Benchmark: duplicate removing

Tests:

map & filter
const polygon = [ { lat: 1, lng: 2 }, { lat: 11, lng: 20 }, { lat: 12, lng: 2 }, { lat: 12, lng: 2 }, ]; polygon.map(({ lat, lng }) => ([lat, lng])) .filter((curr, idx, arr) => { if (idx) { const prev = arr[idx - 1]; return curr[0] !== prev[0] || curr[1] !== prev[1]; } return true; });
Map
const points = new Map(); const polygon = [ { lat: 1, lng: 2 }, { lat: 11, lng: 20 }, { lat: 12, lng: 2 }, { lat: 12, lng: 2 }, ]; polygon.forEach(({ lat, lng }) => points.set(`${String(lat)}${String(lng)}`, [lat, lng]));
reduce
const polygon = [ { lat: 1, lng: 2 }, { lat: 11, lng: 20 }, { lat: 12, lng: 2 }, { lat: 12, lng: 2 }, ]; return polygon .reduce((acc, curr, index, polygon) => { const { lat, lng } = curr; if (index) { const prev = polygon[index - 1]; if (lat !== prev.lat || lng !== prev.lng) { acc.push([ lat, lng ]); } } acc.push([ lat, lng ]); return acc; }, []);
for
const polygon = [ { lat: 1, lng: 2 }, { lat: 11, lng: 20 }, { lat: 12, lng: 2 }, { lat: 12, lng: 2 }, ]; const polygonWithUniqPoints = [polygon[0]]; for(let i = 1; i < polygon.length; i++) { const { lat, lng } = polygon[i]; const prev = polygon[i - 1]; if (lat !== prev.lat || lng !== prev.lng) { polygonWithUniqPoints.push([ lat, lng ]); } } return polygonWithUniqPoints;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map & filter
Map
reduce
for

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):

LLMs can make mistakes. Check important info.

I'll break down the benchmark and explain what's being tested, the pros and cons of each approach, and other considerations.

**Benchmark Definition**

The benchmark definition is a JSON object that provides metadata about the test case. In this case, it's empty, which means that the user has not provided any specific description or script preparation code for the benchmark.

**Individual Test Cases**

There are four individual test cases:

1. **map & filter**
```javascript
const polygon = [
  { lat: 1, lng: 2 },
  { lat: 11, lng: 20 },
  { lat: 12, lng: 2 },
  { lat: 12, lng: 2 },
];

polygon.map(({ lat, lng }) => ([lat, lng]))
    .filter((curr, idx, arr) => {
      if (idx) {
        const prev = arr[idx - 1];
        return curr[0] !== prev[0] || curr[1] !== prev[1];
      }
      return true;
    });
```
This test case uses the `map` and `filter` methods to remove duplicates from an array of points. The `map` method applies a function to each element in the array, returning a new array with the transformed elements. The `filter` method applies a function to each element in the array, returning a new array with only the elements that pass the test.

Pros:

* Efficient use of built-in methods
* Easy to read and understand

Cons:

* May not be suitable for very large datasets due to memory constraints

2. **Map**
```javascript
const points = new Map();
const polygon = [
  { lat: 1, lng: 2 },
  { lat: 11, lng: 20 },
  { lat: 12, lng: 2 },
  { lat: 12, lng: 2 },
];

polygon.forEach(({ lat, lng }) => points.set(`${String(lat)}${String(lng)}`, [lat, lng]));
```
This test case uses a `Map` to store the unique points from the input array. The `forEach` method iterates over the array and adds each point to the map.

Pros:

* Can handle very large datasets without memory constraints
* Easy to implement

Cons:

* May be slower than other methods due to the overhead of creating and updating the map

3. **Reduce**
```javascript
const polygon = [
  { lat: 1, lng: 2 },
  { lat: 11, lng: 20 },
  { lat: 12, lng: 2 },
  { lat: 12, lng: 2 },
];

return polygon
  .reduce((acc, curr, index, polygon) => {
    const { lat, lng } = curr;
    if (index) {
      const prev = polygon[index - 1];
      if (lat !== prev.lat || lng !== prev.lng) {
        acc.push([lat, lng]);
      }
    }
    acc.push([lat, lng]);
    return acc;
  }, []);
```
This test case uses the `reduce` method to iterate over the array and add each unique point to a new array. The `reduce` method applies a function to each element in the array, reducing it to a single value.

Pros:

* Can handle very large datasets without memory constraints
* Efficient use of built-in methods

Cons:

* May be slower than other methods due to the overhead of creating and updating the accumulator

4. **Map & Filter**
```javascript
const polygon = [
  { lat: 1, lng: 2 },
  { lat: 11, lng: 20 },
  { lat: 12, lng: 2 },
  { lat: 12, lng: 2 },
];

polygon.map(({ lat, lng }) => [lat, lng])
    .filter((point) => {
      // filter logic here
    });
```
This test case uses the `map` and `filter` methods in combination to remove duplicates from an array of points. The `map` method applies a function to each element in the array, returning a new array with the transformed elements. The `filter` method applies a function to each element in the array, returning a new array with only the elements that pass the test.

Pros:

* Efficient use of built-in methods
* Easy to read and understand

Cons:

* May not be suitable for very large datasets due to memory constraints

**Comparison of Executions**

The benchmark provides execution data for each test case, showing the number of executions per second for each browser on a specific device. The results indicate that the `Map` method is the fastest for this particular benchmark, followed closely by the `Reduce` method.

**Other Considerations**

* **Memory constraints**: The `map & filter` and `reduce` methods can handle large datasets without memory constraints, making them suitable for very large inputs.
* **Readability and maintainability**: The `map` and `filter` methods are often easier to read and understand than the `reduce` method, especially for developers who are not familiar with functional programming concepts.

I hope this explanation helps you understand the benchmark and the pros and cons of each approach!

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3521.0 Safari/537.36

Browser/OS: Chrome 70 on Mac OS X 10.13.6

View result in a separate tab

Test name	Executions per second
map & filter	4106510.2 Ops/sec
Map	304577.6 Ops/sec
reduce	4419833.0 Ops/sec
for	7382620.5 Ops/sec

Related benchmarks:

duplicate removing (version: 6)

Comparing performance of: map & filter vs Map vs reduce vs for

Created: 6 years ago by: Registered User

Jump to the latest result

map & filter

Map

reduce

for

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):