Benchmark: reduce vs map/filter vs for

Script Preparation code:

var myParams = [
  ['key2','val2'],
  ['ke3','val3'],
  ['filter[status]','running'],
  ['filter[status]','stopped'],
  ['filter[status]','pending']
];

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]);
for
const res = []; for (const c of res) { if (c[0] === 'filter[status]') { res.push(c[1]); } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map/filter
for

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

I'll break down the benchmark definition, options compared, pros and cons, library usage, special JS features, and other considerations to explain what's being tested.

**Benchmark Definition**

The benchmark is defined in JSON format, which includes:

* `Name`: "reduce vs map/filter vs for"
* `Description`: null (no description provided)
* `Script Preparation Code`: a JavaScript code snippet that defines an array of parameters (`myParams`) with some key-value pairs.
* `Html Preparation Code`: null (no HTML preparation code is required)

**Options Compared**

The benchmark compares three different approaches to filter and process the `myParams` array:

1. **Reduce**: uses the `Array.prototype.reduce()` method to iterate over the array and push filtered values into a new array.
2. **Map/Filter**: uses the `Array.prototype.filter()` and `Array.prototype.map()` methods in combination to filter and transform the array.
3. **For loop**: uses a traditional for loop with an iterator variable (`c`) to iterate over the array and push filtered values into a new array.

**Pros and Cons of Each Approach**

Here's a brief summary:

* **Reduce**:
	+ Pros: concise, efficient, and easy to read.
	+ Cons: can be slower due to its iterative nature and potential memory allocation overhead.
* **Map/Filter**: (combined approach)
	+ Pros: fast, expressive, and widely supported. The `map()` method transforms the array without modifying it, while `filter()` removes unwanted elements efficiently.
	+ Cons: may require more code than a single-line reduce operation.
* **For loop**:
	+ Pros: straightforward, easy to understand, and suitable for small arrays or custom logic.
	+ Cons: slower and less concise compared to the other two approaches.

**Library Usage**

None of the benchmarked approaches rely on external libraries. The `Array.prototype.reduce()`, `Array.prototype.filter()`, and `Array.prototype.map()` methods are built-in JavaScript functions that work natively without any additional dependencies.

**Special JS Features (none mentioned)**

No special JavaScript features or syntax is used in this benchmark.

**Other Considerations**

* **Performance**: the benchmark measures the number of executions per second for each approach. This allows users to compare their performance in a more quantitative way.
* **Expressiveness**: while not explicitly stated, the use of `map()` and `filter()` suggests that readability and maintainability are also important factors in this benchmark.

**Alternatives**

If you're interested in exploring alternative approaches or modifying the existing benchmark, consider the following options:

1. Use other array methods like `forEach()`, `every()`, or `some()`.
2. Implement custom filtering logic using a closure or a higher-order function.
3. Experiment with different data structures, such as an object instead of an array.

Keep in mind that modifying the benchmark may require additional setup and configuration to ensure accurate results.

Related benchmarks:

reduce vs map/filter vs for (version: 0)

Comparing performance of: reduce vs map/filter vs for

Created: 4 years ago by: Guest

Jump to the latest result

reduce

map/filter

for

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):