Benchmark: map filter vs reduce

Script Preparation code:

var arr = [];
for (var i = 0; i < 12345; i++) {
    arr[i] = i > 2 ? 1 : i
}

Tests:

map.filter
arr.filter((a) => a === 1).map(() => 'wow')
.reduce
arr.reduce((acc, a) => a === 1 ? [...acc, ] : acc, [])

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 llama3.2:3b, generated one month ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its options.

**Benchmark Definition**
The benchmark measures the performance difference between two approaches: `map.filter` vs `.reduce`. This is useful for understanding time vs space complexity when dealing with arrays in JavaScript.

**Script Preparation Code**
The script preparation code creates an array of 12345 elements, where each element is either 1 or a boolean value. The condition `i > 2 ? 1 : i` generates these values.

**Options Compared**

1. **`map.filter`**: This approach uses the `filter()` method to create a new array with only the elements that satisfy the predicate (in this case, `a === 1`). Then, it uses the `map()` method to apply a transformation function (`=> () => 'wow'`) to each element in the filtered array.
2. **`.reduce()`**: This approach uses the `.reduce()` method to accumulate an array of elements into a single value (an array in this case). The initial value is initialized with an empty array (`[]`). It iterates over the original array, and for each element that satisfies the predicate (`a === 1`), it appends the current accumulated array to the accumulator.

**Pros and Cons**

* **`map.filter`**:
	+ Pros: More straightforward and concise code. It's often easier to understand what's happening.
	+ Cons: Creates a new array, which can lead to increased memory usage (space complexity). Additionally, it might not be as efficient as other approaches if the predicate is computationally expensive.
* **`.reduce()`**:
	+ Pros: Accumulates elements into an array, reducing the number of allocations and copies required. It's often faster than creating a new array.
	+ Cons: Can be less readable due to its more functional programming style. The initial value needs to be initialized explicitly.

**Library Usage**
There is no library used in this benchmark.

**Special JS Features or Syntax**
None mentioned in the provided code snippets.

**Other Considerations**

* Performance optimizations, such as using `for...of` loops instead of `forEach()`, might also impact performance.
* Browser and hardware variations can affect results. This benchmark uses Chrome 101 on a Mac OS X system, so results may not generalize to other browsers or platforms.
* For larger datasets, other approaches like using `Set` or `Map` data structures might be more efficient.

**Alternatives**

Other alternatives for measuring performance in JavaScript benchmarks include:

1. **Using `Array.prototype.forEach()` instead of `for...of` loops**.
2. **Implementing a custom sorting algorithm**, like merge sort, to compare different algorithms.
3. **Measuring the performance of other array methods**, such as `every()`, `some()`, or `find()`.
4. **Using a profiling tool**, like Chrome DevTools' Profiler, to measure the performance impact of specific functions or code paths.

Keep in mind that different benchmarks may prioritize different aspects of performance, so it's essential to understand the focus and scope of each benchmark before comparing results.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.0.0 Safari/537.36

Browser/OS: Chrome 101 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
map.filter	6873.2 Ops/sec
.reduce	3854.7 Ops/sec

Related benchmarks:

map filter vs reduce (version: 0)

Show time vs space complexity of either solution

Comparing performance of: map.filter vs .reduce

Created: 4 years 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 llama3.2:3b, generated one month ago):