Benchmark: object-hash vs hash-sum

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 context = {
  title: 'fish',
  position: 1
}

Tests:

Sum
sum(context)
ObjectHash
objectHash(context)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Sum
ObjectHash

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 6 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36

Browser/OS: Chrome 141 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Sum	1287207.5 Ops/sec
ObjectHash	74055.2 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided JSON data represents a JavaScript microbenchmark that compares the performance of two different hash functions: `object-hash` and `hash-sum`. The benchmark is designed to measure the execution speed of these two functions on an object with specific properties.

**Hash Functions**

1. **Hash-Sum**: Hash-Sum is a hashing function developed by John Resig in 2009. It's a deterministic algorithm that takes a string or an array as input and returns a fixed-size hash value. The `hash-sum` library used in the benchmark provides a JavaScript implementation of this algorithm.
2. **Object-Hash**: Object-Hash is another hashing function designed to hash objects. It works by creating a unique fingerprint of the object's properties, allowing for efficient storage and comparison of objects.

**Benchmark Comparison**

The benchmark compares the performance of these two hash functions on an input object with specific properties: `title` and `position`. The objective is to measure which function is faster and more efficient in terms of execution speed.

**Pros and Cons**

* **Hash-Sum**:
	+ Pros:
		- Simple and lightweight implementation.
		- Fast execution speed due to its deterministic nature.
	+ Cons:
		- May not be suitable for large or complex inputs, as it can produce collisions.
* **Object-Hash**:
	+ Pros:
		- Designed specifically for hashing objects, making it a good choice for this benchmark.
		- Can handle larger and more complex input objects efficiently.
	+ Cons:
		- May require more CPU cycles due to its object-specific implementation.

**Library Use**

In the provided JSON data, both `hash-sum` and `object-hash` libraries are used. These libraries provide efficient JavaScript implementations of their respective hashing algorithms.

* **Hash-Sum**: The `hash-sum.min.js` file from the CDN is included in the HTML preparation code.
* **Object-Hash**: The `object_hash.min.js` file from the CDN is also included in the HTML preparation code.

**Special JS Features or Syntax**

There are no special JavaScript features or syntaxes mentioned in this benchmark. However, it's worth noting that some modern JavaScript engines may optimize certain operations (like hashing) to achieve better performance on specific hardware configurations.

**Alternatives**

If you're interested in exploring alternative hash functions for your use cases, here are a few options:

* **FNV-1a**: A widely used hashing algorithm known for its speed and reliability.
* **Blowfish**: Another popular hashing algorithm that provides strong security features.
* **MurmurHash3**: A modern hashing algorithm designed to be fast and efficient while maintaining high collision resistance.

Keep in mind that the choice of hash function depends on your specific use case, performance requirements, and security needs.

Related benchmarks:

object-hash vs hash-sum (version: 0)

Comparing performance of: Sum vs ObjectHash

Created: 5 years ago by: Guest

Jump to the latest result

Sum

ObjectHash

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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