Benchmark: Array.find vs. Map.get fork2

Script Preparation code:

function getRandomElement(id) {
  return {
    id,
    a: Math.random(),
    b: Math.random(),
    c: Math.random(),
  }
}

function getArray(length) {
  const result = [];
  for (let i = 0; i < length; i++) {
    result.push(getRandomElement(i))
  }
  
  return result;
}

function arrayToMap(array) {
  return new Map(array.map(el => [el.id, el]));
}

function getRandomInt(max) {
  return Math.floor(Math.random() * max);
}

array_small = getArray(3);
array_large = getArray(10);

map_small = arrayToMap(array_small);
map_large = arrayToMap(array_large);

Tests:

Array.find, 10 000 elements
const target = getRandomInt(2); array_small.find(el => el.id === target);
Map.get, 10 000 elements
const target = getRandomInt(2); map_small.get(target);
Array.find, 1 000 000 elements
const target = getRandomInt(9); array_large.find(el => el.id === target);
Map.get, 1 000 000 elements
const target = getRandomInt(9); map_large.get(target)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array.find, 10 000 elements
Map.get, 10 000 elements
Array.find, 1 000 000 elements
Map.get, 1 000 000 elements

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The test measures the performance of two approaches: `Array.find` and `Map.get`, with varying array sizes (3,000 to 1,000,000 elements) and without converting the array to a map.

**Options Compared**

Two options are being compared:

1. **Array.find**: Iterates through an array to find the first element that matches a given condition.
2. **Map.get**: Retrieves a value from a Map (a data structure that stores key-value pairs) using its key.

**Pros and Cons of Each Approach**

**Array.find:**

Pros:

* Can be more efficient for small arrays or when the condition is simple.
* Does not require extra memory to store the array's index-to-element mapping.

Cons:

* Requires iterating through the entire array, which can be slower for large arrays.
* May involve more CPU cycles and memory accesses compared to `Map.get`.

**Map.get:**

Pros:

* Can be faster for large arrays or when the condition is complex, as it directly accesses the indexed value without iteration.
* Reduces CPU cycles and memory accesses since Map data structures provide constant-time lookups.

Cons:

* Requires extra memory to store the array-to-index mapping, which can impact performance in high-memory scenarios.
* May be slower for small arrays or simple conditions due to the overhead of Map operations.

**Library:**

The `arrayToMap` function is used to convert an array to a Map. It's not a built-in JavaScript method; instead, it's a utility function that creates a new Map by mapping each element in the array to its own key-value pair (in this case, the element's ID as the key and the element itself as the value).

**Special JS Feature/Syntax:**

None are explicitly mentioned. However, `Map.get` is a relatively modern feature introduced in ECMAScript 2015 (ES6) for accessing values in Maps.

**Other Considerations:**

* The benchmark uses Chrome 121 on a Mac OS X 10.15.7 device, which might impact the results due to platform-specific factors.
* The `getRandomElement` function is used to generate random elements for the array and Map initialization. This could introduce variability in the benchmark results.

**Alternatives:**

Other approaches that could be considered for large arrays or performance-critical scenarios:

1. **Linear Search**: Instead of using `Array.find`, a linear search algorithm like binary search could be used, which has a faster time complexity (O(log n)) compared to iteration (O(n)).
2. **Sparse Arrays**: For very large datasets where only a subset of elements are accessed frequently, sparse array data structures or optimized storage formats could reduce memory usage and improve performance.
3. **Native Web Workers**: If the benchmark is CPU-bound, using native web workers for parallel processing could offload computations to separate threads, potentially improving overall performance.

Keep in mind that these alternatives may have their own trade-offs and complexities, and not all may be suitable for every use case.

Latest run results:

Run details: (Test run date: 9 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36

Browser/OS: Chrome 121 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Array.find, 10 000 elements	1624539.6 Ops/sec
Map.get, 10 000 elements	1634853.6 Ops/sec
Array.find, 1 000 000 elements	1606226.5 Ops/sec
Map.get, 1 000 000 elements	1720538.9 Ops/sec

Related benchmarks:

Array.find vs. Map.get fork2 (version: 0)

Test the performance of Array.find vs. Map.get, with various array sizes, and with/without conversion from array to map

Comparing performance of: Array.find, 10 000 elements vs Map.get, 10 000 elements vs Array.find, 1 000 000 elements vs Map.get, 1 000 000 elements

Created: 9 months ago by: Guest

Jump to the latest result

Array.find, 10 000 elements

Map.get, 10 000 elements

Array.find, 1 000 000 elements

Map.get, 1 000 000 elements

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):