Benchmark: flatMap vs reduce test 3

Script Preparation code:

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

Tests:

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

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

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided JavaScript microbenchmark.

**Benchmark Overview**

The benchmark compares two methods for transforming an array: `flatMap` and `reduce`. Both methods are used to create a new array by performing some operation on each element of the original array.

**Options Compared**

In this benchmark, we have two options being compared:

1. **flatMap**: This method creates a new array with the results of applying a provided function to each element in the original array.
2. **reduce**: This method reduces the array to a single value by iterating over its elements and applying a provided function.

**Pros and Cons**

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

* **flatMap**:
	+ Pros: More concise, easier to read, and can be more efficient for certain use cases (e.g., when dealing with arrays that contain only values).
	+ Cons: Can be slower than `reduce` for large datasets due to its overhead.
* **reduce**:
	+ Pros: More flexible, allowing for cumulative computation of the result value. Also, it's often faster than `flatMap` due to its ability to take advantage of array operations (e.g., concatenation).
	+ Cons: Can be more verbose and harder to read, especially when dealing with complex computations.

**Library and Purpose**

In this benchmark, no libraries are explicitly mentioned, but we can assume that the `Array.prototype.flatMap()` method is being compared. The `flatMap()` method was introduced in ECMAScript 2019 (ES10) as a replacement for the older `map()` followed by `concat()`. Its purpose is to create a new array from an existing array by mapping each element and then concatenating the results.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntax mentioned in this benchmark. The code uses standard ECMAScript 2019 syntax, including arrow functions and template literals (for string interpolation).

**Other Alternatives**

If you need to transform an array in other ways, consider using:

* `map()`: Similar to `flatMap`, but does not concatenate the results.
* `forEach()`: Not recommended for performance-critical code, as it's slower than `map()` or `flatMap`.
* `filter()` and then `concat()`: Less efficient than `flatMap` or `reduce`.
* Custom loops: While not recommended, you can use explicit loops to achieve similar transformations.

**Benchmark Preparation Code**

The provided preparation code generates an array of 1000 integers using a simple `for` loop:
```javascript
var values = [];
for (i = 0; i < 1000; i++) {
  values.push(i);
}
```
This setup ensures that both `flatMap` and `reduce` methods are operating on the same dataset.

**Benchmark Results**

The latest benchmark results show the execution counts per second for each browser platform:

| TestName | ExecutionsPerSecond |
| --- | --- |
| flatMap | 3229.2421875 |
| reduce | 2143.2861328125 |

These results suggest that `flatMap` is generally faster than `reduce`, but this may vary depending on the specific use case, dataset size, and browser platform.

Keep in mind that these results are just a single benchmark and might not reflect real-world performance differences.

Related benchmarks:

flatMap vs reduce test 3 (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):