Benchmark: for-push vs for-noop vs flatMap

Script Preparation code:

var arr = Array.from({length: 10_000}, () => Math.floor(Math.random() * 2));

Tests:

for-noop
for (var i = 0; i < arr.length; i++) {}
for-push
var result = []; for (var i = 0; i < arr.length; i++) { var item = arr[i]; if (item !== 0) { result.push(item); } }
flatMap
arr.flatMap((x) => (x !== 0 ? [x] : []));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for-noop
for-push
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 benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The benchmark is designed to compare the performance of three different approaches for iterating over an array:

1. `for-noop` (no-op loop): a simple loop that iterates over the array without doing anything.
2. `for-push`: a loop that pushes each element onto an array.
3. `flatMap`: the flatMap method, which returns a new array with the results of applying the provided function to each element.

**Test Cases**

Each test case represents one of these approaches:

* `for-noop`: A simple loop that iterates over the array without doing anything. This is likely used as a baseline to compare performance.
* `for-push`: A loop that pushes each element onto an array. This approach can be slow due to the overhead of creating and managing arrays.
* `flatMap`: The flatMap method, which returns a new array with the results of applying the provided function to each element.

**Library and Syntax**

The benchmark uses the JavaScript built-in `Array` class, specifically its `length` property and indexing syntax (`arr[i]`). No external libraries are required for this benchmark.

**Special JS Features or Syntax**

None mentioned. The benchmark only uses standard JavaScript features and syntax.

**Comparison and Pros/Cons**

Here's a brief comparison of the three approaches:

* **for-noop**: Fastest, as it doesn't perform any additional operations beyond iterating over the array.
	+ Pros: Simple to implement, minimal overhead.
	+ Cons: Does nothing useful, only serves as a baseline.
* **for-push**: Slower due to the overhead of creating and managing arrays.
	+ Pros: Can be used in situations where elements need to be retained or processed further.
	+ Cons: Creates unnecessary array objects, leading to memory allocation and garbage collection overhead.
* **flatMap**: Faster than `for-push`, but slower than `for-noop`.
	+ Pros: Returns a new array with the results of applying the provided function, which can be useful in certain situations.
	+ Cons: Creates an additional array object, which can lead to memory allocation and garbage collection overhead.

**Other Alternatives**

Some alternative approaches for iterating over arrays include:

* `forEach`: Iterates over the array without creating a new array or modifying the original one. This is often used when you need to perform side effects, such as updating DOM elements.
* Array methods like `map`, `filter`, and `reduce` can also be used instead of loops.

Keep in mind that these alternatives may have different performance characteristics depending on the specific use case and JavaScript engine.

**Benchmark Results**

The provided benchmark results show the number of executions per second for each test case. The fastest execution rate is for the `for-noop` approach, followed by `flatMap`, and then `for-push`.

Overall, this benchmark provides a good comparison of different approaches for iterating over arrays in JavaScript, helping developers choose the most suitable method for their specific use cases.

Related benchmarks:

for-push vs for-noop vs flatMap (version: 0)

Comparing performance of: for-push vs for-noop vs flatMap

Comparing performance of: for-noop vs for-push vs flatMap

Created: 3 years ago by: Guest

Jump to the latest result

for-noop

for-push

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):