Benchmark: Array vs Iterators

Script Preparation code:

var numbers = Array.from({length: 5}, (v, i) => i)

Tests:

array methods
numbers .map(x => x+1) .map(x => x*2) .map(x => x-1) .map(x => x/2) .forEach(console.log)
iterators
function * map(mapperFn, iterable) { for (const item of iterable) { yield mapperFn(item); } } numbers = map(x => x+1, numbers); numbers = map(x => x*2, numbers); numbers = map(x => x-1, numbers); numbers = map(x => x/2, numbers); for (const item of numbers) { console.log(item); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array methods
iterators

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/113.0

Browser/OS: Firefox 113 on Ubuntu

View result in a separate tab

Test name	Executions per second
array methods	13217.8 Ops/sec
iterators	394601.5 Ops/sec

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

LLMs can make mistakes. Check important info.

**What is being tested?**

MeasureThat.net is testing the performance of two approaches to process an array of numbers: using traditional array methods (e.g., `map()`, `forEach()`) versus using iterators.

The test case "Array vs Iterators" compares the execution time and memory usage of both approaches when performing a series of operations on an array of 5 numbers generated by `Array.from()`.

**Options compared**

1. **Traditional Array Methods**: The first option uses traditional array methods, such as:
	* `map()`: applies a callback function to each element in the array.
	* `forEach()`: executes a callback function for each element in the array.
	* These methods create new arrays or objects in memory, which can be expensive.
2. **Iterators**: The second option uses iterators, specifically:
	* A generator function (`function * map(mapperFn, iterable) {...}`): creates an iterator that yields values from the input iterable.
	* The `for...of` loop: iterates over the yielded values.

**Pros and Cons of each approach**

1. **Traditional Array Methods**:
	* Pros:
		+ Easy to understand and implement for developers familiar with array methods.
		+ Can be more intuitive for simple transformations.
	* Cons:
		+ Creates new arrays or objects in memory, which can lead to increased memory usage.
		+ May be slower due to the overhead of creating and manipulating new data structures.
2. **Iterators**:
	* Pros:
		+ More efficient memory usage, as only necessary values are stored in memory.
		+ Can reduce garbage collection overhead, leading to faster performance.
	* Cons:
		+ Requires understanding of iterators and generator functions.
		+ May be more complex to implement and debug.

**Library**

The `function * map(mapperFn, iterable)` code uses a custom iterator library or a polyfill for the `yield` keyword, which is not part of the standard JavaScript language. This allows the iterator function to yield values from the input iterable.

**Special JS feature or syntax**

No special features or syntax are used in this benchmark. It only relies on standard JavaScript language features and iterators.

**Other alternatives**

If you want to explore alternative approaches, here are a few options:

1. **Using `Promise.all()`**: Instead of using `map()` and `forEach()`, you could use `Promise.all()` to process the array elements in parallel.
2. **Using `reduce()`**: You could also use `reduce()` to perform aggregations on the array elements, instead of using `map()` and `forEach()`.
3. **Using Web Workers**: For large datasets or performance-critical applications, you might consider using Web Workers to offload the processing work to a separate thread.

Keep in mind that each alternative approach has its own trade-offs and may not be suitable for all use cases.

Related benchmarks:

Array vs Iterators (version: 0)

Comparing performance of: array methods vs iterators

Created: 2 years ago by: Guest

Jump to the latest result

array methods

iterators

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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