Benchmark: flatMap vs Reduce with push - test2

Script Preparation code:

var arr = Array(10_000).fill(0)

Tests:

reduce
arr.reduce((acc, x) => { const c = [x, x]; acc.push(...c); 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
flatmap

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):

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Let's dive into the provided JSON and benchmark preparation code.

**Benchmark Description**

The benchmark is named "flatMap vs Reduce with push - test2". It tests two different approaches: using `reduce` method with a callback function, and using `flatMap` method. The purpose of this benchmark is to compare the performance of these two methods when it comes to creating an array by pushing elements into it.

**Preparation Code**

The preparation code initializes an array `arr` with 10,000 elements filled with zeros: `var arr = Array(10_000).fill(0)`. This prepares the input data for the benchmark tests.

**Test Cases**

There are two test cases:

1. **reduce**
	* Benchmark Definition: `arr.reduce((acc, x) => { const c = [x, x]; acc.push(...c); return acc;}, [])`
	* Test Name: "reduce"
2. **flatmap**
	* Benchmark Definition: `arr.flatMap(x => [x, x])`
	* Test Name: "flatmap"

**Test Case Analysis**

Let's break down each test case:

1. **reduce**
	* The benchmark uses the `reduce` method with a callback function to accumulate an array of elements.
	* The callback function creates an array `c` containing two copies of the current element `x`, and then pushes these elements into the accumulator array `acc`.
	* This approach is similar to using a `for` loop or a `while` loop to push elements into an array.
2. **flatmap**
	* The benchmark uses the `flatMap` method to create an array by flattening the result of calling a callback function for each element in the input array `arr`.
	* The callback function returns an array containing two copies of the current element `x`, which is then flattened into the final output array.

**Library and Feature Used**

No external libraries or special JavaScript features are used in this benchmark.

**Options Compared**

The two options being compared are:

1. **reduce**: using the `reduce` method with a callback function to accumulate an array of elements.
2. **flatmap**: using the `flatMap` method to create an array by flattening the result of calling a callback function for each element in the input array.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **reduce**
	* Pros: more control over the accumulation process, can be used with other methods like `map` or `filter`.
	* Cons: requires manual handling of the accumulator, can be less readable for complex scenarios.
2. **flatmap**
	* Pros: concise and expressive syntax, easy to use with callbacks that return arrays.
	* Cons: might be less efficient than reduce in some cases due to extra overhead.

**Other Alternatives**

Other alternatives to consider when accumulating an array include:

1. **for loop**: using a traditional `for` loop or a `while` loop to push elements into an array.
2. **concat**: using the `concat` method to concatenate arrays together.
3. **map and reduce**: using the `map` method to transform each element, followed by the `reduce` method to accumulate the results.

These alternatives might be more suitable depending on the specific use case and requirements of your project.

**Latest Benchmark Result**

The latest benchmark result shows that the "reduce" test case executes at approximately 13541.75 EPS (executions per second), while the "flatmap" test case executes at around 685.94 EPS on an Opera 103 browser running on a Mac OS X 10.15.7 system.

Please note that these results are specific to this particular benchmark and might not reflect real-world performance differences between these two methods.

Related benchmarks:

flatMap vs Reduce with push - test2 (version: 0)

Comparing performance of: reduce vs flatmap

Created: 2 years ago by: Guest

Jump to the latest result

reduce

flatmap

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):