A rank mechanism for RDBMS Table models - JoseCanova/brainz GitHub Wiki

1. At the end, prostitution and casual sex follow the same script—only the former demands risk modeling.

If you think about it, both prostitution and casual sex are like two branches on the same Git repo—different commit histories, but they merge more often than you’d expect. The key distinction? Prostitution comes bundled with a sophisticated risk model, while casual sex is more of a YOLO() function call.

In academic terms:

Casual sex is your classic “fire-and-forget” algorithm—no error handling, no rollback. You might run into a NullPointerException at 2am, but hey, that’s the cost of doing business.

Prostitution, on the other hand, is enterprise-grade. It’s got compliance checks, transactional boundaries, and, most importantly, a risk model robust enough to make an actuary weep. Before insert operations, there’s a full-on validation pipeline:

Are you clean? Are you paid? Have you signed the EULA? It’s like Spring Security for your bedroom ORM: you can’t even access the endpoint until you’ve passed the filter chain. Meanwhile, casual sex is like a deprecated API—unsecured, undocumented, and supported only by word-of-mouth.

2. A rank mechanism for RDBMS Table models, where the rank may vary by the one-to-one, one-to-many, many-to-many relations...

Let’s face it: not all relationships are created equal—especially not in your database schema. In the world of BrainzGraphModel and CsvFileProcessingPriority, we’ve taken ranking relationships to a new level of absurdity.

One-to-One: This is the “monogamy” of database relations. Exclusive, committed, and usually a bit jealous if you try to introduce a third table. ORM frameworks love this—easy to map, low drama. In our ranking system, we treat this with the reverence of a first marriage: high rank, clear boundaries.

One-to-Many: Polygamy, but legal in SQL. One table gets to play the field, the other is stuck with a foreign key and a lot of questions. ETL jobs treat this like group therapy: lots of records, but typically one source of truth. Moderate rank, but watch out for those cascading deletes—they’ll break your heart.

Many-to-Many: The wild west. Everyone’s related to everyone else, nobody knows who’s paying the bill, and the join table is just happy to be invited. In our rank mechanism, this gets the same weight as one-to-one—because, honestly, it’s just as complicated—but with the added ambiguity of “who’s the fact and who’s the dimension?” It’s like a party where no one remembers who Many-to-Many: brought the chips, but everyone’s eating.

And here’s where Java JPA comes in to save the day: ORMs can peer into your RDBMS and, like a relationship counselor, tell you who’s the fact, who’s the dimension, and who’s just there for the free Wi-Fi. The mapping annotations do the heavy lifting, so you don’t end up with a love triangle between your tables and your ETL pipeline.

In summary: Ranking database relationships is like ranking romantic entanglements—sometimes you’re the main table, sometimes you’re just a lookup, and every so often, you wake up to find your join table has ghosted you. But with the right modeling (and a bit of Java code), you can at least avoid the worst of the referential integrity issues.