Benchmark: hash-sum vs object-hash 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@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:

Sum
sum(context1) === sum(context2)
Object-hash
objectHash(context1) === objectHash(context2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Sum
Object-hash

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

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I'll break down the benchmark and explain what's being tested, compared, and the pros/cons of each approach.

**Benchmark Overview**

The `MeasureThat.net` benchmark is designed to compare the performance of two JavaScript functions: `hash-sum` (from the `hash-sum` library) and `object-hash` (from the `object-hash` library).

**Script Preparation Code**

The script preparation code defines two objects, `context1` and `context2`, which are identical except for their property values. This setup is used to test the functions on both objects.

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

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

**Html Preparation Code**

The HTML preparation code includes links to external JavaScript libraries:

```html
<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@3.0.0/dist/object_hash.min.js"></script>
```

These libraries are loaded before running the benchmark.

**Individual Test Cases**

There are two test cases:

1. **Sum**: Tests if the `hash-sum` function returns the same result for both objects, `context1` and `context2`.
```javascript
sum(context1) === sum(context2)
```
2. **Object-hash**: Tests if the `object-hash` function returns the same result for both objects, `context1` and `context2`.
```javascript
objectHash(context1) === objectHash(context2)
```

**Library Explanations**

*   **hash-sum library**: The `hash-sum` library provides a fast and efficient way to calculate the sum of all keys in an object. It works by converting the object's keys into a hash value and then summing up these values.
*   **object-hash library**: The `object-hash` library provides a way to generate a unique hash value for any JavaScript object. It does this by iterating over each key-value pair in the object, generating a hash value for the key, and combining them into a single hash value.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax used in this benchmark.

**Pros and Cons of Each Approach**

*   **hash-sum library**: Pros:
    *   Fast and efficient
    *   Works well with large objects
Cons:
    *   May not be suitable for small objects due to overhead
*   **object-hash library**: Pros:
    *   Suitable for any JavaScript object, regardless of size
    *   Generates a unique hash value for each object
Cons:
    *   May be slower than `hash-sum` for very large objects

**Other Alternatives**

There are other libraries and approaches available that could be used to calculate the sum or hash of an object. Some examples include:

*   **lodash.sum**: A utility function from the Lodash library that calculates the sum of all values in an array.
*   **JSON.stringify() + JSON.parse()**: A simple approach that converts an object to a string, sums up the key-value pairs, and then parses the string back into an object.

Keep in mind that these alternatives might not be as efficient or flexible as `hash-sum` and `object-hash`, but they can serve as alternative options depending on specific use cases.

Related benchmarks:

hash-sum vs object-hash 2 (version: 0)

Comparing performance of: Sum vs Object-hash

Created: 3 years ago by: Guest

Jump to the latest result

Sum

Object-hash

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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