Benchmark: array of objects to object with mapper

HTML Preparation code:

<script>
const parts = [{
        "type": "currency",
        "value": "€"
    },
    {
        "type": "integer",
        "value": "1"
    },
    {
        "type": "group",
        "value": ","
    },
    {
        "type": "integer",
        "value": "000"
    },
    {
        "type": "decimal",
        "value": "."
    },
    {
        "type": "fraction",
        "value": "00"
    }
]
  
   const mapper = {
    currency: 'currencySign',
    decimal: 'decimalSeparator',
    group: 'thousandSeparator',
  };
</script>

Tests:

reduce with mapped assign
parts.reduce((result, { type, value }) => { switch (type) { case 'currency': result.currencySign = value; break; case 'decimal': result.decimalSeparator = value; break; case 'group': result.thousandSeparator = value; break; default: break; } return result; }, {});
reduce with mapper
parts.reduce((result, { type, value }) => { if (mapper[type]) { result[mapper[type]] = value; } return result; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce with mapped assign
reduce with mapper

Fastest: N/A

Slowest: N/A

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

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**Benchmark Explanation**

The provided benchmark tests the performance of two approaches to map and assign values from an array of objects to a single object.

**Approach 1: Direct Assignment (`"reduce with mapped assign"`)**

This approach uses the `Array.prototype.reduce()` method to iterate over the `parts` array, where each element is an object with a `type` and a `value`. The callback function updates the resulting object by directly assigning the value to the corresponding key (e.g., `currencySign`, `decimalSeparator`, etc.).

**Approach 2: Mapper-based Assignment (`"reduce with mapper"`)**

This approach uses a similar `Array.prototype.reduce()` method, but introduces a `mapper` object that maps each type to its corresponding property name in the resulting object. The callback function looks up the mapping for each element's `type` and assigns the value to the corresponding property.

**Pros and Cons**

* **Direct Assignment:**
	+ Pros:
		- Simple and straightforward implementation
		- Easy to understand and maintain
	+ Cons:
		- May be slower due to direct assignment, especially if the resulting object is large or has many properties
		- Less flexible than mapper-based approach
* **Mapper-based Assignment:**
	+ Pros:
		- More efficient and scalable for larger datasets or objects with many properties
		- Allows for easier maintenance and modification of the mapping logic
	+ Cons:
		- Slightly more complex implementation due to the need to define a `mapper` object

**Library and Special JS Features**

* **Array.prototype.reduce()**: A built-in JavaScript method that applies a function to each element in an array, accumulating a result.
* No special JS features or syntax are used in this benchmark.

**Considerations**

The choice between these two approaches depends on the specific requirements of your use case. If simplicity and ease of maintenance are more important than performance, direct assignment might be sufficient. However, if scalability and flexibility are crucial, the mapper-based approach is likely a better choice.

**Other Alternatives**

Other possible implementations for this benchmark could include:

* Using `Object.assign()` or `Object.merge()` to assign values from an object to another object.
* Implementing a custom function that manually iterates over the array and updates the resulting object.
* Using a library like Lodash that provides a `mapAssign()` function for this purpose.

However, these alternatives would likely introduce additional complexity and may not be as efficient or scalable as the two approaches tested in this benchmark.

Related benchmarks:

array of objects to object with mapper (version: 0)

Comparing performance of: reduce with mapped assign vs reduce with mapper

Created: 2 years ago by: Guest

Jump to the latest result

reduce with mapped assign

reduce with mapper

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):