Benchmark: Regexp creation vs memoization

Script Preparation code:

var cache = {}
const regexps = [
  /^((?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))*$/,
  /^(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|6011[0-9]{12}|622((12[6-9]|1[3-9][0-9])|([2-8][0-9][0-9])|(9(([0-1][0-9])|(2[0-5]))))[0-9]{10}|64[4-9][0-9]{13}|65[0-9]{14}|3(?:0[0-5]|[68][0-9])[0-9]{11}|3[47][0-9]{13})*$/,
  /[^@/]+@[^@/]+/,
  /\\s+/,
  /[^a-zA-Z0-9]/,
  /^$/,
  /^[1-9]+[0-9]*$/,
  /(^\d*\.?\d*[0-9]+\d*$)|(^[0-9]+\d*\.\d*$)/,
  /^-?[0-9]{0,2}(\.[0-9]{1,2})?$|^-?(100)(\.[0]{1,2})?$/,
]
const sources = regexps.map(r => r.source)
regexps.forEach(r => {
  cache[r.source] = r
})

const n = sources.length
const floor = Math.floor
const random = Math.random
function getRandomSource () {
  return sources[floor(random() * n)]
}

Tests:

constructor
window.result = new RegExp(getRandomSource())
memoize
window.result = cache[getRandomSource()]
common boilerplate
window.result = getRandomSource()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
constructor
memoize
common boilerplate

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down what's being tested in this benchmark.

**Benchmark Definition**

The test compares two approaches to creating regular expressions (regexps) in JavaScript:

1. **Constructor**: This approach creates a new regexp object every time it's needed, without caching the results.
2. **Memoization**: This approach caches the compiled regexps in an object (`cache`) and reuses them instead of recreating them from scratch.

**Options Compared**

The two options being compared are:

* Creating a new regexp object from scratch (constructor)
* Caching the compiled regexp objects (memoization)

**Pros and Cons of Each Approach**

### Constructor

Pros:

* Easy to implement
* No overhead in terms of memory usage or performance impact on first use

Cons:

* Creates a new regexp object every time it's needed, which can be inefficient if the same regexp is used multiple times.
* Can lead to performance issues if the number of regexps grows large.

### Memoization

Pros:

* Reuses cached compiled regexps, reducing memory usage and improving performance for repeated use.
* Reduces the overhead of creating a new regexp object from scratch.

Cons:

* Requires additional memory to store the cache.
* May increase memory usage if the cache grows too large.

**Library Used**

In this benchmark, no external libraries are used. The regexps are defined directly in the JavaScript code.

**Special JS Feature/Syntax (None)**

There are no special JavaScript features or syntaxes being tested in this benchmark.

**Other Considerations**

When deciding between these two approaches, consider the following:

* If you expect to use the same regexp multiple times, memoization might be a better choice.
* If you're working with a large number of unique regexps, memoization could help reduce memory usage and improve performance.
* If simplicity is more important than optimization, the constructor approach might be sufficient.

**Alternatives**

Other approaches to creating regexps in JavaScript include:

* Using a library like RegExCache or RegExp- cache to cache compiled regexps.
* Using a JavaScript engine that supports Just-In-Time (JIT) compilation of regexps, which can improve performance.
* Using a different programming language or framework that provides built-in support for caching and reusing compiled regexps.

Related benchmarks:

Regexp creation vs memoization (version: 7)

I need to obtain and use regexps from a string. I'm testing wether I should cache regexps or just construct them on demand.

Comparing performance of: constructor vs memoize vs common boilerplate

Created: 8 years ago by: Registered User

Jump to the latest result

constructor

memoize

common boilerplate

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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