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Spliced array search
(version: 0)
Comparing performance of:
1 vs 2
Created:
4 years ago
by:
Guest
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Script Preparation code:
var list = []; for (var i = 0; i < 100; i++) { list.push(i); }
Tests:
1
const arr = list.splice(0, 90); arr.find(el => el === 92)
2
list.find(el => el === 92)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
1
2
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what's being tested. **Benchmark Overview** The benchmark measures the performance of two approaches to searching for an element in an array: using `Array.prototype.splice()` followed by `Array.prototype.find()`, and using only `Array.prototype.find()` directly on the original array. **Script Preparation Code** The script preparation code creates a JavaScript array `list` with 100 elements, each containing a unique integer value from 0 to 99. This array is used as the input data for both test cases. **Html Preparation Code** There is no HTML preparation code provided, which means that only JavaScript code is being executed and measured. **Benchmark Definition JSON** The benchmark definition consists of two test cases: 1. **Test Case 1**: `const arr = list.splice(0, 90); arr.find(el => el === 92)`. This approach first creates a new array `arr` by splicing the first 90 elements from the original array `list`. Then, it uses the `find()` method to search for an element with value 92 in the new array. 2. **Test Case 2**: `list.find(el => el === 92)`. This approach directly searches for an element with value 92 in the original array `list` using the `find()` method. **Pros and Cons of Each Approach** **Approach 1: Splicing and then searching** * Pros: + Can be faster for large arrays, since it creates a new array with only the desired elements. + Allows for more control over the search process (e.g., filtering out unwanted elements). * Cons: + Creates a new array, which can consume memory and slow down performance. + Requires additional allocation and copying of data. **Approach 2: Directly searching** * Pros: + More memory-efficient, since it doesn't create a new array. + Faster execution time, as it only searches the original array once. * Cons: + May be slower for very large arrays, due to the overhead of finding a single element. **Library and Special JS Features** There are no external libraries or special JavaScript features being used in this benchmark. **Other Considerations** When dealing with large datasets, other factors such as caching, memoization, and parallel processing can also impact performance. Additionally, the browser's garbage collection mechanism may affect the results, especially if the array is dynamically allocated. **Alternative Approaches** Some alternative approaches that could be considered in this benchmark include: * Using a different data structure, such as a linked list or a binary search tree. * Employing more efficient searching algorithms, like binary search or hash table lookup. * Utilizing browser-specific optimizations, such as SIMD instructions or GPU acceleration. Keep in mind that these alternatives would require significant changes to the benchmark definition and script preparation code.
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splice vs push
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