Benchmark: test flatMap vs reduce - with push

Script Preparation code:

var arr = Array(10_000).fill(0)
var itemGenerator = (x) => [x, x]
var itemReducer = (acc, x) => {
  acc.push(x);
  acc.push(x);
  return acc;
}

Tests:

flatMap
arr.flatMap(itemGenerator)
reduce
arr.reduce(itemReducer, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap
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 dive into the provided JSON benchmark data and explore what is being tested.

**Benchmark Overview**

The provided benchmark compares two different approaches to process an array of 10,000 elements: `arr.flatMap(itemGenerator)` and `arr.reduce(itemReducer, [])`. The goal is to determine which approach is faster.

**Options Compared**

Two options are compared:

1. **flatMap**: This method returns a new array with the results of applying the provided function (in this case, an array comprehension) to each element in the original array.
2. **reduce**: This method applies a reducer function (in this case, `itemReducer`) to each element in the original array, accumulating a result.

**Pros and Cons**

*   **flatMap**
    *   Pros:
        *   More concise and readable code
        *   Returns an iterable sequence of elements from the original array
    *   Cons:
        *   Creates a new array with all intermediate results, potentially consuming more memory
        *   May not be as efficient for very large datasets due to the creation of multiple arrays
*   **reduce**
    *   Pros:
        *   More flexible and customizable reducer function
        *   Can accumulate data in a single array, reducing memory allocation overhead
    *   Cons:
        *   Requires more code to achieve the same result as flatMap (in this case)
        *   May be less readable for complex operations due to the need to manage an accumulator

**Library and Purpose**

No libraries are explicitly mentioned in the provided benchmark data. However, it's worth noting that the `Array.prototype.flatMap` and `Array.prototype.reduce` methods rely on JavaScript's built-in array functions.

**Special JS Feature or Syntax**

There is no mention of any special JavaScript features or syntax in this benchmark. The code uses standard JavaScript language constructs (arrays, functions, arrow functions) to implement the test cases.

**Other Alternatives**

For a more comprehensive understanding of alternative approaches for processing arrays in JavaScript, consider the following:

*   **forEach**: A simple and straightforward method that executes a function for each element in an array.
    *   Pros: Easy to use and understand
    *   Cons: Does not return any value (like flatMap or reduce) and does not accumulate data
*   **filter**, **map**, and **some** methods can be used to process arrays, but they are more limited compared to flatMap and reduce.

To further explore alternative approaches for processing large datasets in JavaScript, consider using libraries like Lodash or Ramda that provide a wide range of utility functions for working with arrays.

Related benchmarks:

test flatMap vs reduce - with push (version: 0)

Comparing performance of: flatMap vs reduce

Created: 4 years ago by: Guest

Jump to the latest result

flatMap

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):