Benchmark: Lodash map & filter vs reduce with push and desctructuring

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var arr = [];
for (var i = 0; i < 1000000; i++) {
  arr[i] = { num: i };
}

Tests:

map & filter
_.filter(_.map(arr, item => item.num), num => num > 15 && num < 30);
reduce
_.reduce(arr, (newArr, { num }) => { if (num > 15 && num < 30) { newArr.push(num); } return newArr; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map & filter
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous 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 benchmark and explain what's being tested.

**What is being tested?**

The provided JSON represents two individual test cases for measuring performance differences between two approaches: Lodash's `map` function followed by `filter`, and using the `reduce` method with a custom callback to achieve similar functionality, along with some added logic using the spread operator (`push`) and destructuring.

**Options being compared**

The options being compared are:

1. **Lodash's `map` and `filter` combination**: This approach uses Lodash's `map` function to transform an array of objects into a new array, and then filters out elements that don't meet the condition (`num > 15 && num < 30`). The resulting filtered array is not returned explicitly.
2. **Reduce with push and destructuring**: This approach uses the `reduce` method to iterate over the original array, pushing elements that meet the condition into a new array. The initial value of the accumulator is an empty array `[]`.

**Pros and Cons**

Here are some pros and cons for each approach:

**Lodash's `map` and `filter` combination:**

Pros:

* Simple and concise code
* Lodash provides optimized implementations for common operations

Cons:

* Creates a new array, which can lead to memory allocation overhead
* Filtering is done after mapping, which may not be optimal if filtering is the dominant operation

**Reduce with push and destructuring:**

Pros:

* Avoids creating an intermediate array, reducing memory allocations
* Allows for more control over the filtering process, as it's done directly in the callback function

Cons:

* Code can become complex and harder to read, especially for those not familiar with `reduce` or destructuring
* May incur additional overhead due to the need to access properties on objects using destructuring syntax

**Other considerations**

Both approaches have their trade-offs. The Lodash approach is generally faster since it leverages optimized implementations, but may create an intermediate array that needs to be garbage collected. The reduce-with-push-and-destructuring approach avoids creating an intermediate array but can lead to more complex code and potential performance overhead.

**Library: Lodash**

Lodash (formerly known as Underscore.js) is a popular JavaScript utility library providing a comprehensive set of functions for tasks like:

* Array manipulation (`map`, `filter`, `reduce`)
* String manipulation
* Object manipulation
* Functional programming utilities

In this benchmark, Lodash's `map` and `filter` combination are used to simplify the filtering process.

**Special JS feature or syntax: Destructuring**

The reduce-with-push-and-destructuring approach uses destructuring syntax (`{ num }`) to access properties on objects. This is a modern JavaScript feature introduced in ECMAScript 2015 (ES6), which allows for more concise and expressive code when working with objects.

**Alternatives**

If you're interested in exploring alternative approaches, some options include:

* Using other libraries or frameworks that provide optimized implementations for array manipulation and filtering
* Implementing custom loops using `for`-`loop`, `while`-`loop`, or `async/await`
* Utilizing WebAssembly (WASM) or other low-level optimization techniques

Keep in mind that each approach has its pros and cons, and the best choice depends on your specific use case, performance requirements, and coding style preferences.

Related benchmarks:

Lodash map & filter vs reduce with push and desctructuring (version: 0)

Lodash map & filter vs reduce with push and desctructuring

Comparing performance of: map & filter vs reduce

Created: 4 years ago by: Registered User

Jump to the latest result

map & 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):