litestar-databases（莱特星数据库）

Databases

Use this skill when persistence architecture and ORM integration are core to the task, especially SQLAlchemy plugin wiring or Piccolo DTO-based API flows.

Execution Workflow

1. Choose ORM path (SQLAlchemy or Piccolo) based on ecosystem and project constraints.
2. For SQLAlchemy, choose plugin strategy first (SQLAlchemyPlugin vs SQLAlchemyInitPlugin + optional SQLAlchemySerializationPlugin).
3. Configure engine + session factory centrally at app initialization (async or sync).
4. Define model/repository/service boundaries and keep transaction ownership explicit.
5. Integrate dependency injection so handlers receive a scoped session/unit-of-work.
6. Define DTO and serialization boundaries at API edges (never leak raw ORM internals unintentionally).
7. Validate lifecycle behavior: startup initialization, request cleanup/rollback, and lazy-loading behavior in hot paths.

Implementation Rules

• Keep transactions explicit and short-lived; commit where business operations complete, rollback on failure paths.
• Keep repositories/services free of HTTP transport concerns.
• Keep session ownership deterministic; handlers should not guess who closes/rolls back a session.
• Prefer async SQLAlchemy for IO-heavy API workloads unless sync architecture is a deliberate requirement.
• Avoid returning ORM entities blindly when relationships/lazy attributes can trigger unexpected DB access.
• Use DTO shaping for both inbound and outbound payloads to protect private/internal fields.

SQLAlchemy: Decision Guide

• Use SQLAlchemyPlugin for most applications that need both app/session tooling and SQLAlchemy model serialization support.
• Use SQLAlchemyInitPlugin only when you need engine/session injection and lifecycle management but do not want automatic SQLAlchemy DTO serialization.
• Add SQLAlchemySerializationPlugin when you want automatic SQLAlchemy DTO generation for handler data and return annotations.
• Use separate init + serialization plugins when you need explicit composition control; otherwise prefer the combined SQLAlchemyPlugin.

SQLAlchemy: Models and Repository Patterns

Litestar SQLAlchemy support includes built-in repository utilities and base model patterns:

• Repository classes:
• SQLAlchemyAsyncRepository for async session workflows.
• Generic repository support for CRUD plus filtering, sorting, pagination, and bulk operations.
• Base model options include UUID and BigInt primary-key variants with optional audit columns:
• UUIDBase, UUIDAuditBase
• BigIntBase, BigIntAuditBase

Implementation expectations:

• Choose one base strategy early (UUID vs BigInt) and keep it consistent.
• Keep query logic in repositories/services, not route handlers.
• Use repository filtering/pagination primitives in list endpoints rather than ad-hoc SQL in handlers.
• Treat relationship loading strategy as part of API design to avoid N+1 regressions.

SQLAlchemy: Plugin Configuration Patterns

Pattern 1: Combined plugin (recommended)

from litestar import Litestar
from litestar.plugins.sqlalchemy import SQLAlchemyAsyncConfig, SQLAlchemyPlugin

config = SQLAlchemyAsyncConfig(
    connection_string="sqlite+aiosqlite:///app.sqlite",
    create_all=True,
    metadata=Base.metadata,
)
sqlalchemy = SQLAlchemyPlugin(config=config)
app = Litestar(route_handlers=[...], plugins=[sqlalchemy])

Pattern 2: Split init + serialization plugins

from litestar import Litestar
from litestar.plugins.sqlalchemy import (
    SQLAlchemyAsyncConfig,
    SQLAlchemyInitPlugin,
    SQLAlchemySerializationPlugin,
)

config = SQLAlchemyAsyncConfig(connection_string="sqlite+aiosqlite:///app.sqlite")
app = Litestar(
    route_handlers=[...],
    plugins=[SQLAlchemyInitPlugin(config=config), SQLAlchemySerializationPlugin()],
)

Pattern 3: Sync configuration

from litestar.plugins.sqlalchemy import SQLAlchemyPlugin, SQLAlchemySyncConfig

config = SQLAlchemySyncConfig(connection_string="sqlite:///app.sqlite")
plugin = SQLAlchemyPlugin(config=config)

SQLAlchemy: Dependency Injection and Lifecycle

SQLAlchemyInitPlugin provides:

• Engine and session availability via dependency injection.
• Engine and session factory stored on app state.
• A before_send handler for request-lifecycle cleanup behavior.
• Signature namespace support for SQLAlchemy-annotated handler dependencies.

Design guidance:

• Inject session dependencies into handlers/services instead of constructing sessions ad hoc.
• Keep one clear per-request unit-of-work path.
• Validate rollback and cleanup behavior during exception paths.

SQLAlchemy: Serialization and DTO Boundaries

SQLAlchemySerializationPlugin automatically creates SQLAlchemy DTO types for handler data and return annotations that use SQLAlchemy models (including collections), unless an explicit DTO is already provided.

Practical guidance:

• Use automatic serialization for straightforward CRUD APIs.
• Use explicit DTO classes when fields, nesting, or security requirements need tighter control.
• Mark model fields (for example via DTO field controls) to prevent exposing private data.
• Verify generated OpenAPI schemas and serialized payloads after model changes.

Piccolo ORM Guidance

Litestar supports Piccolo-centric API flows via PiccoloDTO.

Core pattern:

• Define Piccolo Table models.
• Use PiccoloDTO[Model] for request/response shaping.
• Use custom DTO subclasses with DTOConfig for partial updates and field exclusions.

Example:

from litestar.contrib.piccolo import PiccoloDTO
from litestar.dto import DTOConfig

class PatchDTO(PiccoloDTO[Task]):
    config = DTOConfig(exclude={"id"}, partial=True)

Piccolo implementation guidance:

• Keep table definitions and DB config centralized.
• Use DTO-level controls for patch semantics and hidden/internal columns.
• Keep query and persistence logic out of transport handlers where possible.

Validation Checklist

• Confirm selected plugin strategy matches requirements (combined vs split plugins).
• Confirm async/sync config matches deployed runtime and DB driver.
• Confirm migrations/model metadata align with runtime models.
• Confirm session injection works and per-request cleanup runs reliably.
• Confirm rollback behavior on exceptions is tested.
• Confirm DTO boundaries prevent internal/private field leakage.
• Confirm N+1 and lazy-loading pitfalls are addressed in hot paths.
• Confirm list endpoints enforce deterministic filtering/sorting/pagination.

Cross-Skill Handoffs

• Use litestar-dependency-injection for session provisioning patterns.
• Use litestar-dto and litestar-responses for safe transport shaping.
• Use litestar-testing for transactional test isolation.
• Use litestar-openapi to verify schema output after DTO/plugin changes.

Litestar References

• https://docs.litestar.dev/2/usage/databases/sqlalchemy/models_and_repository.html
• https://docs.litestar.dev/2/usage/databases/sqlalchemy/plugins/index.html
• https://docs.litestar.dev/2/usage/databases/sqlalchemy/plugins/sqlalchemy_plugin.html
• https://docs.litestar.dev/2/usage/databases/sqlalchemy/plugins/sqlalchemy_init_plugin.html
• https://docs.litestar.dev/2/usage/databases/sqlalchemy/plugins/sqlalchemy_serialization_plugin.html
• https://docs.litestar.dev/2/usage/databases/piccolo.html