Benchmark: flatMap vs reduce test

Script Preparation code:

var values = []
for(i=0; i<1000; i++){  values.push(1) }

Tests:

flatMap
values.flatMap(e => [e, e])
reduce
values.reduce((acc, e) => [...acc, e, e], [])

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 break down what's being tested in the provided JSON.

**Benchmark Description**

The benchmark is comparing two different ways to transform an array of values: using `flatMap` and `reduce`.

**Options Compared**

1. **`flatMap`**: This method creates a new array with the results of applying a provided function on every element in this array. In this case, the function takes each value `e` and returns an array containing `e` and `e`.
2. **`reduce`**: This method applies a user-defined function to each element in the array and reduces it to a single output value.

**Pros and Cons**

* **`flatMap`**:
	+ Pros: Can be more efficient for simple transformations, as it avoids the overhead of creating an accumulator array.
	+ Cons: Can lead to memory issues if dealing with large datasets, since each iteration creates a new array.
* **`reduce`**:
	+ Pros: More flexible and can handle complex transformations, as well as accumulating values in an array.
	+ Cons: Can be slower due to the overhead of creating an accumulator array.

In this benchmark, `flatMap` appears to be faster for smaller inputs, while `reduce` might be more suitable for larger datasets or more complex transformations. However, it's essential to note that these are general pros and cons, and actual performance can vary depending on specific use cases and input sizes.

**Libraries Used**

In this benchmark, the following libraries are used:

1. **`Array.prototype.flatMap()`**: A built-in JavaScript method for flattening arrays.
2. **`Array.prototype.reduce()`**: A built-in JavaScript method for reducing arrays to a single value.

These libraries provide efficient implementations of the `flatMap` and `reduce` methods, making them suitable for performance comparisons.

**Special JS Feature/Syntax**

There is no specific special JavaScript feature or syntax used in this benchmark.

**Benchmark Preparation Code**

The script preparation code creates an array `values` with 1000 elements, all initialized to 1. This ensures a consistent input size for both `flatMap` and `reduce` implementations.

**Other Alternatives**

For similar benchmarks, you might consider exploring other methods for transforming arrays, such as:

1. **Using `map()`**: Similar to `flatMap`, but returns an array with the results of applying a function on each element.
2. **Using `forEach()`**: Iterates over the array and executes a callback function on each element, without returning any value.
3. **Implementing custom iteration logic using `for` loops or `while` loops**.

Keep in mind that these alternatives might not provide the same level of performance as built-in methods like `flatMap` and `reduce`, but can be useful for educational purposes or specific use cases where specialized libraries are not available.

Related benchmarks:

flatMap vs reduce test (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):