Benchmark: loop options performance

Script Preparation code:

var arr = Array.from({length:100000}, (v, i) => ({name: i, shouldNotBeFiltered: i % 2 === 0}));

Tests:

flatMap
arr.flatMap(({ name, shouldNotBeFiltered }) => (shouldNotBeFiltered ? [name] : []));
reduce
arr.reduce((acc, { name, shouldNotBeFiltered }) => { if (shouldNotBeFiltered) { const elementToPush = name; acc.push(elementToPush); } return acc; }, []);
for of loop
const result = []; for (const { name, shouldNotBeFiltered } of arr) { if (shouldNotBeFiltered) { const elementToPush = name; result.push(elementToPush); } }
filter and map
arr.filter(({ shouldNotBeFiltered }) => shouldNotBeFiltered).map(({ name }) => name);
forEach
const result = []; arr.forEach(({ name, shouldNotBeFiltered }) => { if (shouldNotBeFiltered) { const elementToPush = name; result.push(elementToPush); } });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap
reduce
for of loop
filter and map
forEach

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 6 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0

Browser/OS: Chrome 125 on Windows

View result in a separate tab

Test name	Executions per second
flatMap	401.2 Ops/sec
reduce	742.0 Ops/sec
for of loop	1640.3 Ops/sec
filter and map	456.3 Ops/sec
forEach	1266.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation.

**Benchmark Definition and Script Preparation Code**

The benchmark measures the performance of different loop options in JavaScript. The script preparation code creates an array `arr` with 100,000 elements, each containing `name` and `shouldNotBeFiltered` properties. The purpose of this array is to serve as a test data structure for the various loop options.

**Loop Options Compared**

The benchmark compares four different loop options:

1. **FlatMap**: This option uses the `flatMap` method to create a new array with the filtered elements.
2. **Reduce**: This option uses the `reduce` method to accumulate the filtered elements in an accumulator array.
3. **For of loop**: This option uses a traditional for loop to iterate over the array and push filtered elements into a result array.
4. **Filter and Map**: This option uses the `filter` method to create a new array with only the filtered elements, followed by the `map` method to create a new array with the names of the filtered elements.

**Pros and Cons**

Here's a brief summary of each loop option:

1. **FlatMap**:
	* Pros: Concise syntax, efficient implementation.
	* Cons: May not be as readable for complex data structures or nested loops.
2. **Reduce**:
	* Pros: Can handle complex data structures and accumulations, more flexible than `flatMap`.
	* Cons: May require more setup and understanding of the accumulator array.
3. **For of loop**:
	* Pros: Trivial to understand and implement, can be used with any data structure.
	* Cons: May be less efficient due to overhead of iterating over each element individually.
4. **Filter and Map**:
	* Pros: Easy to read and understand, can be chained together for complex filtering.
	* Cons: May not be as efficient as `flatMap` or `reduce`, requires two separate method calls.

**Library Usage**

The benchmark uses the built-in JavaScript Array methods: `flatMap`, `reduce`, `filter`, and `map`. These methods are part of the ECMAScript standard and do not require any external libraries.

**Special JS Features or Syntax**

None mentioned in this specific benchmark. However, some loop options may rely on special features like:

* Iterators and generators (e.g., for loops with iterators)
* Async/await syntax
* Promises

These features are not explicitly used in the provided benchmark code.

**Alternatives**

If you're looking for alternative loop options or methods, consider the following:

1. **Higher-order functions**: Instead of `flatMap` and `map`, use higher-order functions like `forEach` or `every`.
2. **Async/await syntax**: Use async/await to simplify looping over asynchronous operations.
3. **Closures**: Utilize closures to create private loops that can be reused throughout your codebase.

Keep in mind that the choice of loop option ultimately depends on your specific use case, performance requirements, and coding style preferences.

Related benchmarks:

loop options performance (version: 0)

Comparing performance of: flatMap vs reduce vs for of loop vs filter and map vs forEach

Created: 7 months ago by: Guest

Jump to the latest result

flatMap

reduce

for of loop

filter and map

forEach

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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