Benchmark: test obj vs arr - MeasureThat.net

Script Preparation code:

var supported = ['AValue', 'BValue', 'CValue']
var supportedObj = {
  'AValue': true,
  'BValue': true,
"CValue": true
}
var data = []
for (let i = 0; i < 300; i++) {
  data.push('AValue')
  data.push('BValue')
  data.push('CValue')
  data.push('DValue')
}

Tests:

obj
const filtered = data.filter(item => supportedObj[item])
arr
const filtered = data.filter(item => supported.includes(item))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
obj
arr

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain the provided benchmark.

**Benchmark Overview**

The benchmark is designed to compare the performance of using an object (`supportedObj`) versus an array (`supported`) to filter data in JavaScript. The test creates an array `data` with 300 elements, each being one of four values: 'AValue', 'BValue', 'CValue', or 'DValue'. It then uses two different approaches to filter the data:

1. **Object-based filtering**: The benchmark definition includes a script that creates an object `supportedObj` with the same keys as in the array, and uses this object to filter the data.
2. **Array-based filtering**: The second benchmark definition includes a script that creates an array `supported` with the same values as in the original array, and uses this array to filter the data.

**Options Compared**

The two options being compared are:

1. **Object-based filtering (`supportedObj`)**
	* Pros:
		+ Can be more efficient when working with large datasets, as object lookups are generally faster than array lookups.
		+ Allows for more flexibility in terms of data structure and indexing.
	* Cons:
		+ Requires creating an additional object to store the filtering criteria, which can add overhead.
		+ May not perform well if the filtering criteria is dynamic or complex.
2. **Array-based filtering (`supported`)**
	* Pros:
		+ Does not require creating additional data structures, making it simpler and more lightweight.
		+ Can be faster when working with large datasets, as array lookups are optimized for performance.
	* Cons:
		+ May perform slower than object-based filtering when the filtering criteria is complex or dynamic.
		+ Requires creating an additional array to store the filtering criteria.

**Library Usage**

There is no explicit library usage in this benchmark. However, it's worth noting that some JavaScript engines may have built-in optimizations for specific data structures or operations (e.g., array lookups), which could affect the performance of these options.

**Special JS Features/Syntax**

The benchmark does not use any special JavaScript features or syntax that would require additional explanation. It only uses standard JavaScript language features and data structures.

**Other Alternatives**

In addition to the two options being compared, other alternatives for filtering data in JavaScript include:

1. **Using `Array.prototype.includes()`**: This method is similar to array-based filtering but can be slower due to its complexity.
2. **Using `Array.prototype.findIndex()`**: This method finds the first index of a value in an array and returns it; it's often used in conjunction with other methods to filter data.
3. **Using a `Map` or `Set`**: These data structures can be used for filtering data, especially when working with unique values or complex criteria.

In summary, this benchmark compares the performance of object-based versus array-based filtering in JavaScript. The choice between these options depends on the specific use case and requirements, as well as considerations such as data structure complexity and overhead.

Related benchmarks:

test obj vs arr (version: 0)

Comparing performance of: obj vs arr

Created: 3 years ago by: Guest

Jump to the latest result

obj

arr

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):