Benchmark: Tagsads - MeasureThat.net

Script Preparation code:

var arr = Array(10000).fill(0).map((_, i) => [i, i + 1])

Tests:

T
arr.reduce((acc, x) => [...acc, ...x], [])
S
arr.flatMap(x => [...x])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
T
S

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 world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition**

The benchmark definition is a JSON object that describes the test to be performed. In this case, we have two individual test cases:

1. `arr.reduce((acc, x) => [...acc, ...x], [])`
2. `arr.flatMap(x => [...x])`

These test cases are comparing the performance of different approaches for array reduction and flattening.

**Options Compared**

The options being compared in these test cases are:

* `reduce`: an array reduction function that takes a callback function as an argument.
* `flatMap`: an array method that flattens an array by mapping over it and returning a new array with the results.

**Pros and Cons of Different Approaches**

Let's analyze the pros and cons of each approach:

1. `reduce`:
	* Pros: efficient for small to medium-sized arrays, easy to understand and use.
	* Cons: can be slow for large arrays due to the overhead of the callback function, may not be suitable for all data types (e.g., strings).
2. `flatMap`:
	* Pros: generally faster than `reduce` for large arrays, concise and expressive syntax.
	* Cons: may have higher memory usage due to the creation of a new array, can be less efficient if the callback function is complex.

**Library Use**

Neither of these test cases uses any libraries. They are purely JavaScript built-in functions.

**Special JS Features or Syntax**

There are no special JS features or syntax used in this benchmark. The code is straightforward and doesn't rely on any advanced techniques.

**Other Considerations**

When writing microbenchmarks, it's essential to consider the following:

* Keep your benchmarks small and focused on a specific aspect of performance.
* Use a representative dataset (in this case, an array of 10,000 elements).
* Ensure that the benchmark is deterministic, meaning the output should be the same every time it's run.
* Consider using multiple browsers or devices to get a more comprehensive picture of performance.

**Alternatives**

If you were to rewrite these test cases in another language or framework, you might consider:

* Using a functional programming language like Haskell or Scala, which often have built-in support for array reduction and flattening.
* Using a JavaScript alternative like TypeScript or Flow, which offer type checking and other features that can improve performance.

Keep in mind that the choice of language or framework will depend on your specific use case and requirements.

Related benchmarks:

Tagsads (version: 0)

Comparing performance of: T vs S

Created: 3 years ago by: Guest

Jump to the latest result

T

S

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):