Benchmark: native lodash filter map

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>

Script Preparation code:

var data = Array(1000000).fill({ filtering: true, mapping: 42 });

Tests:

native lodash filter map
data.filter(({ filtering }) => filtering).map(({ mapping }) => mapping)
Lazy Lodash filter-map
_.map(_.filter('filtering'), 'mapping')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
native lodash filter map
Lazy Lodash filter-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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Let's dive into the benchmark!

**Overview**

The provided JSON represents two JavaScript microbenchmarks, which test the performance of different approaches to filtering and mapping data using the Lodash library. The benchmarks compare the native implementation of Lodash with the lazy, asynchronous version.

**Benchmark Definitions**

There are two benchmark definitions:

1. **Native Lodash filter map**: This is the most straightforward approach, where the `filter` method is called directly on the array, followed by the `map` method.
2. **Lazy Lodash filter-map**: This approach uses the `_` alias for Lodash functions and calls them asynchronously using the `_.map()` and `_.filter()` methods.

**Library**

Lodash (version 4.17.4) is a popular JavaScript utility library that provides various functional programming helpers, including filtering and mapping functions. The library is included in the benchmark via an external script tag (`<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>`).

**Special JS Features/Syntax**

None mentioned explicitly in the provided JSON.

**Options Compared**

The two benchmarks compare the following options:

* **Native Lodash**: Directly calling `filter` and `map` methods on the array, which requires the entire data to be loaded into memory.
* **Lazy Lodash**: Using the `_` alias for Lodash functions and calling them asynchronously using `_.map()` and `_.filter()`, which can take advantage of asynchronous iteration.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Native Lodash**
	+ Pros:
		- Simple, straightforward implementation.
		- Can be faster for small datasets since the entire data is loaded into memory.
	- Cons:
		- Requires more memory to store the dataset in RAM.
		- May not scale well for large datasets due to memory constraints.
* **Lazy Lodash**
	+ Pros:
		- Can handle larger datasets without running out of memory.
		- Can take advantage of asynchronous iteration, which can lead to better performance on multi-core processors.
	- Cons:
		- Requires the `_` alias and async functions, which may add complexity to the codebase.
		- May have overhead due to async function calls.

**Other Alternatives**

Other alternatives for filtering and mapping data in JavaScript include:

* **Vanilla JavaScript**: Using built-in `Array.prototype.filter()` and `Array.prototype.map()` methods without any libraries.
* **Async iterables**: Using async iterables like `async/await` or `Generator` functions to handle large datasets without loading them into memory.

In summary, the two benchmarks compare different approaches to filtering and mapping data using Lodash. The native approach is simple but may not scale well for large datasets, while the lazy approach can handle larger datasets but requires additional complexity due to async function calls.

Related benchmarks:

native lodash filter map (version: 0)

Comparing performance of: native lodash filter map vs Lazy Lodash filter-map

Created: 5 years ago by: Guest

Jump to the latest result

native lodash filter map

Lazy Lodash filter-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):