Benchmark: mybenchmark123313131 sadas

Script Preparation code:

function f(n) { return ++n; }
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];
var newValue = [];

Tests:

array
var addedIds = newValue.filter(userId => !oldValue.includes(userId)); var removedIds = oldValue.filter(userId => !newValue.includes(userId)); addedIds.forEach(userId => f(userId)); removedIds.forEach(userId => f(userId));
set
var oldSet = new Set(oldValue); var newSet = new Set(newValue); var addedIds = newValue.filter(userId => !oldSet.has(userId)); var removedIds = oldValue.filter(userId => !newSet.has(userId)); addedIds.forEach(userId => f(userId)); removedIds.forEach(userId => f(userId));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array
set

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one month ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Windows

View result in a separate tab

Test name	Executions per second
array	3985.6 Ops/sec
set	5629.7 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):

LLMs can make mistakes. Check important info.

This benchmark measures the performance of two different approaches for comparing elements in two lists of user IDs, identified as `oldValue` and `newValue`. The focus is on identifying which user IDs were added or removed when transitioning from one list to the other. Both approaches utilize JavaScript's `filter()` method, but they differ significantly in their underlying implementations.

### Approaches Compared

1. **Array Approach**
   - **Code:** 
     ```javascript
     var addedIds = newValue.filter(userId => !oldValue.includes(userId));
     var removedIds = oldValue.filter(userId => !newValue.includes(userId));
     addedIds.forEach(userId => f(userId));
     removedIds.forEach(userId => f(userId));
     ```
   - **Description:**
     - In this implementation, the `filter()` method is used to create new arrays containing user IDs that are either added to or removed from the `oldValue` list. The `includes()` method checks for membership in the other list.
     - The `f(userId)` function is called for each added or removed user ID, which serves to increment the user ID (though the utility of this operation in the performance test itself is limited).

2. **Set Approach**
   - **Code:**
     ```javascript
     var oldSet = new Set(oldValue);
     var newSet = new Set(newValue);
     var addedIds = newValue.filter(userId => !oldSet.has(userId));
     var removedIds = oldValue.filter(userId => !newSet.has(userId));
     addedIds.forEach(userId => f(userId));
     removedIds.forEach(userId => f(userId));
     ```
   - **Description:**
     - This method leverages JavaScript's `Set` object, which allows for faster membership checks utilizing `has()`. Sets are optimized for operations like checking existence, making them typically faster than arrays for this purpose.
     - Similar to the previous approach, the performance will ultimately rely on the size of the input data.

### Performance Results
The results of the benchmark indicate that the `set` approach executed an average of approximately 5629.71 operations per second, while the `array` approach achieved about 3985.63 operations per second. This illustrates that using `Set` significantly improved performance over traditional array checks.

### Pros and Cons

- **Array Approach:**
  - **Pros:**
    - Simple and straightforward implementation that is easy to understand.
  - **Cons:**
    - Performance can degrade significantly as the list sizes increase, due to the O(n) complexity of the `includes()` method. This means that for each call to `includes()`, it potentially traverses the entire `oldValue` or `newValue` array, making this solution inefficient for large datasets.

- **Set Approach:**
  - **Pros:**
    - More efficient due to the O(1) average time complexity for membership checks using `Set`. This is particularly beneficial when dealing with larger arrays.
    - Clears up memory use since Sets eliminate duplicates by design.
  - **Cons:**
    - Slightly more complex, which might introduce a learning curve for developers unfamiliar with the `Set` object.

### Other Considerations
- The context in which these benchmarks are run can significantly affect performance results, including the environment (browser version, operating system, etc.).
- Depending on the size of user ID lists and specific use cases, one might consider alternative data structures or methods, such as frequency counting with an auxiliary object to track occurrences.
- For very large datasets or high-performance applications, further optimizations, including asynchronous processing or more complex data structures, could be worth exploring.

### Alternatives
- **Using Objects for Counting:** Instead of arrays, use plain JavaScript objects to count occurrences which can be an even more efficient way to track elements during comparisons.
- **Third-party Libraries:** Libraries like `lodash` offer utility functions that can optimize performance and simplify operations, but they may add overhead overhead in terms of bundle size.
- **Functional Programming Approaches:** Explore libraries like `Ramda` that lean heavily on functional programming patterns, offering potential performance improvements for certain types of transformations and fluid chains of operations.

In conclusion, while the `Set` approach shows a clear performance advantage over the array-based method in this benchmark, the choice ultimately depends on the specific context and requirements of the application, as well as familiarity with the language constructs.

Related benchmarks:

mybenchmark123313131 sadas (version: 1)

Comparing performance of: array vs set

Created: one month ago by: Guest

Jump to the latest result

array

set

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):