Benchmark: map&join vs reduce 2

Script Preparation code:

var a = ''
for (const i of Array(10).fill(0)) a += Math.random().toString(36)

function caseReduce(str = '') {
  return str.split(/[^\w]+/).reduce((acc, cur) => {
    return acc ? `${acc}#${cur[0].toUpperCase()}${cur.slice(1)}` : cur
  }, '')
}

function caseMapJoin(str = '') {
  return str.split(/[^\w]+/).map(str => `${str[0].toUpperCase()}${str.slice(1)}`).join('#')
}

Tests:

map&join
caseMapJoin(a)
reduce
caseReduce(a)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map&join
reduce

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark compares two approaches to process a string: `caseMapJoin` and `caseReduce`. Both functions take a string as input and produce an output based on some transformation rules.

**Transformation Rules**

In this case, the transformation rule is to:

* Take each word in the input string
* Convert the first character of each word to uppercase
* Remove any non-alphanumeric characters from the rest of the word

The `caseMapJoin` function uses a map and join approach, while the `caseReduce` function uses a reduce approach.

**Options Compared**

Two options are compared:

1. **Map & Join**: This approach uses the `map()` method to apply the transformation rule to each word in the input string, and then uses the `join()` method to concatenate the transformed words with a delimiter (`#`).
2. **Reduce**: This approach uses the `reduce()` method to apply the transformation rule to each word in the input string, aggregating the results.

**Pros and Cons**

* **Map & Join**
	+ Pros: Easy to read and understand, allows for parallel processing using `map()`.
	+ Cons: May not be as efficient due to the overhead of creating an array and then joining it.
* **Reduce**
	+ Pros: Can be more efficient since it avoids the overhead of creating an array and joining it. Also, can take advantage of optimizations provided by the browser's reduce implementation.
	+ Cons: May be harder to read and understand for complex transformations.

**Library**

Both functions use the `split()` method to split the input string into words, but no external libraries are required.

**Special JS Feature/Syntax**

None mentioned in this specific benchmark.

**Other Alternatives**

If you need to process strings in JavaScript, other alternatives might include:

* Using regular expressions to apply transformations directly to the string.
* Using a library like `lodash` or `underscore` for utility functions that can help with string processing.
* Using a different programming language or framework that provides built-in support for string processing.

Keep in mind that the choice of approach depends on the specific requirements and constraints of your use case.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36

Browser/OS: Chrome 121 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
map&join	1120016.9 Ops/sec
reduce	1333908.1 Ops/sec

Related benchmarks:

map&join vs reduce 2 (version: 0)

Comparing performance of: map&join vs reduce

Created: one year ago by: Registered User

Jump to the latest result

map&join

reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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