Benchmark: Testing array normalizastion2

Script Preparation code:

Array.from(Array(100).keys()).map((i) => ({
  id: i+1,
  name: 'Bar',
  asd: 123123
}));

Array.from(Array(50).keys()).map((i) => ({
  id: i+5,
  title: 'Bar',
  age: 12
}));

Tests:

Object based
const normalizeLiveUpdateData = ( newData, oldaData ) => { if (!oldAnnotations) return newAnnotations; const ids = []; const data = {}; oldAnnotations.forEach((a) => { ids.push(a.id); data[a.id] = a; }); newAnnotations.forEach((a) => { if (ids.indexOf(a.id) === -1) { ids.push(a.id); } data[a.id] = a; }); return ids.map(id => data[id]); };
Array based
const normalizeLiveUpdateData = ( newAnnotations, oldAnnotations) => { if (!oldAnnotations) return newAnnotations; let normalizedAnnotations = oldAnnotations; newAnnotations.forEach((newAnnotation) => { const existingAnnotation = oldAnnotations.find((oldAnnotation) => oldAnnotation.id === newAnnotation.id); if (existingAnnotation) { normalizedAnnotations = normalizedAnnotations.map(obj => newAnnotations.find(o => o.id === obj.id) || obj); } else { normalizedAnnotations.push(newAnnotation); } }); return normalizedAnnotations; };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object based
Array based

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):

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided JSON represents a benchmark test case for normalizing live update data, specifically comparing two approaches: object-based and array-based normalization.

**Script Preparation Code**

The script preparation code generates sample data:

* `Array.from(Array(100).keys()).map((i) => ({ id: i+1, name: 'Bar', asd: 123123 }));`: Creates an array of 100 objects with unique IDs (2-101), a fixed "name" property, and a random "asd" value.
* `Array.from(Array(50).keys()).map((i) => ({ id: i+5, title: 'Bar', age: 12 }));`: Creates an array of 50 objects with unique IDs (6-55), a fixed "title" property, and a fixed "age" value.

**Individual Test Cases**

There are two test cases:

### Object-Based Approach

```javascript
const normalizeLiveUpdateData = (
  newData,
  oldaData
) => {
  // ...
};
```

This approach iterates over the `oldAnnotations` array, adding new annotations to an object that maps IDs to annotation objects. Then, it iterates over the `newAnnotations` array and updates the existing annotation objects in the map.

Pros:

* Easy to understand and maintain
* Does not require explicit loop management

Cons:

* May lead to performance issues due to frequent object lookups
* Can result in slower execution times for large datasets

### Array-Based Approach

```javascript
const normalizeLiveUpdateData = (
  newAnnotations,
  oldAnnotations
) => {
  // ...
};
```

This approach iterates over the `newAnnotations` array and updates the existing annotation objects in the `oldAnnotations` array. If a new annotation has an ID that does not exist in `oldAnnotations`, it is appended to the end of the array.

Pros:

* Can result in faster execution times due to reduced object lookups
* Does not require explicit loop management

Cons:

* Requires additional memory for storing the updated `oldAnnotations` array
* May lead to slower initialization times due to array resizing

**Library and Special Features**

None of the provided code snippet indicates any specific JavaScript libraries or special features, such as async/await, Promises, or ES6+ syntax.

**Alternatives**

Other approaches for normalizing live update data could include:

1. Using a hybrid approach that combines elements of both object-based and array-based methods.
2. Utilizing a data structure like a hash table or a graph to store annotations, which can provide efficient lookup and insertion operations.
3. Implementing an incremental update mechanism that only updates existing annotations, rather than re-normalizing the entire dataset.

**Benchmark Results**

The latest benchmark result shows:

* The "Object-Based" approach executing approximately 107.5 million times per second.
* The "Array-Based" approach executing approximately 26.7 million times per second on a Safari 14 browser on a Mac OS X 11.0 system.

Please note that these results are specific to the provided benchmark test case and may not reflect real-world performance differences between these approaches.

Related benchmarks:

Testing array normalizastion2 (version: 0)

Comparing performance of: Object based vs Array based

Created: 5 years ago by: Guest

Jump to the latest result

Object based

Array based

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):