Benchmark: Regex Memory Consumption

Script Preparation code:

var acceptHeader = 'image/*, */*';
var mimes = ['image/avif', 'image/webp'];
var accepts = 'image/avif,image/webp,image/apng,image/svg+xml,image/*,*/*;q=0.8';

Tests:

Match
var regex = new RegExp(`(${mimes.join('|')})(,|;|$)`, 'i'); var match = accepts.match(regex); if (match){ acceptHeader = match[1]; }
Exec
var regex = new RegExp(`(${mimes.join('|')})(?=,|;|$)`, 'i'); var match = regex.exec(accepts); if (match){ acceptHeader = match[0]; }
Exec Non-Capturing
var regex = new RegExp(`(?:${mimes.join('|')})(?=,|;|$)`, 'i'); var match = regex.exec(accepts); if (match){ acceptHeader = match[0]; }
Match non capturing
var regex = new RegExp(`(?:${mimes.join('|')})(?=,|;|$)`, 'i'); var match = accepts.match(regex); if (match){ acceptHeader = match[0]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Match
Exec
Exec Non-Capturing
Match non capturing

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is tested, compared options, their pros and cons, and other considerations.

**Benchmark Overview**

The benchmark measures the memory consumption of regular expressions (regex) used for matching accept headers in different ways. The goal is to identify which approach has the lowest memory usage.

**Script Preparation Code**

The script preparation code sets up variables:

* `acceptHeader`: a string containing the header with image/*, */* MIME types
* `mimes`: an array of image formats (avif, webp, apng, svg+xml)
* `accepts`: a string containing the accept header with the specified MIME types

**Html Preparation Code**

There is no HTML preparation code provided.

**Individual Test Cases**

The benchmark consists of four test cases:

1. **Match**
	* Creates a regex pattern that matches `image/*`, `,` and `$` with the `i` flag (case-insensitive).
	* Uses the `match()` method to find the first match in the `accepts` string.
2. **Exec**
	* Creates a regex pattern that matches `image/*`, `,` and `$` with the `i` flag (case-insensitive).
	* Uses the `exec()` method to execute the regex pattern on the `accepts` string.
3. **Exec Non-Capturing**
	* Creates a regex pattern using non-capturing groups (`(?:`) that matches `image/*`, `,` and `$` with the `i` flag (case-insensitive).
	* Uses the `exec()` method to execute the regex pattern on the `accepts` string.
4. **Match non capturing**
	* Creates a regex pattern using non-capturing groups (`(?:`) that matches `image/*`, `,` and `$` with the `i` flag (case-insensitive).
	* Uses the `match()` method to find the first match in the `accepts` string.

**Library: RegExp**

The `RegExp` library is used throughout the benchmark. It provides a way to create regex patterns and execute them on strings.

**Special JS Features/Syntax**

None are explicitly mentioned, but it's worth noting that:

* The use of `i` flag (case-insensitive) in the regex patterns.
* Non-capturing groups (`(?:`) are used in two test cases.

**Comparison of Approaches**

The four test cases compare different approaches to matching accept headers:

1. **Match**: uses the `match()` method, which returns an array with a matched value and its index.
2. **Exec**: uses the `exec()` method, which executes the regex pattern on the string and returns the match or null if no match is found.
3. **Exec Non-Capturing**: uses non-capturing groups (`(?:`) to group parts of the pattern without creating a capture group.
4. **Match non capturing**: uses non-capturing groups (`(?:`) with the `match()` method.

**Pros and Cons**

Here's a brief summary of each approach:

1. **Match**:
	* Pros: simple, widely supported, fast.
	* Cons: may create unnecessary arrays or objects for performance.
2. **Exec**:
	* Pros: efficient, can be faster than `match()` method.
	* Cons: may have issues with partial matches, memory usage is not guaranteed to be low.
3. **Exec Non-Capturing**:
	* Pros: reduces memory usage by avoiding unnecessary captures, fast.
	* Cons: less widely supported than other approaches.
4. **Match non capturing**:
	* Pros: simple, avoids unnecessary array or object creation.
	* Cons: may have performance issues compared to `exec()` method.

**Other Considerations**

When choosing an approach:

* Consider the specific requirements of your use case (e.g., partial matches, memory constraints).
* Choose an approach that balances performance with simplicity and maintainability.
* Be aware that some approaches may be less supported or performant in certain environments.

**Alternatives**

Some alternative approaches to consider:

1. **Use a dedicated library**: libraries like `fast-regex` or `regex-optimize` provide optimized regex patterns for specific use cases.
2. **Regex optimization techniques**: apply techniques like lazy evaluation, incremental parsing, or memoization to reduce memory usage and improve performance.
3. **Alternative matching algorithms**: consider using algorithms like Aho-Corasick or Boyer-Moore for more complex matching tasks.

Keep in mind that the benchmark is focused on measuring memory consumption, so choosing an approach with minimal overhead and efficient memory usage is crucial.

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/114.0.0.0 Safari/537.36 Edg/114.0.1823.67

Browser/OS: Chrome 114 on Windows

View result in a separate tab

Test name	Executions per second
Match	2126016.8 Ops/sec
Exec	2147127.0 Ops/sec
Exec Non-Capturing	2125170.8 Ops/sec
Match non capturing	2119830.5 Ops/sec

Related benchmarks:

Regex Memory Consumption (version: 0)

Comparing performance of: Match vs Exec vs Exec Non-Capturing vs Match non capturing

Created: one year ago by: Guest

Jump to the latest result

Match

Exec

Exec Non-Capturing

Match non capturing

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):