Benchmark: flatMap vs reduce (inner object11)

Script Preparation code:

var arr = Array(100).fill({ subsections: Array(100).fill({ foo: "bar" }) })

Tests:

reduce with concat
arr.reduce((memo, { subsections }) => [...memo, ...subsections], [])
flatMap
arr.flatMap((x) => x.subsections)

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 JSON and explain what's being tested.

**Benchmark Overview**

The benchmark measures the performance of two JavaScript methods: `reduce` with concatenation and `flatMap`. The test is designed to compare these two approaches on an inner object with 100 subsections, each containing a string value "bar".

**Test Case Descriptions**

1. **"reduce with concat"`**: This test case uses the `Array.prototype.reduce()` method to concatenate the `subsections` arrays inside the `arr` array. The initial value of the accumulator is an empty array (`[]`). Each iteration, it appends a subset of `subsections` to the accumulator.
2. **"flatMap"`**: This test case uses the `Array.prototype.flatMap()` method to create a new array with the flattened elements from each subsection. It simply iterates over the `x.subsections` arrays and returns a new array containing all the concatenated values.

**Library/Functionality Used**

* `Array.prototype.reduce()`: A built-in JavaScript method that reduces an array by applying a function to each element. The purpose of this method is to perform aggregation or computation on an array.
* `Array.prototype.flatMap()`: Also a built-in JavaScript method, which creates a new array with the results of applying a provided mapping function on every element in the calling array.

**Pros and Cons**

* **`reduce()`**: Pros:
	+ More efficient for small arrays (e.g., 100 elements).
	+ Can be useful for aggregating data or performing computations.
Cons:
	+ May lead to slower performance due to repeated concatenations of arrays.
	+ Requires more memory due to the temporary accumulator array.
* **`flatMap()`**: Pros:
	+ More efficient and scalable for large datasets (e.g., 1000 elements).
	+ Reduces memory usage by avoiding unnecessary array concatenation.
Cons:
	+ May not be suitable for small arrays or aggregation use cases.

**Special JavaScript Features/Syntax**

This benchmark does not explicitly mention any special JavaScript features or syntax. However, it's worth noting that both `reduce()` and `flatMap()` are widely supported in modern browsers, including the ones used by MeasureThat.net.

**Other Alternatives**

For similar tasks:

* If you want to flatten an array using a custom function, you could use `Array.prototype.map()` and then concatenate the results.
* For aggregation or computation on an array, consider using `reduce()`, as it provides more control over the iteration process.

Keep in mind that the choice between `reduce()` and `flatMap()` ultimately depends on your specific use case and performance requirements. MeasureThat.net's benchmark helps you compare the relative performance of these two methods for a particular task.

Related benchmarks:

flatMap vs reduce (inner object11) (version: 0)

Comparing performance of: reduce with concat vs flatMap

Created: 4 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):