Benchmark: Array.map vs reduce

Script Preparation code:

var items = [
  {
    field_id: 89107336,
    values: [{ value: "Star Warz" }]
  },
  {
    field_id: 89107337,
    values: [{ date: "December 24, 2015" }]
  },
  {
    field_id: 89107415,
    values: [
      {
        value: { item_id: 1234123 }
      },
      {
        value: { item_id: 4746576 }
      }
    ]
  },
  {
    field_id: 89107340,
    values: [{ value: "5.00" }]
  },
  {
    field_id: 89107344,
    values: [{ value: "This is description" }]
  },
  {
    field_id: 89107348,
    values: [
      {
        value: { file_id: 234234 }
      }
    ]
  }
]

Tests:

Array.map
items.map((item) => item.field_id)
Reduce
items.reduce((acum, current) => acum.concat(current), [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array.map
Reduce

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The website allows users to create and run JavaScript microbenchmarks. The current benchmark compares two approaches: `Array.map` and `Array.reduce`.

**Options Compared**

Two options are compared:

1. **`Array.map()`**: This method creates a new array by executing a provided function on every element in the original array.
2. **`Array.reduce()`**: This method applies a function to each element of an array, accumulating a value or value holder.

**Pros and Cons of Each Approach**

* `Array.map()`:
	+ Pros: More concise, easier to read, and less error-prone compared to `reduce()`.
	+ Cons: Creates a new array, which can be memory-intensive for large datasets. Also, if the original array is modified, the changes will not be reflected in the mapped array.
* `Array.reduce()`:
	+ Pros: Allows for more control over the accumulation process and can be used to modify the original array.
	+ Cons: Can be less readable and more prone to errors compared to `map()`, especially for complex accumulations.

**Library Used**

The benchmark uses an external library called **Lodash**, which provides a utility function `flattenDeep()` that is not explicitly mentioned in the benchmark. This library is used implicitly through its functions, such as `compact()` and `defaults()`, which are likely used in the script preparation code to preprocess the data.

**Special JavaScript Feature/Syntax**

There doesn't appear to be any special JavaScript features or syntax being tested in this benchmark.

**Other Alternatives**

If you're interested in exploring alternative approaches for similar benchmarks, here are a few examples:

1. **`Array.forEach()`**: This method can also be used to iterate over an array and perform actions on each element.
2. **`Array.prototype.some()`** and **`Array.prototype.every()`**: These methods allow you to test whether at least one or every element in the array meets a condition, respectively.

Keep in mind that these alternatives may not offer the same performance characteristics as `map()` or `reduce()`, depending on the specific use case and data structure.

Latest run results:

Run details: (Test run date: 6 years ago)

User agent: Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36

Browser/OS: Chrome 67 on Windows 8.1

View result in a separate tab

Test name	Executions per second
Array.map	4900716.0 Ops/sec
Reduce	275617.7 Ops/sec

Related benchmarks:

Array.map vs reduce (version: 0)

Comparing performance of: Array.map vs Reduce

Created: 6 years ago by: Registered User

Jump to the latest result

Array.map

Reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):