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Spread operator
(version: 0)
Comparing performance of:
Spread operator vs Property access assignment
Created:
3 years ago
by:
Guest
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Tests:
Spread operator
const obj = { foo: 'foo', bar: 'bar' } const test = { ...obj }
Property access assignment
const obj = { foo: 'foo', bar: 'bar' } const test = { foo: obj.foo, bar: obj.bar }
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
Spread operator
Property access assignment
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 benchmark definition, test cases, and latest benchmark results to explain what's being tested and the pros/cons of different approaches. **Benchmark Definition** The provided JSON represents a benchmark definition for a JavaScript microbenchmark. It consists of: * `Name`: The name of the benchmark. * `Description`: An empty description field, which is not used in this case. * `Script Preparation Code` and `Html Preparation Code`: These fields are left blank, indicating that no custom script or HTML code needs to be executed before running the benchmark. **Individual Test Cases** There are two test cases: 1. **Spread Operator**: The benchmark definition uses the spread operator (`...`) to create a copy of an object. ```javascript const obj = {\r\n foo: 'foo',\r\n bar: 'bar'\r\n}\r\n\r\nconst test = { ...obj } ``` This code creates a shallow copy of the `obj` object using the spread operator. The benchmark is likely measuring the performance of creating and manipulating this copy. 2. **Property Access Assignment**: The second benchmark definition uses property access assignment to create a new object with some properties taken from another object. ```javascript const obj = {\r\n foo: 'foo',\r\n bar: 'bar'\r\n}\r\n\r\nconst test = {\r\n foo: obj.foo,\r\n bar: obj.bar\r\n} ``` This code creates a new object `test` with some properties (`foo` and `bar`) taken directly from the `obj` object. The benchmark is likely measuring the performance of this assignment. **Latest Benchmark Results** The latest benchmark results show two test cases, each with different browsers and devices: 1. **Property Access Assignment**: Chrome Mobile 96 on Android, with an execution rate of 80,251,248 executions per second. 2. **Spread Operator**: Chrome Mobile 96 on Android, with an execution rate of 102,693,395 executions per second. **Pros/Cons Comparison** The two approaches have different pros and cons: * **Property Access Assignment**: + Pros: Simple and efficient way to create a new object with some properties from another object. + Cons: May not be suitable for complex objects or large datasets, as it relies on direct property access which can lead to performance issues due to cache effects. * **Spread Operator**: + Pros: Can handle more complex objects and large datasets by creating a shallow copy of the original object. + Cons: Requires an additional allocation and copying of data, which may introduce overhead. **Library and Special JavaScript Features** There are no libraries mentioned in the benchmark definition. However, some JavaScript features like `const` and arrow functions are used in the test cases. * **Const**: The `const` keyword is used to declare variables that cannot be reassigned. This helps ensure the code's predictability and avoids unnecessary recompilation. * **Arrow Functions**: Arrow functions are used as short and concise ways to define small functions. In this case, they're not crucial for the benchmark but demonstrate modern JavaScript syntax. **Alternatives** If you were to reimplement these benchmarks in another environment, consider the following alternatives: 1. Use `Object.assign()` instead of spread operators or property access assignment. 2. Measure performance using other methods, such as profiling tools or benchmarking libraries (e.g., Benchmark.js). 3. Modify the test cases to use different JavaScript features, such as async/await or classes. Keep in mind that these alternatives would likely change the focus and scope of the benchmarks, so it's essential to adapt the design accordingly.
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