Defines the data shape, invariants, keys, relationships, lifecycle, and selection criteria for data validation. This reference fixes the adoption boundary, failure behavior, and observable evidence while leaving environment-specific limits configurable.
Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.
Decision
For Data validation, adopt database constraints plus one transactional domain write path. Choose the model from access patterns and invariants, with canonical identity, lifecycle, transaction, migration, and deletion rules. This data block focuses specifically on modeling decision. This package is a reference contract: the adopting team must choose numeric limits, providers, jurisdictions, and operational owners from evidence in its own environment.
Scope
- The Data validation actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes required for the modeling decision focus of this data block.
- Primary and alternate interfaces, background work, caches, integrations, support paths, and evidence that can exercise the same boundary.
- Adoption-specific configuration, rollout, recovery, and verification responsibilities needed to apply the reference safely.
Outside this block
- Choosing a universal vendor, framework, jurisdiction, numeric threshold, retention period, or service-level objective for every adopter.
- Claiming that packaged scenarios have run against a downstream implementation or that this reference grants legal, security, or accessibility certification.
Contract
- Encode stable invariants with types, keys, uniqueness, foreign keys, and checks rather than relying only on caller convention.
- Define transaction and concurrency boundaries, including how stale writes, duplicate commands, and partial failures resolve.
- Roll out additive schema and dual-compatible code before destructive changes; backfills are resumable and observable.
- Document ownership, retention, deletion, backup, restore, and index implications for every material record or relation.
- Represent absence, unknown, empty, pending, historical, and deleted states distinctly when their business behavior differs.
- Prevent duplicate logical relationships with a canonical key and constraint rather than cleanup after conflicting writes.
- Define types, required and nullable fields, ranges, formats, enumerations, cross-field invariants, referential checks, normalization, and stable error codes in a versioned authoritative schema.
- Validate again at the trusted write boundary, preserve user input for recovery, reject ambiguous coercion, and migrate stored invalid or legacy values through an explicit compatibility policy.
- For the modeling decision variant, enforce this declared boundary: defines the data shape, invariants, keys, relationships, lifecycle, and selection criteria for data validation.
Implementation guidance
- Validate the model against representative reads, writes, bulk operations, retention, audit, and restore before committing to storage details.
- Model Data validation inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
- Store the applicable Data validation contract or policy version with material state so migrations, replay, and support decisions remain attributable.
- Introduce the path behind controlled rollout, compare expected and observed outcomes, and keep a tested recovery route until adoption evidence is complete.
Failure handling and safeguards
- Constraint or migration failure preserves the last valid state and exposes a resumable, operator-owned recovery point.
- Invalid or contradictory Data validation state is rejected or quarantined; the implementation never guesses a value that broadens authority or duplicates an effect.
- Retries are bounded and use durable identity; after exhaustion, work reaches an inspectable terminal state with an accountable owner.
Verification and operations
- Test constraints and concurrent transitions directly, then restore a backup and re-run invariant checks on the recovered data.
- Monitor Data validation success, denial, validation failure, dependency failure, retry exhaustion, and recovery by contract version without sensitive payload dimensions.
- Re-run paired positive and negative fixtures after policy, schema, dependency, migration, or boundary changes and preserve the resulting evidence.
Adoption assumptions
- Names and values in the example are a concrete fixture, not universal defaults; adopters replace them through documented evidence and ownership.
- The adopting system can provide authenticated identity, durable operation or record versions, bounded telemetry, and a controlled path for change.
The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.