Benchmark: 2D to 1D Test - MeasureThat.net

Tests:

Test5
const testArray = [ [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]] var newTestArray = new Array(); for (var x = 0; x < testArray.length; x++){ for (var y = 0; y < testArray[x].length; y++){ newTestArray.push(testArray[x][y]) } }
Test4
const testArray = [ [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]] const newTestArray = testArray.reduce((prev, next) => prev.concat(next))
Test3
const testArray = [ [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]] const newTestArray = [].concat(...testArray)
Test2
const testArray = [ [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]] const newTestArray = testArray.join().split(",");
Test1
const testArray = [ [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]] const newTestArray = [...new Set(testArray.flat())]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Test5
Test4
Test3
Test2
Test1

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Measuring the performance of JavaScript code is crucial in understanding how efficient different approaches are. Let's break down the provided benchmark and explain what each option tests, their pros and cons, and other considerations.

The provided benchmark consists of four test cases: `Test1`, `Test2`, `Test3`, and `Test4`. These test cases aim to measure the performance difference in flattening a 2D array into a 1D array using various methods.

**Option 1: Using a `for` loop (`Test1`)**
```javascript
const newTestArray = [...new Set(testArray.flat())];
```
This option uses the `flat()` method to flatten the 2D array and then converts it to a set using `Set()`. Finally, it converts the set back to an array using the spread operator (`[...new Set(...)]`). This approach has the advantage of being straightforward and easy to understand. However, it may have performance overhead due to the additional iterations.

**Pros:** Easy to understand, straightforward implementation.
**Cons:** Potential performance overhead due to extra iterations.

**Option 2: Using `Array.prototype.concat()` (`Test4`)**
```javascript
const newTestArray = testArray.reduce((prev, next) => prev.concat(next));
```
This option uses the `concat()` method on each inner array in the 2D array and then reduces it to a single array using the `reduce()` method. This approach has the advantage of being concise but may have performance overhead due to repeated concatenations.

**Pros:** Concise implementation.
**Cons:** Potential performance overhead due to repeated concatenations.

**Option 3: Using `Array.prototype.flat()` (`Test2`)**
```javascript
const newTestArray = [...new Set(testArray.flat())];
```
This option uses the `flat()` method to flatten the 2D array and then converts it to a set using `Set()`. Finally, it converts the set back to an array using the spread operator. This approach has the advantage of being more efficient than options 1 and 4.

**Pros:** More efficient than options 1 and 4.
**Cons:** May still have performance overhead due to the additional iteration.

**Option 4: Using `Array.prototype.flat()` with `Infinity` (`Test5`)**
```javascript
const newTestArray = testArray.reduce((acc, curr) => acc.concat(curr), []);
```
This option uses the `flat()` method with a maximum depth of `Infinity` to flatten the 2D array and then reduces it to a single array using the `reduce()` method. This approach has the advantage of being more efficient than options 1, 2, and 4.

**Pros:** Most efficient among all options.
**Cons:** May have higher memory usage due to the use of `Infinity`.

In conclusion, the choice of option depends on the trade-off between performance and readability. Option 5 is the most efficient but may require more memory due to the use of `Infinity`. Options 1 and 2 are straightforward but may have performance overhead due to extra iterations or repeated concatenations. Option 4 offers a good balance between efficiency and simplicity.

The latest benchmark results show that `Test3` is the fastest among all options, followed by `Test5`, which is also efficient but uses more memory due to the use of `Infinity`.

Related benchmarks:

2D to 1D Test (version: 0)

Comparing performance of: Test5 vs Test4 vs Test3 vs Test2 vs Test1

Created: 2 years ago by: Guest

Jump to the latest result

Test5

Test4

Test3

Test2

Test1

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):