Benchmark: Precompile RegExp vs Runtime (on the fly)

Script Preparation code:

const str = 'someTextWith333Digits'

const p1 = /[a-z](?=\d)|\d(?=[a-z])/gi
const p2 = /([A-Z]|([a-zA-Z]\d))/g
const p3 = /^./
  
var toSpacePascalCasePrecompile = value => (
  value
    // insert a space before/after number
    .replace(p1, '$& ')
    // insert a space before all caps
    .replace(p2, ' $1')
    // uppercase the first character
    .replace(p3, str => str.toUpperCase())
)

var toSpacePascalCaseRuntime = value => (
  value
    // insert a space before/after number
    .replace(/[a-z](?=\d)|\d(?=[a-z])/gi, '$& ')
    // insert a space before all caps
    .replace(/([A-Z]|([a-zA-Z]\d))/g, ' $1')
    // uppercase the first character
    .replace(/^./, str => str.toUpperCase())
)

Tests:

Precompile
toSpacePascalCasePrecompile('someTextWith333Digits')
Runtime
toSpacePascalCaseRuntime('someTextWith333Digits')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Precompile
Runtime

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/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Windows

View result in a separate tab

Test name	Executions per second
Precompile	1648323.2 Ops/sec
Runtime	1502985.0 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.

**Benchmark Overview**

The benchmark is comparing two approaches to converting a string from "someTextWith333Digits" to "Some Text With 333 Digits". There are three functions involved:

1. `toSpacePascalCasePrecompile`: This function takes the input string as an argument and uses precompiled regular expressions to perform the conversion.
2. `toSpacePascalCaseRuntime`: This function takes the input string as an argument and performs the conversion at runtime using JavaScript's built-in methods.

**Options Compared**

The benchmark is comparing two options:

1. **Precompile**: This approach compiles the regular expression patterns beforehand, which can lead to faster execution times since the regex engine can cache the compiled patterns.
2. **Runtime**: This approach uses JavaScript's built-in string manipulation methods at runtime, without compiling any patterns beforehand.

**Pros and Cons of Each Approach**

1. **Precompile**
	* Pros:
		+ Can lead to faster execution times due to cached compiled patterns.
		+ Can be more efficient for large datasets or frequent use cases.
	* Cons:
		+ Requires additional setup time for compilation, which might not be worth it for small inputs.
2. **Runtime**
	+ Pros:
		+ No additional setup required; just execute the JavaScript code.
		+ Suitable for small inputs where compilation overhead is negligible.
	* Cons:
		+ Can lead to slower execution times due to repeated compilation of patterns.

**Library and Special JS Features**

The benchmark does not use any external libraries. However, it utilizes JavaScript's built-in features:

1. **String manipulation**: The `replace()` method with regex patterns is used in both functions.
2. **Regular expressions**: Both functions use regular expressions, but the precompile approach compiles them beforehand.

**Considerations**

When deciding between these approaches, consider the following factors:

* Input size: For large inputs, precompilation might provide a performance boost.
* Frequency of use: If this function is called frequently, precompilation might be beneficial. Otherwise, runtime execution could be sufficient.
* Development time and complexity: Precompiling requires additional setup and testing, which might add to development time.

**Alternatives**

Other approaches that could be considered for this benchmark include:

1. **Interpreting the expression**: Instead of compiling or precompiling the regex patterns, you could interpret them at runtime using a library like `esregex`.
2. **Using a just-in-time (JIT) compiler**: Some JavaScript engines, like V8 in Chrome, have JIT compilers that can improve performance by compiling code into native machine code.
3. **Caching compiled patterns**: If the precompilation approach is chosen, consider caching the compiled patterns to avoid recompiling them on every execution.

Keep in mind that these alternatives might introduce additional complexity or require specific setup and configuration.

Related benchmarks:

Precompile RegExp vs Runtime (on the fly) (version: 0)

Is precompile version faster then on runtime? Function toSpacePascalCase() convert 'someTextWith333Digits' to 'Some Text With 333 Digits'

Comparing performance of: Precompile vs Runtime

Created: 3 years ago by: Registered User

Jump to the latest result

Precompile

Runtime

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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