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Rule Catalog — DB Schema Design

.agents/skills/backend-code-review/references/db-schema-rule.md

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Rule Catalog — DB Schema Design

Scope

  • Covers: SQLModel model definitions, schema boundaries in model properties, user-scoped schema design, index redundancy checks, dialect portability in models (SQLite + PostgreSQL), and cross-database compatibility in Alembic migrations.
  • Key directories:
    • Models: src/backend/base/langflow/services/database/models/
    • Migrations: src/backend/base/langflow/alembic/versions/
  • Does NOT cover: session lifecycle, transaction boundaries, and query execution patterns (handled by sqlalchemy-rule.md).

Rules

Do not query other tables inside @property

  • Category: [maintainability, performance]
  • Severity: critical
  • Description: A model @property must not open sessions or query other tables. This hides dependencies across models, tightly couples schema objects to data access, and can cause N+1 query explosions when iterating collections. Langflow uses SQLModel which combines SQLAlchemy table models with Pydantic validation; properties should only derive values from already-loaded fields.
  • Suggested fix:
    • Keep model properties pure and local to already-loaded fields.
    • Move cross-table data fetching to service methods or CRUD functions under the model's module (e.g., models/flow/utils.py).
    • For list/batch reads, fetch required related data explicitly (join/preload/bulk query) before rendering derived values.
  • Example:
    • Bad:
      python
      class Flow(FlowBase, table=True):
    __tablename__ = "flow"

    @property
    def folder_name(self) -> str:
        from lfx.services.deps import session_scope
        import asyncio
        loop = asyncio.get_event_loop()
        async def _get():
            async with session_scope() as session:
                folder = (await session.execute(
                    select(Folder).where(Folder.id == self.folder_id)
                )).scalar_one()
                return folder.name
        return loop.run_until_complete(_get())
    • Good:
      python
      class Flow(FlowBase, table=True):
    __tablename__ = "flow"

    @property
    def display_name(self) -> str:
        return self.name or "Untitled Flow"

# Service or CRUD layer performs explicit fetch for related Folder rows.
async def get_flow_with_folder(flow_id: UUID, session: AsyncSession) -> tuple[Flow, Folder]:
    stmt = select(Flow, Folder).join(Folder, Flow.folder_id == Folder.id).where(Flow.id == flow_id)
    result = (await session.execute(stmt)).one_or_none()
    return result

Use user_id for user-scoped data

  • Category: maintainability
  • Severity: suggestion
  • Description: Langflow scopes user-owned data by user_id (not tenant_id). When an entity belongs to a specific user, include user_id in the model definition. This improves data isolation safety and keeps future multi-user or partitioning strategies practical. The user_id column should reference user.id via a foreign key where appropriate.
  • Suggested fix:
    • Add a user_id column and ensure related unique/index constraints include the user dimension when applicable.
    • Propagate user_id through service interfaces to keep access paths user-scoped.
    • Exception: if a table is explicitly designed as globally shared metadata (e.g., system settings), document that design decision clearly.
  • Example:
    • Bad:
      python
      class CustomComponent(SQLModel, table=True):
    __tablename__ = "custom_component"
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    name: str = Field(index=True)
    code: str = Field(sa_column=Column(Text))
    # Missing user_id: any user can see/modify any custom component
    • Good:
      python
      class CustomComponent(SQLModel, table=True):
    __tablename__ = "custom_component"
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    user_id: UUID = Field(foreign_key="user.id", index=True)
    name: str = Field(index=True)
    code: str = Field(sa_column=Column(Text))

Detect and avoid duplicate/redundant indexes

  • Category: performance
  • Severity: suggestion
  • Description: Review index definitions for leftmost-prefix redundancy. For example, index (a, b, c) can safely cover most lookups for (a, b). Keeping both may increase write overhead and can mislead the optimizer into suboptimal execution plans. This applies to both SQLModel Field(index=True) declarations and explicit __table_args__ index definitions.
  • Suggested fix:
    • Before adding an index, compare against existing composite indexes by leftmost-prefix rules.
    • Drop or avoid creating redundant prefixes unless there is a proven query-pattern need.
    • Apply the same review standard in both model __table_args__ and Alembic migration index DDL.
  • Example:
    • Bad:
      python
      class Message(SQLModel, table=True):
    __tablename__ = "message"
    __table_args__ = (
        sa.Index("idx_msg_user_flow", "user_id", "flow_id"),
        sa.Index("idx_msg_user_flow_created", "user_id", "flow_id", "created_at"),
    )
    • Good:
      python
      class Message(SQLModel, table=True):
    __tablename__ = "message"
    __table_args__ = (
        # Keep the wider index unless profiling proves a dedicated short index is needed.
        sa.Index("idx_msg_user_flow_created", "user_id", "flow_id", "created_at"),
    )

Avoid dialect-specific constructs directly in models; use portable types

  • Category: maintainability
  • Severity: critical
  • Description: Langflow supports both SQLite (development) and PostgreSQL 15+ (production). Model/schema definitions should avoid PostgreSQL-only or SQLite-only constructs directly in business models. When database-specific behavior is required, encapsulate it behind a portable abstraction. SQLModel's Field() and Column() already provide most portability, but direct use of dialect-specific types (e.g., postgresql.JSONB, postgresql.ARRAY) breaks SQLite compatibility.
  • Suggested fix:
    • Use SQLModel's JSON column type (which maps to the appropriate dialect type) instead of postgresql.JSONB.
    • Use sa.Text or sa.String instead of dialect-specific text types.
    • If a dialect-specific feature is truly needed, add a conditional wrapper and document the rationale.
  • Example:
    • Bad:
      python
      from sqlalchemy.dialects.postgresql import JSONB

class ToolConfig(SQLModel, table=True):
    __tablename__ = "tool_config"
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    config: dict = Field(sa_column=Column(JSONB, nullable=False))  # Breaks on SQLite
    • Good:
      python
      from sqlmodel import JSON

class ToolConfig(SQLModel, table=True):
    __tablename__ = "tool_config"
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    config: dict | None = Field(default=None, sa_column=Column(JSON))  # Works on both dialects

Guard migration incompatibilities with dialect checks

  • Category: maintainability
  • Severity: critical
  • Description: Alembic migration scripts under src/backend/base/langflow/alembic/versions/ must account for SQLite/PostgreSQL incompatibilities explicitly. SQLite has limited ALTER TABLE support (no DROP COLUMN before 3.35, no ADD CONSTRAINT). For dialect-sensitive DDL or defaults, branch on the active dialect or use op.batch_alter_table() for SQLite compatibility.
  • Suggested fix:
    • In migration upgrades/downgrades, bind connection and branch by dialect for incompatible SQL fragments.
    • Use op.batch_alter_table() for column modifications on SQLite.
    • Avoid one-dialect-only migration logic unless there is a documented, deliberate compatibility exception.
  • Example:
    • Bad:
      python
      def upgrade():
    op.add_column("flow", sa.Column(
        "access_type",
        sa.String(50),
        server_default=sa.text("'PRIVATE'::character varying"),  # PostgreSQL-only cast
        nullable=False,
    ))
    • Good:
      python
      def upgrade():
    conn = op.get_bind()
    if conn.dialect.name == "postgresql":
        default_expr = sa.text("'PRIVATE'::character varying")
    else:
        default_expr = sa.text("'PRIVATE'")

    with op.batch_alter_table("flow") as batch_op:
        batch_op.add_column(sa.Column(
            "access_type",
            sa.String(50),
            server_default=default_expr,
            nullable=False,
        ))

SQLModel-specific patterns

  • Category: best practices
  • Severity: suggestion
  • Description: Langflow uses SQLModel which merges SQLAlchemy ORM and Pydantic validation into a single class hierarchy. Follow these conventions for model definitions:
    • Use SQLModel as the base class with table=True for database-backed models.
    • Use SQLModel without table=True for schema/validation-only models (e.g., FlowCreate, FlowRead, FlowUpdate).
    • Use Field() for column definitions; use sa_column=Column(...) only when Field() alone cannot express the constraint.
    • Use Relationship() for ORM relationships; put related model type hints behind TYPE_CHECKING to avoid circular imports.
  • Suggested fix: Follow the existing patterns in src/backend/base/langflow/services/database/models/flow/model.py for reference on how to define base models, table models, and CRUD schema models.
  • Example:
    • Bad:
      python
      # Mixing table model and API schema in one class
class Flow(SQLModel, table=True):
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    name: str
    data: dict | None = None
    # API-only fields mixed in
    component_count: int = 0  # Not a DB column, computed at API time
    • Good:
      python
      # Table model: only DB columns
class Flow(FlowBase, table=True):
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    user_id: UUID = Field(foreign_key="user.id", index=True)

# Schema model: includes computed fields for API responses
class FlowRead(FlowBase):
    id: UUID
    user_id: UUID
    component_count: int = 0

Follow the Base → Table → Create → Read → Update schema pattern

  • Category: best practices
  • Severity: suggestion
  • Description: Langflow uses a layered SQLModel schema pattern. {Entity}Base(SQLModel) defines shared fields, validators (@field_validator), and serializers (@field_serializer). {Entity}(Base, table=True) adds primary key and relationships. Separate Create, Read, and Update schemas control API boundaries. Use model_dump(exclude_unset=True, exclude_none=True) for partial updates. Use FlowRead.model_validate(db_flow, from_attributes=True) to convert ORM→schema while session is active.
  • Suggested fix:
    • Define Base → Table → Create → Read → Update for every entity.
    • Put @field_validator and @field_serializer on the Base so they apply everywhere.
    • Never return the Table model directly from a route — always convert to the Read schema.
  • Example:
    • Bad:
      python
      # Returning the table model directly (leaks internal fields, relationships)
@router.get("/{flow_id}")
async def get_flow(flow_id: UUID, session: DbSession) -> Flow:
    return (await session.execute(select(Flow).where(Flow.id == flow_id))).scalar_one()
    • Good:
      python
      class FlowBase(SQLModel):
    name: str = Field(index=True)
    description: str | None = Field(default=None)

    @field_validator("endpoint_name")
    @classmethod
    def validate_endpoint_name(cls, v):
        if v is not None and not re.match(r"^[a-zA-Z0-9_-]+$", v):
            raise HTTPException(status_code=422, detail="Invalid endpoint name")
        return v

    @field_serializer("updated_at")
    def serialize_datetime(self, value):
        if isinstance(value, datetime):
            return value.replace(microsecond=0).isoformat()
        return value

class Flow(FlowBase, table=True):
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    user_id: UUID = Field(foreign_key="user.id")

class FlowCreate(FlowBase):
    user_id: UUID | None = None
    folder_id: UUID | None = None

class FlowRead(FlowBase):
    id: UUID
    user_id: UUID | None = Field()

@router.get("/{flow_id}", response_model=FlowRead)
async def get_flow(*, flow_id: UUID, session: DbSession, current_user: CurrentActiveUser) -> FlowRead:
    flow = (await session.execute(select(Flow).where(Flow.id == flow_id, Flow.user_id == current_user.id))).scalar_one()
    return FlowRead.model_validate(flow, from_attributes=True)