Benchmark: Current vs suggestions

Script Preparation code:

var teams = [];
var filterTeams = [];


for (var i = 0; i < 1000; i++) {
  var teamUsers = [];
  for (var j = 0; j < 1000; j++) {
    Math.random() > 0.5 && teamUsers.push({userId: j});
  }
  teams.push({
    teamUsers: teamUsers
  });
  
  Math.random() > 0.5 && filterTeams.push(i);
}

var userId = 999;

Tests:

Current
var tempResult = teams .filter((team) => filterTeams.some((teamId) => teamId === team.id)) .some((team) => team.teamUsers.some((teamUser) => teamUser.userId === userId));
Optimize option 1
var tempResult = teams .filter((team) => filterTeams.includes(team.id)) .some((team) => team.teamUsers.some((teamUser) => teamUser.userId === userId));
Optimize option 2
var tempResult = teams .filter((team) => ~filterTeams.indexOf(team.id)) .some((team) => team.teamUsers.some((teamUser) => teamUser.userId === userId));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Current
Optimize option 1
Optimize option 2

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark definition represents a JavaScript microbenchmark that tests the performance of three different approaches to filter teams based on some IDs. The script preparation code generates an array of 1000 teams, where each team has another array of 1000 users. Then, it randomly filters out either 500 or all teams based on their ID.

**Script Preparation Code**

```javascript
for (var i = 0; i < 1000; i++) {
  var teamUsers = [];
  for (var j = 0; j < 1000; j++) {
    Math.random() > 0.5 && teamUsers.push({userId: j});
  }
  teams.push({
    teamUsers: teamUsers
  });
  
  Math.random() > 0.5 && filterTeams.push(i);
}
```

**Html Preparation Code**

There is no HTML preparation code provided, which means the benchmark does not depend on any external HTML elements or structures.

**Individual Test Cases**

The benchmark consists of three test cases:

1. **Current**: This test case uses the `some()` method with a callback function to filter out teams based on their ID.
2. **Optimize option 1**: This test case uses the `includes()` method to check if a team's ID is present in the `filterTeams` array.
3. **Optimize option 2**: This test case uses bitwise NOT (`~`) with the `indexOf()` method to filter out teams based on their ID.

**Libraries and Features Used**

* None

**Pro and Cons of Each Approach**

1. **Current (some())**
	* Pros:
		+ Easy to read and understand.
		+ Works well for small arrays.
	* Cons:
		+ May be slower than other approaches for large arrays.
		+ Requires a callback function, which can add overhead.
2. **Optimize option 1 (includes())**
	* Pros:
		+ Fast lookup time for `filterTeams` array.
		+ Can be faster than `some()` for large arrays.
	* Cons:
		+ May not work as expected if `filterTeams` is not an array.
3. **Optimize option 2 (~indexOf())**
	* Pros:
		+ Similar to `includes()`, but can be faster due to bitwise NOT optimization.
		+ Can be used for arrays of any type, not just strings or numbers.
	* Cons:
		+ Requires understanding of bitwise operations.
		+ May not work as expected if `filterTeams` is an empty array.

**Other Considerations**

* The benchmark does not take into account the size of the teams array. This might affect the performance differences between the approaches.
* The use of `Math.random()` to generate random IDs can introduce variability in the results, but this is likely intended for performance testing purposes only.
* There are no error handling or input validation mechanisms in place, which could lead to issues if the input data is malformed.

**Alternatives**

Other alternatives for filtering teams based on their ID might include:

* Using a `Set` data structure instead of an array for `filterTeams`.
* Using a more advanced filtering algorithm, such as a Bloom filter.
* Implementing a custom filtering function using a different programming paradigm (e.g., functional programming).

Keep in mind that these alternatives are not necessarily better or worse than the approaches tested in this benchmark. The choice of approach often depends on the specific requirements and constraints of the project.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:106.0) Gecko/20100101 Firefox/106.0

Browser/OS: Firefox 106 on Ubuntu

View result in a separate tab

Test name	Executions per second
Current	125.8 Ops/sec
Optimize option 1	877.5 Ops/sec
Optimize option 2	7847.4 Ops/sec

Related benchmarks:

Current vs suggestions (version: 0)

Comparing performance of: Current vs Optimize option 1 vs Optimize option 2

Created: 2 years ago by: Guest

Jump to the latest result

Current

Optimize option 1

Optimize option 2

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):