Benchmark: Map vs Object (models case) Performance

Script Preparation code:

var keys = []
var map = new Map()
var obj = {}

var langs = ['en', 'pt', 'es']
var sequels = ['rw', 'ro']

langs.forEach(lang => {
  sequels.forEach(sequel => {
    let val = Math.random()
    let key = sequel + '|' + lang
    keys.push(key)
    map.set(key,val)
	obj[key] = val
  })
})

Tests:

Map lookup
for (let i=0; i<keys.length; i++) { let key = keys[i] a = map.get(key) }
Obj lookup
for (let i=0; i<keys.length; i++) { let key = keys[i] a = obj[key] }
Map lookup 2
for (let i=0; i<keys.length; i++) { let key = keys[i] a = map.get(key) }
Obj lookup 2
for (let i=0; i<keys.length; i++) { let key = keys[i] a = obj[key] }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map lookup
Obj lookup
Map lookup 2
Obj lookup 2

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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I'll break down the provided JSON and explain what's being tested, the options compared, their pros and cons, and other considerations.

**Benchmark Definition**

The benchmark measures the performance of two data structures: `Map` and `Object`. The test case creates an array of keys (`keys`) and initializes both a `Map` and an `Object` with the same key-value pairs. The benchmark then performs three types of lookups on each data structure:

1. `Map.lookup()`: Using the `get()` method to retrieve a value from the map.
2. `Obj.lookup()`: Directly accessing the value associated with a key in the object using the square bracket notation (`obj[key]`).
3. `Map.lookup 2` and `Obj.lookup 2`: Similar to `Map.lookup()` and `Obj.lookup()`, but with some minor differences.

**Options Compared**

The benchmark compares the performance of three approaches:

1. **Direct Object Access**: Using the object's square bracket notation (`obj[key]`) to access values.
2. **Map Lookup**: Using the `get()` method on a Map instance to retrieve values.
3. Two variations of each approach: `Map.lookup 2` and `Obj.lookup 2`, which introduce minor differences.

**Pros and Cons**

1. **Direct Object Access**
	* Pros: Fast, lightweight, and easy to implement.
	* Cons: May not be as efficient as Map lookup for large datasets or complex lookups.
2. **Map Lookup**
	* Pros: Efficient for large datasets or complex lookups, as it uses a hash table under the hood.
	* Cons: May have overhead due to the `get()` method's internal implementation.

**Other Considerations**

The benchmark also takes into account:

* **Language Variations**: The test case creates data in multiple languages (English, Portuguese, and Spanish) to ensure that the results are language-agnostic.
* **Execution Frequency**: Each test case is executed a large number of times (637,752.625 executions per second for one test case), which helps to mitigate any overhead due to benchmarking itself.

**Additional Notes**

The `Script Preparation Code` initializes an array of keys (`keys`) and creates a map and object with the same key-value pairs. The `Html Preparation Code` is empty, indicating that this benchmark does not rely on HTML rendering or other graphical aspects.

The test cases use JavaScript features such as:

* `Map` and `Object` data structures
* Arrow functions ( implicit in the `forEach` loops)
* Template literals (used to create the keys string)

If you're interested in exploring alternative approaches, some options include:

* Using a different data structure, like an array of objects or a custom data structure.
* Implementing a more optimized lookup mechanism using JavaScript native methods or third-party libraries.
* Experimenting with language-specific optimizations, such as caching or memoization.

Related benchmarks:

Map vs Object (models case) Performance (version: 0)

Lookup of map vs object

Comparing performance of: Map lookup vs Obj lookup vs Map lookup 2 vs Obj lookup 2

Created: 3 years ago by: Guest

Jump to the latest result

Map lookup

Obj lookup

Map lookup 2

Obj lookup 2

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):