Benchmark: RegExp unnecessary matches implications

Script Preparation code:

var matches = [
  'this_is_a_match',
  'another_match',
  'match_with_numbers_9',
  '100_200_300_400_500'
];

var no_matches = [
  'this_is_not_a-match',
  'another_no_match@',
  'no_match_with_numbers_9_*',
  '100_200_300_400_500_#'
];

Tests:

With unnecessary matches
var result_matches = matches.map((str) => /^[\w\d_]+$/.test(str)); var result_no_matches = no_matches.map((str) => /^[\w\d_]+$/.test(str));
Without unnecessary matches
var result_matches = matches.map((str) => /^\w+$/.test(str)); var result_no_matches = no_matches.map((str) => /^\w+$/.test(str));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
With unnecessary matches
Without unnecessary matches

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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.

**Benchmark Definition**

The benchmark measures whether having unnecessary matches in regular expressions has any performance implications. In other words, it wants to know if adding unnecessary characters to a regex pattern affects its execution speed.

**Options Compared**

Two options are compared:

1. **With unnecessary matches**: This option uses the regex pattern `^[\\w\\d_]+$` on both test cases (`matches` and `no_matches`). The `_` character is added to the pattern, which is not present in the original string.
2. **Without unnecessary matches**: This option uses the simplified regex pattern `^\\w+$` on both test cases.

**Pros and Cons of each approach**

**With unnecessary matches:**

Pros:

* Simulates a real-world scenario where unnecessary characters are added to the regex pattern.
* Helps identify potential performance issues with regex patterns that contain unnecessary characters.

Cons:

* May introduce additional overhead due to the extra character in the regex pattern.
* Could potentially mask underlying performance issues if the added character is not significant.

**Without unnecessary matches:**

Pros:

* Simplifies the regex pattern and reduces overhead.
* Helps isolate the effect of the regex pattern itself on execution speed.

Cons:

* May not accurately simulate real-world scenarios where unnecessary characters are present in the input data.

**Library Used (if any)**

In this benchmark, no explicit library is used. However, it's worth noting that some JavaScript engines might use built-in libraries or utilities to optimize regex performance, which could affect the results.

**Special JS feature or syntax**

There are a few special cases in the benchmark:

* The `^` character is used as an anchor to match the start of the string.
* The `\w` character class matches word characters (letters, numbers, and underscores).
* The `_` character is explicitly included in the pattern, which might not be necessary.

**Other Alternatives**

To further optimize or test regex performance, you could consider using alternative approaches, such as:

* Using a more efficient regex engine, like `RegExp.prototype.test() with flags`
* Compiling and recompiling regex patterns for optimal caching
* Using a library specifically designed for regex optimization, like [regex-ast](https://github.com/kevlinghagen/regex-ast)

Keep in mind that the specific approach used in this benchmark might not be the most efficient or effective way to measure regex performance.

Related benchmarks:

RegExp unnecessary matches implications (version: 7)

Check if having unnecessary matches in a regular expression has some performance implication

Comparing performance of: With unnecessary matches vs Without unnecessary matches

Created: 2 years ago by: Registered User

Jump to the latest result

With unnecessary matches

Without unnecessary matches

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):