Toggle navigation
MeasureThat.net
Create a benchmark
Tools
Feedback
FAQ
Register
Log In
flatMap vs reduce 999912345
(version: 0)
Comparing performance of:
flatMap vs reduce
Created:
3 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var values = [] for(i=0; i<1000; i++){ values.push([i, i]) }
Tests:
flatMap
values.flatMap(e => e.map(i => i * 2))
reduce
values.reduce((acc, x) => { acc.push(...x.map(i => i * 2)); return acc; }, [])
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
Fork
Test case name
Result
flatMap
reduce
Fastest:
N/A
Slowest:
N/A
Latest run results:
No previous run results
This benchmark does not have any results yet. Be the first one
to run it!
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
I'd be happy to help explain the provided benchmark. **What is being tested?** MeasureThat.net tests the performance of two JavaScript methods: `flatMap` and `reduce`. The test creates an array of arrays, where each inner array contains a number and its square. The goal is to compare the execution time of these two methods when applied to this dataset. **Options compared** The benchmark compares the following options: 1. `flatMap`: A method that flattens an array by mapping over it and then concatenating the results. 2. `reduce`: A method that accumulates a value by applying a function to each element in the array, passing the accumulated value as an argument. **Pros and Cons of each approach** * `flatMap`: + Pros: More concise and expressive code, can be more efficient for small datasets. + Cons: Can be less readable for complex transformations, may not work correctly with null or undefined values. * `reduce`: + Pros: Can handle large datasets, provides a flexible way to accumulate values, is widely supported by JavaScript engines. + Cons: More verbose code, can lead to performance issues if the accumulator becomes too large. **Library usage** The test uses the built-in `Array.prototype.flatMap` and `Array.prototype.reduce` methods, which are part of the ECMAScript standard. These methods are implemented in the browser's JavaScript engine and are not specific to any library. **Special JS feature or syntax** There is no special JavaScript feature or syntax used in this benchmark that would require additional explanation. **Other considerations** When writing microbenchmarks like this, it's essential to consider factors such as: * Dataset size: In this case, the dataset consists of 1000 arrays with two elements each. * Data structure: The use of an array of arrays can affect performance due to the overhead of indexing and bounds checking. * JavaScript engine: Different engines may have varying degrees of optimization and caching for specific methods like `flatMap` and `reduce`. **Alternative approaches** Other alternatives for implementing the same functionality could include: * Using a library like Lodash, which provides an implementation of `flatMap` and `reduce`. * Implementing the transformation manually using loops or recursion. * Using a different data structure, such as a linked list or a custom array implementation. Keep in mind that these alternatives may have varying performance characteristics and code readability compared to the built-in methods.
Related benchmarks:
flatMap vs reduce test
flatMap vs reduce test 2
flatMap vs reduce test 3
Reduce Push vs. flatMap with subarrays
flatMap vs reduce (push)
Comments
Confirm delete:
Do you really want to delete benchmark?