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(version: 0)
a
Comparing performance of:
for-in vs object.keys
Created:
5 years ago
by:
Guest
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Tests:
for-in
const List = [ { id: '111', Count: 2 }, { id: '222', Count: 2 }, { id: '333', Count: 2 }, { id: '323333', Count: 1 }, { id: '555111', Count: 1 }, { id: '700000', Count: 1 }, { id: '700001', Count: 1 }, { id: '900111', Count: 1 }, { id: '989222', Count: 1 }, { id: '999999', Count: 1 } ]; const target = [{"id":"111","subject":"SAMPLE","comments":10012},{},{"id":"333","subject":"SAMPLE","comments":0},{},{},{},{},{},{},{}]; const xx =[]; for (let i = 0; i < List.length; i+=1){ if('id' in target[i]){ xx.push(Object.assign(target[i],List[i])) } }
object.keys
const List = [ { id: '111', Count: 2 }, { id: '222', Count: 2 }, { id: '333', Count: 2 }, { id: '323333', Count: 1 }, { id: '555111', Count: 1 }, { id: '700000', Count: 1 }, { id: '700001', Count: 1 }, { id: '900111', Count: 1 }, { id: '989222', Count: 1 }, { id: '999999', Count: 1 } ]; const target = [{"id":"111","subject":"SAMPLE","comments":10012},{},{"id":"333","subject":"SAMPLE","comments":0},{},{},{},{},{},{},{}]; const xx =[]; for (let i = 0; i < List.length; i+=1){ if(Object.keys(target[i]).length){ xx.push(Object.assign(target[i],List[i])) } }
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Test case name
Result
for-in
object.keys
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
The provided JSON represents two test cases for measuring the performance of JavaScript microbenchmarks on Measurthat.net. **Overview** Both test cases use an array `List` and an object `target`. The goal is to iterate through the `List` array and create a new object by merging elements from `List` with existing objects in `target`. However, there are differences in how the iteration is performed. **Test Case 1: "for-in"** This test case uses the traditional `for` loop syntax with an index variable `i`, where `i` ranges from 0 to `List.length - 1`. The iteration condition checks if the key `'id'` exists in the current object of `target[i]`. If it does, the object is merged using `Object.assign`. **Test Case 2: "object.keys"** This test case uses the `Object.keys()` method to iterate through the properties of each object in `target`. The condition checks if the length of the resulting array from `Object.keys(target[i])` is greater than 0. If it is, the object is merged using `Object.assign`. **Comparison and Analysis** Both approaches have their pros and cons: * **for-in**: This approach uses a traditional loop and can be more predictable in terms of performance, as the iteration is not dependent on the properties of the objects. + Pros: Easy to understand and implement, potential for better performance due to less overhead from dynamic property checks. + Cons: May have slower performance due to the need to check if the key exists in each object, which can lead to additional iterations. * **object.keys**: This approach uses a more modern and concise syntax to iterate through properties. It's also more flexible, as it doesn't require checking for existing keys explicitly. + Pros: More concise and readable, less overhead from property checks, and potentially faster performance due to fewer iterations. + Cons: May have slower performance if the objects are not sparse (i.e., all properties exist), or if the length of `Object.keys(target[i])` is very large. **Library Usage** Neither test case uses an external library explicitly. However, it's worth noting that the use of `Object.assign()` and the fact that the objects being iterated through have keys with specific names (e.g., `'id'`) might be considered edge cases or conventions rather than using a specific library. **Special JS Features** There are no special JavaScript features or syntax used in these test cases beyond what's described above. **Other Alternatives** If you wanted to improve the performance of these tests, you could consider: * Using `for...of` loop instead of traditional `for` loops for iteration. * Utilizing a `Map` data structure for fast property lookups. * Implementing a more efficient merge function using techniques like object spreading or merging strategies. * Optimizing the array and object sizes to reduce overhead. Keep in mind that the performance differences between these approaches will likely be negligible unless you're working with extremely large datasets.
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