Implement (migrate) constraints and validation from postgres betydb

[dlebauer]

Background

The Postgres implementation of betydb had a number of data constraints. These did a great job of enforcing data integrity (value ranges, foreign keys, uniqueness, etc). When migrating to a CSV based dataset, these database constraints are no longer automatically enforced.

Constraints are

• described / documented in Constraints for BETYdb.
• Enumerated in the Constraints Spreadsheet
• Implemented in the postgres schema structure db/structure.sql in the bety repository, but these were not completely implemented.

Scope

It is not necessary to replicate all constraints, which would be a lot of work with diminishing returns (there are a lot!).

• translate PostgreSQL constraints to validation within R, and run by GH Actions
• focus on useful constraints only

⚠️ Do not use the original design and implementation as a checklist.
⚠️ Don't overthink it. the original set of constraints took a lot of time to develop.

Approach

1. Prioritize constraints
2. Discuss implementation approach here. Some options:
   1. validation functions, e.g. in data-raw/validation.R, called by data-raw/make-data.R
   2. testthat tests, e.g. tests/testthat/test-data-constraints.R
   3. combination of above
   4. Other

Large chunks of this work, especially translating constraints to R, may be well suited to LLMs because they are formally defined and the most important ones are implemented in SQL.

Priorities

General Approach

Prioritize constraints that:

• prevent real data corruption
• avoid complex cross-table logic
• are easy to understand and maintain

Value constraints

• numeric ranges (e.g., percentages between 0–100, precipitation ≥0)
• positive counts (n ≥ 0)
• sanity bounds for variables (min, max from variables table)

Uniqueness constraints

• natural keys that prevent duplicate rows

Non-NULL constraints

On critical fields required to interpret measurements; natural keys.

Standardization

• whitespace normalization
• canonical values for units or categorical variables

Deliverables

• R validation functions implementing key constraints
• automated tests ensuring they run in CI
• documentation describing which constraints are enforced

[dlebauer]

Follow-up from review of #12. Using this issue as the umbrella for validation and constraints work that remains after the PR-specific fixes land.

• Validate foreign-key coverage between traitsview and shipped support tables.
• Decide and enforce row-identity constraints for traitsview.
• Define how sentinel 0 IDs should be treated in packaged data.
• Add CI / testthat checks for these invariants.
• Document which integrity rules are enforced at build time versus only reported.

PR-specific blockers from #12 should stay on the PR review rather than move here.