Benchmark: Spread in reduce - more entries

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", another1: "aa", another3: "vv", another2: "cc", another4: "aaa" });
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[currentFilterKey] = currentFilterValue return acc; } acc.optional[currentFilterKey] = currentFilterValue; return acc; }, { mandatory: {}, optional: {}, } ); }; getMandatoryAndOptionalFilters({ type: "aa", category: "cat1;cat2", paymentType: "payment", otherFilter: "otherFilter", another1: "aa", another3: "vv", another2: "cc", another4: "aaa" });

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 provided benchmark and explain what is being tested, compared options, pros and cons of those approaches, library usage, and special JavaScript features.

**Benchmark Overview**

The benchmark measures the performance difference between two approaches to filter out excluded filters from an object using the `reduce` method. The test case consists of a JavaScript function `getMandatoryAndOptionalFilters`, which takes an object `filters` as input. It returns an object with two properties: `mandatory` and `optional`, where each property contains the filtered values.

**Benchmark Comparison**

There are two benchmark cases:

1. **"With spread"`**: This approach uses the spread operator (`...`) to create a new object for the `mandatory` and `optional` properties.
```javascript
return Object.entries(filters).reduce((acc, [currentFilterKey, currentFilterValue]) => {
  // ...
}, { mandatory: {}, optional: {} });
```
2. **"Without spread"`**: This approach does not use the spread operator and instead modifies the existing object (`acc`) directly.
```javascript
return Object.entries(filters).reduce((acc, [currentFilterKey, currentFilterValue]) => {
  // ...
}, {});
```
**Pros and Cons**

* **"With spread"`**:
	+ Pros: This approach creates a new object for each property, which can be beneficial for performance and readability.
	+ Cons: Creating a new object for each property may incur additional overhead due to memory allocation and copy operations.
* **"Without spread"`**:
	+ Pros: Modifying the existing object directly can be more efficient since it avoids creating unnecessary copies.
	+ Cons: This approach modifies the original object, which might not be desirable if the object is expected to remain unchanged.

**Library Usage**

There are no external libraries used in this benchmark. However, the `Object.entries` method and the `reduce` function are built-in JavaScript methods.

**Special JavaScript Features**

None of the special JavaScript features are used in this benchmark. It only relies on standard JavaScript syntax and built-in methods.

**Alternatives**

Other approaches to filter out excluded filters from an object using the `reduce` method might include:

* Using a different data structure, such as a Map or an array, instead of an object.
* Utilizing a library or framework that provides optimized filtering or sorting functions.
* Implementing a custom filtering algorithm using bitwise operations or other low-level techniques.

Keep in mind that these alternatives would likely have their own pros and cons, and the choice of approach would depend on the specific requirements and constraints of the use case.

Related benchmarks:

Spread in reduce - more entries (version: 1)

Comparing performance of: With spread vs Without spread

Created: 2 years ago by: Registered User

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):