Benchmark: highestFrequencyString-2

Tests:

METHOD MANIA
const highestFrequencyString = (strArray) => { // so janky can we do better? let res = {}; strArray.forEach((str) => (res[str]) ? res[str]++ : res[str]=1); // var reuse because i'm a degenerate res = Object.entries(res).sort((a,b) => b[1]-a[1])[0]; console.log(`${res[0]} appears ${res[1]} times in the array.`); } const fruits = ["apple", "orange", "apple", "peach", "pear", "apple", "peach"]; highestFrequencyString(fruits);
FOR LOOP MANIA
const highestFrequencyString = (strArray) => { strArray.sort((a,b) => a <= b ? -1 : 1); let max = 0; let curr = 0; let res; for (let i = 0; i < strArray.length-1; i++) { if (strArray[i] === strArray[i+1]) { curr++; } else { if (max < ++curr) { max = curr; res = strArray[i]; } curr = 0; } } console.log( `${res} appears ${max} times in the array.`); } const fruits = ["apple", "orange", "apple", "peach", "pear", "apple", "peach"]; highestFrequencyString(fruits);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
METHOD MANIA
FOR LOOP MANIA

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

The benchmark described tests two different implementations of a function named `highestFrequencyString`, which takes an array of strings and determines which string appears most frequently, along with the count of its occurrences. The benchmark compares performance using two distinct methods:

1. **METHOD MANIA**: This implementation utilizes a hash-map-like approach to count occurrences.
2. **FOR LOOP MANIA**: This implementation first sorts the array and uses a for-loop to track occurrences.

### Comparison of the Approaches:

#### 1. METHOD MANIA:
- **Method**: 
  - It initializes an empty object `res` to store occurrences of each string.
  - It iterates through the input array using `forEach`, incrementing the count of each string in `res`.
  - Finally, it converts the object to an array of key-value pairs, sorts it by frequency in descending order, and retrieves the highest frequency string.

- **Pros**:
  - **Time Complexity**: The overall complexity is O(n) for counting and O(m log m) for sorting the frequencies where `m` is the number of unique strings, making it generally efficient for most cases.
  - **Simplicity**: The logic is straightforward and easy to understand.

- **Cons**:
  - **Memory Usage**: Utilizes additional memory for the object to store string counts, which may be a consideration for very large input arrays with many unique strings.

#### 2. FOR LOOP MANIA:
- **Method**:
  - This implementation begins by sorting the array. After sorting, it iterates through the sorted array with a simple loop, maintaining a count of consecutive identical strings.
  - When a different string appears, it resets the count and compares it with the maximum count recorded.

- **Pros**:
  - **Memory Footprint**: Avoids the additional memory overhead associated with using an object for counting by maintaining counts inline during iteration.
  
- **Cons**:
  - **Time Complexity**: This method has a higher time complexity due to the sorting step, which is O(n log n). It can be less efficient for large datasets, especially when there are a significant number of unique strings.
  - **Complexity**: The sorting step can introduce unnecessary overhead if the data is already in a somewhat organized state or if the unique strings are few and identifiable quickly.

### Other Considerations:
- The use of `console.log` in both implementations serves only for displaying the results but is not part of the core algorithm. Practically, this can be removed or replaced with a return statement in production code.
- The benchmarks were executed in a controlled environment (Chrome 130 on macOS), and performance may vary on different environments or with different browsers.

### Alternative Approaches:
- **Using Map**: Instead of a plain object, using a `Map` to keep counts can provide performance benefits when there are a large number of unique keys, as it can handle non-string keys better.
- **Reduce Method**: Implementing the counting logic using `Array.prototype.reduce` could offer a more functional programming style but may not necessarily improve performance.
- **Two-pass algorithm**: Similar to `FOR LOOP MANIA` but without sorting could be implemented by first counting occurrences and then finding the maximum count in a second pass. This will keep it linear: O(n).

### Conclusion:
The benchmark highlights different methodologies for solving the same problem, emphasizing trade-offs between time complexity, memory usage, and implementation simplicity. Depending on the specific context and dataset characteristics, one method may be favored over the other, and further optimizations could be considered for production-ready code to enhance efficiency.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
METHOD MANIA	404238.9 Ops/sec
FOR LOOP MANIA	428746.8 Ops/sec

Related benchmarks:

highestFrequencyString-2 (version: 0)

Write a function named `highestFrequencyString` that takes in an array of strings and returns the string with the highest frequency in the array and how many times it appears.

Comparing performance of: METHOD MANIA vs FOR LOOP MANIA

Created: one month ago by: Guest

Jump to the latest result

METHOD MANIA

FOR LOOP MANIA

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):