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checking maping vs mapingfiltering
(version: 0)
Comparing performance of:
mapping + filtering vs filtering
Created:
one year ago
by:
Guest
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Script Preparation code:
var table = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]; var ids = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15"];
Tests:
mapping + filtering
ids.map(Number).forEach((id) => { const areEqual = table[0] === id; });
filtering
ids.forEach((id) => { const areEqual = table[0] === Number(id); });
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
mapping + filtering
filtering
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year ago
)
User agent:
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36
Browser/OS:
Chrome 127 on Mac OS X 10.15.7
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Test name
Executions per second
mapping + filtering
506196.9 Ops/sec
filtering
226033.7 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
**Benchmark Explanation** The provided JSON represents a JavaScript benchmark test created using MeasureThat.net. The test aims to compare the performance of two approaches: mapping and filtering. **Options Compared** There are two options being compared: 1. **Mapping**: This approach uses the `map()` method to create a new array with the same length as the original array, but with transformed elements. In this case, it's used to convert the `ids` string array to a numeric array using `Number()`. 2. **Filtering**: This approach uses the `forEach()` method to iterate over the `ids` array and compare each element to the first element of the `table` array. **Pros and Cons** * **Mapping**: Pros: + More concise and expressive code. + Less prone to errors, as it avoids manual iteration. * Cons: + Creates a new array, which can be memory-intensive for large datasets. + May not be suitable for very large datasets due to performance constraints. * **Filtering**: Pros: + Does not create a new array, which can be beneficial for memory-constrained scenarios. + Can be more flexible, as it allows for manual iteration and logic control. * Cons: + More verbose code. + Prone to errors, as manual iteration requires careful attention. **Library: Lodash** The `map()` method is part of the JavaScript standard library. However, if you're looking at the benchmark definition JSON provided, it's likely that this test uses a version of JavaScript that doesn't support the `map()` method natively. In such cases, you might be using a library like Lodash to provide the `map()` function. **Special JS Feature/Syntax: None** There are no special JS features or syntax being used in this benchmark. **Benchmark Preparation Code Explanation** The script preparation code creates two arrays: * `table`: an array of numbers from 1 to 15, repeated twice. * `ids`: an array of strings representing the same numbers as `table`, but with quotes around each value. This setup allows for easy comparison between the mapping and filtering approaches. **Alternative Approaches** Other alternatives could be explored: 1. **Using Array.prototype.reduce()**: Instead of using `map()` or `forEach()`, you could use `reduce()` to iterate over the `ids` array. 2. **Using a library like Lodash's 'filter' function**: As mentioned earlier, Lodash provides a `filter()` function that can be used instead of `forEach()` for filtering arrays. 3. **Using a custom iteration loop**: If you're looking for extreme optimization or memory efficiency, you could consider implementing your own custom iteration loop using a `for` loop or another iteration method. Keep in mind that each approach has its trade-offs, and the choice ultimately depends on your specific requirements and performance constraints.
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