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Functional Vs Imperative
(version: 0)
Comparing performance of:
Functional vs Imperative
Created:
3 years ago
by:
Guest
Go to the latest result
Script Preparation code:
window.nums = new Array(1000).fill(0).map((x,i) => i)
Tests:
Functional
const y = nums.filter(x => x%2).map(x => x*3).filter(x => x < 100) return y
Imperative
const y = [] for(const x of nums) { if(x%2) { const x3 = x*3; if(x3 < 100) { y.push(x3) } } } return y
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
Functional
Imperative
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
7 months ago
)
User agent:
Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/143.0.0.0 Mobile Safari/537.36
Browser/OS:
Chrome Mobile 143 on Android
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=623166" width="100%" height="500px"></iframe>
Imperative
293K/s
Functional
32K/s
View exact numbers
Test name
Executions per second
✓
Imperative
293,421 Ops/sec
Functional
31,941 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided JSON and explain what is being tested, the options compared, and their pros and cons. **Benchmark Definition** The benchmark definition represents a JavaScript microbenchmark that compares two approaches: functional programming and imperative programming. * **Script Preparation Code**: The script preparation code initializes an array `nums` with 1000 zeros, mapped to indices from 0 to 999. This array will be used as input for both test cases. * **Html Preparation Code**: There is no HTML preparation code, which means that only the JavaScript code is executed. **Individual Test Cases** There are two test cases: 1. **Functional** ```javascript const y = nums.filter(x => x%2).map(x => x*3).filter(x => x < 100) return y ``` This code uses the `filter()` and `map()` array methods to transform the `nums` array in a functional programming style. 2. **Imperative** ```javascript const y = [] for(const x of nums) { if(x%2) { const x3 = x*3; if(x3 < 100) { y.push(x3) } } } return y ``` This code uses a `for...of` loop and conditional statements to iterate over the `nums` array in an imperative programming style. **Comparison of Options** The two approaches differ in their use of array methods (`filter()` and `map()`) versus explicit loops (`for...of` and conditional statements). This comparison highlights the differences between functional and imperative programming styles. Pros and Cons: * **Functional Programming** + Pros: - More concise code - Easier to write and maintain - Less prone to errors due to immutability + Cons: - Can be less efficient due to the creation of intermediate arrays - May not perform as well on large datasets * **Imperative Programming** + Pros: - More control over the execution flow - Can be more efficient for large datasets - May be more familiar to developers with traditional programming backgrounds + Cons: - Code can be longer and more complex - Prone to errors due to mutable state **Library/Dependency** There is no library or dependency mentioned in the benchmark definition. The tests only rely on built-in JavaScript features. **Special JS Feature/Syntax** None of the test cases use any special JavaScript features or syntax. **Other Alternatives** Other alternatives for comparing functional and imperative programming styles include: * **Loops vs. Recursion**: Comparing traditional loops (e.g., `for`, `while`) to recursive functions. * **Array Spreads vs. Array Concatenation**: Comparing the performance of array spreads (`[...array]`) versus concatenating arrays using the `+` operator (`array + array`). * **Lambda Functions vs. Closures**: Comparing the performance of lambda functions (e.g., `x => x*3`) versus closures (e.g., `function multiply(x) { return x*3; }`). These alternatives can provide additional insights into the trade-offs between different programming styles and execution strategies in JavaScript.
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