mitsuhiko/flask-sqlalchemy

Features

• Object-Relational Mapping - Provides a full object-relational mapper to bridge Python classes with relational database records.
• Relational Database Toolkits - Provides a comprehensive set of helpers for schema definition, session management, and query execution in web apps.
• ORM Model Declarations - Uses a declarative class system to automatically manage database metadata and naming conventions.
• Transactional Updates - Ensures data consistency by grouping multiple record insertions, updates, and deletions into atomic transactional units.
• Database Query Execution - Manages the execution of SQL queries and data retrieval using a session-based flow.
• Database Schema Generation - Automatically creates physical database tables based on declarative model definitions.
• Object-Relational Mapping Models - Implements a system for mapping application data structures to relational database table columns and types.
• ORM Integrations - Simplifies the integration of SQLAlchemy ORM with web application connection pools and configurations.
• Relational Data Modeling - Provides tools for organizing data into structured tables and relationships using Python classes.
• Database Session Lifecycles - Automatically manages database connection lifecycles and transaction states within the scope of a web request.
• Connection Management - Manages engine setup and model associations while automatically cleaning up connections and sessions after every request.
• Request-Bound Session Management - Binds database session lifecycles to the active web request context to ensure automatic connection cleanup.
• Multi-Database Routing - Implements architectural routing of model queries to multiple distinct database engines using bind-keys.
• Application-Context Session Scoping - Restricts database session scoping to the active application context to ensure connections close automatically.
• SQLAlchemy Integrations - Integrates SQLAlchemy ORM capabilities directly into the Flask web framework for model and connection management.
• Database Connection Configurations - Provides configuration for connection parameters like pool recycling and pre-pinging to maintain database stability.
• Multi-Bind Schema Management - Coordinates schema creation and deletion across multiple configured database engines or specific bind subsets.
• Model-Based Query Routing - Provides a routing engine that maps specific ORM model classes to different database engine instances.
• Multi-Database Connections - Coordinates multiple independent database connections within a single application runtime via unique keys.
• Paginated Results - Splits large database result sets into smaller, numbered pages to optimize application performance.
• Custom Query Interface Methods - Allows extending the query object with custom methods to streamline specialized data retrieval for models.
• SQL Execution Logging - Captures and logs executed SQL statements and timing data to identify performance bottlenecks.
• SQLAlchemy Model Wrappers - Provides a wrapper for declarative models that simplifies table creation and record retrieval with integrated error handling.
• Database Model Mixins - Supports extending base classes or using mixins to share common attributes and logic across database models.
• Database Schema Reflection - Detects the structure of existing databases to automatically generate corresponding application-level table objects.
• Database Table Generators - Automatically produces physical database tables by interpreting metadata defined in declarative class definitions.
• Query Logging - Tracks executed SQL statements and timing data during requests to facilitate performance monitoring and debugging.
• Data Management - Adds SQLAlchemy support to Flask.

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