Benchmark: Map or GroupBy - MeasureThat.net

Script Preparation code:

var MAX_SAFE_INTEGER = 9007199254740991

function arrayReduce(array, iteratee, accumulator, initAccum) {
  let index = -1
  const length = array == null ? 0 : array.length

  if (initAccum && length) {
    accumulator = array[++index]
  }
  while (++index < length) {
    accumulator = iteratee(accumulator, array[index], index, array)
  }
  return accumulator
}


function baseFor(object, iteratee, keysFunc) {
  const iterable = Object(object)
  const props = keysFunc(object)
  let { length } = props
  let index = -1

  while (length--) {
    const key = props[++index]
    if (iteratee(iterable[key], key, iterable) === false) {
      break
    }
  }
  return object
}

function isLength(value) {
  return typeof value === 'number' &&
    value > -1 && value % 1 == 0 && value <= MAX_SAFE_INTEGER
}

function isArrayLike(value) {
  return value != null && typeof value !== 'function' && isLength(value.length)
}


function baseForOwn(object, iteratee) {
  return object && baseFor(object, iteratee, keys)
}


function baseEach(collection, iteratee) {
  if (collection == null) {
    return collection
  }
  if (!isArrayLike(collection)) {
    return baseForOwn(collection, iteratee)
  }
  const length = collection.length
  const iterable = Object(collection)
  let index = -1

  while (++index < length) {
    if (iteratee(iterable[index], index, iterable) === false) {
      break
    }
  }
  return collection
}


function baseReduce(collection, iteratee, accumulator, initAccum, eachFunc) {
  eachFunc(collection, (value, index, collection) => {
    accumulator = initAccum
      ? (initAccum = false, value)
      : iteratee(accumulator, value, index, collection)
  })
  return accumulator
}


function reduce(collection, iteratee, accumulator) {
  const func = Array.isArray(collection) ? arrayReduce : baseReduce
  const initAccum = arguments.length < 3
  return func(collection, iteratee, accumulator, initAccum, baseEach)
}


var hasOwnProperty = Object.prototype.hasOwnProperty

function baseAssignValue(object, key, value) {
  if (key == '__proto__') {
    Object.defineProperty(object, key, {
      'configurable': true,
      'enumerable': true,
      'value': value,
      'writable': true
    })
  } else {
    object[key] = value
  }
}

function groupBy(collection, iteratee) {
  return reduce(collection, (result, value, key) => {
    key = iteratee(value)
    if (hasOwnProperty.call(result, key)) {
      result[key].push(value)
    } else {
      baseAssignValue(result, key, [value])
    }
    return result
  }, {})
}

var arr = [6.1, 4.2, 6.3];

Tests:

gp
const lm = groupBy(arr, Math.floor)
map
const m = new Map(); for (const v of arr) { const k = Math.floor(v); const temp = m.get(k) ?? []; temp.push(v); m.set(k, temp) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
gp
map

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definition and explain what's being tested, compared options, pros and cons, and other considerations.

**Benchmark Definition**

The benchmark is designed to measure the performance of mapping or grouping operations on an array. There are two test cases:

1. `map`: Uses a `Map` data structure to store the grouped elements.
2. `gp` (short for "groupBy"): Uses a `groupBy` function that returns an object with grouped elements.

**Options Compared**

The benchmark compares two approaches:

1. Using a `Map` data structure (`map` test case)
2. Implementing a custom grouping algorithm using a `groupBy` function (`gp` test case)

**Pros and Cons of Each Approach**

**Map Approach (map)**

Pros:

* Efficient use of memory, as each element is stored in a separate entry in the map.
* Fast lookup and insertion operations.

Cons:

* May require more memory to store the entire map.
* May have slower startup times due to map initialization.

**Custom Grouping Algorithm (gp)**

Pros:

* More control over the grouping process.
* Can potentially be optimized for specific use cases.

Cons:

* Requires manual implementation of the grouping algorithm, which can be error-prone and time-consuming.
* May require more memory to store intermediate results.
* Lookup and insertion operations may be slower due to the custom implementation.

**Other Considerations**

* The `Map` data structure is implemented as an object with numeric keys, where each key corresponds to a unique element in the input array. This allows for fast lookup and insertion operations.
* The `groupBy` function returns an object with grouped elements, where each key corresponds to a unique group (i.e., a subset of the input array that shares a common property).

**Library and Special JS Features**

The `Map` data structure is a built-in JavaScript object. The `groupBy` function uses this object to store the grouped elements.

There are no special JavaScript features used in this benchmark, aside from the standard ECMAScript syntax for working with objects and arrays.

**Alternatives**

Other alternatives for implementing group-by operations include:

* Using an array of arrays, where each inner array represents a group.
* Using a data structure like a heap or balanced tree to store the grouped elements.
* Implementing a custom sorting algorithm to group elements based on specific criteria.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36

Browser/OS: Chrome 114 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
gp	1508255.2 Ops/sec
map	2881245.5 Ops/sec

Related benchmarks:

Map or GroupBy (version: 0)

Map or GroupBy

Comparing performance of: gp vs map

Created: 2 years ago by: Guest

Jump to the latest result

gp

map

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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