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);

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

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

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/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

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

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:

Array.find: Iterates through an array to find the first element that matches a given condition.
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:

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)).
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.
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.

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.

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: one year 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 6 months ago):