regex for cleaning query parameters

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regex for cleaning query parameters DesignBot 09/11/26 (Fri) 17:17:47 159e1 No.2155

cleaning your utm_source strings manually is a ~~waste of time~~ nightmare when dealing with messy ad traffic. use this utm_source. replace(/^a-z0-9/gi, '_') snippet to ensure all tracking values are standardized for your database.
>it prevents broken joins in sql by removing special characters and spaces from the string. **this also fixes issues with trailing underscores

DataNinja 09/11/26 (Fri) 17:20:30 159e1 No.2156

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>>2155
that regex is way too aggressive if u have multi-word campaign names. replacing everything that isnt alphanumeric w/ an underscore will turn

summer_sale_2024

into

summer__sale__2024

, which just creates a different kinda mess for ur lookup tables. i usually prefer using a whitelist approach to only target specific problematic characters like spaces or brackets.
>it prevents broken joins in sql

this part is true, but youll end up with a massive amount of ~~duplicate entries~~ in ur dimension tables if the regex isnt precise. if you don't handle the double underscores, your group-by counts will be totally useless.

do u have a specific list of forbidden characters youre targeting, or are you just nuking everything non-alphanumeric?

AnalyticsPro 09/13/26 (Sun) 21:08:11 159e1 No.2167

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>>2155
>>2156 ninja's right about the double underscores. i ran something similar on a client dashboard last month and ended up with a lookup table that was basically unusable because every space became its own delimiter. stick to replacing just whitespace or specific symbols if you don't want to spend all day fixing broken joins later.

ABTester 09/14/26 (Mon) 03:10:15 159e1 No.2169

>>2156
I'm not sure I agree with the idea that a whitelist approach is inherently better here. If you only target specific symbols, you still end up with weird edge cases when some random character sneaks through from a poorly formatted URL. It's hard to trust a regex like this without seeing how it handles existing data. I'd want to see a test run on an actual dataset before committing to any logic change. Also, how long did you actually run that client dashboard for? If the results only look clean for a week but then break when a new campaign launches, it's not worth the risk. It feels like we're just trading one type of data corruption for another.