Toggle navigation
MeasureThat.net
Create a benchmark
Tools
Feedback
FAQ
Register
Log In
flatMap() vs filter().map() Bruno
(version: 0)
flatMap vs filter map
Comparing performance of:
filter().map() vs flatMap()
Created:
3 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var arr = []; var i = 0; while (i <= 400) arr[i] = i++;
Tests:
filter().map()
arr.filter(x => x > 1).map(x => x * 2)
flatMap()
arr.flatMap(x => x <= 1 ? [] : x*2)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
Fork
Test case name
Result
filter().map()
flatMap()
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one year ago
)
User agent:
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36
Browser/OS:
Chrome 133 on Windows
View result in a separate tab
Embed
Embed Benchmark Result
Test name
Executions per second
filter().map()
238317.3 Ops/sec
flatMap()
92905.9 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the benchmark! **What is tested?** The provided JSON represents two test cases: `flatMap()` and `filter().map()`. The goal of this benchmark is to compare the performance of these two approaches in JavaScript. **Options compared** Two options are being compared: 1. **flatMap()**: This method was introduced in ECMAScript 2019 (ES2020) as part of the Array.prototype.flatMap() method. 2. **filter().map()**: This approach involves first filtering an array to remove elements that don't meet a certain condition, and then mapping over the remaining elements. **Pros and Cons** **flatMap():** Pros: * More concise and expressive code * Can be faster for large datasets, as it avoids creating intermediate arrays Cons: * Only supported in ECMAScript 2019 (ES2020) and later versions * May not work correctly in older JavaScript engines or environments that don't support this method **filter().map():** Pros: * Widely supported across different JavaScript engines and environments * Can be more readable for developers who are familiar with the `filter()` and `map()` methods Cons: * Creates intermediate arrays, which can lead to increased memory usage and slower performance for large datasets. **Other considerations:** When choosing between these two approaches, consider the following factors: * **Memory constraints:** If you're working with very large datasets, `flatMap()` might be a better choice since it avoids creating intermediate arrays. * **Code readability:** If you need to write code that's easy for others to understand, `filter().map()` might be a better option since it uses familiar methods. * **Browser compatibility:** If you need your code to run in older browsers or environments, `filter().map()` is a safer choice. **Library usage** There are no libraries mentioned in the provided JSON. However, if any libraries are used in the benchmark, they would be used to set up the test environment and prepare the data. **Special JavaScript features or syntax** There are some special JavaScript features used in this benchmark: * **Template literals:** The `Script Preparation Code` uses template literals to create an array with dynamic values. * **Arrow functions:** Both benchmarks use arrow functions for concise code. * **Let and const declarations:** The `Script Preparation Code` uses the `let` and `const` keywords for variable declarations. **Other alternatives** If you want to explore alternative approaches, consider the following: * Using other array methods like `reduce()` or `forEach()` * Implementing a custom solution using a loop * Using a different programming paradigm, such as functional programming with currying Keep in mind that these alternatives might not be suitable for your specific use case and may require more code and maintenance.
Related benchmarks:
javascript array.filter().map() vs array.flatMap()
Array flatMap() vs filter().map()
flatMap() vs filter().map() - arrays
comparing flatMap vs filter and map in little arr length
Comments
Confirm delete:
Do you really want to delete benchmark?