Benchmark: Spread in reduce 2

Tests:

With spread
const EXCLUDED_FILTERS_FOR_COUNT = [ 'type', 'product', ]; const getMandatoryAndOptionalFilters = ( filters ) => { if (!filters) return {}; return Object.entries(filters).reduce( (acc, [currentFilterKey, currentFilterValue]) => { if ( EXCLUDED_FILTERS_FOR_COUNT.includes(currentFilterKey) ) { return { ...acc, mandatory: { ...(acc.mandatory ?? {}), [currentFilterKey]: currentFilterValue, }, }; } return { ...acc, optional: { ...(acc.optional ?? {}), [currentFilterKey]: currentFilterValue, }, }; }, {} ); }; getMandatoryAndOptionalFilters({ type: "aa", category: "cat1;cat2", paymentType: "payment", otherFilter: "otherFilter" });
Without spread
const EXCLUDED_FILTERS_FOR_COUNT = ["type", "product"]; const getMandatoryAndOptionalFilters = ( filters ) => { if (!filters) return {}; return Object.entries(filters).reduce( (acc, [currentFilterKey, currentFilterValue]) => { if ( EXCLUDED_FILTERS_FOR_COUNT.includes(currentFilterKey) ) { acc.mandatory = { ...(acc.mandatory ?? {}), [currentFilterKey]: currentFilterValue, }; } else { acc.optional = { ...(acc.optional ?? {}), [currentFilterKey]: currentFilterValue, }; } return acc; }, {} ); }; getMandatoryAndOptionalFilters({ type: "aa", category: "cat1;cat2", paymentType: "payment", otherFilter: "otherFilter", });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
With spread
Without spread

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

**Benchmark Definition**

The benchmark is defined in two parts:

1. **Script Preparation Code**: This section contains the code that sets up the benchmark environment, but it's empty in this case. It might include variables, functions, or other setup tasks.
2. **Html Preparation Code**: This section is also empty, which means there are no HTML-specific setup or cleanup tasks.

**Individual Test Cases**

There are two test cases:

1. **"With spread"**
	* The benchmark definition uses the spread operator (`...`) to create a new object in the `getMandatoryAndOptionalFilters` function.
	* In this implementation, if a filter key is excluded from `EXCLUDED_FILTERS_FOR_COUNT`, it's added directly to the `acc.mandatory` or `acc.optional` objects using the spread operator.
2. **"Without spread"**
	* The benchmark definition uses a more traditional approach without the spread operator. Instead of creating a new object, it updates the existing `acc` object directly.

**Options being compared**

The two test cases compare the performance of using the spread operator versus not using it in the `getMandatoryAndOptionalFilters` function.

**Pros and Cons**

* **Using the spread operator (`...`)**:
	+ Pros:
		- Creates a new object, which can be more efficient for large datasets.
		- Reduces the need for manual key-value pair assignment.
	+ Cons:
		- May incur additional overhead due to the creation of a new object.
		- Can lead to slower performance if the resulting objects are too large.
* **Not using the spread operator**:
	+ Pros:
		- Avoids creating a new object, which can be more efficient for small datasets.
		- Reduces memory allocation and garbage collection overhead.
	+ Cons:
		- Requires manual key-value pair assignment, which can lead to errors if not done correctly.
		- May result in slower performance due to the need for explicit updates.

**Other considerations**

* The benchmark doesn't account for other factors that might affect performance, such as array sizes or data types.
* The `EXCLUDED_FILTERS_FOR_COUNT` constant is hardcoded and might not be representative of real-world scenarios where filter keys are dynamic.

**Library usage**

There's no library explicitly mentioned in the benchmark definition. However, it uses built-in JavaScript features like the spread operator (`...`) and object methods (e.g., `Object.entries()`, `includes()`).

**Special JS feature or syntax**

* The use of template literals (e.g., `"type", "product"`) is not particularly notable.
* There are no other special features or syntaxes used in this benchmark.

**Alternatives**

To write similar benchmarks, you could explore different approaches to creating and updating objects, such as:

1. Using the `Object.assign()` method instead of the spread operator.
2. Utilizing libraries like Lodash for functional programming and object manipulation.
3. Implementing custom optimization techniques, such as caching or memoization.

Keep in mind that these alternatives might not produce identical results and may introduce new complexities.

Related benchmarks:

Spread in reduce 2 (version: 0)

Comparing performance of: With spread vs Without spread

Created: 2 years ago by: Guest

Jump to the latest result

With spread

Without spread

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):