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getTime
(version: 0)
Comparing performance of:
Sort first vs Map first
Created:
5 years ago
by:
Guest
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Tests:
Sort first
const sample = {date: new Date(), value:1}; const usage = Array(10).fill(sample); usage.sort((a, b) => a.date.getTime() - b.date.getTime()).map(({ date, value }) => [date.getTime(), value])
Map first
const sample = {date: new Date(), value:1}; const usage = Array(10).fill(sample); usage.map(({ date, value }) => [date.getTime(), value]).sort((a, b) => a[0] - b[0])
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
Sort first
Map first
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided JSON and explain what's being tested, compared, and analyzed in the benchmark. **Benchmark Definition** The benchmark definition is empty, which means that the script preparation code and HTML preparation code are not specified. This could be due to various reasons such as: * The script is very simple and doesn't require any setup or initialization. * The script is already executed before running the test case. * The script is too long and complex to be included in the benchmark definition. **Individual Test Cases** The benchmark consists of two test cases: "Sort first" and "Map first". Both test cases are designed to measure the performance of sorting a large array of objects based on a specific property. **Test Case 1: Sort First** In this test case, the `sort` method is called on the `usage` array before mapping over it. The `sort` method takes a compare function as an argument, which defines the sort order. In this case, the compare function is `(a, b) => a.date.getTime() - b.date.getTime()`, which compares the timestamps of the dates in ascending order. **Test Case 2: Map First** In this test case, the `map` method is called on the `usage` array before sorting it. The `map` method returns a new array with the results of applying the provided function to each element in the original array. In this case, the function is `{ ({ date, value }) => [date.getTime(), value] }`, which extracts the timestamp and value from each object in the array. **Comparison** The two test cases are designed to compare the performance of sorting and mapping arrays in different orders: first by sorting and then by mapping, or first by mapping and then by sorting. This allows users to see how the order of operations affects the performance of their code. **Pros and Cons** * **Sorting First**: + Pros: Can be more efficient if the data is already partially sorted or has a specific structure. + Cons: May require additional memory allocation and copying of elements in case of nested arrays or objects. * **Mapping First**: + Pros: Can reduce the number of iterations required for sorting, especially when dealing with large datasets. + Cons: May not be suitable for all use cases, as it may change the data structure of the array. **Other Considerations** * The benchmark uses the `getTime` method to compare dates, which is a relatively fast way to compare timestamps. However, this method is not universally supported by all browsers or platforms. * Both test cases use the same JavaScript engine and browser version to ensure consistent results across different environments. **Library and Special JS Features** There are no libraries mentioned in the benchmark definition. However, it's worth noting that some modern JavaScript features like async/await, decorators, and classes may not be supported by all browsers or platforms. **Alternatives** If you want to create a similar benchmark for other programming languages or frameworks, here are some alternatives: * Node.js: You can use the `benchmark` library to create a benchmarking framework. * Python: You can use the `timeit` module to measure execution times of small code snippets. * Java: You can use the `Benchmark` class in the Java Standard Edition (SE) API to create benchmarks. These alternatives may not provide exactly the same level of customization and control as MeasureThat.net, but they can still be useful for measuring performance and identifying bottlenecks in your code.
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