Benchmark: reduce vs map,filter

Script Preparation code:

a = [];
for (let i = 0; i < 10000; i++) {
    a.push(Number(i) / 1000 * 1000);
}
var mapping = x => x % 5 !== 0 ? x : undefined;
var filtering = x => x;
var reducing = (acc, x) => {
    const value = mapping(x);
    if (filtering(value)) acc.push(value);
    return acc;
}

Tests:

map-filter
a.map(mapping).filter(filtering);
reduce
a.reduce(reducing,[]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map-filter
reduce

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 27 days ago):

LLMs can make mistakes. Check important info.

The benchmark defined in the provided JSON compares two different approaches for processing an array of numbers in JavaScript: using the combination of `map` followed by `filter` versus using `reduce`.

### Options Being Compared

1. **`map` and `filter` Combination**: 
   - **Benchmark Definition**: `a.map(mapping).filter(filtering);`
   - **Test Name**: "map-filter"
   
   The `map` function is used to transform each element of the array `a` through a mapping function (`mapping`). After that, `filter` is applied to the results of `map` to exclude unwanted elements based on filtering criteria (`filtering`).

2. **`reduce`**:
   - **Benchmark Definition**: `a.reduce(reducing,[]);`
   - **Test Name**: "reduce"
   
   The `reduce` function processes each element of the array and accumulates values based on the `reducing` function which includes both mapping and filtering logic.

### Pros and Cons of Each Approach

#### **1. `map` and `filter` Combination:**
   - **Pros**:
     - Readability: The intent of using `map` and `filter` is typically clearer and more declarative, making the code easier to understand at a glance.
     - Separation of concerns: Each function has a single responsibility, which can lead to cleaner code.

- **Cons**:
     - Performance: This approach may be less efficient because it creates an intermediate array from the `map` operation, which is then processed by the `filter`. This can lead to higher memory usage and slower execution time, especially for larger datasets.

#### **2. `reduce`:**
   - **Pros**:
     - Efficiency: It processes the array in a single pass and avoids the creation of an intermediate array. This can lead to better performance and lower memory usage since it accumulates results directly.
   
   - **Cons**:
     - Complexity: The combined logic of mapping and filtering within a single function can make the `reduce` code harder to read and understand, particularly for less experienced developers.

### Considerations
- **Performance**: As shown in the benchmark results, the `map-filter` combination executed with approximately 2299 operations per second, while the `reduce` method performed at about 839 operations per second. This stark difference highlights that while `reduce` may be efficient in memory usage, it can come at the cost of speed in this specific case.
  
- **Code Clarity**: Choosing between these methods also depends on the context in which they are used. If the goal is to write clean and easily maintainable code, `map` and `filter` might be preferable. In performance-critical applications, choosing `reduce` could be beneficial, especially if the overhead of multiple passes through data can be avoided.

### Libraries and Special Features
There are no external libraries used in this benchmark, nor are there any special JavaScript features beyond standard array methods (i.e., `map`, `filter`, and `reduce`), which are widely supported and well-understood in the JavaScript ecosystem.

### Alternatives
Beyond these two approaches, there are several alternatives to processing arrays in JavaScript:
1. **For-loops**: A traditional loop can offer the most fine-tuned performance and control. However, it often sacrifices readability.
  
2. **`forEach`**: It provides a method to iterate over elements but doesn’t allow for returning a new array or accumulating results like `map`, `filter`, or `reduce`.

3. **Third-party libraries**: Libraries like Lodash offer utility methods that can sometimes provide optimized performance or chained operations that can be cleaner and more flexible.

In conclusion, engineers can choose between `map-filter` and `reduce` based on priorities such as clarity, efficiency, and the specific use case at hand. Understanding these trade-offs is crucial for writing effective JavaScript code.

Latest run results:

Run details: (Test run date: 27 days ago)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:131.0) Gecko/20100101 Firefox/131.0

Browser/OS: Firefox 131 on Linux

View result in a separate tab

Test name	Executions per second
map-filter	11327.6 Ops/sec
reduce	15486.3 Ops/sec

Related benchmarks:

reduce vs map,filter (version: 0)

Comparing performance of: map-filter vs reduce

Created: 28 days ago by: Guest

Jump to the latest result

map-filter

reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 27 days ago):