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Spread Operator VS Array.prototype.slice() VS Array.prototype.map()
(version: 0)
Spread Operator VS Array.prototype.slice() VS Array.prototype.map()
Comparing performance of:
Spread Operator vs Array.prototype.slice() vs Array.prototype.map()
Created:
2 years ago
by:
Registered User
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Script Preparation code:
var randomNumbers = []; for (let i = 0; i < 2000; i++) { randomNumbers.push(Math.random()); }
Tests:
Spread Operator
const spreadOperator = [...randomNumbers];
Array.prototype.slice()
const slice = randomNumbers.slice();
Array.prototype.map()
const map = randomNumbers.map(num=>num);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
Previous results
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Test case name
Result
Spread Operator
Array.prototype.slice()
Array.prototype.map()
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
2 years ago
)
User agent:
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36
Browser/OS:
Chrome 120 on Windows
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Embed Benchmark Result
Test name
Executions per second
Spread Operator
1680759.9 Ops/sec
Array.prototype.slice()
1725002.2 Ops/sec
Array.prototype.map()
295365.9 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided JSON and benchmark preparation code to understand what is being tested. **Benchmark Definition** The `Spread Operator VS Array.prototype.slice() VS Array.prototype.map()` benchmark tests three different approaches to create a shallow copy of an array: 1. **Spread Operator**: The spread operator (`...`) creates a new array by copying elements from the original array. 2. **Array.prototype.slice()**: The `slice()` method returns a shallow copy of a portion of an array, starting from the beginning and ending at the specified index. 3. **Array.prototype.map()**: The `map()` method applies a provided function to each element in the array and returns a new array with the results. **Pros and Cons** * **Spread Operator**: Pros: + Concise and readable syntax + Fast performance (since it's a native operator) Cons: + Can be slower for very large arrays due to JavaScript's garbage collection overhead * **Array.prototype.slice()**: Pros: + Efficient and optimized for large datasets Cons: + Less readable syntax than the spread operator or `map()` + May have performance issues if not implemented correctly (e.g., using `slice()` with a negative index) * **Array.prototype.map()**: Pros: + More expressive syntax than `slice()`, making it easier to understand and maintain Cons: + Can be slower for very large arrays due to the overhead of applying the provided function to each element **Library Usage** None of the test cases use any external libraries, so there are no additional dependencies to consider. **Special JS Features or Syntax** * **Spread Operator**: Introduced in ECMAScript 2018 (ES2018), the spread operator is a new syntax for creating arrays from iterables. It's supported by most modern browsers and JavaScript engines. * **Array.prototype.slice()**: A standard method of the Array prototype, which has been available since ECMAScript 5 (ES5). * **Array.prototype.map()**: Also a standard method of the Array prototype, introduced in ECMAScript 5 (ES5). **Other Alternatives** For creating shallow copies of arrays, other approaches include: * Using `Array.from()` with an iterable argument * Using `Object.assign()` with an array as the first argument * Using `Lodash` or other utility libraries that provide a `cloneDeep()` function for deep copying arrays Keep in mind that the choice of approach often depends on specific use cases, performance requirements, and personal coding style. **Benchmark Considerations** When interpreting the benchmark results: * Look at the raw execution count per second (e.g., 1725002.25) to understand which method performs best for a given number of executions. * Compare the results across different browsers or devices to see if there are any platform-specific differences in performance. * Check if the benchmark is running on a stable, repeatable environment to ensure accurate results. These considerations help you make informed decisions about choosing the right approach for your specific use cases.
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