Benchmark: Object lookup vs array lookup

Script Preparation code:

var items = Array.from(Array(1000), (_,x) => x + '');

var objContainer = {};
var arrContainer = [];

for (let i = 100; i >= 0; i--) {
  const index = Math.floor(Math.random() * 1000) + '';
  const item = items[index];
  objContainer[item] = item;
  arrContainer.push(item)
}

Tests:

Object access
items.forEach(item => objContainer[item])
Array find
items.forEach(item => arrContainer[arrContainer.indexOf(item)])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object access
Array find

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 months ago)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:128.0) Gecko/20100101 Firefox/128.0

Browser/OS: Firefox 128 on Linux

View result in a separate tab

Test name	Executions per second
Object access	100276.9 Ops/sec
Array find	1430.1 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what's being tested, compared, and some pros/cons of each approach.

**Benchmark Definition**

The benchmark is testing the speed of two different approaches:

1. **Object Access**: This method uses the `forEach` loop to iterate over an array (`items`) and access elements in an object container (`objContainer`) by key.
2. **Array Find**: This method uses the `forEach` loop to iterate over the same array (`items`) and find an element in another array container (`arrContainer`) using the `indexOf` method.

**Comparison**

The two approaches are compared on a single test case, where 1000 elements are generated randomly and stored in both arrays. The benchmark measures the time it takes to execute each loop.

**Pros/Cons of Each Approach:**

1. **Object Access**: This approach is likely faster because:
	* Objects are hash-based, allowing for O(1) lookups on average.
	* Arrays, on the other hand, have a linear search algorithm (O(n)) that scales poorly with large datasets.
However, this approach requires accessing an object by key using the bracket notation (`objContainer[item]`), which might incur additional overhead due to string concatenation and lookup in the object's internal hash table.
2. **Array Find**: This approach is likely slower because:
	* Arrays have a linear search algorithm (O(n)) that scales poorly with large datasets.
	* The `indexOf` method has to iterate through the array until finding the element, which can be slow for large arrays.

**Library and Special JS Features**

There are no libraries mentioned in the benchmark definition. However, some general notes:

* No special JavaScript features or syntax (e.g., async/await, Promises, etc.) are used in this benchmark.
* The `Array.from` method is used to generate an array of 1000 elements, which is a modern JavaScript feature.

**Alternative Approaches**

Some alternative approaches could be:

1. **Using a data structure specifically designed for fast lookup**: Instead of using arrays and objects, a data structure like a hash table or a trie could be used to store the data, allowing for faster lookups.
2. **Using caching mechanisms**: Implementing caching mechanisms, such as memoization or LRU cache, can improve performance by reducing the number of iterations required to find an element in the array.
3. **Parallelizing the loops**: If multiple CPU cores are available, parallelizing the loops using techniques like parallel iteration or concurrent programming could lead to significant performance improvements.

Keep in mind that these alternative approaches would likely require additional implementation and might not be suitable for all use cases.

Related benchmarks:

Object lookup vs array lookup (version: 0)

Test speed of object access by key vs array find to find object

Comparing performance of: Object access vs Array find

Created: one year ago by: Guest

Jump to the latest result

Object access

Array find

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):