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Join vs map + filter + includes
(version: 0)
Comparing performance of:
Join vs map + filter + includes
Created:
4 years ago
by:
Registered User
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Script Preparation code:
var columns = Array.from(new Array(10000)).map(() => ({ fieldName: 'column' })); var preselectedColumns = Array.from(new Array(10000)).map(() => 'column') var result = []
Tests:
Join
columns.map((child) => { const childChecked = preselectedColumns.includes(child.fieldName); if (childChecked) { result.push(child.fieldName); } })
map + filter + includes
result = columns .map(child => child.fieldName) .filter(fieldName => preselectedColumns.includes(fieldName))
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
Join
map + filter + includes
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
I'll break down the provided benchmark and its options for you. **Benchmark Overview** The benchmark measures the performance of two approaches to filter an array of 10,000 objects based on whether a specific field exists in another predefined array. The two approaches are: 1. **Join**: A single line of code that uses the `includes` method to check if each object's field name is present in the preselected columns array. 2. **map + filter + includes**: A multi-step approach that: * Creates a new array of filtered objects using `map`, which creates a new array with transformed objects. * Uses `filter` to create a new array with only the objects that pass the filter condition. * Uses `includes` to check if each object's field name is present in the preselected columns array. **Options Compared** The benchmark compares the performance of these two approaches: Pros and Cons: * **Join**: This approach uses a single line of code and is likely to be faster since it only needs to perform one iteration over the data. However, this method can be less readable and maintainable due to its concise nature. * **map + filter + includes**: This approach creates multiple intermediate arrays, which can lead to slower performance. However, this method can provide better readability and maintainability due to its more explicit nature. **Libraries Used** None of the provided benchmark code uses any external libraries. Both approaches rely on built-in JavaScript methods: `Array.prototype.map`, `Array.prototype.filter`, and `String.prototype.includes`. **Special JS Features/Syntax** There are no special JavaScript features or syntax used in this benchmark. **Alternative Approaches** Some alternative approaches that could be considered for optimization: * **Using a Set**: Instead of using the `includes` method, which has an average time complexity of O(n), using a Set data structure can provide faster lookups with an average time complexity of O(1). * **Parallelization**: If the benchmark is run on a multi-core processor, parallelizing the filtering process could lead to significant performance gains. * **Caching**: If the preselected columns array does not change frequently, caching its contents in memory could reduce the number of lookups required. Keep in mind that these alternative approaches may introduce additional complexity and trade-offs, and their effectiveness would depend on the specific use case and requirements.
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