Benchmark: transrorm template 2

HTML Preparation code:

<!--your preparation HTML code goes here-->

Tests:

transform1
var transform1 = (rowTemplate) => { if (!rowTemplate?.length) { return [[]]; } const rows = rowTemplate.length; const cols = rowTemplate[0].length; const tiles = new Map(); for (let row = 0; row < rows; row++) { for (let col = 0; col < cols; col++) { const id = rowTemplate[row][col]; if (!tiles.has(id)) { tiles.set(id, { positions: [] }); } tiles.get(id)?.positions.push({ row, col }); } } const tilesByColumn = Array.from(tiles.keys()).sort((idA, idB) => { const posA = tiles.get(idA)?.positions[0]; const posB = tiles.get(idB)?.positions[0]; return posA.col === posB.col ? posA.row - posB.row : posA.col - posB.col; }); const tilesByRow = Array.from(tiles.keys()).sort((idA, idB) => { const posA = tiles.get(idA)?.positions[0]; const posB = tiles.get(idB)?.positions[0]; return posA.row === posB.row ? posA.col - posB.col : posA.row - posB.row; }); const tileMap = {}; tilesByRow.forEach((rowId, index) => { tileMap[rowId] = tilesByColumn[index]; }); const result = Array(rows).fill(null) .map(() => Array(cols).fill(null)); for (let row = 0; row < rows; row++) { for (let col = 0; col < cols; col++) { const originalId = rowTemplate[row][col]; result[row][col] = tileMap[originalId]; } } return result; }; var t1 = [ ["1", "1", "2", "3", "4", "4"], ["1", "1", "5", "6", "4", "4"] ]; for (var i=0; i < 10000; i++) { transform1(t1); }
transform2
var transform2 = (rowTemplate) => { if (!rowTemplate?.length) return [[]]; const rows = rowTemplate.length; const cols = rowTemplate[0].length; const firstPositions = {}; for (let row = 0; row < rows; row++) { for (let col = 0; col < cols; col++) { const id = rowTemplate[row][col]; if (firstPositions[id] === undefined) { firstPositions[id] = {row, col}; } } } const uniqueIds = Object.keys(firstPositions); const rowIds = [...uniqueIds].sort((a, b) => { const posA = firstPositions[a]; const posB = firstPositions[b]; return posA.row === posB.row ? posA.col - posB.col : posA.row - posB.row; }); const colIds = [...uniqueIds].sort((a, b) => { const posA = firstPositions[a]; const posB = firstPositions[b]; return posA.col === posB.col ? posA.row - posB.row : posA.col - posB.col; }); const tileMap = {}; for (let i = 0; i < rowIds.length; i++) { tileMap[rowIds[i]] = colIds[i]; } const result = Array(rows); for (let row = 0; row < rows; row++) { result[row] = new Array(cols); for (let col = 0; col < cols; col++) { result[row][col] = tileMap[rowTemplate[row][col]]; } } return result; }; var t1 = [ ["1", "1", "2", "3", "4", "4"], ["1", "1", "5", "6", "4", "4"] ]; for (var i=0; i < 10000; i++) { transform2(t1); }
transform3
const transform3 = (rowTemplate) => { if (!rowTemplate?.length) { return []; } const rows = rowTemplate.length; const cols = rowTemplate[0].length; const result = []; for (let col = 0; col < cols; col++) { for (let row = 0; row < rows; row++) { result.push(rowTemplate[row][col]); } } return result; }; var t1 = [ ["1", "1", "2", "3", "4", "4"], ["1", "1", "5", "6", "4", "4"] ]; for (var i=0; i < 10000; i++) { transform3(t1); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
transform1
transform2
transform3

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36

Browser/OS: Chrome 133 on Windows

View result in a separate tab

Test name	Executions per second
transform1	72.4 Ops/sec
transform2	98.8 Ops/sec
transform3	3693.3 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):

LLMs can make mistakes. Check important info.

The benchmark provided tests the performance of three different JavaScript functions designed to transform a 2D array (called `rowTemplate`) based on certain rules about how the elements are arranged. Below, I will explain each function, the differences between them, their pros and cons, and some considerations regarding their performance.

### Test Cases Overview

1. **transform1**
   - **Description**: This function first collects the positions of each unique tile in the `rowTemplate`. It then creates two sorted arrays: one sorted by rows and another by columns. Finally, it constructs a `tileMap` that associates each unique ID with its respective position, which is then used to populate the result matrix.
   - **Pros**:
     - Efficient use of a `Map` for storing tile positions, which helps in quick lookups and modifications.
     - Careful sorting ensures that data can be organized effectively.
   - **Cons**:
     - Complexity might lead to higher execution time in terms of initializing and managing additional structures, especially the two sorts.
     - Performance may degrade with larger matrices due to the sorting operations.

2. **transform2**
   - **Description**: This function simplifies the previous approach by using an object (`firstPositions`) instead of a `Map`. It records the first occurrence of each unique tile ID. This time, it only sorts once to produce the `rowIds` and `colIds`, then constructs a `tileMap` for results.
   - **Pros**:
     - Uses a plain object for storing first positions, which can be beneficial in terms of performance since it avoids the overhead of a `Map`.
     - Simpler logic and fewer sorts compared to `transform1` could enhance performance.
   - **Cons**:
     - Still involves multiple loops and sorting, which may still be limiting in large datasets.
     - Potentially less readable due to less abstracted logic than in `transform1`.

3. **transform3**
   - **Description**: This function takes a simpler approach by directly flattening the 2D array by iterating through columns and rows, pushing values into a result array without any mapping or sorting.
   - **Pros**:
     - The simplest implementation which is often the easiest to read and debug.
     - Likely to have the best performance because it minimizes data structure management, merely executing two nested loops.
   - **Cons**:
     - Does not cater to any transformation logic beyond flattening the array, which might limit its application for more complex transformations.

### Performance Results

The performance results show the number of executions per second for each function:

- **transform3**: 3693.27 executions/second - This is significantly faster because it’s using a straightforward approach without additional overhead.
- **transform2**: 98.85 executions/second - This is slower due to its dual sorting and additional logic, but still reasonable.
- **transform1**: 72.40 executions/second - The most complex, hence the slowest, primarily due to multiple sorts and a broader scope of processing.

### Considerations

- **Array Size**: The performance of all three functions will generally decrease as the size of `rowTemplate` increases. Thus, the choice of function can depend on the expected size of input data.
- **Code Readability vs Performance**: More optimized code can sometimes become complex and harder to read; developers must balance efficiency with maintainability.
- **Use of Modern JS Features**: Both `?.` (optional chaining) and `const` are used, which are modern JavaScript features that improve code robustness and clarity.

### Alternatives

1. **Lodash**: A utility library that could be used to simplify operations on arrays and objects, which may contribute to improved code readability. However, it may add overhead due to its size.
2. **Functional Programming Libraries**: Libraries such as Ramda or RxJS can provide an alternative paradigm that might lead to clearer expressions of transformations but can also increase complexity if not properly understood.
3. **Native Maps and Sets**: Leveraging native ES6 data structures like `Set` for unique items could simplify logic in similar transformations.

In summary, each of the three functions has its own trade-offs regarding performance, readability, and the complexity of the implemented logic. The optimal choice will depend on the specific needs of the application, data sizes, and the expected frequency of such transformations.

Related benchmarks:

transrorm template 2 (version: 1)

Comparing performance of: transform1 vs transform2 vs transform3

Created: one month ago by: Guest

Jump to the latest result

transform1

transform2

transform3

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):