Benchmark: map and find may vs array find may

Script Preparation code:

var arr = Array.from({length: 1000000}).map((_,i) => ({id: i}));
var findTargets = Array.from({length: 1000}).map((_,i) => idGen());
function idGen() {
  return Math.floor(Math.random() * 1000000) % 1000000
}

Tests:

create map and find may
const arrMap = new Map(arr.entries()); findTargets.forEach(target => arrMap.get(target));
array find may
findTargets.forEach(target => arr.find((i) => i.id === target));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
create map and find may
array find may

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and its pros and cons.

**Benchmark Overview**

The benchmark measures the performance of two different approaches to search for elements in an array: creating a Map and searching using the `find` method versus searching directly in the array using the same approach. The test case uses JavaScript, which is the programming language being measured.

**Script Preparation Code**

The script preparation code generates two arrays:

1. `arr`: An array of 1,000,000 objects with an `id` property, created using `Array.from()` and mapping over a range of numbers.
2. `findTargets`: An array of 1,000 random IDs generated by the `idGen()` function.

**Library: `Math.random()`**

The `idGen()` function uses `Math.random()` to generate a random number between 0 and 999,999. This is used to create unique IDs for the objects in the `arr` array.

**Individual Test Cases**

There are two test cases:

1. **"create map and find may"**: Creates a Map from the `arr` entries and then searches for elements using the `get()` method.
2. **"array find may"**: Searches directly for elements in the `arr` array using the same approach as the first test case.

**Comparison**

The two approaches are compared based on their performance measured by the number of executions per second (ExecutionsPerSecond). The browser, device platform, operating system, and raw UA string are also reported to provide context about the execution environment.

**Pros and Cons**

Here's a brief summary of the pros and cons for each approach:

1. **Creating a Map**: Pros:
	* Can be faster than searching directly in the array, especially for larger datasets.
	* Can reduce memory allocation and garbage collection overhead compared to searching directly in the array.
Cons:
	* Requires additional memory allocation for the Map data structure.
2. **Searching Directly in the Array**: Pros:
	* No additional memory allocation or garbage collection overhead.
Cons:
	* May be slower than creating a Map, especially for larger datasets.

**Other Considerations**

The benchmark does not consider other factors that might affect performance, such as:

* Array indexing vs. object property access
* Using `Array.prototype.forEach()` vs. using `for` loops
* Using iterators or generators

Keep in mind that this is just a simplified comparison and real-world scenarios may have additional complexities.

**Alternatives**

If you wanted to explore alternative approaches, here are some options:

1. **Using `Set`**: Instead of creating a Map, you could use a Set to store unique IDs.
2. **Using `SparseArray`**: If the array is sparse (i.e., most elements have missing indices), using a SparseArray data structure might be more efficient than searching directly in the array.
3. **Parallelizing the search**: You could consider parallelizing the search operation using Web Workers or other concurrency techniques to take advantage of multiple CPU cores.

Please note that these alternatives are not necessarily applicable to this specific benchmark and may require additional setup and testing to verify their effectiveness.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36

Browser/OS: Chrome 107 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
create map and find may	5.9 Ops/sec
array find may	0.4 Ops/sec

Related benchmarks:

map and find may vs array find may (version: 0)

Comparing performance of: create map and find may vs array find may

Created: one year ago by: Guest

Jump to the latest result

create map and find may

array find may

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):