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flatMap vs for in vs forEach
(version: 2)
Comparing performance of: flatMap vs for of
Comparing performance of:
flatMap vs forEach vs for...in
Created:
3 years ago
by:
Registered User
Go to the latest result
HTML Preparation code:
var n = 100000
Script Preparation code:
var n = 100000
Tests:
flatMap
[...Array(n)].flatMap((x,i) => [++i,i++])
forEach
let res = []; Array(n).forEach((_,i) => res.push(i+1, i+1))
for...in
let res = []; for (const el in n) { res.push(el, el); }
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (3)
Previous results
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Test case name
Result
flatMap
forEach
for...in
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
2 years ago
)
User agent:
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36
Browser/OS:
Chrome 123 on Mac OS X 10.15.7
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=517814" width="100%" height="500px"></iframe>
for...in
32.9M/s
forEach
10K/s
flatMap
433/s
View exact numbers
Test name
Executions per second
✓
for...in
32,887,646 Ops/sec
forEach
9,824 Ops/sec
flatMap
433 Ops/sec
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 is comparing the performance of three different JavaScript iteration methods: 1. `flatMap` 2. `for...in` 3. `forEach` These methods are used to iterate over an array and perform some operation on each element. The benchmark wants to know which method is the fastest for a specific use case. **Options Compared** The options being compared are: * `flatMap`: A method that returns a new array with the results of applying a provided function on every element in this array. * `for...in`: A loop that iterates over an object's properties, including inherited ones. It can be used to iterate over arrays by casting it as an object using `Array.prototype.slice.call()`. * `forEach`: A method that calls a callback function once for each element in an array. **Pros and Cons of Each Approach** 1. **flatMap**: Fastest approach, but uses more memory since it creates a new array. * Pros: Efficient, compact code, easy to read. * Cons: Memory-intensive, requires iteration over the entire array. 2. **for...in**: Uses less memory, but can be slower due to object property lookup. * Pros: Memory-efficient, allows for early termination of loops. * Cons: Less readable than other approaches, may require `Array.prototype.slice.call()` to convert the array to an object. 3. **forEach**: Medium performance and memory usage. * Pros: Easy to read, uses less memory than flatMap. * Cons: Can be slower than flatMap due to callback function overhead. **Library Used** None of these approaches require any external libraries. They are all part of the JavaScript standard library or built-in methods. **Special JS Feature/Syntax** The benchmark doesn't use any special JavaScript features or syntax beyond what's required for the three iteration methods being compared. **Other Considerations** When writing performance-critical code, it's essential to consider the trade-offs between execution speed and memory usage. In this case, `flatMap` is the fastest approach but requires more memory than the other two. If memory efficiency is crucial, `for...in` might be a better choice, but it can be slower. **Alternative Approaches** If you need to iterate over an array and perform some operation on each element, here are some alternative approaches: * **forEach** with callback function: While not as efficient as flatMap, forEach with a callback function is still a viable option for many use cases. * **map**, **filter**, or **reduce**: These methods can be used in combination to achieve similar results to flatMap. However, they may require more code and are less readable in some cases. * **Looping manually**: Forcing the developer to write their own loop using a traditional for loop can lead to slower performance and more code. Keep in mind that performance differences between these approaches might not be significant unless you're dealing with very large datasets or performance-critical applications.
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