Benchmark: Lodash test-01 - MeasureThat.net

HTML Preparation code:

https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.15/lodash.core.js

Script Preparation code:

var nestedArray = [
  {
    sectionTitle: "Section title",
    groups: [
      {
        title: "Group title",
        icon: "Group icon",
        links: [
          {
            text: "Link 1 text",
            url: "Link 1 url"
          },
          {
            text: "Link 1 text",
            url: "Link 1 url"
          }
        ]
      }
    ]
  },
  {
    sectionTitle: "Section title 2",
    groups: [
      {
        title: "Group title 2",
        icon: "Group icon 2",
        links: [
          {
            text: "Link 1 text 2",
            url: "Link 1 url 2"
          },
          {
            text: "Link 1 text 2",
            url: "Link 1 url 2"
          }
        ]
      }
    ]
  }
];

Tests:

withlodasd
_.flatMap( _.flatMap(nestedArray, item => item.groups), item => item.links.map(link => link.url) )
native
nestedArray.map(item => item.groups.map(group => group.links.map(link => link.url))).flat(2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
withlodasd
native

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):

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definition and explain what's being tested, compared options, pros and cons, library usage, special JavaScript features, and other considerations.

**Benchmark Definition**

The benchmark definition consists of two test cases:

1. `withlodasd`: This test case uses the Lodash `flatMap` function to flatten a nested array of objects. The input is a `nestedArray` defined in the Script Preparation Code.
2. `native`: This test case manually flattens the same `nestedArray` using an arrow function and the `flat()` method.

**Options Compared**

The two options being compared are:

* `withlodasd`: Using the Lodash `flatMap` function to flatten the array.
* `native`: Manually flattening the array using a combination of arrow functions and the `flat()` method.

**Pros and Cons**

**withlodasd (Lodash)**

Pros:

* Faster execution time, likely due to the optimized implementation of the `flatMap` function in Lodash.
* Reduced code maintenance effort, as the developer doesn't need to manually implement the flattening logic.

Cons:

* Requires an external library (Lodash) to be included, which may add overhead to the test.
* May not be suitable for all environments where Lodash is not available or disabled.

**native**

Pros:

* Does not require any external libraries, making it more robust and compatible with a wider range of environments.
* Provides direct insight into the performance characteristics of the JavaScript engine's built-in `flat()` method.

Cons:

* Requires manual implementation of the flattening logic, which may increase code maintenance effort.
* May be slower than the optimized Lodash implementation.

**Library Usage**

The benchmark definition uses the Lodash library for the `flatMap` function in the `withlodasd` test case. The HTML Preparation Code includes the URL of the Lodash library file (`lodash.core.js`) to be loaded before running the tests.

**Special JavaScript Features**

None of the benchmark definitions explicitly use any special JavaScript features or syntax beyond standard ES6 features (e.g., arrow functions, template literals). However, the `flat()` method used in both test cases is a relatively recent addition to the ECMAScript standard and may not be supported in older browsers or environments.

**Other Considerations**

* The benchmark definition assumes that the `nestedArray` input is available when running the tests. This may require additional setup or preparation steps, such as loading the array into memory before running the test.
* The `ExecutionsPerSecond` metric used in the benchmark result indicates the average number of executions per second for each test case. This provides insight into the performance characteristics of both options.

**Alternatives**

Other alternatives to compare include:

* Using a different flattening algorithm, such as recursion or iteration.
* Comparing the performance of `flat()` vs. `slice().concat()`.
* Using a benchmarking library or framework that supports more advanced testing features, such as parallelism or caching.
* Adding additional test cases to cover other aspects of the Lodash function or native implementation.

Related benchmarks:

Lodash test-01 (version: 0)

Comparing performance of: withlodasd vs native

Created: 6 years ago by: Guest

Jump to the latest result

withlodasd

native

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):