Benchmark: flatMap() vs filter().map() vs reduce() - filtered 1 time out of 10 - small array

Script Preparation code:

var arr = [];
var i = 0;

while (i <= 100) arr[i] = i++;

Tests:

filter().map()
arr.filter(x => x % 10).map(x => x/100)
flatMap()
arr.flatMap(x => x % 10 ? [x/100] : [])
reduce()
arr.reduce((acc, x) => { if (x % 10) acc.push(x / 100) return acc }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter().map()
flatMap()
reduce()

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

**Benchmark Analysis**

MeasureThat.net provides a JSON representation of a JavaScript microbenchmark, allowing users to compare the performance of different approaches for a specific task.

The benchmark is defined by two parts: Script Preparation Code and Html Preparation Code (which is empty in this case). The Script Preparation Code creates an array `arr` with values from 0 to 100 using a while loop. This script is executed before each test case.

**Test Cases**

There are three individual test cases:

1. **filter().map()**: This test case applies the `filter()` method to remove elements from `arr` that don't meet a condition (in this case, `x % 10 == 0`), and then applies the `map()` method to transform each remaining element.
2. **flatMap()**: This test case applies the `flatMap()` method to create a new array with the transformed elements, without filtering out elements first.
3. **reduce()**: This test case uses the `reduce()` method to accumulate values in an accumulator array, transforming each value as it is added.

**Options Compared**

The benchmark compares three different approaches:

* `filter().map()`: This approach involves two separate operations: filtering and mapping. It can be beneficial for small arrays because it allows the browser's garbage collector to free up memory between these operations.
* `flatMap()`: This approach applies both filtering and transformation in a single operation, which may result in faster performance since there is less overhead compared to using `filter()` and then `map()`.
* `reduce()`: This approach accumulates values directly without creating an intermediate array. It can be beneficial for large arrays because it avoids the need to create temporary arrays.

**Pros and Cons**

Here are some pros and cons of each approach:

* **filter().map()**
	+ Pros: Easy to understand, works well with small arrays.
	+ Cons: Involves two separate operations, which may slow down performance.
* **flatMap()**
	+ Pros: Reduces overhead compared to `filter()` and then `map()`, can be beneficial for large arrays.
	+ Cons: May be less intuitive than other approaches.
* **reduce()**
	+ Pros: Accumulates values directly without creating temporary arrays, efficient for large arrays.
	+ Cons: Requires more memory management knowledge and can be slower for small arrays.

**Library**

None of the test cases explicitly use a JavaScript library. However, it's worth noting that some modern browsers may implement polyfills or extensions to these methods that could affect their performance.

**Special JS Feature/Syntax**

There are no special JS features or syntax mentioned in this benchmark. All operations follow standard JavaScript syntax and semantics.

**Alternatives**

For large arrays, other approaches like `map()` followed by `forEach()` or using a `for` loop with a temporary array might also be used to compare performance. Additionally, some optimization techniques like memoization or caching could be applied to these methods for better performance.

Related benchmarks:

flatMap() vs filter().map() vs reduce() - filtered 1 time out of 10 - small array (version: 0)

flatMap vs filter map vs reduce()

Comparing performance of: filter().map() vs flatMap() vs reduce()

Created: 4 years ago by: Registered User

Jump to the latest result

filter().map()

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):