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for-noop vs flatMap
(version: 0)
Comparing performance of: for-noop vs flatMap
Comparing performance of:
for-noop vs flatMap
Created:
3 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var arr = Array.from({length: 10_000}, () => Math.floor(Math.random() * 2));
Tests:
for-noop
for (var i = 0; i < arr.length; i++) {}
flatMap
arr.flatMap((x) => (x !== 0 ? [x] : []));
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
for-noop
flatMap
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
9 months ago
)
User agent:
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.5 Safari/605.1.15
Browser/OS:
Safari 18 on Mac OS X 10.15.7
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Embed Benchmark Result
Test name
Executions per second
for-noop
336701.9 Ops/sec
flatMap
6117.2 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the explanation of the provided benchmark. **Benchmark Overview** The benchmark is designed to compare the performance of two JavaScript constructs: `for-noop` and `flatMap`. The goal is to determine which approach provides better performance for iterating over an array. **What is being tested?** In this benchmark, two test cases are compared: 1. **For-noop**: This test case uses a traditional `for` loop to iterate over the array. 2. **FlatMap**: This test case uses the `flatMap()` method, which flattens an array of arrays into a single array. **Options Compared** The benchmark compares two options: * Traditional `for` loop (``` for (var i = 0; i < arr.length; i++) {} ``` * `flatMap()` method (``` arr.flatMap((x) => (x !== 0 ? [x] : [])); ``` **Pros and Cons** Here's a brief analysis of each approach: ### For-Noop Loop **Pros:** * More traditional and familiar syntax for many developers. * Less overhead due to method calls. **Cons:** * May have more overhead due to the need to increment the loop counter (`i`). * Can be slower due to the extra operation. ### FlatMap Method **Pros:** * More concise and expressive syntax, which can lead to better code readability. * Reduces overhead by using a built-in method with native implementation. **Cons:** * May have additional overhead due to the need to create a new array and perform type checks. * Less familiar syntax for some developers. **Other Considerations** When evaluating performance, it's essential to consider other factors beyond just the loop or method used: * **Array Size**: The size of the array being iterated over can significantly impact performance. In this benchmark, the array has 10,000 elements. * **Browser and Device**: The browser version, device platform, and operating system can affect performance due to differences in JavaScript engine implementation. **Library Used** The `flatMap()` method is a built-in JavaScript method that uses the `Array.prototype.flat()` method with an optional `depth` parameter set to 1. This means it performs a depth-first traversal of the array and returns a new array with flattened elements. **Special JS Feature or Syntax** There are no special features or syntaxes mentioned in this benchmark, but some modern JavaScript versions (ECMAScript 2019 and later) introduce additional features that can affect performance: * **Arrow functions**: Used in both `for-noop` and `flatMap` test cases. * **Template literals**: Not used in this benchmark. **Alternatives** If you're interested in exploring alternative approaches, here are a few options: * **Iterators**: Using iterators can provide more flexibility and performance for large datasets. * **Closures**: Closures can be used to create custom iteration mechanisms with better control over performance. * **Native Web APIs**: For specific use cases, using native web APIs like `WebAssembly` or `Canvas` might be a good alternative. For this benchmark, the focus is on comparing traditional loops with modern array methods. However, understanding other alternatives can help you make informed decisions when working with JavaScript and optimizing performance in your projects.
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