Benchmark: Performance of delete on large arrays

Script Preparation code:

var N0 = 76300;

Tests:

76300
const N = N0; const o = {}; for ( let i = 0; i < N; i++ ) { o[i] = i; } for ( let i = 0; i < N; i++ ) { delete o[i]; }
76301
const N = N0 + 1; const o = {}; for ( let i = 0; i < N; i++ ) { o[i] = i; } for ( let i = 0; i < N; i++ ) { delete o[i]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
76300
76301

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'd be happy to explain the provided JSON data and the benchmark.

**Benchmark Definition**

The provided JSON data represents a JavaScript microbenchmark for measuring the performance of deleting elements from an array. The benchmark consists of two test cases:

1. **Test Case 1**: This test case creates an array `o` with a size of `N0` (which is set to 76300 in the script preparation code) and then deletes each element one by one using the `delete` keyword.
2. **Test Case 2**: This test case creates an array `o` with a size of `N0 + 1` and then deletes each element one by one using the `delete` keyword.

**Options Compared**

The two test cases are designed to compare the performance of deleting elements from an array in different scenarios:

* **Direct Access**: In Test Case 1, we directly access the array element using the `o[i]` syntax and delete it using `delete o[i];`. This approach assumes that the browser can optimize direct access to the array.
* **Indirect Access**: In Test Case 2, we create a proxy object `o` with a size of `N0 + 1`, but don't use it. Then, we directly access each element using the `o[i]` syntax and delete it using `delete o[i];`. This approach assumes that the browser can optimize indirect access to the array.

**Pros and Cons**

* **Direct Access**: Pros:
	+ May be faster since direct access is less computationally expensive.
	+ Can take advantage of browser optimizations for direct array access.
* Cons:
	+ May not work correctly in all scenarios, especially when dealing with large arrays or complex data structures.
* **Indirect Access**: Pros:
	+ May be more robust and reliable, as it avoids potential issues with direct access.
	+ Can provide a better representation of real-world usage scenarios where indirect access is necessary.
* Cons:
	+ May be slower due to the indirect access pattern.
	+ Requires the browser to optimize indirect access, which may not always be possible.

**Library Usage**

None of the test cases use any external libraries. The benchmark only relies on built-in JavaScript features and the `delete` keyword.

**Special JS Features/Syntax**

There are no special JS features or syntax used in these test cases. However, it's worth noting that the use of `const` and `let` variables may affect the behavior of the benchmark depending on the browser version and platform.

**Other Alternatives**

Some alternative approaches to measure the performance of deleting elements from an array could include:

* Using a custom implementation with a different data structure (e.g., linked lists, trees)
* Adding noise or variations to the input data to simulate real-world scenarios
* Comparing performance across multiple browsers and platforms
* Measuring the performance of alternative deletion methods (e.g., `splice()`, `indexOf()` followed by `shift()`)

Keep in mind that the specific approach used will depend on the goals and requirements of the benchmark.

Latest run results:

Run details: (Test run date: 7 years ago)

User agent: Mozilla/5.0 (Linux; Android 6.0.1; Nexus 5 Build/M4B30Z) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.132 Mobile Safari/537.36

Browser/OS: Chrome Mobile 57 on Android 6.0.1

View result in a separate tab

Test name	Executions per second
76300	32.7 Ops/sec
76301	33.8 Ops/sec

Related benchmarks:

Performance of delete on large arrays (version: 0)

Comparing performance of: 76300 vs 76301

Created: 7 years ago by: Guest

Jump to the latest result

76300

76301

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):