Benchmark: Reduce deserialize vs JSON deserialize

Script Preparation code:

const address_raw = [{ childrenIndex: 0 }, { toggleIndex: 1 }, { instanceIndex: 2 }, { childrenIndex: 0 }, { toggleIndex: 1 }, { instanceIndex: 2 }, { childrenIndex: 0 }, { toggleIndex: 1 }, { instanceIndex: 2 }, { childrenIndex: 0 }, { toggleIndex: 1 }, { instanceIndex: 2 }, { childrenIndex: 0 }, { toggleIndex: 1 }, { instanceIndex: 2 }];

const serializeAddressJSON = (address) => JSON.stringify(address);
const serializeAddressReduce = (address) =>
	address.reduce((serialized, point) => {
		const { childrenIndex, toggleIndex, instanceIndex } = point;

		let pointCode;
		if (childrenIndex !== undefined) {
			pointCode = `c${childrenIndex}`;
		}
		if (toggleIndex !== undefined) {
			pointCode = `t${toggleIndex}`;
		}
		if (instanceIndex !== undefined) {
			pointCode = `i${instanceIndex}`;
		}

		return `${serialized}${pointCode}`;
	}, '');

window.addressJSON = serializeAddressJSON(address_raw);
window.addressReduce = serializeAddressReduce(address_raw);

window.deserializeAddressJSON = (address) => JSON.parse(address);

window.deserializeAddressReduce = (serializedAddress) => {
	const pointCodeRegex = /([cti])(\d+)/g;
	const addressPointsMatches = serializedAddress.matchAll(pointCodeRegex);
	const address = [...addressPointsMatches].map(([_match, pointType, index]) => {
		switch (pointType) {
			case 'c':
				return { childrenIndex: +index };
			case 't':
				return { toggleIndex: +index };
			case 'i':
				return { instanceIndex: +index };
		}
	});
	return address;
};

Tests:

JSON deserialize
deserializeAddressJSON(window.addressJSON)
Reduce deserialize
deserializeAddressReduce(window.addressReduce)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
JSON deserialize
Reduce deserialize

Fastest: N/A

Slowest: N/A

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Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):

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Let's dive into the explanation.

**What is being tested?**

The provided JSON represents a benchmark test that compares the performance of two different approaches to deserialize (convert from string back to original format) a complex data structure in JavaScript:

1. **JSON Deserialization**: Using the built-in `JSON.parse()` function to deserialize the data.
2. **Reduce Deserialization**: Using the `reduce()` method, a higher-order function in JavaScript, to manually iterate over the serialized data and reconstruct the original format.

**What options are compared?**

The two test cases compare the execution time of deserializing the same complex data structure using:

1. The traditional `JSON.parse()` approach (using the built-in JSON library).
2. A custom implementation using the `reduce()` method to manually parse the serialized data.

**Pros and Cons:**

* **JSON Deserialization (Built-in)**:
	+ Pros: Easy to use, widely supported, and often optimized by browsers.
	+ Cons: May be slower than custom implementations for large or complex datasets.
* **Reduce Deserialization (Custom)**:
	+ Pros: Can provide better performance for specific use cases, such as large datasets or complex data structures.
	+ Cons: Requires manual implementation, which can lead to errors and maintenance issues.

**Other considerations:**

The benchmark also considers the overhead of using a custom library (in this case, the `reduce()` method) versus the built-in JSON library. The results show that the `JSON.parse()` approach is generally faster than the custom implementation using `reduce()` for deserializing complex data structures.

**Library used:**

None, apart from the built-in JavaScript JSON library and the standard library's higher-order functions (such as `reduce()`).

**Special JS features or syntax:**

The benchmark uses the following features:

1. **Destructuring**: The use of destructuring assignment (`const { childrenIndex, toggleIndex, instanceIndex } = point;`) to extract values from objects.
2. **Regular expressions**: The use of a regular expression (`pointCodeRegex`) to match and extract specific patterns from the serialized data.

**Other alternatives:**

In general, when deserializing complex data structures, developers may also consider using:

1. **Other built-in libraries**, such as `DOMParser` or `XMLSerializer`, depending on the type of data being serialized.
2. **Third-party libraries**, such as JSON utilities or parsing libraries, which can provide optimized performance for specific use cases.

Keep in mind that this explanation is focused on the provided benchmark test and might not cover all possible scenarios or edge cases.

Related benchmarks:

Reduce deserialize vs JSON deserialize (version: 0)

Comparing performance of: JSON deserialize vs Reduce deserialize

Created: 3 years ago by: Registered User

Jump to the latest result

JSON deserialize

Reduce deserialize

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):