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flat map vs reduce concat for real
(version: 0)
Comparing performance of:
reduce vs flatmap
Created:
3 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var arr = Array(10_000).fill({a:[0]})
Tests:
reduce
arr.reduce((acc, x) => acc.concat(x.a), [])
flatmap
arr.flatMap(x => x.a)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
reduce
flatmap
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 JSON and explain what's being tested. **Benchmark Definition** The benchmark is comparing two approaches: `reduce` and `flatMap` (also known as `flatMap`) for concatenating arrays in JavaScript. **Options Compared** * **Reduce**: The `reduce` method takes an accumulator array and iterates over the input array, applying a callback function to each element. In this case, the callback function is used to concatenate the `a` property of each object in the array into the accumulator array. * **FlatMap**: The `flatMap` method returns a new array with the results of calling a provided function on every element in the original array. In this case, the function is used to flatten the `a` property of each object in the array. **Pros and Cons** * **Reduce**: + Pros: Can be more efficient for small arrays or when the accumulator needs to be modified. + Cons: Creates a new array, which can be memory-intensive for large inputs. The callback function needs to be carefully optimized to avoid unnecessary work. * **FlatMap**: + Pros: Returns a new array in a single operation, making it potentially more efficient and safer than `reduce`. Reduces the risk of accumulator corruption or unexpected side effects. + Cons: May create intermediate arrays that are not immediately necessary for the computation. **Library** None mentioned. **Special JS Feature/Syntax** `flatMap` is an ES6 addition to JavaScript (introduced in 2015). It's a shorthand for `map.concat()`, but returns a new array instead of modifying the original one. Now, let's analyze the provided benchmark result: The test compares the performance of `reduce` and `flatMap` on two separate iterations. The results are: * **FlatMap**: 986 executions per second (on Chrome 108/ Mac OS X 10.15.7) * **Reduce**: 57 executions per second (on Chrome 108/ Mac OS X 10.15.7) This suggests that `flatMap` is significantly faster than `reduce` in this specific scenario. **Other Alternatives** If you're interested in exploring alternative approaches, here are a few options: * **Array.prototype.concat()**: A simple but slower approach that creates a new array by concatenating the input arrays. * **Set-based approaches**: For large inputs, using sets to accumulate elements or avoid unnecessary concatenations can be more efficient. However, this requires careful consideration of data structure choices and potential memory limitations. Keep in mind that these alternatives may not offer significant performance improvements over `reduce` and `flatMap`, especially for smaller inputs.
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