Benchmark: hash-sum vs object-hash vs JSON (with array) 2

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/hash-sum@2.0.0/hash-sum.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/object-hash@2.0.3/dist/object_hash.min.js"></script>

Script Preparation code:

var context1 = {
    title: 'fish',
    position: 1,
    array: [1, 2, 3, [4, 5, 6]],
}

var context2 = {
    title: 'fish',
    position: 1,
    array: [1, 2, 3, [4, 5, 6]],
    array2: [{a:{b:{c:{d:123}}}}, {}, {}],
}

Tests:

Sum
sum(context1) === sum(context2)
Object-hash
objectHash(context1) === objectHash(context2)
JSON.stringify
JSON.stringify(context1) === JSON.stringify(context2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Sum
Object-hash
JSON.stringify

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36

Browser/OS: Chrome 111 on Windows

View result in a separate tab

Test name	Executions per second
Sum	79855.9 Ops/sec
Object-hash	2844.3 Ops/sec
JSON.stringify	581040.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases.

**Benchmark Definition:**

The benchmark is testing three different approaches to compare the results of hashing and summing data:

1. **Hash-Sum**: This approach uses the `hash-sum` library to calculate the hash sum of the input data.
2. **Object-Hash**: This approach uses the `object-hash` library to generate a hash value from the input data, which represents the object's structure and content.
3. **JSON Stringify**: This approach converts the input data into a JSON string and then compares it with the same data in a different format.

**Options Compared:**

The three approaches are compared based on their execution speed (measured in executions per second). The goal is to determine which approach is the fastest.

**Pros and Cons of Each Approach:**

1. **Hash-Sum**:
	* Pros: Fast and efficient, suitable for large datasets.
	* Cons: May not capture the structure and content of complex data objects, can be sensitive to input data formatting.
2. **Object-Hash**:
	* Pros: Captures the structure and content of complex data objects, provides a more robust comparison than hash-sum.
	* Cons: Can be slower than hash-sum for large datasets, may not be suitable for very small inputs.
3. **JSON Stringify**:
	* Pros: Provides a human-readable representation of the input data, can be useful for debugging and logging purposes.
	* Cons: May not be as efficient as other approaches, can lead to false positives or negatives if the JSON string is malformed.

**Library Descriptions:**

1. **Hash-Sum**: The `hash-sum` library provides a fast and efficient way to calculate the hash sum of input data. It uses a combination of hashing algorithms to generate a unique hash value.
2. **Object-Hash**: The `object-hash` library generates a hash value from an object based on its structure and content. It uses a recursive approach to traverse the object and identify key characteristics.

**Special JS Feature/Syntax:**

The test cases do not use any special JavaScript features or syntax that would affect the benchmarking results.

**Alternative Approaches:**

Other approaches that could be used for this benchmark include:

1. **JSON Parse**: Instead of comparing JSON strings, you could parse them into objects and compare their contents.
2. **Deep Copy**: You could use a deep copy algorithm to create a separate copy of the input data, allowing you to compare its hash sum or object hash with the original data.
3. **Custom Hash Function**: Depending on the specific requirements of the benchmark, you could define a custom hash function that takes into account additional factors such as data encoding or compression.

Overall, the choice of approach depends on the specific use case and requirements of the benchmark. The provided test cases aim to evaluate the performance characteristics of each approach under different conditions.

Related benchmarks:

hash-sum vs object-hash vs JSON (with array) 2 (version: 0)

Comparing performance of: Sum vs Object-hash vs JSON.stringify

Created: 2 years ago by: Guest

Jump to the latest result

Sum

Object-hash

JSON.stringify

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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