Benchmark: lodash chain - MeasureThat.net

HTML Preparation code:

https://cdn.jsdelivr.net/npm/lodash@4.17.21/lodash.min.js

Script Preparation code:

var data = [{
  "name": "jim",
  "color": "blue",
  "age": "22"
}, {
  "name": "Sam",
  "color": "blue",
  "age": "33"
}, {
  "name": "eddie",
  "color": "green",
  "age": "77"
}];

var chainableFunctions = {
  map: _.map,
  groupBy: _.groupBy,
  value: _.value,
};

var chain = (input) => {
  var value = input;
  var wrapper = {
    ..._.mapValues(
      chainableFunctions,
      (f) => (...args) => {
        // lodash always puts input as the first argument
        value = f(value, ...args);
        return wrapper;
      },
    ),
    value: () => value,
  };
  return wrapper;
};

Tests:

chain
console.log( chain(data) // Group the elements of Array based on `color` property .groupBy("color") // `key` is group's name (color), `value` is the array of objects .map((value, key) => ({ color: key, users: value })) .value() );
custom
var custom = (x) => { var group = _.groupBy(x, 'color'); return _.map(_.entries(group), ([ color, val ]) => ({ color, val })) } console.log(custom(data))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
chain
custom

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.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmark on MeasureThat.net, which compares the performance of two approaches for grouping data by color using Lodash's `groupBy` function.

**Approaches Compared**

Two test cases are compared:

1. **Lodash Chain**: This approach uses Lodash's chainable functions to group the data.
2. **Custom Implementation**: This approach implements a custom solution using Lodash's `groupBy` function directly.

**Pros and Cons of Each Approach**

**Lodash Chain:**

Pros:

* Cleaner code with less boilerplate
* Easier to read and maintain

Cons:

* May incur additional overhead due to the use of an intermediate wrapper object (`wrapper`)
* Performance might be impacted by the number of nested functions

**Custom Implementation:**

Pros:

* Can potentially outperform Lodash Chain if optimization is done carefully
* No additional overhead from using an intermediate wrapper object

Cons:

* Code can become more complex and harder to read
* Requires manual handling of errors and edge cases

**Library Usage - Lodash**

Lodash is a popular JavaScript utility library that provides a comprehensive set of functions for various tasks, such as array manipulation, object transformation, and functional programming. In this benchmark, Lodash's `groupBy` function is used to group the data by color.

**Special JS Feature/Syntax**

None mentioned in the provided code.

**Other Considerations**

* The use of `console.log` statements in the test cases might impact performance due to the overhead of logging output.
* The `data` array contains objects with string properties (`color` and `age`) that may not be optimized for comparison or grouping purposes.
* The benchmark results show a small difference between the two approaches, but further testing would be required to determine if this is statistically significant.

**Alternatives**

Other alternatives for grouping data by color include:

1. Using native JavaScript methods (e.g., `Array.prototype.reduce()`, `Array.prototype.forEach()`).
2. Utilizing third-party libraries that provide similar functionality (e.g., `lodash`, `underscore`).
3. Implementing a custom solution using a different programming paradigm or language (e.g., using a data structure like a trie).

However, the choice of approach ultimately depends on the specific requirements and constraints of the project, as well as personal preference and expertise.

Related benchmarks:

lodash chain (version: 0)

Comparing performance of: chain vs custom

Created: 2 years ago by: Guest

Jump to the latest result

chain

custom

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):