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example-7
(version: 6)
Comparing performance of:
multiple loops vs single loop
Created:
6 years ago
by:
Registered User
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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>
Run tests (2)
Previous results
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Test case name
Result
multiple loops
single loop
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
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.
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