Benchmark: wrapped to original111

Script Preparation code:

function generateRecords(count) {
  const records = [];
  for (let i = 1; i <= count; i++) {
    // Генерация случайного original.id (от 1 до count/2 для появления дубликатов)
    const originalId = Math.floor(Math.random() * (count / 2)) + 1;
    records.push({ id: i, original: { id: originalId } });
  }
  return records;
}


function getUniqueRecords (records) {
  const seenIds = [];
  const uniqueRecords = [];

  for (const record of records) {
    if (!seenIds.includes(record.original.id)) {
      seenIds.push(record.original.id);
      uniqueRecords.push(record);
    }
  }

  return uniqueRecords;
}

function getUniqueRecordsSet(records) {
  const seenIds = new Set();
  const uniqueRecords = [];

  for (const record of records) {
    if (!seenIds.has(record.original.id)) {
      seenIds.add(record.original.id);
      uniqueRecords.push(record);
    }
  }

  return uniqueRecords;
}

function getUniqueRecords3(records) {
    const uniqueRecords = [];
    const seenIds = new Set();

    for (const record of records) {
      if (!seenIds.has(record.original.id)) {
        seenIds.add(record.original.id);
        uniqueRecords.push(record.original);
      }
    }

    return uniqueRecords;
 }

var records = generateRecords(1000)

Tests:

list
getUniqueRecords(records)
set
getUniqueRecordsSet(records)
gpt
getUniqueRecords3(records)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
list
set
gpt

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 12 days ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Linux

View result in a separate tab

Test name	Executions per second
list	23641.5 Ops/sec
set	53120.2 Ops/sec
gpt	45953.6 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 12 days ago):

LLMs can make mistakes. Check important info.

The provided JSON represents a benchmark that tests three different approaches to filtering unique records from a dataset. The benchmark includes different implementations of a function to extract unique records based on an `original.id`. Here are the key details regarding what is tested, the alternatives being compared, and some considerations for each approach.

### Approaches Compared

1. **Array-based Implementation (`getUniqueRecords`)**
   - This implementation utilizes a simple array to track seen `original.id` values.
   - It iterates through the records, checking the `seenIds` array with `.includes()` to determine uniqueness.

2. **Set-based Implementation (`getUniqueRecordsSet`)**
   - This version uses the `Set` object to store seen `original.id` values.
   - The `Set` provides faster checks for existence with `.has()` compared to arrays, which can improve performance, especially with larger datasets.

3. **Alternative Set Implementation (`getUniqueRecords3`)**
   - Similar to the second approach, but it populates the `uniqueRecords` array differently. It pushes the `record.original` instead of the full `record`.
   - The operational mechanics are the same as the previous `Set` implementation, focusing on tracking `original.id`.

### Performance Analysis

The benchmark results show the number of executions per second for each implementation:

- **Set-based implementation (`getUniqueRecordsSet`)**: 53,120 executions/second - shows the highest performance. The use of `Set` for uniqueness checks enables more efficient operations than using an array, primarily due to faster lookups.
  
- **Alternative Set implementation (`getUniqueRecords3`)**: 45,953 executions/second - this has a similar performance to the `Set` approach but slightly slower due to the implementation differences, as it pushes only the `original` object.

- **Array implementation (`getUniqueRecords`)**: 23,641 executions/second - the slowest of the three. The repeated use of the `includes()` method on an array creates heavier computational overhead due to linear search time complexity, making it inefficient for larger datasets.

### Pros and Cons

- **Array-Based Approach (`getUniqueRecords`)**
  - **Pros**: Simplicity and straightforwardness. Easy to understand for developers who may not be familiar with `Set`.
  - **Cons**: Poor performance on larger datasets due to the need for linear search operations; less efficient.

- **Set-Based Approach (`getUniqueRecordsSet` & `getUniqueRecords3`)**
  - **Pros**: Provides significantly better performance due to O(1) average time complexity for lookups with `Set`. Scales better with larger datasets.
  - **Cons**: Slightly more complex than a simple array approach; still requires an understanding of `Set`.

### Other Considerations

When comparing these benchmarked methods, consider the following:

- **Memory Usage**: While the `Set` implementation does consume more memory than the array-based approach (due to the overhead of maintaining a `Set` object), performance gain often outweighs additional memory consumption in scenarios where performance is critical.

- **JavaScript Engine Optimizations**: Different JavaScript engines (like V8 in Chrome) may optimize certain structures, so it's vital to test across platforms for critical applications.

- **Data Characteristics**: If the nature of the data changes, the results may vary. For example, if the dataset has high duplication, the method with the best average case (Set) would still outperform the array-based method.

### Alternative Approaches

In addition to the methods tested, other alternatives include:

- **Using Object as a Lookup Table**: You can create an object to map `original.id` values to their respective records. This can also provide O(1) lookups, akin to using `Set`.

- **Sorting and Filtering**: For datasets where the levels of uniqueness can be predicted, sorting the records by `original.id` and then filtering duplicates can be a viable approach, although it usually incurs an O(n log n) complexity due to the sort operation.

Ultimately, the choice of the method should consider the dataset size, performance requirements, and readability/maintainability of the code.

Related benchmarks:

wrapped to original111 (version: 1)

asd asdasd

Comparing performance of: list vs set vs gpt

Created: 12 days ago by: Guest

Jump to the latest result

list

set

gpt

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 12 days ago):