Benchmark: flatMap vs reduce on array dict3

Script Preparation code:

var arr = Array(1000).fill({data: Array(1000).fill(0)})

Tests:

reduce with concat
arr.reduce((acc, { data }) => [...acc, ...data], [])
flatMap
arr.flatMap(({ data }) => data)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce with concat
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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Let's break down the provided benchmark and explain what's being tested, compared options, pros and cons of those approaches, and other considerations.

**Benchmark Definition**

The benchmark is comparing two approaches to transform an array: `flatMap` and `reduce` with concatenation. The input data is a large array of objects, each containing another array of 1000 zeros. This data structure resembles a nested array or a graph.

**Options Compared**

1. **flatMap**: This method flattens the inner array into the outer array, effectively removing the nesting.
2. **Reduce with concat**: This approach uses the `reduce` method to iterate over the outer array and concatenate each inner array to an accumulator array.

**Pros and Cons of Each Approach**

1. **flatMap**:
	* Pros:
		+ More concise and expressive code
		+ Can be more efficient since it avoids creating intermediate arrays
	* Cons:
		+ May have performance issues if the inner array is very large, as it needs to flatten all elements at once
2. **Reduce with concat**:
	* Pros:
		+ More flexible, as you can easily modify or extend the reduction process
		+ Can be more predictable in terms of performance, since it only iterates over each element once
	* Cons:
		+ More verbose code compared to `flatMap`
		+ May require more memory, since intermediate arrays are created

**Other Considerations**

* **Memory usage**: `flatMap` is likely to use less memory since it doesn't create intermediate arrays. However, if the inner array is very large, this might not be a significant concern.
* **Cache locality**: The performance difference between these approaches may depend on cache locality. `reduce with concat` might have better cache locality, as it iterates over each element sequentially.

**Library and Special JS Features**

There are no libraries used in this benchmark that aren't part of the standard JavaScript language. However, some browsers (like Chrome 110) have a special feature called UAString, which is not a standard JavaScript concept. The provided browser string includes the version number of Chrome and Safari.

**Alternatives**

Other alternatives to these approaches include:

* **Array.prototype.map()**: This method creates a new array with transformed elements but doesn't eliminate nesting.
* **Array.prototype.forEach() + Array.prototype.push()`: This approach iterates over each element in the outer array, pushing each inner array to an accumulator array.
* **Looping and Array.prototype.splice()**: This approach uses explicit looping and `splice()` to modify the accumulator array.

Keep in mind that these alternatives might not provide significant performance differences for small input sizes. However, as the input size grows, optimizations like those found in `flatMap` or `reduce with concat` can become more important.

Related benchmarks:

flatMap vs reduce on array dict3 (version: 0)

Comparing performance of: reduce with concat vs flatMap

Created: 3 years ago by: Guest

Jump to the latest result

reduce with concat

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):