Benchmark: HelloDDDDAA - MeasureThat.net

Script Preparation code:

    var sharedInfo = {
      chromecastVersion: 'cafReceiver',
      activeTrackIds: [],
      tracks: [],
      mediaInfo: null,
      errors: [],
      availabilityStartTime: undefined,
      accessToken: null,
      authorizationKey: null,
      entitlementToken: null,
      senderDeviceId: null,
      initialAudioTrackLanguage: '',
      initialTextTrackLanguage: '',
      viewableId: null,
      playableId: null,
      isLive: false,
      isStarted: false,
      isPlaying: false,
      isSeeking: false,
      isBuffering: false,
      isPlayingDaiAds: false,
      currentTime: 0,
      volume: 0,
      duration: 0,
      playSession: null,
      viewable: {},
      preflight: {
        playlistDuration: 0,
        playlist: null,
        mediaTailor: null,
        license: null,
        headers: null,
      },
      startLivePoint: {},
      sar: '1:1',
    };

var volumeUpdate = {
  volume:1, 
  duration:2,
}

Tests:

Using the spread operator
const finalObject = { ...sharedInfo, ...volumeUpdate };
Using Object.assign
const finalObject = Object.assign(sharedInfo, volumeUpdate);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Using the spread operator
Using Object.assign

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.

Let's break down the provided benchmark and explain what's being tested, compared, and considered.

**Benchmark Definition**

The benchmark is comparing two approaches to merge an object with another using JavaScript:

1. **Using the spread operator**: `const finalObject = {...sharedInfo, ...volumeUpdate};`
2. **Using Object.assign**: `const finalObject = Object.assign(sharedInfo, volumeUpdate);`

Both approaches aim to create a new object by combining `sharedInfo` and `volumeUpdate`. The difference lies in how they handle the merging process.

**Options Compared**

The two options are compared in terms of:

1. **Performance**: Measured in executions per second (FPS).
2. **Browser/Device Platform**: The benchmark is run on a Chrome 88 browser on a Windows desktop.
3. **Language Features**: Neither option explicitly uses any special JavaScript features or syntax.

**Pros and Cons**

1. **Using the spread operator (`...`)**:
	* Pros: Often considered more readable and concise, as it clearly expresses the intention of merging two objects.
	* Cons: Can be slower due to the need for dynamic property creation at runtime.
2. **Using Object.assign**:
	* Pros: Generally faster than the spread operator, as it uses a built-in method optimized for performance.
	* Cons: May be less readable and more verbose, requiring explicit reference to the `Object` global object.

**Library and Purpose**

In this benchmark, no libraries are explicitly mentioned or used. The only notable mention is of the Chrome browser's UA string (User-Agent), which indicates the browser's version and platform.

**Special JS Features/Syntax**

Neither option explicitly uses any special JavaScript features or syntax beyond what's built-in to the language.

**Other Alternatives**

If you were to explore alternative approaches, some options might include:

* Using a library like Lodash (e.g., `_.merge(sharedInfo, volumeUpdate)`).
* Implementing a custom merging function.
* Using a different data structure, such as an array or a class, to represent the objects being merged.

Keep in mind that these alternatives may not directly address the performance trade-off between using the spread operator and Object.assign, but rather offer alternative solutions for achieving similar results.

Related benchmarks:

HelloDDDDAA (version: 0)

Comparing performance of: Using the spread operator vs Using Object.assign

Created: 5 years ago by: Guest

Jump to the latest result

Using the spread operator

Using Object.assign

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):