Benchmark: Test flatmap reduce

Script Preparation code:

const test = [
  {
    state: 'lorem',
  },
  {
    items: [
      {
        state: 'ipsum'
      },
      {
        items: [
          {
            state: 'dolor'
          },
          {
            items: [
              {
                state: 'sit'
              }
            ]
          }
        ]
      }
    ]
  },
  {
    items: [
      {
        state: 'foo'
      }
    ]
  },
  {
    state: 'bar'
  }
]

Tests:

flatMap
const test = [ { state: 'lorem', }, { items: [ { state: 'ipsum' }, { items: [ { state: 'dolor' }, { items: [ { state: 'sit' } ] } ] } ] }, { items: [ { state: 'foo' } ] }, { state: 'bar' } ] const res = (arr) => { return arr.flatMap((x) => { if (x.items?.length) { return res(x.items) } return x }) } console.log(res(test))
reduce
const test = [ { state: 'lorem', }, { items: [ { state: 'ipsum' }, { items: [ { state: 'dolor' }, { items: [ { state: 'sit' } ] } ] } ] }, { items: [ { state: 'foo' } ] }, { state: 'bar' } ] const res = (arr) => { return arr.reduce((acc, x) => { if (x.items?.length) { acc = [...acc, ...res(x.items)] } else { acc.push(x) } return acc }, []) } console.log(res(test))

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 break down the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is designed to compare two approaches: `flatMap` and `reduce`. Both methods are used to process an array of objects, but they differ in their handling of nested arrays.

**Test Case 1: flatMap**

In this test case, we have the following JSON data:
```json
{
    "state": "lorem",
    "items": [
        {
            "state": "ipsum"
        },
        {
            "items": [
                {
                    "state": "dolor"
                },
                {
                    "items": [
                        {
                            "state": "sit"
                        }
                    ]
                }
            ]
        }
    ]
}
```
The test code uses the `flatMap` method to process this array:
```javascript
const res = (arr) => {
    return arr.flatMap((x) => {
        if (x.items?.length) {
            return res(x.items)
        }
        return x
    })
}
```
**Test Case 2: reduce**

In this test case, we have the same JSON data as before. The test code uses the `reduce` method to process this array:
```javascript
const res = (arr) => {
    return arr.reduce((acc, x) => {
        if (x.items?.length) {
            acc = [...acc, ...res(x.items)]
        } else {
            acc.push(x)
        }
        return acc
    }, [])
}
```
**Comparison**

The main difference between these two approaches is how they handle nested arrays. `flatMap` returns a new array with the flattened elements, while `reduce` accumulates the results in an array.

**Pros and Cons**

* `flatMap`:
	+ Pros: Efficient use of memory, as it doesn't create intermediate arrays.
	+ Cons: Can be slower for large datasets due to the overhead of recursion.
* `reduce`:
	+ Pros: More flexible, as it can accumulate multiple values in a single array.
	+ Cons: Can lead to increased memory usage if not handled carefully.

**Library and Special Features**

There is no specific library used in this benchmark. However, the use of template literals (`\r\n`) and backticks (`\``) suggests that the test code is using modern JavaScript features.

**Other Considerations**

The benchmark measures the execution speed of both `flatMap` and `reduce`. The results are presented in terms of executions per second, which gives an idea of the performance difference between the two methods.

**Alternatives**

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

* `forEach`: This method can be used to process each element individually, but it's not suitable for this specific use case.
* `map` and `filter`: These methods can be combined with `reduce` to achieve similar results, but they may lead to increased memory usage due to the creation of intermediate arrays.
* Third-party libraries: Depending on your specific requirements, you might consider using a library like Lodash or Ramda, which provide optimized implementations of these methods.

Related benchmarks:

Test flatmap reduce (version: 0)

Comparing performance of: flatMap vs reduce

Created: 2 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):