Benchmark: flatMap() vs filter().map() teste

Script Preparation code:

var arr = [];
var i = 0;

while (i <= 1e5) arr[i] = i++;

Tests:

filter().map()
arr.filter(x => x % 2 === 0).map(x => x * 2)
flatMap()
arr.flatMap(x => x % 2 === 0 ? [] : x*2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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.

**What is being tested?**

The provided JSON represents two benchmark test cases: `filter().map()` and `flatMap()`. Both tests are comparing the performance of two different approaches to process an array of numbers. The script preparation code generates a large array of consecutive integers from 0 to 100,000.

In the first test case, `filter().map()`, we're filtering out even numbers using the `filter()` method and then mapping each remaining number to double its value using the `map()` method.

In the second test case, `flatMap()`, we're directly flattening an array of arrays using the `flatMap()` method, where each inner array contains a single odd number multiplied by 2.

**Options compared**

The two approaches being compared are:

1. **Filtering and mapping**: `filter().map()` (Test Case 1)
	* Pros:
		+ Easy to understand and implement
		+ Works well with small to medium-sized arrays
	* Cons:
		+ Creates temporary intermediate arrays, which can be memory-intensive for large arrays
2. **Flattening**: `flatMap()` (Test Case 2)
	* Pros:
		+ Eliminates the need for intermediate arrays, reducing memory usage and potential performance bottlenecks
		+ Can lead to better cache locality and reduced overhead due to fewer function calls
	* Cons:
		+ Less intuitive and less well-supported in older browsers or JavaScript engines
		+ May require more complex code and handling of edge cases

**Special considerations**

Both test cases assume the presence of modern JavaScript features, such as:

1. **Arrow functions**: Used for concise and readable implementations of the filtering and mapping operations.
2. **Array methods**: `filter()`, `map()`, and `flatMap()` are supported by most modern browsers and JavaScript engines.

**Other alternatives**

If you were to design alternative approaches, here are a few possibilities:

1. **Looping**: Using traditional loops with indexing variables (e.g., `for` loops or `forEach()` loops) instead of array methods.
2. **Recursive solutions**: Implementing filtering and mapping using recursive functions, which can be more complex and less efficient than array methods.
3. **Native WebAssembly code**: Compiling the benchmark to native WebAssembly bytecode for improved performance (although this would require significant changes to the test implementation).

In conclusion, the `filter().map()` and `flatMap()` benchmarks provide a clear comparison of two common approaches to processing arrays in JavaScript. Understanding the pros and cons of each approach can help developers make informed decisions about which method to use in their own projects.

Related benchmarks:

flatMap() vs filter().map() teste (version: 0)

flatMap vs filter map

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

Created: 3 years ago by: Guest

Jump to the latest result

filter().map()

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):