Benchmark: Map with filter vs reduce

Script Preparation code:

const array1 = [1, 2, 3, 4, 5];

a=[];


for(i=0; i < 10000; i++) a.push({
  id: i+1,
  text: `Iteration number ${i+1}`,
  group: `Group number ${i+1}`,
  color: {
    name: 'red',
    hex: '#FF0000'
  },
  relatedValues: array1.map(x => ({
    id: x * 2,
    isValid: x % 2 === 0
  })),
});
var filtering= x => x.id % 2 === 0;
var mapping= x => x.test = true;
var reducing=(acc,x)=>{
  var value = mapping(x);
  if(filtering(value)) acc.push(value);
  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

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

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Let's break down the provided benchmark and explain what is tested, compared, and their pros/cons.

**Benchmark Definition JSON**

The benchmark consists of two test cases:

1. `Map with filter vs reduce`
2. `map-filter` and `reduce`

The first test case is a single script that creates an array of 10,000 objects and then applies various operations to it.

**Options Compared**

In the benchmark, we have two approaches compared:

*   **Filtering**: The filtering operation (`a.filter(filtering).map(mapping)`) removes elements from the array based on a condition.
*   **Reducing**: The reducing operation (`a.reduce(reducing, [])`) combines all elements in the array into a single value.

**Pros and Cons**

Here's a brief overview of each approach:

### Filtering

*   **Pros:**
    *   Faster for small arrays or when only a few elements need to be removed.
    *   More memory-efficient since it doesn't create a new array with filtered elements.
*   **Cons:**
    *   Slower for large arrays due to the overhead of iterating over each element.
    *   Less concise and more verbose than reducing.

### Reducing

*   **Pros:**
    *   Faster for large arrays since it only needs to iterate over each element once.
    *   More concise and easier to read, especially when dealing with complex logic.
*   **Cons:**
    *   More memory-intensive since it creates a new array with the combined elements.

**Library/Functions Used**

The benchmark uses two library functions:

1.  `Array.prototype.filter()`: Removes all elements from an array that do not pass the test implemented by the provided function.
2.  `Array.prototype.map()`: Creates a new array with the results of applying the provided function on every element in this array.

Additionally, it defines custom functions:

*   `filtering(x)`: Returns `true` if the `id` property of an object is even (using the modulo operator).
*   `mapping(x)`: Sets the `text` property of an object to `'true'`.
*   `reducing((acc,x))`: Takes a value and pushes it into the accumulator array only if the filtering condition is met.

**Other Considerations**

When choosing between filtering and reducing, consider the following:

*   **Array Size**: For small arrays, filtering might be faster. For large arrays, reducing is usually more efficient.
*   **Logic Complexity**: When dealing with complex logic or multiple conditions, reducing can provide a cleaner implementation.
*   **Memory Constraints**: If memory usage is a concern, using filtering will consume less memory.

**Alternative Approaches**

Some alternative approaches to filtering and reducing include:

*   **Using `forEach()` instead of `map()` for filtering**: This eliminates the need for an intermediate array.
*   **Using `Array.prototype.every()` or `Array.prototype.some()` as alternatives to `filter()`**: These can be more concise, but might be slower for very large arrays.

Other considerations include using libraries like Lodash (e.g., `_filter()`, `_map()`) that provide optimized versions of these functions.

Related benchmarks:

Map with filter vs reduce (version: 0)

Comparing performance of: map-filter vs reduce

Created: 3 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):