This guide describes the next layer of database objects and schema details that matter once basic table synchronization is already stable.
Why This Guide Exists
After tables, indexes, views, triggers, and routines are in place, the next source of complexity is not a new engine. It is deeper object coverage inside the existing engines.
That usually includes:
generated columns
more complex index variants
richer default expressions
stricter check constraints
deeper dependency graphs across views, routines, and foreign keys
dialect-specific objects that affect planning or rebuild safety
These cases are where a runtime evolution engine either matures or starts producing too many conservative rebuilds.
Coverage Tiers
The most useful way to think about advanced object coverage is by risk tier.
Tier 1: Safe Structural Extensions
Examples:
additional non-unique indexes
richer default expressions with clear equivalence rules
basic check constraints that can be inspected and compared reliably
These are the first candidates for broader automation.
Tier 2: Planner-Sensitive Features
Examples:
generated columns
expression-based indexes
partial or filtered indexes where dialects support them
defaults whose semantics are engine-specific rather than purely textual
These require stronger semantic comparison before automation can be trusted.
Tier 3: Deep Dependency Objects
Examples:
nested views
routines referencing tables under rebuild
triggers with object-level dependency implications
rebuilds that cross ownership boundaries through foreign keys or procedural objects
These are the cases most likely to need orchestration depth, explicit review, and engine-specific policy.
Recommended Scope For Existing Engines
SQLite
SQLite should prioritize:
generated-column behavior only if it can be inspected and compared reliably
deeper rebuild clarity for triggers and copy-mapping paths
avoiding false destructive detection for narrow schema changes
SQLite should not chase every advanced object feature before its comparison rules are stable.
PostgreSQL
PostgreSQL is the natural home for the broadest advanced-object coverage.
Priority areas:
deeper dependency graph coverage
richer index and materialized-view handling
stronger semantic equivalence for advanced defaults and constraint changes
This is the engine where advanced object depth is most aligned with the current architecture.
MySQL
MySQL should prioritize:
generated-column and default-expression handling only where semantics can be compared safely
stronger dependency awareness around routines, views, and external foreign keys
avoiding over-promising on features that are dialect-limited or emulated
The goal should be dependable support, not superficial parity wording.
How To Decide Whether A New Object Type Is Ready
A good readiness test is:
can it be inspected reliably?
can it be represented in code or metadata clearly?
can change detection distinguish equivalent from destructive cases?
can the plan artifact explain the result?
can observability expose the decision path?
If too many answers are no, the object type is not ready for full automation yet.
Relationship To Compatibility Contracts
New advanced object support should update three places together:
DATABASE_ENGINE.md
engine-specific runtime guide
plan, observability, or semantic-detection guides if the new object changes planning logic
That keeps the public contract aligned with actual runtime behavior.
Relationship To Semantic Detection
Advanced object coverage should only expand when semantic detection can keep up.
Otherwise the library may technically “support” a feature while still producing unreliable rebuild decisions.
That is why advanced-object work and semantic-detection work should stay linked.
Minimum Useful Next Step
The most practical next step is not “support every rare object.” It is:
pick one advanced object family
define inspection support
define semantic comparison rules
define plan-artifact representation
add engine-specific tests and documentation
That approach scales better than trying to widen all surfaces at once.
Future Extensions
Likely future focus areas include:
generated columns
expression or filtered indexes where supported
deeper dependency graph traversal
stricter handling of advanced defaults and checks
explicit modeling of more object families beyond tables in persisted plan artifacts