Benchmark: filter + map vs reduce 12345

Script Preparation code:

var values = []
for(i=0; i<1000; i++){ values.push([i, i]) }

Tests:

flatMap
values.flatMap(e => [e, e * 2])
reduce
values.reduce((acc, x) => { acc.push(i, i * 2); return acc; }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap
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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Let's dive into the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is designed to compare the performance of two different JavaScript methods: `filter()` + `map()` and `reduce()`. The test case uses an array of 1000 elements, where each element is a pair of numbers `[i, i]`.

**Options Compared**

Two options are being compared:

1. **Filter() + Map()**: This approach involves filtering the array to remove unwanted elements (in this case, no filtering is performed), and then mapping each remaining element to a new array with the same element and its value multiplied by 2.
2. **Reduce()**: This approach involves using the `reduce()` method to accumulate the results of an operation on each element in the array.

**Pros and Cons**

Here's a brief analysis of the pros and cons of each approach:

1. **Filter() + Map()**
	* Pros: This approach can be more readable and maintainable, as it separates filtering and mapping operations.
	* Cons:
		+ May require additional memory allocation for intermediate arrays (filtering).
		+ Can be slower due to the overhead of creating and garbage-collecting temporary arrays.
2. **Reduce()**
	* Pros: This approach can be faster and more memory-efficient, as it avoids creating intermediate arrays.
	* Cons:
		+ Can be less readable and maintainable, especially for complex operations.

**Library/Features Used**

There are no external libraries used in this benchmark, but some features of the JavaScript language are being utilized:

1. **Template literals**: The `Script Preparation Code` uses template literals (`var values = []\r\nfor(i=0; i<1000; i++){ values.push([i, i]) }`) to create a string with multiple lines.
2. **Arithmetic operations**: The `flatMap` and `reduce` operations involve simple arithmetic calculations.

**Other Considerations**

Some other factors that might affect the performance of these benchmarks include:

1. **Browser caching**: If the same benchmark is run repeatedly, some browsers may cache the results or use faster versions of the code.
2. **Garbage collection**: JavaScript's garbage collector can influence the performance of memory-intensive operations like mapping or reducing large arrays.
3. **Parallelism**: The `ExecutionsPerSecond` value indicates how many times each test case is executed per second, but it doesn't account for parallelism (e.g., running multiple tests simultaneously).

**Alternatives**

If you're interested in exploring alternative approaches, consider:

1. **Looping instead of mapping/reducing**: Using traditional loops to iterate over the array elements.
2. **Other filtering and mapping methods**: Such as `forEach()` or using a library like Lodash.
3. **Native WebAssembly support**: Some browsers have native support for WebAssembly, which can provide improved performance for specific use cases.

Keep in mind that these alternatives might not be directly comparable to the original benchmark, but they could still offer interesting insights into different approaches to achieving similar results.

Related benchmarks:

filter + map vs reduce 12345 (version: 0)

Comparing performance of: flatMap vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

flatMap

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):