Benchmark: flatMap() vs filter().map() vs reduce() - filtered 9 times out of 10 - large array

Script Preparation code:

var arr = [];
var i = 0;

while (i <= 1E5) 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):

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Let's break down the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark compares three different approaches to process an array of numbers: `flatMap()`, `filter().map()`, and `reduce()`.

**What's Being Tested?**

Here are the specific options being compared:

1. **`flatMap()`**: The `flatMap()` method is used to flatten an array (or return it unchanged if it's already flat). In this case, it's used to filter out numbers that don't meet a certain condition (`!(x % 10)`) and then map each remaining number to its quotient divided by 100.
2. **`filter().map()`**: This approach uses the `filter()` method to remove elements from an array that don't meet a certain condition, followed by the `map()` method to transform each remaining element. In this case, it's used in combination with `flatMap()`.
3. **`reduce()`**: The `reduce()` method is used to apply a function to each element of an array and reduce it to a single value (in this case, an array).

**Pros and Cons of Each Approach**

Here are some pros and cons of each approach:

1. **`flatMap()`**:
	* Pros: Can be more efficient than `filter().map()` when dealing with large arrays, as it avoids the overhead of filtering and mapping individually.
	* Cons: May not be as readable or maintainable for complex transformations, as it relies on a single method to handle multiple operations.
2. **`filter().map()`**:
	* Pros: Can be more readable and maintainable than `flatMap()`, as each operation is performed separately.
	* Cons: May be less efficient than `flatMap()` when dealing with large arrays, due to the additional filtering and mapping steps.
3. **`reduce()`**:
	* Pros: Can be a concise way to perform complex transformations, especially when dealing with large datasets.
	* Cons: May require more understanding of the accumulator function and its behavior.

**Library or Special JS Feature**

None of these approaches rely on any specific libraries or special JavaScript features beyond the standard array methods. The code is pure JavaScript.

**Other Considerations**

The benchmark also includes a "large array" generated by a script, which may impact performance depending on the size of the array and the browser being tested.

As for alternatives to these approaches, some other options might include:

* Using `every()` and `then()` with `Promise` for filtering and mapping
* Using `forEach()` with a callback function for iterating over the array and performing transformations
* Using a library like Lodash or Ramda for functional programming utilities

However, it's worth noting that these alternatives may not provide the same performance benefits as using native JavaScript methods like `flatMap()`, `filter().map()`, or `reduce()`.

Related benchmarks:

flatMap() vs filter().map() vs reduce() - filtered 9 times out of 10 - large 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):