Benchmark: dedplicate - MeasureThat.net

Tests:

option 1
let arr1 = ["123", "234", "345", "456", "567", "678", "789", "890"]; let arr2 = ["123", "234", "A45", "456", "567", "A78", "A89", "890"]; const result = [...arr1]; for (const item2 of arr2) { if (!result.some((item1) => item1 === item2)) { result.push(item2); } } return result;
option 2
let arr1 = ["123", "234", "345", "456", "567", "678", "789", "890"]; let arr2 = ["123", "234", "A45", "456", "567", "A78", "A89", "890"]; let SOURCE = [...arr1, ...arr2]; return [...new Set(SOURCE)];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
option 1
option 2

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.

Let's dive into the provided benchmark JSON and explain what's being tested, compared, and some considerations.

**Benchmark Overview**

MeasureThat.net is a platform where users can create and run JavaScript microbenchmarks to compare different approaches. In this case, we have two individual test cases: "option 1" and "option 2".

The test cases aim to measure the performance of duplicate detection in an array using JavaScript. The input arrays contain some duplicates and some non-duplicates.

**Option 1**

For "option 1", the benchmark definition is:
```javascript
let arr1 = [\"123\", \"234\", \"345\", \"456\", \"567\", \"678\", \"789\", \"890\"];\r\nlet arr2 = [\"123\", \"234\", \"A45\", \"456\", \"567\", \"A78\", \"A89\", \"890\"];\r\nconst result = [...arr1];\r\n  for (const item2 of arr2) {\r\n    if (!result.some((item1) => item1 === item2)) {\r\n      result.push(item2);\r\n    }\r\n  }\r\n  return result;
```
This approach uses a simple loop to iterate through `arr2` and checks for duplicates using the `some()` method. If no duplicate is found, it adds the element to `result`.

**Option 2**

For "option 2", the benchmark definition is:
```javascript
let arr1 = [\"123\", \"234\", \"345\", \"456\", \"567\", \"678\", \"789\", \"890\"];\r\nlet arr2 = [\"123\", \"234\", \"A45\", \"456\", \"567\", \"A78\", \"A89\", \"890\"];\r\nlet SOURCE = [...arr1, ...arr2];\r\n\r\n return [...new Set(SOURCE)];
```
This approach uses the spread operator to concatenate `arr1` and `arr2`, and then converts the resulting array to a set using `Set()`. The `Set()` function automatically removes duplicates.

**Comparison**

The two approaches differ in their complexity:

* Option 1: O(n^2) time complexity, where n is the length of `arr1`.
* Option 2: O(n) time complexity, as it uses a set to remove duplicates.

In general, Option 2 is more efficient for large input sizes.

**Pros and Cons**

Pros of Option 2:

* Faster execution times
* Less memory usage (set only stores unique elements)

Cons of Option 2:

* Limited control over duplicate detection (no explicit loop)
* May not work as expected if the input array contains non-unique elements with different values

Pros of Option 1:

* More explicit control over duplicate detection
* Works with any input array, including those with non-unique elements

Cons of Option 1:

* Slower execution times
* Higher memory usage (creates a new array `result`)

**Other Considerations**

When to use each approach?

* Use Option 2 when:
	+ You need to process large input arrays and want optimal performance.
	+ You're working with unique elements only.
* Use Option 1 when:
	+ You need more control over the duplicate detection process.
	+ You're working with non-unique elements that require explicit handling.

**Library Used**

None is explicitly mentioned in this benchmark. However, if you use `Set()` or `some()`, you might be using built-in JavaScript features.

**Special JS Feature/Syntax**

There are no special JS features or syntax used in these test cases.

Related benchmarks:

dedplicate (version: 0)

Comparing performance of: option 1 vs option 2

Created: 2 years ago by: Guest

Jump to the latest result

option 1

option 2

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):