Benchmark: filter vs object, massive data

Script Preparation code:

value = new Array(1000);
objTest = {};
arrTest = [];
arr = [...value];
arr.forEach((value, idx) => {
   objTest[idx] = idx;
})
arr.forEach((value, idx) => {
   arrTest.push(idx)
})

Tests:

filter
arrTest.filter(i => i === 990);
obj
objTest[990]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter
obj

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36

Browser/OS: Chrome 81 on Windows

View result in a separate tab

Test name	Executions per second
filter	253787.7 Ops/sec
obj	3512142.2 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll explain what's tested in the provided JSON, comparing different approaches and discussing their pros and cons.

The benchmark measures the performance of two different ways to access an element in an array: using the `objTest[990]` syntax (direct indexing) versus using the `arr.filter(i => i === 990)` syntax (filtering). The test case uses a JavaScript library called Lodash, which is not explicitly mentioned in the JSON.

**Direct Indexing (`objTest[990]`)**

This approach directly accesses an element in the object `objTest` at index 990. It's a simple and straightforward way to access an element by its index.

Pros:

* Simple and easy to understand
* Fast, as it only requires accessing an element in the object

Cons:

* Limited to accessing elements by their index, not by any other criteria
* Can be slow if the object is very large or has many properties

**Filtering (`arr.filter(i => i === 990)`)**

This approach uses the `filter()` method to create a new array containing only the elements that match the condition (in this case, `i === 990`). It then returns an iterator over these matching elements.

Pros:

* Flexible and can be used to access elements based on any criteria
* Can be faster than direct indexing if the object is very large or has many properties

Cons:

* Requires creating a new array and iterating over it, which can be slower than direct indexing
* More complex than direct indexing

Now, let's consider the specific implementation of the benchmark. The script preparation code creates two arrays: `arrTest` and `arr`. `arrTest` is initialized with some values, while `arr` is created by copying the elements from `value`, an array of 1000 numbers.

The script preparation code also creates objects `objTest` and `arrTest`, where `objTest` has a property at index 990 (which happens to be equal to 990).

**Other considerations**

* The benchmark uses Chrome 81, which is a relatively recent version. This may affect the results, as newer versions of browsers may have optimized performance for certain operations.
* The benchmark runs on a desktop platform, which may influence the results compared to running on mobile devices or other platforms.

**Alternative approaches**

Other ways to access elements in an array include:

* `arr[990]`: Direct indexing, similar to `objTest[990]`
* `arr.map(i => i === 990)`: Using the `map()` method to create a new array containing only the matching elements ( slower than filtering)
* `arr.indexOf(990)`: Using the `indexOf()` method to find the index of the first occurrence of the specified value (slower than direct indexing or filtering)

These alternative approaches may have different performance characteristics and are not necessarily better or worse than the two approaches tested in this benchmark.

Related benchmarks:

filter vs object, massive data (version: 0)

Comparing performance of: filter vs obj

Created: 4 years ago by: Guest

Jump to the latest result

filter

obj

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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