Benchmark: Lodash.isEqual vs JSON.stringify Equality Comparison for Shallow Array of Strings. 22

HTML Preparation code:

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

Script Preparation code:

window.foo = {
  "appNavigation": {
    "currentRoute": "Profile"
  },
  "appState": {
    "previousState": "active",
    "currentState": "active"
  },
  "appVersion": {
    "appVersionState": "ok",
    "error": "Network request failed: http://127.0.0.1:8888/delivery-courier/deliveryCourier/appOpened?version=UI.0.68&device_id=9796748A-0496-480B-96DA-9BA5A4100E70&courier_id=-1&country=ee&device_type=ios&device_name=iPhone%208&device_os_version=13.2.2&device_locale=en-US&language=en-US&session_id=9796748A-0496-480B-96DA-9BA5A4100E70courier1593698168"
  },
  "cashOnHands": {
    "balance": {
      "value": 0,
      "currency": "EUR",
      "value_str": "0 €"
    },
    "cashAmount": {
      "value": 0,
      "currency": "EUR",
      "value_str": "0 €"
    }
  },
  "earningsSummary": {},
  "externalNavigator": {
    "currentApp": "apple-maps",
    "_persist": {
      "version": 0,
      "rehydrated": true
    }
  },
  "interactions": {
    "payProviderRequestSent": {},
    "receiveEaterCash": {
      "69": 123
    }
  },
  "ipLocation": null,
  "isGpsEnabled": true,
  "keepAliveOrders": [
    {
      "order_id": 69
    }
  ],};
window.bar = {
  "appNavigation": {
    "currentRoute": "Profile"
  },
  "appState": {
    "previousState": "active",
    "currentState": "active"
  },
  "appVersion": {
    "appVersionState": "ok",
    "error": "Network request failed: http://127.0.0.1:8888/delivery-courier/deliveryCourier/appOpened?version=UI.0.68&device_id=9796748A-0496-480B-96DA-9BA5A4100E70&courier_id=-1&country=ee&device_type=ios&device_name=iPhone%208&device_os_version=13.2.2&device_locale=en-US&language=en-US&session_id=9796748A-0496-480B-96DA-9BA5A4100E70courier1593698168"
  },
  "cashOnHands": {
    "balance": {
      "value": 0,
      "currency": "EUR",
      "value_str": "0 €"
    },
    "cashAmount": {
      "value": 0,
      "currency": "EUR",
      "value_str": "0 €"
    }
  },
  "earningsSummary": {},
  "externalNavigator": {
    "currentApp": "apple-maps",
    "_persist": {
      "version": 0,
      "rehydrated": true
    }
  },
  "interactions": {
    "payProviderRequestSent": {},
    "receiveEaterCash": {
      "69": 123
    }
  },
  "ipLocation": null,
  "isGpsEnabled": true,
  "keepAliveOrders": [
    {
      "order_id": 69
    }
  ],};

Tests:

_.isEqual
_.isEqual(window.foo, window.bar)
JSON.stringify
JSON.stringify(window.foo) === JSON.stringify(window.bar);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
_.isEqual
JSON.stringify

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 definition and options being compared.

**Benchmark Definition:**

The benchmark compares two methods for equality checking:

1. `_.isEqual(window.foo, window.bar)` using Lodash library
2. `JSON.stringify(window.foo) === JSON.stringify(window.bar)` (without using any external libraries)

**What is being tested?**

Both methods are testing for shallow array of strings equality. The input objects `window.foo` and `window.bar` contain similar properties with the same values, but the order of some properties might differ.

**Options compared:**

The benchmark compares two options:

A) Using Lodash's `_.isEqual()` function to check for equality.
B) Using stringification (`JSON.stringify()`) to compare two objects.

**Pros and Cons of each approach:**

1. **Lodash's _.isEqual():**
	* Pros:
		+ More efficient, as it uses a specialized implementation optimized for performance.
		+ Handles more complex data structures (e.g., nested objects).
	* Cons:
		+ Requires the Lodash library to be included in the test.
2. **Stringification (JSON.stringify()):**
	* Pros:
		+ No external libraries are required, making it a self-contained comparison.
		+ Easy to implement and understand.
	* Cons:
		+ Less efficient than _.isEqual() as it involves creating a string representation of each object.

**Considerations:**

When using Lodash's `_.isEqual()` function, it's essential to note that the library is not included in this benchmark by default. This means you'll need to include it manually or use an alternative implementation.

The stringification approach might seem less efficient, but it's a straightforward and easy-to-understand method for comparing two objects. However, keep in mind that creating a string representation of each object can lead to slower performance compared to using a specialized library like Lodash.

**Special JS feature:**

In this benchmark, the `window.foo` and `window.bar` objects are not created directly within the test code. Instead, they are populated with data from external sources (the provided JSON). This suggests that the goal is to compare these pre-existing objects without introducing any additional dependencies or overhead.

**Other alternatives:**

If you need more advanced equality checking capabilities, consider using alternative libraries like:

* `deep-equal`: A lightweight library for deep object comparison.
* `fast-json-stamp`: A fast and efficient way to compare JSON strings.
* `eqops`: A library providing a variety of equality checks for JavaScript objects.

However, keep in mind that these alternatives might introduce additional dependencies or complexities not present in the original benchmark.

Related benchmarks:

Lodash.isEqual vs JSON.stringify Equality Comparison for Shallow Array of Strings. 22 (version: 0)

Test on isEqual performance

Comparing performance of: _.isEqual vs JSON.stringify

Created: 5 years ago by: Guest

Jump to the latest result

_.isEqual

JSON.stringify

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):