mapping=x=> `${x} to String`;

var reducing=(acc,x)=>{

  if(filtering(x)) acc.push(mapping(x));
  return acc;
}

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map-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.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks, comparing the performance of different approaches to solve problems. The provided benchmark definition and test cases focus on evaluating two methods: `map-filter` vs `reduce`.

**Script Preparation Code Analysis**

The script preparation code creates an array `a` with 1000 elements, each being a product of a random number between 1 and 1000 multiplied by 5000. It then defines three functions:

*   `filtering(x)`: Returns `true` if the input value is greater than 5000.
*   `mapping(x)`: Converts the input value to a string using template literals.
*   `reducing(acc, x)`: Pushes the result of `mapping(x)` to the accumulator array (`acc`) if `filtering(x)` returns `true`. It returns the accumulator.

The script then prepares two test cases: `map-filter` and `reduce`.

**Test Cases**

The individual test cases are defined in the following format:

*   `"Benchmark Definition": "JavaScript code"`
    *   Example 1: `a.filter(filtering).map(mapping);`
        +   This approach uses the `filter()` method to remove elements that don't meet the filtering condition and then applies the `map()` method to transform the remaining elements.
    *   Example 2: `a.reduce(reducing,[]);`
        +   This approach uses the `reduce()` method to accumulate values in an array.

**Options Comparison**

The benchmark compares two options:

1.  `map-filter`: Uses both `filter()` and `map()` methods sequentially.
2.  `reduce`: Uses the `reduce()` method alone.

**Pros and Cons of Each Approach:**

*   **Map-Filter (Sequential):**
    *   Pros:
        +   Can be more readable and maintainable for complex filtering logic.
        +   Easier to understand and debug individual parts of the code.
    *   Cons:
        +   Requires two separate function calls (`filter()` and `map()`), which can be slower due to function call overhead.
*   **Reduce:**
    *   Pros:
        +   Can be more efficient, as it eliminates the need for sequential function calls.
        +   Allows for a single, self-contained operation that can be optimized by the JavaScript engine.
    *   Cons:
        +   Can be less readable and maintainable, especially for complex filtering logic.

**Library/Functionality Considerations**

*   `filter()`, `map()`, and `reduce()` are built-in JavaScript functions provided by the ECMAScript standard. They are widely supported across different browsers and environments.
    *   The `filter()` function creates a new array with only the elements that pass the test implemented by the provided function.
    *   The `map()` function creates a new array with the results of applying the provided function to each element in the original array.
    *   The `reduce()` function applies a function against an accumulator and each element in the array (from left to right) to reduce it to a single value.

**Other Alternatives**

*   If you need more control over the filtering logic or want to avoid using built-in JavaScript functions, you can use custom loops with conditional statements.
*   For other programming languages, you may have alternative methods for achieving similar results.

Related benchmarks:

filter-map vs reduce PSOW (version: 0)

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

Comparing performance of: map-filter vs reduce

Created: 4 years ago by: Guest

Jump to the latest result

map-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):