Benchmark: example-7 - MeasureThat.net

Script Preparation code:

var records = [];
for (var i = 0; i < 1000; i++) {
  records.push({
    'class': 'Group',
    'groupGUID': `${i}-${i}`,
    'id': i,
  });
  records.push({
    'class': 'test',
  });
}

Tests:

multiple loops
var groupsMapping = records .filter(record => record.class === 'Group') .reduce((collector, record) => { collector[record.groupGUID] = record.id; return collector; }, {});
single loop
var groupsMapping = {}; records .forEach(record => { if (record.class === 'Group') { groupsMapping[record.groupGUID] = record.id; } });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
multiple loops
single loop

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark JSON and explain what's being tested, compared options, pros and cons of each approach, and other considerations.

**Benchmark Definition:**

The benchmark definition provides a JavaScript script that prepares data for testing. In this case:

* The `records` array is populated with 1000 objects, each containing an ID and two class names (`'Group'` and `'test'`). The `groupGUID` property is generated by concatenating the `i` index with itself (e.g., `0-0`, `1-1`, etc.).
* Two scripts are provided for preparing data:
	+ The first script uses a loop to iterate through the records array, pushing objects onto the `records` array and then using `filter()` and `reduce()` methods to create an object (`groupsMapping`) that maps group GUIDs to IDs.
	+ The second script also uses a loop to iterate through the records array but only checks for class `'Group'`, directly assigning group GUID-IDs mappings into the `groupsMapping` object.

**Individual Test Cases:**

The benchmark tests two different approaches:

1. **Multiple Loops (Test Case 1)**:
	* The script prepares data using a single loop and then uses `filter()` and `reduce()` to create an object (`groupsMapping`) that maps group GUIDs to IDs.
	* This approach may be slower due to the overhead of filtering and reducing an array, especially for larger inputs.
2. **Single Loop (Test Case 2)**:
	* The script prepares data using a single loop and then directly assigns group GUID-IDs mappings into the `groupsMapping` object without filtering or reducing.
	* This approach may be faster since it avoids the overhead of filtering and reducing an array.

**Pros and Cons:**

* **Multiple Loops (Test Case 1)**:
	+ Pros: Easy to understand and implement, can handle larger inputs.
	+ Cons: May be slower due to filtering and reduction overhead.
* **Single Loop (Test Case 2)**:
	+ Pros: Potential for faster execution, reduced filtering/reduction overhead.
	+ Cons: More complex implementation, may not handle larger inputs as well.

**Other Considerations:**

* Both test cases use the `filter()` method, which can be slow for large arrays. Using a more efficient data structure or algorithm might improve performance.
* The `reduce()` method is used in both test cases, but its performance can vary depending on the size of the array and the complexity of the accumulator function.

**Libraries Used:**

The benchmark uses built-in JavaScript methods like `filter()`, `reduce()`, and `forEach()`. No external libraries are required.

**Special JS Features/Syntax:**

None mentioned in the provided code. However, it's worth noting that modern browsers support various features like async/await, generators, and arrow functions, which can impact performance. The benchmark doesn't explicitly test for these features, but their presence might affect the execution results.

**Alternatives:**

If you're interested in exploring alternative approaches or optimizing the existing tests, consider the following:

* Using a different data structure (e.g., an object instead of an array) to reduce filtering/reduction overhead.
* Implementing a custom loop that avoids filtering and reduction altogether.
* Investigating more efficient algorithms for mapping group GUIDs to IDs (e.g., using a hash table or a trie).
* Adding additional test cases to cover different scenarios, such as:
	+ Handling edge cases (e.g., empty arrays, null values).
	+ Testing performance with varying input sizes.
	+ Exploring the impact of parallelization on execution time.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:71.0) Gecko/20100101 Firefox/71.0

Browser/OS: Firefox 71 on Ubuntu

View result in a separate tab

Test name	Executions per second
multiple loops	3213.8 Ops/sec
single loop	3587.1 Ops/sec

Related benchmarks:

example-7 (version: 6)

Comparing performance of: multiple loops vs single loop

Created: 4 years ago by: Registered User

Jump to the latest result

multiple loops

single loop

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):