Benchmark: Deep Clone #2 - MeasureThat.net

Script Preparation code:

function recursiveDeepCopy(obj) {
  return Object.keys(obj).reduce((v, d) => Object.assign(v, {
    [d]: (obj[d].constructor === Object) ? recursiveDeepCopy(obj[d]) : obj[d]
  }), {});
}

function jsonDeepCopy(o) {
  return JSON.parse(JSON.stringify(o));
}

var dimensions = [{
  "dimensions": [{
    "runtime": {
      "common": {
        "client": null,
        "server": null
      }
    }
  }, {
    "device": {
      "android": null,
      "blackberry": null,
      "iemobile": null,
      "iphone": null,
      "ipad": null,
      "kindle": null,
      "opera-mini": null,
      "palm": null
    }
  }, {
    "environment": {
      "development": {
        "dev": null,
        "test": null
      },
      "production": {
        "stage": null,
        "prod": null
      }
    }
  }, {
    "lang": {
      "ar": {
        "ar-JO": null,
        "ar-MA": null,
        "ar-SA": null,
        "ar-EG": null
      },
      "bn": {
        "bn-IN": null
      },
      "ca": {
        "ca-ES": null
      },
      "cs": {
        "cs-CZ": null
      },
      "da": {
        "da-DK": null
      },
      "de": {
        "de-AT": null,
        "de-DE": null
      },
      "el": {
        "el-GR": null
      },
      "en": {
        "en-AU": null,
        "en-BG": null,
        "en-CA": null,
        "en-GB": null,
        "en-GY": null,
        "en-HK": null,
        "en-IE": null,
        "en-IN": null,
        "en-MY": null,
        "en-NZ": null,
        "en-PH": null,
        "en-SG": null,
        "en-US": null,
        "en-ZA": null
      },
      "es": {
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        "es-BO": null,
        "es-CL": null,
        "es-CO": null,
        "es-EC": null,
        "es-ES": null,
        "es-MX": null,
        "es-PE": null,
        "es-PY": null,
        "es-US": null,
        "es-UY": null,
        "es-VE": null
      },
      "fi": {
        "fi-FI": null
      },
      "fr": {
        "fr-BE": null,
        "fr-CA": null,
        "fr-FR": null,
        "fr-GF": null
      },
      "hi": {
        "hi-IN": null
      },
      "hu": {
        "hu-HU": null
      },
      "id": {
        "id-ID": null
      },
      "it": {
        "it-IT": null
      },
      "ja": {
        "ja-JP": null
      },
      "kn": {
        "kn-IN": null
      },
      "ko": {
        "ko-KR": null
      },
      "ml": {
        "ml-IN": null
      },
      "mr": {
        "mr-IN": null
      },
      "ms": {
        "ms-MY": null
      },
      "nb": {
        "nb-NO": null
      },
      "nl": {
        "nl-BE": null,
        "nl-NL": null,
        "nl-SR": null
      },
      "pl": {
        "pl-PL": null
      },
      "pt": {
        "pt-BR": null
      },
      "ro": {
        "ro-RO": null
      },
      "ru": {
        "ru-RU": null
      },
      "sv": {
        "sv-SE": null
      },
      "ta": {
        "ta-IN": null
      },
      "te": {
        "te-IN": null
      },
      "th": {
        "th-TH": null
      },
      "tr": {
        "tr-TR": null
      },
      "vi": {
        "vi-VN": null
      },
      "zh": {
        "zh-Hans": {
          "zh-Hans-CN": null
        },
        "zh-Hant": {
          "zh-Hant-HK": null,
          "zh-Hant-TW": null
        }
      }
    }
  }]
}]

Tests:

Req
var dimensionsCopy = recursiveDeepCopy(dimensions);
json
var dimensionsCopy = jsonDeepCopy(dimensions);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Req
json

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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 other considerations.

**Benchmark Definition**

The benchmark is defined by two scripts: `recursiveDeepCopy` and `jsonDeepCopy`. Both functions are used to create a copy of an object (in this case, the `dimensions` array). The main difference between the two functions lies in how they handle nested objects:

*   `recursiveDeepCopy`: This function uses recursion to iterate over each property of the object. If the value is an object itself, it calls itself recursively with that value.
*   `jsonDeepCopy`: This function uses JSON's built-in serialization and deserialization mechanism to create a copy of the object.

**Individual Test Cases**

There are two test cases:

1.  **`req`**: This test case uses the `recursiveDeepCopy` function to create a copy of the `dimensions` array.
2.  **`json`**: This test case uses the `jsonDeepCopy` function to create a copy of the `dimensions` array.

**Latest Benchmark Result**

The latest benchmark result shows the execution performance of both test cases on a Chrome 84 browser running on Linux:

*   **`req`**: The `recursiveDeepCopy` function executed approximately 267,037 times per second.
*   **`json`**: The `jsonDeepCopy` function executed approximately 35,720 times per second.

**Comparison**

The benchmark comparison highlights the performance difference between using recursion (in the `recursiveDeepCopy` function) and JSON-based serialization/deserialization (in the `jsonDeepCopy` function). It's clear that `recursiveDeepCopy` outperforms `jsonDeepCopy`.

Here are some possible reasons for this performance difference:

*   **Recursion vs. Serialization**: Recursion can lead to a stack overflow if not implemented carefully, whereas JSON-based serialization/deserialization is generally more efficient and less prone to errors.
*   **Overhead of Function Calls**: The overhead of function calls can slow down `recursiveDeepCopy`, especially for large objects with many nested properties.

**Other Considerations**

When working with nested objects, consider the following:

*   **Memoization**: If you need to perform this operation frequently, consider memoizing the results of expensive function calls to avoid redundant computations.
*   **Object Cloning Strategies**: Depending on your specific use case, you might want to explore other object cloning strategies that balance performance and readability.

I hope this explanation helps clarify what's being tested in this benchmark!

Related benchmarks:

Deep Clone #2 (version: 0)

Comparing performance of: Req vs json

Created: 5 years ago by: Guest

Jump to the latest result

Req

json

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):