Benchmark: flatMap vs reduce 99991234

Script Preparation code:

var values = []
for(i=0; i<1000; i++){ values.push([i, i, i, i, i, i, i, i, i, i]) }

Tests:

flatMap
values.flatMap(e => e.map(i => i * 2))
reduce
values.reduce((acc, x) => { acc.push(...x.map(i => i * 2)); return acc; }, [])

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:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

I'll explain the benchmark in detail.

**Benchmark Overview**

The provided benchmark compares the performance of two approaches: `flatMap` and `reduce`. Both methods are used to process an array of arrays, where each inner array has 10 elements.

**Script Preparation Code**

The script preparation code generates a large array of arrays (`values`) with 1000 elements each. Each element is an array with 10 identical integers (`i`). This array will be processed by both `flatMap` and `reduce`.

**Html Preparation Code**

There is no HTML preparation code provided, which means that the benchmark only runs in the browser's JavaScript engine.

**Benchmark Definition**

The benchmark definition consists of two individual test cases:

1. **flatMap**: The benchmark uses the `flatMap` method to process the array of arrays. `flatMap` returns a new array with the results of applying the given function to each element of the input array.
2. **reduce**: The benchmark uses the `reduce` method to process the array of arrays. `reduce` applies a reducer function to each element in the input array, accumulating a result.

**Libraries and Special Features**

Neither of these methods relies on any external libraries or special JavaScript features beyond the standard API.

**Options Comparison**

Here's a brief comparison of the two approaches:

*   **Performance**: Both methods have their own performance characteristics. `reduce` tends to be slower than `flatMap`, especially for larger inputs, because it creates an accumulator array and then flattens it at the end. On the other hand, `flatMap` uses tail recursion which makes it potentially faster in some situations.
*   **Memory Usage**: Both methods use a reasonable amount of memory. However, `reduce` might use slightly more due to creating the initial accumulator array.

**Pros and Cons**

Here's what we can say about each method:

*   **flatMap**
    *   Pros: Can be slightly faster in some situations and uses less memory than reduce.
    *   Cons: Might not be as intuitive for developers, especially those without experience with functional programming concepts. It requires the use of map() to work around the lack of parallelism between flatMap() and map().
*   **reduce**
    *   Pros: More straightforward, familiar method that leverages the power of accumulator functions and can often lead to cleaner code when building up some data.
    *   Cons: Uses more memory due to creating an initial array and then flattening it at the end.

**Other Alternatives**

While `flatMap` and `reduce` are standard methods in JavaScript, other alternatives exist:

*   **forEach()**: This method iterates over each element of an array and executes a provided function once for every element. It doesn't build up any data structure that can be leveraged later.
*   **for...of loop**: The most basic iteration method using the `for` keyword with `of`. While not as powerful or flexible, this is the oldest way to iterate over arrays in JavaScript and still quite widely used.

However, these alternatives do not provide the same level of parallelism that `flatMap` offers.

Related benchmarks:

flatMap vs reduce 99991234 (version: 0)

Comparing performance of: flatMap vs reduce

Created: 3 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):