Benchmark: .flatMap vs .map.flat

Script Preparation code:

var arr = new Array(100000).fill(1);

Tests:

Test flatMap
arr.flatMap(() => [1, 2, [3, 4, true, "another", [{name:"first", age: 24, pref: {shows: ["rick and morty", "adventure time"]} }, {name:"second", age: 24, pref: {shows: ["the orville", "black mirror"]} }]]]);
Test map/flat
arr.map(() => [1, 2, [3, 4, true, "another", [{name:"first", age: 24, pref: {shows: ["rick and morty", "adventure time"]} }, {name:"second", age: 24, pref: {shows: ["the orville", "black mirror"]} }]]]).flat();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Test flatMap
Test map/flat

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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I'll break down the provided benchmark definition and test cases, explaining what's being tested, the options compared, pros and cons of each approach, and other considerations.

**Benchmark Definition**

The benchmark definition is divided into two parts: Script Preparation Code and Html Preparation Code. The Script Preparation Code defines a JavaScript array `arr` with 100,000 elements, all filled with the value `1`. This setup is used as a common base for both test cases.

**Test Cases**

There are two test cases:

1. **Test flatMap**: This test case uses the `flatMap` method on an array to flatten it, which returns a new array with the same length as the original array, containing each element of the original array that produces a value.
2. **Test map/flat**: This test case first applies the `map` method to an array, and then calls the `flat` method on the resulting array.

**Options Compared**

The two options being compared are:

*   Using the `flatMap` method directly
*   Using the `map` method followed by the `flat` method

**Pros and Cons of Each Approach**

1.  **flatMap Method**
    *   Pros:
        *   More efficient: The `flatMap` method is optimized for this specific use case, as it can avoid creating intermediate arrays.
        *   Fewer operations: It directly returns a new array without the need to create an intermediate array using `map`.
    *   Cons:
        *   Less flexible: It's specifically designed for flattening arrays and may not be suitable for other use cases.
2.  **Map/Fat Method**
    *   Pros:
        *   More versatile: The `map` method is widely used and well-understood, making it easier to maintain and debug code that uses it.
        *   Easier to understand: The two-step process can be more intuitive for developers familiar with the `map` method.

**Other Considerations**

*   **Library Usage**: There's no explicit library mentioned in the benchmark definition. However, the use of array methods like `flatMap`, `map`, and `flat` suggests that the JavaScript standard library is being used.
*   **Special JS Features or Syntax**: The benchmark does not explicitly test any special features or syntax beyond what's provided by the JavaScript language.

**Alternatives**

Other alternatives to consider for similar benchmarking scenarios might include:

*   Using a different array flattening method, like `reduce`.
*   Comparing the performance of using `flatMap` vs. manually implementing array flattening using loops and destructuring.
*   Measuring the impact of using a library like Lodash or Ramda on array manipulation performance.

When designing benchmarks for similar use cases, consider factors such as:

*   Using relevant data structures (e.g., arrays) to accurately represent real-world scenarios.
*   Testing different algorithms and techniques to provide insights into their relative efficiency.
*   Including multiple test cases to cover a range of edge cases and normal operation scenarios.

Related benchmarks:

.flatMap vs .map.flat (version: 0)

Comparing performance of: Test flatMap vs Test map/flat

Created: 2 years ago by: Registered User

Jump to the latest result

Test flatMap

Test map/flat

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):