Benchmark: RegExp constructor vs literal (re-do creation)

Script Preparation code:

var reConstructor = new RegExp('^[0-9a-fA-F]{24}$')
var reLiteral = /^[0-9a-fA-F]{24}$/

Tests:

new RegExp()
(new RegExp('^[0-9a-fA-F]{24}$')).test('132abc67219f019afe12901a')
Literal
/^[0-9a-fA-F]{24}$/.test('132abc67219f019afe12901a')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
new RegExp()
Literal

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36

Browser/OS: Chrome 122 on Windows

View result in a separate tab

Test name	Executions per second
new RegExp()	1396474.4 Ops/sec
Literal	3394795.2 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 is tested, compared, and considered.

**Benchmark Overview**

The MeasureThat.net website provides a microbenchmarking platform to compare performance of different approaches in JavaScript. The current benchmark measures the performance of two ways to create regular expressions:

1. **RegExp constructor**: Creating a new RegExp object using the `new` keyword.
2. **Literal regex**: Using a literal regex pattern without the `new` keyword.

**Benchmark Definition**

The benchmark definition is provided as JSON, which includes:

* **Name**: A unique name for the benchmark.
* **Description**: A brief description of the test case.
* **Script Preparation Code**: The JavaScript code that prepares the script before execution. In this case, it creates two RegExp objects: `reConstructor` and `reLiteral`.
* **Html Preparation Code**: Not applicable in this case.

**Individual Test Cases**

The benchmark consists of two individual test cases:

1. **new RegExp()**
	* Benchmark Definition: `(new RegExp('^[0-9a-fA-F]{24}$')).test('132abc67219f019afe12901a')`
	* Test Name: `new RegExp()`
2. **Literal**
	* Benchmark Definition: `/^[0-9a-fA-F]{24}$/ .test('132abc67219f019afe12901a')`
	* Test Name: `Literal`

**Library and Purpose**

In the script preparation code, two RegExp objects are created:

1. `reConstructor = new RegExp('^[0-9a-fA-F]{24}$')`: This creates a new RegExp object using the constructor.
2. `reLiteral = /^[0-9a-fA-F]{24}$/`: This is a literal regex pattern without the `new` keyword.

The `test()` method is called on both RegExp objects with the same input string to measure their performance.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in this benchmark. It only relies on standard JavaScript functionality.

**Pros and Cons of Different Approaches**

1. **RegExp constructor**:
	* Pros: Can be useful when creating complex regex patterns with flags (e.g., `new RegExp('pattern', 'flags')`).
	* Cons: May incur overhead due to the creation of a new object.
2. **Literal regex**:
	* Pros: Faster execution, as it avoids object creation overhead.
	* Cons: Limited to simple regex patterns without flags.

**Other Alternatives**

If you wanted to create similar benchmarks for other ways to create RegExp objects or test different aspects of performance, consider the following:

1. Using a RegExp literal with flags (e.g., `/pattern.flags/`).
2. Creating a RegExp object using a string constructor (e.g., `new RegExp(pattern, flags)`).
3. Comparing performance of different regex engines (e.g., built-in vs. third-party libraries like RegEx.js).

Keep in mind that the specific benchmarks and test cases will depend on your use case and requirements.

Related benchmarks:

RegExp constructor vs literal (re-do creation) (version: 0)

Testing performance when using a new RegExp object vs a literal regex

Comparing performance of: new RegExp() vs Literal

Created: 2 years ago by: Guest

Jump to the latest result

new RegExp()

Literal

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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