Benchmark: reduce() vs filter().map() V2

Script Preparation code:

var arr = [];
var i = 0;

while (i <= 1E5) arr[i] = i++;

Tests:

filter().map()
arr.filter(x => x % 3).map(x => x/100)
reduce()
arr.reduce((acc, x) => { if (x % 3) acc.push(x); return acc; }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter().map()
reduce()

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

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I'd be happy to help you understand the JavaScript benchmarking process on MeasureThat.net.

**Benchmark Definition and Preparation**

The benchmark is defined as a comparison between two approaches: `reduce()` and `filter().map()`. The preparation code for this benchmark creates an array of 100,000 elements using a while loop, where each element is set to its index value (`i`) in the array. This creation process is intentionally slow to ensure that the benchmark focuses on the performance difference between the two approaches.

**Options Compared**

The comparison involves two main options:

1. `reduce()`: This method applies a reduction function to an array, accumulating a result. In this case, the reduction function checks if each element (`x`) is divisible by 3, and if so, pushes it to the accumulator (`acc`). The final result is an array of numbers that are multiples of 3.
2. `filter().map()`: This approach first filters out elements from the array using a callback function (in this case, one that checks if `x` is divisible by 3), and then maps each remaining element to its value divided by 100.

**Pros and Cons**

Both approaches have their trade-offs:

* **Reduce()**: Pros:
	+ More memory-efficient since it only requires a single accumulator array.
	+ Can be more concise and expressive for specific use cases.
	+ Can be optimized for cache performance, as the same memory location is used throughout the reduction process.
* Cons:
	+ Can lead to slower execution times due to the overhead of function calls and object property lookups.
	+ Requires a callback function that iterates over the array, which can introduce additional overhead.
* **Filter().map()**: Pros:
	+ Generally faster than `reduce()` since it avoids the overhead of callback functions and repeated accumulator array updates.
	+ Can be easier to understand and maintain, especially for those familiar with functional programming concepts.
* Cons:
	+ Requires more memory allocation due to the creation of a new array (`acc`) for intermediate results.
	+ Can lead to higher cache fragmentation if the filter function is not optimized.

**Library: Lodash**

The `filter()` and `map()` functions are part of the Lodash library, which provides a set of utility functions for functional programming in JavaScript. Lodash's implementation of these methods can significantly impact performance due to its caching and optimization strategies.

**Special JS Feature/Syntax**

This benchmark does not explicitly use any special JavaScript features or syntax beyond the standard `for...of` loop for array iteration (not shown). However, it's worth noting that some browser implementations may optimize certain functions like `reduce()` or `filter()` based on their specific architecture and cache properties.

**Other Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

1. **Array.prototype.every()**: Instead of using `filter()` and then `map()`, consider using `every()` with a callback function to check if all elements meet the condition.
2. **Array.prototype.forEach()**: Use `forEach()` with an anonymous callback function to perform the same operation as `reduce()`.
3. **Closures**: Consider implementing the reduction logic within a closure, which can help optimize performance by minimizing memory allocations and garbage collection.

Keep in mind that each alternative approach may have its own trade-offs and potential impact on performance. The MeasureThat.net benchmark helps evaluate these variations under various JavaScript environments and browsers.

Related benchmarks:

reduce() vs filter().map() V2 (version: 0)

reduce vs filter map

Comparing performance of: filter().map() vs reduce()

Created: 4 years ago by: Guest

Jump to the latest result

filter().map()

reduce()

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):