Benchmark: f v r obj blabla

Script Preparation code:

var arr = [];
for (var i = 0; i < 1234567; i++) {
  arr[i] = {
    name: "test-name" + i,
  	id: i,
    value: i,
    metadata: {
      position: i,
      wierdName: {
      	weird: "obj" + name
      }
    },
  };
}

function someFn(i) {
  return i.metadata.position % 2 === 0;
}

var sumReduce = 0,
  sumFilter = 0;

Tests:

filter
sumFilter = arr.filter(elem => elem.metadata.position % 2 === 0).length
reduce
sumReduce = arr.reduce((prevValue, item) => item.metadata.position % 2 === 0 ? ++prevValue : prevValue, 0);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and considered.

**Benchmark Overview**

The benchmark is designed to measure the performance of two different approaches in JavaScript: using `Array.prototype.filter()` (Filter) and `Array.prototype.reduce()` (Reduce). The benchmark creates a large array of objects, where each object has a `metadata.position` property that needs to be checked for even values.

**Benchmark Definition JSON**

The benchmark definition includes:

* A script preparation code that creates a large array of objects with the specified properties.
* An HTML preparation code (which is empty in this case).
* Two test cases:
	+ "filter": Measures the time taken by `Array.prototype.filter()` to filter out the odd-positioned elements and return the count of even-positioned elements.
	+ "reduce": Measures the time taken by `Array.prototype.reduce()` to accumulate the sum of the metadata.position values for only the objects with even-positioned elements.

**Options Compared**

The benchmark compares two options:

1. **Filter**: Uses `Array.prototype.filter()` to filter out the odd-positioned elements and return the count of even-positioned elements.
2. **Reduce**: Uses `Array.prototype.reduce()` to accumulate the sum of the metadata.position values for only the objects with even-positioned elements.

**Pros and Cons**

* **Filter**:
	+ Pros: Simple, concise, and easy to read. Filters out unwanted data in a single step.
	+ Cons: May be less efficient than `Reduce` because it creates a new array with filtered elements, which can lead to extra memory allocations and copy operations.
* **Reduce**:
	+ Pros: Can accumulate values more efficiently than filtering, especially for large datasets. Also, allows for early termination if the accumulator reaches a certain value.
	+ Cons: More complex and harder to read due to its callback function and initial value requirements.

**Library Used**

In this benchmark, `Array.prototype.filter()` and `Array.prototype.reduce()` are used from the JavaScript core library, which is built into most modern browsers and Node.js environments. These methods are part of the ECMAScript standard and provide efficient implementations for filtering and reducing arrays.

**Special JS Feature or Syntax**

None mentioned in this benchmark. However, it's worth noting that `Array.prototype.reduce()` requires an initial value to be provided as a second argument, which can affect performance if not done correctly.

**Other Alternatives**

If you need alternative approaches, consider the following:

* **Using for loops**: Instead of using array methods like `filter()` and `reduce()`, you can use traditional for loops to iterate over the array and perform calculations.
* **Using other libraries or frameworks**: Depending on your specific requirements, you might want to explore other JavaScript libraries or frameworks that provide more efficient algorithms for filtering and reducing arrays.

For example, if you need to filter an array based on a complex condition, you might consider using a library like Lodash, which provides the `filterBy` function. Similarly, for reducing arrays, you can use libraries like Ramda, which offers the `reduceBy` function.

Related benchmarks:

f v r obj blabla (version: 0)

Comparing performance of: filter vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

filter

reduce

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):