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case insensitive array check
(version: 0)
Comparing performance of:
Regex vs ToLower vs Map
Created:
3 years ago
by:
Guest
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Script Preparation code:
var rawArray = [{ key: "Apple" }, { key: "bat" }, { key: "CAT" }]; var lowerArray = rawArray.map(x => ({ key: x.key.toLowerCase() })); var map = lowerArray.reduce((prev, curr) => ({ ...prev, [curr.key]: curr })); var testItem = "CaT";
Tests:
Regex
const re = new RegExp(`^${testItem}$`, "i"); const val = rawArray.find(x => re.test(x.key));
ToLower
const val = lowerArray.find(x => x.key === testItem);
Map
const val = map[testItem];
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
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Test case name
Result
Regex
ToLower
Map
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the world of MeasureThat.net. **Benchmark Definition JSON** The provided JSON represents a benchmark test case that checks for case-insensitive array values in three different ways: 1. **Map Approach**: The `map` function is used to create a new array with the lowercase version of each key in the original array. Then, the `reduce` method is used to merge this new array into a single object. This approach creates a new data structure and performs an operation on it. 2. **ToLower Approach**: This approach uses the `toLowerCase()` method directly on the individual elements of the original array, similar to the `map` function in the previous step. However, instead of merging the results, it simply finds the first match with the specified value using the `find()` method. 3. **Regex Approach**: A regular expression is created that matches any string starting and ending with the specified test item (`testItem`). The `find()` method is then used to search for an element in the original array that satisfies this condition. **Options Compared** The benchmark compares the performance of these three approaches: * **Map Approach**: It creates a new data structure and performs an operation on it. * **ToLower Approach**: It uses a simple iteration approach with the `find()` method, which is relatively lightweight compared to the map approach. * **Regex Approach**: It uses regular expressions for pattern matching. **Pros and Cons** Here's a brief summary of the pros and cons of each approach: 1. **Map Approach**: Pros: * Easier to understand and maintain due to its functional programming style. * More flexible, as it can be easily extended or modified. Cons: * Creates a new data structure, which can lead to performance overhead. * May not be suitable for very large datasets. 2. **ToLower Approach**: Pros: * Lightweight and efficient due to the simple iteration approach. * Does not create an additional data structure, making it more memory-friendly. Cons: * Less readable and maintainable compared to the map approach. * May be less flexible for custom use cases. 3. **Regex Approach**: Pros: * Suitable for pattern matching and can be extended with regular expression features. Cons: * Can be slow due to the complexity of regular expressions. * Less intuitive and readable compared to other approaches. **Library Usage** There is no explicit library mentioned in the benchmark definition. However, if we consider the `find()` method used in all three approaches, it implies that the JavaScript built-in methods are being utilized. No external libraries are required for this specific test case. **Special JS Features or Syntax** None of the provided code snippets utilize any special JavaScript features or syntax beyond what is standard (e.g., `const`, `let`, `var`, arrow functions, and basic operators). **Alternatives** Other alternatives to measure the performance of case-insensitive array checks could include: * Using an array data structure with built-in methods for filtering or searching (e.g., `Array.prototype.includes()`) * Implementing a custom search algorithm using bitwise operations * Utilizing a different data structure, such as a Trie or a hash table MeasureThat.net's approach provides a straightforward and easy-to-understand benchmarking framework for comparing the performance of different algorithms.
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