Benchmark: Bench fast-json-patch vs jsondiffpatch icl. Patching

HTML Preparation code:

        <script type='text/javascript' src="https://cdn.jsdelivr.net/npm/jsondiffpatch/dist/jsondiffpatch.umd.min.js"></script>
		<script src="https://cdn.jsdelivr.net/npm/deep-diff@1/dist/deep-diff.min.js"></script>
		<script src="https://cdn.jsdelivr.net/npm/fast-json-patch/dist/fast-json-patch.min.js"></script>

Script Preparation code:

obj1= {
        name: "Argentina",
        cities: [
        {
            name: 'Buenos Aires',
            population: 13028000,
        },
        {
            name: 'Cordoba',
            population: 1430023,
        },
        {
            name: 'Rosario',
            population: 1136286,
        },
        {
            name: 'Mendoza',
            population: 901126,
        },
        {
            name: 'San Miguel de Tucuman',
            population: 800000,
        }
        ]
    };
    
obj2= {
        name: "Argentina",
        cities: [
        {
            name: 'Cordoba',
            population: 1430023,
        },
        {
            name: 'Mendoza',
            population: 901126,
        },
        {
            name: 'San Miguel de Tucuman',
            population: 550000,
        }
        ]
    };

Tests:

jsondiffpatch
var diff1 = jsondiffpatch.diff(obj1, obj2); var objnew = jsonpatch.deepClone(obj1); jsondiffpatch.unpatch(objnew, diff1);
FAST JSON-patch
var diff3 = jsonpatch.compare(obj1, obj2); var objnew = jsonpatch.deepClone(obj1); jsonpatch.applyPatch(objnew, diff3, false, true);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
jsondiffpatch
FAST JSON-patch

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one month ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36

Browser/OS: Chrome 134 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
jsondiffpatch	121611.6 Ops/sec
FAST JSON-patch	325457.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JSON patching benchmarks!

The provided benchmark measures the performance of two popular libraries for JSON patching: `fast-json-patch` (also known as FAST) and `jsondiffpatch`. The test cases compare the execution speed of these two libraries when applying patches to a sample JSON object.

**Test Case 1: jsondiffpatch**

In this test case, we use the `jsondiffpatch` library to:

1. Create a difference (`diff`) between two objects (`obj1` and `obj2`) using `jsondiffpatch.diff`.
2. Clone the original object (`obj1`) using `jsondiffpatch.unpatch`, applying the created difference.
3. Revert the patch by applying it again, which is essentially no-op.

The purpose of this test case is to measure the performance of creating a patch and then reapplying it, as well as cloning an object.

**Test Case 2: FAST JSON-patch**

In this test case, we use the `fast-json-patch` library to:

1. Compare the two objects (`obj1` and `obj2`) using `jsonpatch.compare`.
2. Clone the original object (`obj1`) using `jsonpatch.deepClone`.
3. Apply a patch to the cloned object using `jsonpatch.applyPatch`, passing in the result of the comparison.

The purpose of this test case is to measure the performance of creating a comparison and then applying it, as well as cloning an object.

**Comparison**

| Library | Creating Patch | Cloning Object | Applying Patch |
| --- | --- | --- | --- |
| jsondiffpatch | Yes | No | No (revert patch) |
| FAST JSON-patch | No | Yes | Yes |

The main difference between the two test cases is how they create and apply patches:

* `jsondiffpatch` creates a patch by taking the difference between two objects, which can be time-consuming. It also doesn't provide a way to apply the patch directly.
* FAST JSON-patch creates a comparison between two objects, which is faster than creating a patch. However, it requires cloning an object first.

**Pros and Cons**

**jsondiffpatch:**

Pros:

* Can create patches using any kind of data structure
* Provides a way to revert patches

Cons:

* Creating patches can be slower compared to comparing objects
* Doesn't provide a direct way to apply patches

**FAST JSON-patch:**

Pros:

* Faster than creating patches
* Provides a convenient way to clone and apply patches

Cons:

* Requires cloning an object first, which may add overhead
* Limited to comparing objects only

**Other Considerations**

When choosing between `jsondiffpatch` and FAST JSON-patch, consider the following factors:

* **Data structure:** If you're working with complex data structures, `jsondiffpatch` might be a better choice. However, if your data is mostly composed of simple objects, FAST JSON-patch could be more efficient.
* **Patch creation speed:** If creating patches is critical for your use case, FAST JSON-patch's faster approach might be beneficial. However, if the patch creation process is not performance-critical, `jsondiffpatch` might provide a safer and more flexible solution.
* **Reverting patches:** If you need to frequently revert patches, `jsondiffpatch` provides a convenient way to do so.

**Alternatives**

Other popular libraries for JSON patching include:

1. **deep-diff**: Similar to `jsondiffpatch`, but with a focus on deep equality checks.
2. **jsonmerge-patch**: A library that allows merging multiple patches into a single patch.
3. **patch-merge**: Another patch merge library that provides a more concise API.

Keep in mind that the choice of library ultimately depends on your specific use case and performance requirements.

Related benchmarks:

Bench fast-json-patch vs jsondiffpatch icl. Patching (version: 0)

Comparing performance of: jsondiffpatch vs FAST JSON-patch

Created: one year ago by: Guest

Jump to the latest result

jsondiffpatch

FAST JSON-patch

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):