Benchmark: Fourjunctions Time Complexity

Script Preparation code:

const numbers = Array.from({ length: 10_000 }).map(() => Math.random())

Tests:

Multiple loops
const result = numbers .map(n => Math.round(n * 10)) .filter(n => n % 2 === 0) .reduce((a, n) => a + n, 0)
Single loop
const result = numbers.reduce((a, n) => { if (n % 2 === 0) return a; return a + n; }, 0);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Multiple loops
Single loop

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36

Browser/OS: Chrome 133 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Multiple loops	3874.4 Ops/sec
Single loop	79113.7 Ops/sec

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

LLMs can make mistakes. Check important info.

The benchmark defined in the JSON you've provided tests and compares the performance of two different approaches to processing an array of numbers generated randomly. Specifically, it measures how long it takes to execute multiple operations on the data using different looping techniques.

### Description of Tests

1. **Test Case: Multiple Loops**
   ```javascript
   const result = numbers
       .map(n => Math.round(n * 10))
       .filter(n => n % 2 === 0)
       .reduce((a, n) => a + n, 0);
   ```

**Description:**
   - This test utilizes three operations on the array `numbers`:
     - **Map**: Transforms each number into a rounded integer between 0 and 10.
     - **Filter**: Retains only even numbers from the mapped results.
     - **Reduce**: Sums the filtered even numbers.
   
   **Pros/Cons:**
   - **Pros**: 
     - Easy to read and understand, as it uses functional programming principles.
     - Each operation is succinct and chained, making the code clear.
   - **Cons**: 
     - Due to the multiple iterations over the data (one for each operation), this approach tends to be less performance-efficient.
     - Each method creates intermediate arrays, which can increase memory usage.

2. **Test Case: Single Loop**
   ```javascript
   const result = numbers.reduce((a, n) => {
       if (n % 2 === 0) return a;
       return a + n;
   }, 0);
   ```

**Description:**
   - This test combines the functionality of filtering and reduction into a single operation by using `reduce` solely.
   - It sums only the odd numbers from the `numbers` array.

**Pros/Cons:**
   - **Pros**: 
     - More efficient than the multiple loops case since it iterates through the array just once.
     - Reduces the overhead of creating additional data structures.
   - **Cons**: 
     - May be less readable to developers unfamiliar with the `reduce` method.
     - Logic is more condensed, which can potentially lead to misunderstandings in complex situations.

### Key Considerations

- **Performance**: The results of the benchmark show a significant difference in execution speed, with the single loop method performing about five times better than the multiple loops method (16803.31 vs. 3331.06 executions per second).
- **Readability vs. Performance**: While the multiple loop approach is clearer due to its step-by-step procedure, the single loop is optimal for performance, especially with larger datasets.

### Alternatives

1. **For Loop**: A traditional `for` loop could be used to both filter and sum values in one pass. This is often seen as more efficient since it avoids the overhead of higher-order functions.

```javascript
   let sum = 0;
   for (let i = 0; i < numbers.length; i++) {
       if (numbers[i] % 2 !== 0) {
           sum += numbers[i];
       }
   }
   ```

2. **ForEach with Conditional Logic**: Similar to the `for` loop but using `forEach`, though it does not inherently support returning a value, it can still sum within a closure.

```javascript
   let sum = 0;
   numbers.forEach(n => {
       if (n % 2 !== 0) sum += n;
   });
   ```

3. **Using External Libraries**: Libraries like lodash can also simplify these operations, providing optimized implementations of various array manipulations.

### Conclusion

In summary, this benchmark provides valuable insight into how different JavaScript methods for processing arrays can have significant impacts on performance. Selecting the best approach often depends on the specific use case, balancing between ease of understanding for future maintenance and performance needs, particularly as data sizes grow.

Related benchmarks:

Fourjunctions Time Complexity (version: 3)

Benchmarking how much time is taken to execute multiple loops and single loops

Comparing performance of: Multiple loops vs Single loop

Created: one year ago by: Registered User

Jump to the latest result

Multiple loops

Single loop

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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