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Lodash inRange vs JS inRange 2
(version: 0)
Comparing performance of:
Lodash inRange vs JS inRange
Created:
5 years ago
by:
Guest
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HTML Preparation code:
<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>
Script Preparation code:
var inRange = function inRangeFunc(num, start, end) { return num >= start && num < end; };
Tests:
Lodash inRange
_.inRange(5, 0, 50); _.inRange(500, 0, 50); _.inRange(500, 0, 500); _.inRange(445, 0, 450); _.inRange(25, 0, 150); _.inRange(0, 0, 5); _.inRange(1, 0, 1); _.inRange(2, 0, 2);
JS inRange
inRange(5, 0, 50); inRange(500, 0, 50); inRange(500, 0, 500); inRange(445, 0, 450); inRange(25, 0, 150); inRange(0, 0, 5); inRange(1, 0, 1); inRange(2, 0, 2);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
Lodash inRange
JS inRange
Fastest:
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Slowest:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the benchmark and its options. **What is being tested?** The provided benchmark measures the performance of two functions: `_.inRange` from Lodash (a popular JavaScript library) and a custom implementation of an "in range" function in vanilla JavaScript (`inRange`). **Options compared** The benchmark compares the execution time of these two functions for different input scenarios: 1. Small numbers: `_.inRange(5, 0, 50)` vs `inRange(5, 0, 50)` 2. Medium-sized numbers: `_.inRange(500, 0, 50)` vs `inRange(500, 0, 50)` 3. Large number range: `_.inRange(500, 0, 500)` vs `inRange(500, 0, 500)` 4. Edge case: `_.inRange(445, 0, 450)` vs `inRange(445, 0, 450)` 5. Non-range input: `_.inRange(25, 0, 150)` vs `inRange(25, 0, 150)` (this test is expected to be fast for both) 6. Edge case with zero start value: `_.inRange(0, 0, 5)` vs `inRange(0, 0, 5)` 7. Edge case with equal bounds: `_.inRange(1, 0, 1)` vs `inRange(1, 0, 1)` **Pros and cons of each approach** Lodash's `_.inRange` is a more concise and readable implementation, but it might be slower due to the overhead of a JavaScript library. The custom `inRange` function is likely to be faster because it doesn't have any additional functions or data structures. However, Lodash provides other benefits, such as: * Code reusability: `_.inRange` can be reused in other parts of the codebase. * Error handling: `_.inRange` will throw an error if the input is invalid, which might be desirable in some cases. * Type safety: `_.inRange` returns a boolean value, making it easier to write correct logic. On the other hand, the custom `inRange` function has: * Fewer lines of code, making it easier to understand and maintain. * No additional dependencies or overhead. **Library used** The Lodash library is used in the benchmark. Lodash provides a set of reusable functions for common tasks, such as string manipulation, array utilities, and more. **Special JS feature or syntax** Neither of the implementations uses any special JavaScript features or syntax. They are both straightforward, vanilla JavaScript functions that rely on basic operations like comparison and arithmetic. **Other alternatives** If you were to reimplement this benchmark with alternative libraries or approaches, some options could include: * Using a different library: Other popular JavaScript libraries like underscore.js or Ramda might provide faster or more concise implementations of `inRange`. * Using native WebAssembly (WASM) implementation: Some modern browsers support WASM-based performance optimizations for common functions. Implementing `inRange` in WASM might lead to significant performance gains. * Using a Just-In-Time (JIT) compiler: A JIT compiler like V8 (used by Chrome) could potentially optimize the custom `inRange` function to match the Lodash implementation's performance. * Optimizing for specific use cases: Depending on your particular requirements, you might be able to optimize one or both of the implementations to better suit your needs. Keep in mind that these alternatives would require significant changes and optimizations, and it's essential to consider the trade-offs between code readability, maintainability, and performance when choosing an approach.
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