Benchmark: reduce vs mapsdfasdfsdf

Script Preparation code:

var codeFields = [
  {value: '5'},
  {value: '3'},
  {value: '1'}
]

function isNumber(obj) {
    return obj !== undefined && typeof (obj) === 'number' && !isNaN(obj)
  }

Tests:

reduce
const enteredValue = codeFields.reduce((acc, el) => acc + (isNumber(parseInt(el.value, 10)) ? el.value : ''), '');
map
const enteredValue2 = codeFields.map(el => el.value).join('').replace(/\s/g, '');

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map

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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**Overview of the Benchmark**

The provided benchmark measures the performance difference between two approaches: using `Array.prototype.reduce()` and `Array.prototype.map()`. The test case uses an array of objects (`codeFields`) to demonstrate the performance of these two methods.

**Approaches Compared**

There are two approaches compared in this benchmark:

1. **Reduce**: This approach accumulates the values in the array by repeatedly applying a callback function to each element.
2. **Map**: This approach creates a new array with the results of applying a provided function to each element in the original array.

**Pros and Cons**

* **Reduce**:
	+ Pros: Can be more efficient when dealing with small arrays or when the accumulator needs to be updated in each iteration.
	+ Cons: May have higher memory overhead due to the creation of an internal array, and can be slower for large datasets.
* **Map**:
	+ Pros: Can be faster for large datasets since it avoids creating an intermediate array, but may require more memory if the resulting array is large.
	+ Cons: Creates a new array with the results, which can be memory-intensive.

**Other Considerations**

* **NaN Handling**: Both approaches handle NaN (Not a Number) values in a similar way. The `isNumber` function used in the benchmark checks for NaN by verifying that the value is not undefined and has a number type.
* **Integer Conversion**: The `parseInt` function is used to convert string values to integers, which affects the performance of the reduce approach.

**Library and Special JS Features**

There are no external libraries used in this benchmark. However, it does use some JavaScript features:

* **Arrow Functions**: The callback functions used in both approaches are defined using arrow functions (`() => ...`).
* **Template Literals**: The `codeFields` array is created using template literals (`\r\n  {value: '5'}\r\n  {value: '3'}\r\n  {value: '1'}`).

**Alternatives**

If you're looking for alternative approaches or libraries, consider the following:

* **Lodash**: A popular utility library that provides a `reduce` and `map` function with various options and shortcuts.
* **Array.prototype.forEach()**: An older approach to iterating over arrays, which can be slower than reduce and map for large datasets.
* **Custom Iterators**: Creating a custom iterator using `Iterator Protocol` or `for...of` loop can provide more control over the iteration process.

In summary, this benchmark provides insight into the performance difference between two common JavaScript array methods. By understanding the pros and cons of each approach, developers can choose the most suitable method for their specific use case.

Related benchmarks:

reduce vs mapsdfasdfsdf (version: 0)

Comparing performance of: reduce vs map

Created: 5 years ago by: Guest

Jump to the latest result

reduce

map

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):