Benchmark: Object compare (raw or stringify) 2

Tests:

stringify
const obj1 = { prop1: 123456, prop2: "123456", prop3: true } const obj2 = { prop1: 123456, prop2: "123456", prop3: true } let equal = JSON.stringify(obj1) === JSON.stringify(obj2);
raw
const obj1 = { prop1: 123456, prop2: "123456", prop3: true } const obj2 = { prop1: 123456, prop3: true, prop2: "123456" } let equal = check(obj1, obj2); function check(obj1, obj2) { for (let key in obj1) { if (!obj2.hasOwnProperty(key) || obj2[key] !== obj1[key]) { return false; } } return true; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
stringify
raw

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

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**Benchmark Definition**

The provided JSON defines two benchmark tests: `Object compare (raw or stringify) 2`. These tests aim to measure the performance difference between comparing objects using their raw representation (`"raw"`) and stringifying them before comparison (`"stringify"`).

**Options Compared**

Two options are compared:

1. **Raw Representation**: This approach compares two objects by checking if they have the same keys with the same values. This method is simple and efficient but may not be suitable for complex objects or large datasets.
2. **Stringifying Objects**: Before comparison, the objects are converted to strings using `JSON.stringify()`. This method creates a new string representation of each object, which can be compared. Stringification can introduce additional overhead due to string creation and comparison.

**Pros and Cons**

* **Raw Representation:**
	+ Pros:
		- Simple and efficient
		- Fast comparison
	+ Cons:
		- May not work correctly for complex objects or large datasets
		- Sensitive to object structure and property names
* **Stringifying Objects:**
	+ Pros:
		- Robust against object structure and property name changes
		- Suitable for complex objects or large datasets
	+ Cons:
		- Introduces additional overhead due to string creation and comparison

**Library and Special JS Features**

Neither of the benchmark tests uses a specific library, but it's worth noting that `JSON.stringify()` is a built-in JavaScript function.

There are no special JavaScript features used in these benchmark tests.

**Other Considerations**

When choosing between raw representation and stringifying objects for object comparison, consider the following:

* If you're working with simple objects or small datasets, raw representation might be sufficient.
* If you're working with complex objects or large datasets, stringifying objects can provide a more robust solution.
* Keep in mind that stringification can introduce additional overhead due to string creation and comparison.

**Alternative Approaches**

Other alternative approaches for object comparison include:

1. **Proxies**: Create a proxy object that traps property access and allows you to implement custom comparison logic.
2. **JSON Diffing Libraries**: Use libraries like jsondiff or diff-match-patch to calculate the difference between two objects.
3. **Serialization Libraries**: Use serialization libraries like Lodash's `isEqual` function or a custom implementation to compare objects.

These alternatives might offer more flexibility and robustness than the raw representation and stringification approaches used in the benchmark tests.

Latest run results:

Run details: (Test run date: 29 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36

Browser/OS: Chrome 128 on Windows

View result in a separate tab

Test name	Executions per second
stringify	2615690.2 Ops/sec
raw	19380728.0 Ops/sec

Related benchmarks:

Object compare (raw or stringify) 2 (version: 0)

Comparing performance of: stringify vs raw

Created: 29 days ago by: Guest

Jump to the latest result

stringify

raw

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):