Benchmark: reduce vs map/filter with more items

Script Preparation code:

var myParams = [
  ['key1','val1'],
  ['key2','val2'],
  ['ke3','val3'],
  ['filter[status]','val4'],
  ['filter[status]','val5'],
  ['filter[status]','val6']
];

Tests:

reduce
myParams.reduce((a, c) => { if (c[0] === 'filter[status]') { a.push(c[1]); } return a; }, []);
map/filter
myParams .filter((param) => param[0] === 'filter[status]') .map((param) => param[1]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map/filter

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in this JavaScript microbenchmark.

**Benchmark Definition**

The benchmark is designed to compare the performance of two approaches: `reduce` and `map/filter`. The test case uses an array of objects, `myParams`, which contains multiple items with nested keys (`filter[status]`). The goal is to extract values from these objects using different methods.

**Options Compared**

1. **Reduce**: This method iterates over the array and accumulates a result in the accumulator (`a`) as it goes through each element (`c`). If an element's key matches `filter[status]`, its value (`c[1]`) is added to the accumulator.
2. **Map/Filters**: This approach uses two intermediate steps:
	* `filter`: Removes elements from the array that don't match a specific condition (in this case, `param[0] === 'filter[status]'`).
	* `map`: Transforms each remaining element in the filtered array into its desired output (`param[1]`).

**Pros and Cons of Each Approach**

* **Reduce**: Pros:
	+ Can be more memory-efficient for large datasets since it only uses a single accumulator.
	+ Can be faster for simple operations with minimal overhead.
* Cons:
	+ May have higher CPU overhead due to repeated lookups in the accumulator.
	+ Less readable code for complex transformations.
* **Map/Filters**: Pros:
	+ More readable code for complex transformations, as each step is separate and explicit.
	+ Can be faster when filtering out most elements quickly.
* Cons:
	+ Requires more memory since it creates new arrays (intermediate results).
	+ May incur higher CPU overhead due to repeated function calls.

**Library Usage**

There is no library usage in this benchmark. It only uses built-in JavaScript methods and syntax.

**Special JS Features or Syntax**

None of the test cases use any special features or syntax beyond standard JavaScript functions like `filter`, `map`, and `reduce`.

**Other Alternatives**

If you were to rewrite this benchmark, other approaches might include:

* Using `forEach` instead of `reduce`.
* Employing a custom loop with indexing (e.g., using `for...of`) for both approaches.
* Utilizing WebAssembly or other low-level languages if performance is critical.
* Experimenting with different data structures (e.g., objects, arrays) to see how they affect performance.

Keep in mind that these alternatives would require additional modifications to the benchmark and might not provide significant improvements over the existing `reduce` vs. `map/filter` comparison.

Related benchmarks:

reduce vs map/filter with more items (version: 0)

Comparing performance of: reduce vs map/filter

Created: 4 years ago by: Guest

Jump to the latest result

reduce

map/filter

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):