data-relational.md

Data Relational — SQLAlchemy Adapter

Package: pyfly.data.relational.sqlalchemy Commons: pyfly.data — shared ports, pagination, query parsing, entity mapping

This guide covers the SQLAlchemy adapter for relational databases. For generic data concepts shared across all adapters (repository ports, Page/Pageable/Sort, QueryMethodParser, Mapper, extensibility), see the Data Module Guide. For document databases, see the Data Document Guide.

Hexagonal by design: your services depend on RepositoryPort[T, ID] (the port), never on Repository[T, ID] (the adapter). SQLAlchemy is the default relational adapter today — but the layer is designed so any relational backend (Tortoise ORM, Django ORM, etc.) can be added by implementing the same ports.

PyFly Data Relational implements the Repository pattern with Spring Data-style derived query methods, composable specifications, pagination, entity mapping, and declarative transaction management — backed by SQLAlchemy's async ORM.

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Table of Contents

• Architecture Overview
• Entity Definition
  • Base (DeclarativeBase)
  • BaseEntity: Audit Trail Fields
  • Defining Your Own Entities
• Repository Pattern
  • Repository Class
  • Creating a Repository
  • CRUD Methods Reference
• Derived Query Methods
  • Complete Derived Query Examples
• Custom Queries with @query
  • JPQL-Like Syntax
  • Native SQL
  • Return Type Inference
  • JPQL Transpilation Details
• Specifications
  • Creating Specifications
  • Combining Specifications
  • Using Specifications with Repositories
• FilterOperator
  • Available Operators
  • Composing Filters
• FilterUtils: Query by Example
• Pagination
  • Paginated Queries
  • Paginated Specification Queries
• Transaction Management
• Data Auditing
  • AuditingEntityListener
  • How Auditing Works
  • Resolving the Current User
  • Registering the Listener
• RepositoryBeanPostProcessor
  • How It Works
  • Stub Detection
• QueryMethodCompiler
• Complete CRUD Example
• See Also

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Architecture Overview

All concrete types live in the SQLAlchemy adapter package. The namespace pyfly.data.relational is a pass-through and does not re-export anything.

from pyfly.data.relational.sqlalchemy import (
    Base, BaseEntity,                   # SQLAlchemy entity base classes
    Repository,                         # Repository[T, ID] implementation
    Specification,                      # Composable query predicates
    FilterOperator, FilterUtils,        # Query-by-example utilities
    QueryExecutor, query,               # Custom @query decorator
    QueryMethodCompiler,                # Derived query → SQLAlchemy compiler
    RepositoryBeanPostProcessor,        # Auto-wires query methods
    reactive_transactional,             # Declarative transaction management
)

Note: Always import concrete types from pyfly.data.relational.sqlalchemy. Commons types (Page, Pageable, RepositoryPort, etc.) are imported from pyfly.data — see the Data Module Guide.

---

Entity Definition

Base (DeclarativeBase)

PyFly exports a pre-configured SQLAlchemy DeclarativeBase:

from pyfly.data.relational.sqlalchemy import Base

Use Base directly when you need SQLAlchemy entities without the built-in audit trail fields.

BaseEntity: Audit Trail Fields

BaseEntity extends Base and provides a UUID primary key plus four audit trail columns. All domain entities should inherit from this class:

from pyfly.data.relational.sqlalchemy import BaseEntity

Inherited fields:

Field	Type	Column Type	Description
id	Mapped[UUID]	Primary key	Auto-generated UUID v4
created_at	Mapped[datetime]	DateTime(tz=True)	Set automatically on insert
updated_at	Mapped[datetime]	DateTime(tz=True)	Set on insert, updated on every save
created_by	Mapped[str|None]	String(255)	Creator identifier (default None)
updated_by	Mapped[str|None]	String(255)	Updater identifier (default None)

BaseEntity is declared with __abstract__ = True, so it does not create its own database table.

Defining Your Own Entities

Extend BaseEntity and declare your domain columns:

from pyfly.data.relational.sqlalchemy import BaseEntity
from sqlalchemy import String, Float, Boolean
from sqlalchemy.orm import Mapped, mapped_column


class Order(BaseEntity):
    __tablename__ = "orders"

    customer_id: Mapped[str] = mapped_column(String(255))
    status: Mapped[str] = mapped_column(String(50), default="PENDING")
    total: Mapped[float] = mapped_column(Float, default=0.0)
    active: Mapped[bool] = mapped_column(Boolean, default=True)

This entity will have all five inherited fields (id, created_at, updated_at, created_by, updated_by) plus your four custom columns.

---

Repository Pattern

Repository Class

The Repository[T, ID] class provides generic async CRUD operations for any SQLAlchemy model. The two type parameters are:

• T — The entity type (any SQLAlchemy model, including BaseEntity subclasses or plain Base subclasses)
• ID — The primary key type (e.g. UUID, int, str)

When you subclass Repository[T, ID] with concrete type parameters, the framework automatically extracts the entity type and ID type via __init_subclass__. The AsyncSession is injected by the DI container from the auto-configured async_sessionmaker. No explicit __init__ is needed:

from uuid import UUID
from pyfly.data.relational.sqlalchemy import Repository
from pyfly.container import repository as repo_stereotype


@repo_stereotype
class OrderRepository(Repository[Order, UUID]):
    pass

# Usage (session is injected by the container):
order = await repo.save(Order(customer_id="abc", status="PENDING"))
found = await repo.find_by_id(order.id)

Repository[T, ID] satisfies the RepositoryPort[T, ID] protocol, enabling hexagonal architecture where your service layer depends on the port, not the adapter.

Creating a Repository

Subclass Repository[T, ID] with concrete type parameters and register it with the @repository stereotype:

from uuid import UUID
from pyfly.data.relational.sqlalchemy import Repository
from pyfly.container import repository as repo_stereotype


@repo_stereotype
class OrderRepository(Repository[Order, UUID]):
    pass

For entities with integer primary keys:

@repo_stereotype
class ProductRepository(Repository[Product, int]):
    pass

How it works:

1. __init_subclass__ inspects __orig_bases__ to extract the entity type (Order) and ID type (UUID) from the generic parameters at class definition time.
2. The AsyncSession is provided as an auto-configured bean by RelationalAutoConfiguration and injected by the container into the repository's constructor.
3. The entity type is used internally for all query operations — no need to pass it manually.

CRUD Methods Reference

Method	Return Type	Description
save(entity)	T	Insert or update; flushes and refreshes
find_by_id(id: ID)	T | None	Find by primary key
find_all(**filters)	list[T]	Find all, optionally filtered by column values
delete(id: ID)	None	Delete by primary key (no-op if not found)
count()	int	Count all entities in the table
exists(id: ID)	bool	Check if an entity with this ID exists
find_paginated(page, size, pageable)	Page[T]	Paginated query with optional sorting
find_all_by_spec(spec)	list[T]	Find all matching a Specification
find_all_by_spec_paged(spec, pageable)	Page[T]	Paginated query with Specification + sorting

save() calls session.add(), then session.flush() and session.refresh() to ensure the returned entity has all database-generated values (ID, defaults, etc.).

find_all() accepts keyword arguments that are translated into equality filters:

orders = await repo.find_all(status="PENDING", customer_id="abc")
# Equivalent to: SELECT * FROM orders WHERE status = 'PENDING' AND customer_id = 'abc'

delete() looks up the entity first and deletes it if found. If not found, it is a no-op.

---

Derived Query Methods

PyFly automatically generates query implementations from method names using the Spring Data naming convention. You define stub methods on your repository and the RepositoryBeanPostProcessor compiles them into real SQLAlchemy queries at startup.

For the full naming convention reference (prefixes, operators, connectors, ordering), see the Data Module Guide — Derived Query Methods.

Complete Derived Query Examples

@repo_stereotype
class OrderRepository(Repository[Order, UUID]):

    # Equals (default operator)
    async def find_by_status(self, status: str) -> list[Order]: ...

    # Multiple conditions with AND
    async def find_by_customer_id_and_status(
        self, customer_id: str, status: str
    ) -> list[Order]: ...

    # Greater than
    async def find_by_total_greater_than(self, min_total: float) -> list[Order]: ...

    # Between (takes 2 arguments)
    async def find_by_total_between(self, low: float, high: float) -> list[Order]: ...

    # LIKE pattern
    async def find_by_customer_id_like(self, pattern: str) -> list[Order]: ...

    # Contains (wraps value in %)
    async def find_by_customer_id_containing(self, fragment: str) -> list[Order]: ...

    # IN a list
    async def find_by_status_in(self, statuses: list[str]) -> list[Order]: ...

    # IS NULL / IS NOT NULL (zero arguments consumed)
    async def find_by_deleted_at_is_null(self) -> list[Order]: ...
    async def find_by_email_is_not_null(self) -> list[User]: ...

    # COUNT prefix
    async def count_by_status(self, status: str) -> int: ...

    # EXISTS prefix
    async def exists_by_customer_id(self, customer_id: str) -> bool: ...

    # DELETE prefix (returns number of rows deleted)
    async def delete_by_status(self, status: str) -> int: ...

    # With ordering
    async def find_by_status_order_by_created_at_desc(
        self, status: str
    ) -> list[Order]: ...

    # Complex: AND + ordering
    async def find_by_status_and_customer_id_order_by_total_desc(
        self, status: str, customer_id: str
    ) -> list[Order]: ...

Each method body should be a stub (... or pass). The RepositoryBeanPostProcessor detects them and replaces them with real implementations at startup.

---

Custom Queries with @query

For complex queries that cannot be expressed through method naming conventions, use the @query decorator:

from pyfly.data.relational.sqlalchemy import query

JPQL-Like Syntax

By default, @query accepts a JPQL-like query string that is transpiled to SQL at startup:

@repo_stereotype
class OrderRepository(Repository[Order, UUID]):

    @query("SELECT o FROM Order o WHERE o.status = :status AND o.total > :min_total")
    async def find_expensive_orders(
        self, status: str, min_total: float
    ) -> list[Order]: ...

    @query("SELECT COUNT(o) FROM Order o WHERE o.role = :role")
    async def count_by_role(self, role: str) -> int: ...

Named parameters (:param_name) are bound from the method's keyword arguments.

Native SQL

Set native=True for raw SQL queries:

@query("SELECT * FROM orders WHERE status = :status", native=True)
async def find_by_status_native(self, status: str) -> list[Order]: ...

Return Type Inference

The QueryExecutor infers the return type from the query shape:

Query Pattern	Return Type
SELECT COUNT(...)	int
Query containing EXISTS	bool
All other SELECT queries	list[entity]

JPQL Transpilation Details

The lightweight JPQL-to-SQL transpiler performs these transformations:

1. FROM Entity alias becomes FROM <tablename> (alias is removed)
2. SELECT alias becomes SELECT *
3. COUNT(alias) becomes COUNT(*)
4. alias.field references become just field (alias prefix stripped)
5. Boolean literals = true / = false become = 1 / = 0

Example transpilation:

JPQL:  SELECT u FROM User u WHERE u.email LIKE :pattern AND u.active = true
SQL:   SELECT * FROM users WHERE email LIKE :pattern AND active = 1

---

Specifications

Specifications provide composable, type-safe query predicates inspired by Spring Data's Specification pattern. They let you build arbitrarily complex WHERE clauses from small, reusable building blocks.

Commons port: The SQLAlchemy Specification[T] subclasses the generic Specification[T, Q] ABC from pyfly.data.specification. This means SQLAlchemy specifications are polymorphic with the commons port — code that accepts pyfly.data.Specification will work with the SQLAlchemy adapter. See the Specification Port section in the Data Commons guide.

Creating Specifications

A Specification[T] wraps a callable that takes an entity class (root) and a SQLAlchemy Select statement, and returns a modified Select:

from pyfly.data.relational.sqlalchemy import Specification

# Inline specification
active = Specification(lambda root, q: q.where(root.active == True))
admin = Specification(lambda root, q: q.where(root.role == "admin"))

Combining Specifications

Specifications support Python's standard operators for composition:

# AND: both conditions must match
active_admins = active & admin

# OR: either condition may match
active_or_admin = active | admin

# NOT: negate a specification
inactive = ~active

# Complex combinations with parentheses
complex_spec = (active & admin) | ~admin

How combination works internally:

• & (AND): Chains the two predicates sequentially. SQLAlchemy naturally combines successive .where() calls with AND.
• | (OR): Applies each predicate independently, extracts the whereclause from each, and combines them using sqlalchemy.or_().
• ~ (NOT): Applies the predicate, extracts the whereclause, and wraps it with sqlalchemy.not_().

Using Specifications with Repositories

# Find all matching a specification
orders = await repo.find_all_by_spec(active & admin)

# Find with pagination
from pyfly.data import Pageable, Sort

pageable = Pageable.of(page=1, size=20, sort=Sort.by("created_at").descending())
page = await repo.find_all_by_spec_paged(active & admin, pageable)

---

FilterOperator

FilterOperator provides a library of static factory methods for creating common Specification predicates without writing lambdas.

Available Operators

Method	SQL Equivalent	Arguments
eq(field, value)	field = value	field, value
neq(field, value)	field != value	field, value
gt(field, value)	field > value	field, value
gte(field, value)	field >= value	field, value
lt(field, value)	field < value	field, value
lte(field, value)	field <= value	field, value
like(field, pattern)	field LIKE pattern	field, pattern
contains(field, value)	field LIKE '%value%'	field, value
in_list(field, values)	field IN (values)	field, list
is_null(field)	field IS NULL	field
is_not_null(field)	field IS NOT NULL	field
between(field, low, high)	field BETWEEN low AND high	field, low, high

Composing Filters

Every FilterOperator method returns a Specification, so they can be composed with &, |, and ~:

from pyfly.data.relational.sqlalchemy import FilterOperator

# Adults between 18 and 65
age_filter = FilterOperator.gte("age", 18) & FilterOperator.lt("age", 65)

# Active users with a verified email
user_filter = FilterOperator.eq("active", True) & FilterOperator.is_not_null("email_verified_at")

# Premium or VIP customers
tier_filter = FilterOperator.in_list("tier", ["PREMIUM", "VIP"])

# Combine everything
final_spec = age_filter & user_filter & tier_filter
results = await repo.find_all_by_spec(final_spec)

---

FilterUtils: Query by Example

Commons port: FilterUtils extends the BaseFilterUtils ABC from pyfly.data.filter. The by(), from_dict(), and from_example() algorithms are inherited from the base class — FilterUtils only implements the adapter-specific hooks _create_eq() and _create_noop(). See the BaseFilterUtils Port section in the Data Commons guide.

FilterUtils generates Specification objects from various input formats, providing a Pythonic take on Spring Data's Query by Example pattern.

from pyfly.data.relational.sqlalchemy import FilterUtils

# From keyword arguments (all eq, ANDed together)
spec = FilterUtils.by(name="Alice", active=True)
results = await repo.find_all_by_spec(spec)

# From a dictionary (None values are automatically skipped)
filters = {"role": "admin", "name": None, "active": True}
spec = FilterUtils.from_dict(filters)
# Produces: role = 'admin' AND active = True (name is skipped)

# From an example object (dataclass or plain object)
# Non-None fields become equality predicates
from dataclasses import dataclass

@dataclass
class UserFilter:
    role: str | None = None
    active: bool | None = None

example = UserFilter(role="admin")
spec = FilterUtils.from_example(example)
# Produces: role = 'admin' (active is None, so skipped)

FilterUtils methods:

Method	Input	Behavior
by(**kwargs)	Keyword arguments	All eq, ANDed together
from_dict(filters)	dict[str, Any]	All eq, ANDed; None values skipped
from_example(example)	Dataclass or object	Non-None fields become eq predicates

---

Pagination

For the full Pageable, Sort, Order, and Page[T] API reference, see the Data Module Guide — Pagination & Sorting.

Paginated Queries

from pyfly.data import Pageable, Sort

# Basic pagination
page = await repo.find_paginated(page=1, size=20)

# With Pageable (overrides page/size and adds sorting)
pageable = Pageable.of(page=2, size=10, sort=Sort.by("name"))
page = await repo.find_paginated(pageable=pageable)

When pageable is provided, its page, size, and sort override the primitive page and size arguments.

Paginated Specification Queries

spec = FilterOperator.eq("status", "ACTIVE")
pageable = Pageable.of(page=1, size=20, sort=Sort.by("created_at").descending())

page = await repo.find_all_by_spec_paged(spec, pageable)
# Returns Page[Order] with filtered, sorted, paginated results

The implementation:

1. Applies the specification's predicate to get the filtered query.
2. Counts total matching rows via a subquery.
3. Applies sort orders from Pageable.sort.
4. Applies offset and limit for pagination.

---

Transaction Management

The @reactive_transactional decorator provides declarative async transaction management:

from pyfly.data.relational.sqlalchemy import reactive_transactional
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker

session_factory: async_sessionmaker[AsyncSession] = ...


@reactive_transactional(session_factory)
async def create_order(session: AsyncSession, customer_id: str) -> Order:
    order = Order(customer_id=customer_id, status="PENDING")
    session.add(order)
    return order
    # Transaction is automatically committed on success
    # Transaction is automatically rolled back on exception

How it works:

1. Opens a new AsyncSession from the session_factory.
2. Begins a transaction with session.begin().
3. Calls the wrapped function with the session as the first argument.
4. On success: the transaction is committed (via the async with context manager).
5. On exception: the transaction is rolled back and the exception is re-raised.

The decorated function's original arguments are passed after the injected session:

@reactive_transactional(session_factory)
async def transfer_funds(session: AsyncSession, from_id: str, to_id: str, amount: float):
    # session is injected; from_id, to_id, amount are passed through
    ...

# Call it without the session argument:
await transfer_funds("acc-1", "acc-2", 100.0)

---

Data Auditing

PyFly provides automatic entity auditing through the AuditingEntityListener. It auto-populates the created_at, updated_at, created_by, and updated_by fields on BaseEntity subclasses via SQLAlchemy ORM events, so you never need to set these fields manually.

AuditingEntityListener

from pyfly.data.relational.sqlalchemy.auditing import AuditingEntityListener

The AuditingEntityListener registers SQLAlchemy before_insert and before_update event listeners on BaseEntity. Because the listeners use propagate=True, they automatically apply to all subclasses of BaseEntity.

How Auditing Works

Event	Fields Set	Behavior
before_insert	created_at, updated_at, created_by, updated_by	Sets both timestamps to datetime.now(UTC). Sets both user fields to the current authenticated user (if available).
before_update	updated_at, updated_by	Sets updated_at to datetime.now(UTC). Sets updated_by to the current authenticated user (if available).

Example:

from pyfly.data.relational.sqlalchemy import BaseEntity
from sqlalchemy import String
from sqlalchemy.orm import Mapped, mapped_column


class Order(BaseEntity):
    __tablename__ = "orders"
    customer_id: Mapped[str] = mapped_column(String(255))
    status: Mapped[str] = mapped_column(String(50))

# When you save a new Order, the audit fields are populated automatically:
order = Order(customer_id="abc", status="PENDING")
saved = await repo.save(order)
# saved.created_at = 2026-02-20T10:30:00+00:00
# saved.updated_at = 2026-02-20T10:30:00+00:00
# saved.created_by = "user-123"  (from SecurityContext)
# saved.updated_by = "user-123"

# On subsequent updates, only updated_at and updated_by change:
saved.status = "SHIPPED"
updated = await repo.save(saved)
# updated.created_at = 2026-02-20T10:30:00+00:00  (unchanged)
# updated.updated_at = 2026-02-20T10:35:00+00:00  (new timestamp)
# updated.created_by = "user-123"                  (unchanged)
# updated.updated_by = "admin-456"                 (new user)

Resolving the Current User

The AuditingEntityListener resolves the current authenticated user from the RequestContext:

1. Calls RequestContext.current() to get the current request context.
2. If a RequestContext is available, reads the security_context attribute.
3. If the SecurityContext is authenticated (is_authenticated is True), uses user_id as the value for created_by / updated_by.
4. If there is no RequestContext or the user is not authenticated, the user fields are left unchanged (they remain None for new entities).

This means auditing works transparently in HTTP request handlers (where the SecurityMiddleware or SecurityFilter populates the SecurityContext) and degrades gracefully in background tasks or CLI scripts where no request context is available.

Registering the Listener

The AuditingEntityListener must be registered once at application startup. Call register() to attach the ORM event listeners:

from pyfly.data.relational.sqlalchemy.auditing import AuditingEntityListener

listener = AuditingEntityListener()
listener.register()

When using auto-configuration, the listener is registered automatically by RelationalAutoConfiguration when pyfly.data.relational.enabled is true. No manual registration is needed in that case.

You can also register it in a custom @configuration class:

from pyfly.container import configuration, bean
from pyfly.data.relational.sqlalchemy.auditing import AuditingEntityListener


@configuration
class DataConfig:

    @bean
    def auditing_listener(self) -> AuditingEntityListener:
        listener = AuditingEntityListener()
        listener.register()
        return listener

Source: src/pyfly/data/relational/sqlalchemy/auditing.py

---

Transaction Management

The @transactional decorator provides Spring-style declarative transaction management with propagation and isolation support.

Basic Usage

from pyfly.data.relational.sqlalchemy import transactional, Propagation, Isolation

@service
class OrderService:
    _session_factory: async_sessionmaker  # injected by DI

    @transactional()
    async def create_order(self, order: Order) -> Order:
        return await self.repo.save(order)

    @transactional(propagation=Propagation.REQUIRES_NEW)
    async def audit_log(self, message: str) -> None:
        # Always opens a new transaction, even if called from within another
        ...

    @transactional(isolation=Isolation.SERIALIZABLE, read_only=True)
    async def generate_report(self) -> Report:
        ...

Propagation Types

Propagation	Behavior
REQUIRED (default)	Join existing transaction or start new
REQUIRES_NEW	Always start a new transaction, suspending existing
SUPPORTS	Run within transaction if one exists, or without
NOT_SUPPORTED	Suspend any existing transaction
MANDATORY	Require existing transaction, raise if none
NEVER	Raise if a transaction exists

Isolation Levels

DEFAULT, READ_UNCOMMITTED, READ_COMMITTED, REPEATABLE_READ, SERIALIZABLE

The decorator resolves async_sessionmaker from self._session_factory and automatically patches Repository instances on the service with the transaction-scoped session.

---

Soft Delete & Optimistic Locking

PyFly provides opt-in mixins for soft delete and optimistic locking, mirroring JPA's @SoftDelete and @Version annotations.

SoftDeleteMixin

from pyfly.data.relational.sqlalchemy import BaseEntity, SoftDeleteMixin

class Order(BaseEntity, SoftDeleteMixin):
    __tablename__ = "orders"
    name: Mapped[str] = mapped_column(String(255))

This adds a deleted_at column. Use SoftDeleteRepository for automatic soft-delete-aware CRUD:

from pyfly.data.relational.sqlalchemy import SoftDeleteRepository

class OrderRepository(SoftDeleteRepository[Order, UUID]):
    pass  # delete() sets deleted_at, find methods exclude deleted entities

Method	Behavior
delete(id)	Sets deleted_at (soft delete)
find_by_id(id)	Excludes soft-deleted entities
find_all()	Excludes soft-deleted entities
find_all_including_deleted()	Includes soft-deleted entities
restore(id)	Clears deleted_at
hard_delete(id)	Permanently removes from DB
count()	Counts only non-deleted entities

VersionedMixin (Optimistic Locking)

from pyfly.data.relational.sqlalchemy import BaseEntity, VersionedMixin

class Order(BaseEntity, VersionedMixin):
    __tablename__ = "orders"
    name: Mapped[str] = mapped_column(String(255))

This adds a version column. SQLAlchemy automatically appends WHERE version = :old to every UPDATE and raises StaleDataError on concurrent modification — the equivalent of JPA's @Version.

---

RepositoryBeanPostProcessor

The RepositoryBeanPostProcessor is a BeanPostProcessor that runs after each repository bean is initialized. It scans the repository class for stub methods and replaces them with real query implementations.

How It Works

The after_init(bean, bean_name) method:

1. Checks if the bean is an instance of Repository. If not, it is returned unchanged.
2. Gets the entity type from bean._model.
3. Iterates over all attributes defined on the bean's class (not inherited from Repository).
4. For @query-decorated methods: compiles them via QueryExecutor.compile_query_method() and replaces the stub with a wrapper that injects bean._session.
5. For derived query methods (find_by_*, count_by_*, exists_by_*, delete_by_*): checks if the method is a stub, parses the method name via QueryMethodParser.parse(), compiles it via QueryMethodCompiler.compile(), and replaces the stub with a wrapper.

Stub Detection

A method is considered a stub when its code object contains no meaningful constants beyond None and Ellipsis. This covers both forms:

async def find_by_status(self, status: str) -> list[Order]: ...    # Ellipsis stub
async def find_by_status(self, status: str) -> list[Order]: pass   # Pass stub

Register the post-processor in your application context:

from pyfly.data.relational.sqlalchemy import RepositoryBeanPostProcessor

context.register_post_processor(RepositoryBeanPostProcessor())

---

QueryMethodCompiler

The SQLAlchemy QueryMethodCompiler implements the QueryMethodCompilerPort protocol. It takes ParsedQuery objects produced by the shared QueryMethodParser and compiles them into SQLAlchemy column expressions.

Prefix	Generated Query Pattern
find_by	SELECT entity WHERE ... ORDER BY ...
count_by	SELECT COUNT(*) FROM entity WHERE ...
exists_by	SELECT COUNT(*) FROM entity WHERE ... > 0
delete_by	DELETE FROM entity WHERE ... (returns rowcount)

The compiler builds SQLAlchemy column expressions from each FieldPredicate, combines them using the connectors, and applies ORDER BY clauses from OrderClause objects.

---

Complete CRUD Example

The following example demonstrates an entity, repository with derived queries, specifications, pagination, and mapping.

# --- Entity ---

from pyfly.data.relational.sqlalchemy import BaseEntity
from sqlalchemy import String, Float, Boolean
from sqlalchemy.orm import Mapped, mapped_column


class Product(BaseEntity):
    __tablename__ = "products"

    name: Mapped[str] = mapped_column(String(255))
    price: Mapped[float] = mapped_column(Float)
    category: Mapped[str] = mapped_column(String(100))
    active: Mapped[bool] = mapped_column(Boolean, default=True)


# --- Repository ---

from pyfly.data.relational.sqlalchemy import Repository, query
from pyfly.container import repository as repo_stereotype
from sqlalchemy.ext.asyncio import AsyncSession


@repo_stereotype
class ProductRepository(Repository[Product, UUID]):

    # Derived query methods (stubs, auto-compiled at startup)
    async def find_by_category(self, category: str) -> list[Product]: ...

    async def find_by_active_and_category(
        self, active: bool, category: str
    ) -> list[Product]: ...

    async def find_by_price_greater_than_order_by_price_desc(
        self, min_price: float
    ) -> list[Product]: ...

    async def find_by_name_containing(self, fragment: str) -> list[Product]: ...

    async def count_by_category(self, category: str) -> int: ...

    async def exists_by_name(self, name: str) -> bool: ...

    async def delete_by_active(self, active: bool) -> int: ...

    # Custom query method
    @query("SELECT p FROM Product p WHERE p.category = :category AND p.price > :min_price")
    async def find_expensive_in_category(
        self, category: str, min_price: float
    ) -> list[Product]: ...


# --- Service ---

from pyfly.container import service
from pyfly.data import Pageable, Sort, Page, Mapper
from pyfly.data.relational.sqlalchemy import FilterOperator, FilterUtils, Specification
from dataclasses import dataclass


@dataclass
class ProductDTO:
    id: str
    name: str
    price: float
    category: str


@service
class ProductService:
    def __init__(self, repo: ProductRepository) -> None:
        self._repo = repo
        self._mapper = Mapper()

    async def find_all_active(self, category: str | None = None) -> list[ProductDTO]:
        spec: Specification = FilterOperator.eq("active", True)
        if category:
            spec = spec & FilterOperator.eq("category", category)
        products = await self._repo.find_all_by_spec(spec)
        return self._mapper.map_list(products, ProductDTO)

    async def find_paginated(
        self,
        page: int = 1,
        size: int = 20,
        category: str | None = None,
    ) -> Page[ProductDTO]:
        spec = FilterOperator.eq("active", True)
        if category:
            spec = spec & FilterOperator.eq("category", category)

        pageable = Pageable.of(
            page=page,
            size=size,
            sort=Sort.by("name"),
        )

        result = await self._repo.find_all_by_spec_paged(spec, pageable)
        return result.map(lambda p: self._mapper.map(p, ProductDTO))

    async def search_by_name(self, query: str) -> list[ProductDTO]:
        products = await self._repo.find_by_name_containing(query)
        return self._mapper.map_list(products, ProductDTO)

    async def find_by_id(self, product_id: str) -> ProductDTO | None:
        from uuid import UUID
        product = await self._repo.find_by_id(UUID(product_id))
        if product is None:
            return None
        return self._mapper.map(product, ProductDTO)

    async def create(self, name: str, price: float, category: str) -> ProductDTO:
        product = Product(name=name, price=price, category=category)
        saved = await self._repo.save(product)
        return self._mapper.map(saved, ProductDTO)

    async def delete(self, product_id: str) -> None:
        from uuid import UUID
        await self._repo.delete(UUID(product_id))

    async def count_in_category(self, category: str) -> int:
        return await self._repo.count_by_category(category)

---

See Also

• Data Module Guide — Generic commons: repository ports, pagination, query parsing, entity mapping, extensibility
• Data Document Guide — MongoDB adapter
• SQLAlchemy Adapter Reference — Setup, configuration, adapter-specific features