Benchmark: object-hash SHA-1 encoding 2

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/object-hash@3.0.0/dist/object_hash.min.js"></script>

Script Preparation code:

var context1 = {
  title: 'fish',
  position: 1
}

var context2 = {
  title: 'fish',
  position: 1
}

Tests:

Object-hash hex
objectHash(context1,{algorithm:'sha1',encoding:'hex'})
Object-hash base64
objectHash(context1,{algorithm:'sha1',encoding:'base64'})
Object-hash binary
objectHash(context1,{algorithm:'sha1',encoding:'binary'})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object-hash hex
Object-hash base64
Object-hash binary

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested.

**What is being tested?**

The provided JSON represents two benchmark definitions: `objectHash` and its usage with three different encoding options: `hex`, `base64`, and `binary`. The goal of this benchmark is to measure the performance of these three encoding options when hashing objects using the `objectHash` function.

**What are the encoding options?**

The encoding options being compared are:

1. **Hex**: Hexadecimal encoding, which represents binary data using 16 characters (0-9 and A-F) in a base-16 number system.
2. **Base64**: Base64 encoding, which represents binary data using a 64-character alphabet to encode binary data into a text format that can be safely transmitted over the internet.
3. **Binary**: Binary encoding, which simply returns the raw binary representation of the object without any additional formatting.

**Pros and Cons of each approach:**

1. **Hex Encoding**
	* Pros: Fastest encoding option, requires less memory than base64 or binary.
	* Cons: Not human-readable, limited to 16 characters per byte.
2. **Base64 Encoding**
	* Pros: Human-readable, widely supported by web applications and protocols. However, it's slower than hex encoding due to the alphabet size (64 characters).
	* Cons: Requires more memory than hex or binary encoding, larger output size compared to hex encoding.
3. **Binary Encoding**
	* Pros: Fastest encoding option, requires minimal memory and processing power.
	* Cons: Not human-readable, requires additional processing to convert to a usable format.

**Other considerations:**

* The `objectHash` function is not defined in the provided code snippet, but it's likely a wrapper around a hashing algorithm like SHA-1.
* The `context1` object is used as input for all three encoding options. It contains two properties: `title` and `position`, both with string values.

**Alternative approaches:**

Other alternatives to these encoding options could include:

* **URL-safe base64**: A variant of Base64 that replaces the `/` and `+` characters with `_` and `-` characters, respectively.
* **ASCII-safe base64**: Another variant of Base64 that uses only ASCII characters (A-Z, a-z, 0-9) to avoid issues with non-ASCII characters.
* **Other hashing algorithms**, such as SHA-256 or MD5, which may offer better security properties than SHA-1 but are also typically slower.

Overall, this benchmark seems to focus on measuring the performance of different encoding options for the `objectHash` function, with a mix of speed and memory considerations.

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36

Browser/OS: Chrome 123 on Windows

View result in a separate tab

Test name	Executions per second
Object-hash hex	33991.8 Ops/sec
Object-hash base64	33096.5 Ops/sec
Object-hash binary	41060.8 Ops/sec

Related benchmarks:

object-hash SHA-1 encoding 2 (version: 1)

Comparing performance of: Object-hash hex vs Object-hash base64 vs Object-hash binary

Created: 8 months ago by: Registered User

Jump to the latest result

Object-hash hex

Object-hash base64

Object-hash binary

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):