Benchmark: flatMap vs reduce Saran

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>

Previous results

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

View result in a separate tab

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):

LLMs can make mistakes. Check important info.

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.

Related benchmarks:

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

reduce with array push

flatMap

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):