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flatMap vs reduce Saran
(version: 0)
Comparing performance of:
reduce with array push vs flatMap
Created:
2 years ago
by:
Guest
Jump to the latest result
Script Preparation code:
var arr = Array(10_000).fill(0)
Tests:
reduce with array push
arr.reduce((acc, x) => { acc.push(x); acc.push(x); return acc; }, [])
flatMap
arr.flatMap(x => [x, x])
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
reduce with array push
flatMap
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
one month ago
)
User agent:
Mozilla/5.0 (iPhone; CPU iPhone OS 18_6_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) CriOS/146.0.7680.151 Mobile/15E148 Safari/604.1
Browser/OS:
Chrome Mobile iOS 146 on iOS 18.6.2
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Embed Benchmark Result
Test name
Executions per second
reduce with array push
19609.3 Ops/sec
flatMap
11918.8 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, compared, and analyzed. **Benchmark Overview** The benchmark is comparing two JavaScript methods: `reduce` with array push (involving explicit array push) versus `flatMap`. The goal is to measure which method performs better in terms of execution speed on a specific dataset. **Options Compared** Two approaches are being compared: 1. **`reduce` with array push**: This approach uses the `reduce` method to iterate over an array, and for each element, it explicitly pushes that element onto an accumulator array. The method returns the final accumulator array. 2. **`flatMap`**: This approach uses the `flatMap` method to create a new array by mapping each element of an input array to an array, and then flattening that array into one level. **Pros and Cons** **`reduce` with array push:** Pros: * Directly applicable to any dataset * Allows for manual control over iteration and accumulation Cons: * Involves explicit array push operations, which can be slower due to the overhead of creating and modifying arrays. * May require more memory allocation due to the accumulator array. **`flatMap`:** Pros: * More concise and expressive than `reduce` with explicit array push * Automatically handles iteration and flattening for you Cons: * May not be directly applicable to all datasets (e.g., those requiring manual accumulation) * Can lead to slower execution if not optimized correctly, as it involves additional function calls and array creation. **Library and Special JS Features** The test case uses the `Array` prototype's `reduce()` method. The purpose of this method is to reduce an input array to a single value or array by applying a reducing function to each element in the array. There are no special JavaScript features mentioned in the provided benchmark. However, it's worth noting that the use of `flatMap` involves the new ES6 array methods introduced with ECMAScript 2017 (ES7), which provide a concise way to flatten arrays. **Other Alternatives** If `reduce` with explicit array push or `flatMap` aren't suitable for your specific use case, you may consider alternative approaches: * **Using `map` and concatenation**: Instead of `reduce`, you can use the `map()` method to create an array of transformed elements and then concatenate them using the `concat()` method. * **Using a different iteration method**: Depending on the dataset structure and requirements, other iteration methods like `forEach()`, `for...of`, or even iterative loops with manual indexing might be more suitable. Keep in mind that each approach has its trade-offs in terms of performance, conciseness, and maintainability.
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