Benchmark: map vs closure vs symbols

Script Preparation code:

function A() {
	var x = 0;
	return {
		incr() { x ++ },
		get() { return x },
	}
}
var closure = A();
closure.incr()

var wm = new Map();
function B() {
	var x = {
		incr() { wm.set(x, (wm.get(x) || 0) + 1) },
		get() { return wm.get(x) },
	}
	return x
}
var weakmap = B();
weakmap.incr()


function C() {
	var me = Symbol('me');
	var x = {
		incr() { x[me]++ },
		get() { return x[me] },
		[me]: 0,
	}
	return x
}
var symbols = C();
symbols.incr()

Tests:

closure get
closure.get()
map get
weakmap.get()
closure incr
closure.incr()
map incr
weakmap.incr()
symbol incr
symbols.incr()
symbol get
symbols.get()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
closure get
map get
closure incr
map incr
symbol incr
symbol get

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is testing three different approaches to incrementing values in JavaScript:

1. Closure (`closure.incr()`)
2. Weak Map (`weakmap.incr()`)
3. Symbol (`symbols.incr()`)

Each approach has a corresponding getter function, which is also being tested.

**Options Compared**

Here's what's being compared:

* Closure: an object with an `incr` method that increments a value stored in the object.
* Weak Map: a map object that stores values and allows for efficient incrementing using the ` incr()` method.
* Symbol: an object with a symbol property (`[me]`) that can be used to store and increment a value.

**Pros and Cons of Each Approach**

Here are some pros and cons of each approach:

1. Closure:
	* Pros: easy to implement, doesn't require any additional libraries or data structures.
	* Cons: can lead to issues with scoping and closure capture.
2. Weak Map:
	* Pros: efficient incrementing using the `incr()` method, supports weak references.
	* Cons: requires additional library support (ES6+), may incur overhead due to garbage collection.
3. Symbol:
	* Pros: allows for efficient incrementing using a symbol as a key, doesn't require any additional libraries or data structures.
	* Cons: requires ES6+ support, may not be familiar to all developers.

**Libraries and Data Structures**

The benchmark uses the following library:

1. `Map` (Weak Map): provides an implementation of a map data structure that supports weak references.

Other considerations:

* The benchmark uses Safari 10 as the browser, which may have some specific features or optimizations enabled.
* The test cases use a fixed script preparation code and HTML preparation code, which means that the results are likely to be repeatable.

**Special JS Features or Syntax**

The benchmark doesn't explicitly mention any special JavaScript features or syntax, but it does rely on ES6+ support for Weak Maps and Symbols. If you're using an older version of JavaScript, you may need to adjust your implementation accordingly.

**Alternatives**

If you don't want to use a weak map, you could consider alternative approaches:

1. Using an array with an incrementing index.
2. Creating a custom data structure that supports efficient incrementing (e.g., a linked list).
3. Using a different data structure altogether (e.g., a binary search tree).

However, these alternatives may incur additional overhead or complexity, so it's essential to weigh the trade-offs before making a decision.

I hope this explanation helps you understand what's being tested in the benchmark!

Related benchmarks:

map vs closure vs symbols (version: 0)

Comparing performance of: closure get vs map get vs closure incr vs map incr vs symbol incr vs symbol get

Created: 5 years ago by: Registered User

Jump to the latest result

closure get

map get

closure incr

map incr

symbol incr

symbol get

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):