Benchmark: reduce vs flatMap, conditional correct

Script Preparation code:

var xs = [{
    text: "a",
    sort: false
}, {
    text: "b",
    sort: true
}, {
    text: "c",
    sort: false
}, {
    text: "d",
    sort: true
}, {
    text: "e",
    sort: false
}]

Tests:

reduce
xs.reduce((acc, x) => { if (x.sort) { acc.push(x.text); } return acc; }, ["z"])
flatMap
["z", ...xs.flatMap((x) => x.sort ? [x.text] : [])]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
flatMap

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 explain what's being tested.

**Benchmark Purpose**

The benchmark compares the performance of two approaches: `reduce` and `flatMap`. Both methods are used to process an array of objects, filtering out elements based on a specific condition. The goal is to determine which approach is faster.

**Options Compared**

Two options are compared:

1. **`reduce`**: A method that applies a reduction function to each element in the array, accumulating a result.
2. **`flatMap`**: A method that flattens an array of arrays or maps into a single array.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

*   `reduce`:
    *   Pros:
        *   Can be more memory-efficient, as it avoids creating new arrays.
        *   Allows for early termination when a condition is met.
    *   Cons:
        *   Can be slower due to its iterative nature.
        *   Requires careful handling of initial values and edge cases.
*   `flatMap`:
    *   Pros:
        *   Often faster, as it leverages the browser's optimized array manipulation code.
        *   Easier to read and maintain, as it uses a more traditional loop structure.
    *   Cons:
        *   May consume more memory, depending on the size of the output array.

**Library Usage**

Neither `reduce` nor `flatMap` requires any external libraries. However, both methods rely on built-in JavaScript functionality.

**Special JS Features/Syntax**

The benchmark uses a few special features:

*   **Template literals**: Used in the script preparation code to define the initial array (`xs`) and the reduction function.
*   **Arrow functions**: Used as the reduction function in both `reduce` and `flatMap` benchmarks. This feature allows for concise, functional-style coding.

**Other Alternatives**

If you wanted to write this benchmark using a different approach, here are some alternatives:

*   **Hand-rolled loops**: Instead of using `reduce` or `flatMap`, you could implement the filtering logic manually using a loop.
*   **Other array methods**: You might consider using other array methods like `filter()`, `map()`, or `forEach()` to compare performance.

Keep in mind that each approach has its trade-offs, and the choice ultimately depends on your specific use case and personal preference.

Related benchmarks:

reduce vs flatMap, conditional correct (version: 0)

Comparing performance of: reduce vs flatMap

Created: 3 years ago by: Guest

Jump to the latest result

reduce

flatMap

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):