Benchmark: JS array chunk - MeasureThat.net

Script Preparation code:

function chunk1(xs, n) {
  return xs.reduce(function(o, x, i) {
    o[Math.floor(i / n)].push(x);
    return o;
  }, Array(Math.ceil(xs.length / n)).fill([]));
}

function chunk2(xs, n) {
  return (xs.length <= n) ? [xs] : [
    xs.slice(0, n),
    ...chunk2(xs.slice(n), n)
  ]
}

function chunk3(array, size) {
  //declaring variable 'chunked' as an empty array
  let chunked = []

  //looping through the array until it has been entirely "manipulated" or split into our subarrays
  while (array.length > 0) {
    //taking the spliced segments completely out of our original array
    //pushing these subarrays into our new "chunked" array
    chunked.push(array.splice(0, size))
  }
  //returning the new array of subarrays
  return chunked
}

function chunk4(array, size) {
  //declaring variable 'chunked' as an empty array
  let chunked = []

  //setting our start point for our while loop at index 0
  let i = 0;
  //looping through the array until we have reached the final index
  while (i < array.length) {
    //push the sliced subarray of length 'size' into our new 'chunked' array
    chunked.push(array.slice(i, i + size))
    //increment by the size as to avoid duplicates
    i += size;
  }
  //return the array of subarrays
  return chunked
}

function chunk5(array, size) {
  //declaring variable 'chunked' as an empty array
  let chunked = [];

  //for loop iterating through every element of our input array
  for (let ele of array) {
    //declaring variable 'last' as the last index of our 'chunked' array
    const last = chunked[chunked.length - 1];

    //checking if last is undefined or if the last subarray is equal to the size
    if (!last || last.length === size) {
      //then we push the element to be a new subarray in 'chunked'
      chunked.push([ele])
    } else {
      //if not, then we add the element to the 'last' subarray
      last.push(ele)
    }
  }
  //return the array of subarrays
  return chunked
}

function chunk6(array, size) {
  const chunked_arr = [];
  for (let i = 0; i < array.length; i++) {
    const last = chunked_arr[chunked_arr.length - 1];
    if (!last || last.length === size) {
      chunked_arr.push([array[i]]);
    } else {
      last.push(array[i]);
    }
  }
  return chunked_arr;
}

function chunk7(array, size) {
  const chunked_arr = [];
  let copied = [...array]; // ES6 destructuring
  const numOfChild = Math.ceil(copied.length / size); // Round up to the nearest integer
  for (let i = 0; i < numOfChild; i++) {
    chunked_arr.push(copied.splice(0, size));
  }
  return chunked_arr;
}

function chunk8(array, size) {
  const chunked_arr = [];
  let index = 0;
  while (index < array.length) {
    chunked_arr.push(array.slice(index, size + index));
    index += size;
  }
  return chunked_arr;
}

function chunk9(array, size) {
  if (!array) return [];
  const firstChunk = array.slice(0, size); // create the first chunk of the given array
  if (!firstChunk.length) {
    return array; // this is the base case to terminal the recursive
  }
  return [firstChunk].concat(chunk9(array.slice(size, array.length), size));
}

Tests:

1
chunk1([1, 2, 3, 4], 2);
2
chunk2([1, 2, 3, 4], 2);
3
chunk3([1, 2, 3, 4], 2);
4
chunk4([1, 2, 3, 4], 2);
5
chunk5([1, 2, 3, 4], 2);
6
chunk6([1, 2, 3, 4], 2);
7
chunk7([1, 2, 3, 4], 2);
8
chunk8([1, 2, 3, 4], 2);
9
chunk9([1, 2, 3, 4], 2);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
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9

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.

It looks like you provided a JSON array of data, which is quite long!

To help me assist you effectively, I'll need to know what specific information or analysis you'd like me to focus on. However, based on the data provided, here are some general observations and potential insights:

1. **Browser usage**: The majority of the data points appear to be from Chrome 81, which is not surprising given its popularity.
2. **Device platform**: Most devices are running on Desktop platforms, while the number of mobile devices (if any) seems to be negligible based on this sample.
3. **Operating System**: Mac OS X 10.15.3 dominates the landscape, suggesting that macOS users make up a significant portion of this group.
4. **Execution per second**: There's considerable variation in execution rates, with some tests running at very high frequencies (e.g., > 5 million executions/second) and others much slower.

Before proceeding further, could you please clarify what your specific goals or questions are regarding this data?

Related benchmarks:

JS array chunk (version: 0)

Comparing performance of: 1 vs 2 vs 3 vs 4 vs 5 vs 6 vs 7 vs 8 vs 9

Created: 6 years ago by: Guest

Jump to the latest result

1

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3

4

5

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7

8

9

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):