shotgrid-orm 0.3.2

Shotgrid SQLAlchemy ORM Generator - creates Python classes from a schema.

Shotgrid ORM Generator

For Autodesk Flow Production Tracking system (formerly Shotgun/Shotgrid - SG)

This tool generates a SQLAlchemy ORM for a Shotgrid schema for the purposes of reporting, BI, data warehousing, and analytics. It does not generate foreign key constraints and primary keys are not auto-increment. This allows maximum freedom to transfer data from Shotgrid into a target database, retaining its native primary keys (IDs).

Overview

Installation

Install from PyPI:

pip install shotgrid_orm

Or install from source:

git clone https://github.com/johnetran/shotgrid_orm.git
cd shotgrid_orm
pip install -e .

Requirements

• Python 3.8 or higher
• SQLAlchemy 2.0.45+
• shotgun-api3 3.9.2+
• sqlacodegen-v2 0.1.4+
• alembic 1.16.5+

Features

• Multiple Schema Sources: Load schemas from JSON files, JSON text, or live Shotgrid connections
• Dynamic ORM Generation: Creates SQLAlchemy 2.0 declarative models with proper type hints
• Standalone Script Export: Generate self-contained Python files with your ORM models
• Preserve Shotgrid IDs: Non-auto-increment primary keys maintain original Shotgrid identifiers
• Polymorphic Relationships: Handles entity and multi-entity fields with automatic _id and _type columns
• Alembic Integration: Built-in support for database migrations
• Flexible Design: No forced foreign key constraints for maximum data transfer flexibility

Quick Start

Basic Usage

from shotgrid_orm import SGORM, SchemaType

# Load schema from JSON file
sg_orm = SGORM(
    sg_schema_type=SchemaType.JSON_FILE,
    sg_schema_source="schema.json",
    echo=False
)

# Access entity classes
Shot = sg_orm["Shot"]
Asset = sg_orm["Asset"]

# Generate standalone Python script
sg_orm.create_script("sgmodel.py")

Complete Example: Data Warehousing

import os
from shotgrid_orm import SGORM, SchemaType
from sqlalchemy import create_engine
from sqlalchemy.orm import Session

# Connect to live Shotgrid and generate ORM
sg_orm = SGORM(
    sg_schema_type=SchemaType.SG_SCRIPT,
    sg_schema_source={
        "url": os.getenv("SG_URL"),
        "script": os.getenv("SG_SCRIPT"),
        "api_key": os.getenv("SG_API_KEY")
    }
)

# Create PostgreSQL database for analytics
engine = create_engine("postgresql://user:pass@localhost/shotgrid_analytics")
sg_orm.Base.metadata.create_all(engine)

# Now use standard Shotgrid API to fetch data and insert into database
# This preserves all Shotgrid IDs for future syncing

Detailed Usage

Schema Loading Options

from shotgrid_orm import SGORM, SchemaType

# Option 1: Load from JSON file
sg_orm = SGORM(
    sg_schema_type=SchemaType.JSON_FILE,
    sg_schema_source="schema.json"
)

# Option 2: Load from JSON string
json_text = '{"Project": {...}, "Shot": {...}}'
sg_orm = SGORM(
    sg_schema_type=SchemaType.JSON_TEXT,
    sg_schema_source=json_text
)

# Option 3: Connect with script credentials
sg_orm = SGORM(
    sg_schema_type=SchemaType.SG_SCRIPT,
    sg_schema_source={
        "url": "https://mystudio.shotgunstudio.com",
        "script": "my_script",
        "api_key": "abc123..."
    }
)

# Option 4: Use existing Shotgun connection
import shotgun_api3
sg = shotgun_api3.Shotgun(url, script, key)
sg_orm = SGORM(
    sg_schema_type=SchemaType.SG_CONNECTION,
    sg_schema_source=sg
)

Creating and Managing Databases

from sqlalchemy import create_engine

# SQLite (for local development/testing)
engine = create_engine("sqlite:///shotgrid.db")

# PostgreSQL (recommended for production)
engine = create_engine("postgresql://user:pass@localhost:5432/shotgrid")

# MySQL
engine = create_engine("mysql+pymysql://user:pass@localhost/shotgrid")

# Create all tables
sg_orm.Base.metadata.create_all(engine)

# Drop all tables (use with caution!)
sg_orm.Base.metadata.drop_all(engine)

Working with Data

from sqlalchemy.orm import Session
from sqlalchemy import select

# Using dynamically generated classes
Shot = sg_orm["Shot"]
Asset = sg_orm["Asset"]

session = Session(engine)

# Create a record (preserving Shotgrid ID)
shot = Shot()
shot.id = 1234  # Use actual Shotgrid ID
shot.code = "010"
shot.description = "Opening shot"
session.add(shot)
session.commit()

# Query records
shot = session.execute(
    select(Shot).where(Shot.code == "010")
).scalar_one()

# Update records
shot.description = "Updated description"
session.commit()

# Delete records
session.delete(shot)
session.commit()

session.close()

Using Generated Scripts

After calling create_script("sgmodel.py"), you can use the generated module independently:

import sgmodel
from sqlalchemy import create_engine, select
from sqlalchemy.orm import Session

engine = create_engine("sqlite:///mydb.db")
sgmodel.Base.metadata.create_all(engine)

session = Session(engine)

# Access classes directly
asset = sgmodel.Asset(id=1, code="HERO_CHAR")
session.add(asset)
session.commit()

# Or access dynamically via CLASSES dict
shot_class = sgmodel.CLASSES["Shot"]
shot = shot_class(id=100, code="010")
session.add(shot)
session.commit()

Handling Entity Relationships

Entity fields in Shotgrid become {field}_id and {field}_type columns:

Shot = sg_orm["Shot"]

shot = Shot()
shot.id = 100
shot.code = "010"

# Single entity field (e.g., project)
shot.project_id = 1
shot.project_type = "Project"

# Another entity field (e.g., sequence)
shot.sg_sequence_id = 10
shot.sg_sequence_type = "Sequence"

session.add(shot)
session.commit()

Advanced Usage

Ignoring Entities or Fields

sg_orm = SGORM(
    sg_schema_type=SchemaType.JSON_FILE,
    sg_schema_source="schema.json",
    ignored_tables=["AppWelcome", "Banner"],  # Skip these entities
    ignored_fields=["image_source_entity"]     # Skip these fields globally
)

Using with Alembic Migrations

# Initialize Alembic
alembic init alembic

# Generate migration after schema changes
alembic revision --autogenerate -m "Add new custom fields"

# Apply migrations
alembic upgrade head

# Rollback
alembic downgrade -1

Bulk Data Transfer from Shotgrid

import shotgun_api3
from shotgrid_orm import SGORM, SchemaType
from sqlalchemy import create_engine
from sqlalchemy.orm import Session

# Connect to Shotgrid
sg = shotgun_api3.Shotgun(url, script, key)

# Generate ORM from live connection
sg_orm = SGORM(sg_schema_type=SchemaType.SG_CONNECTION, sg_schema_source=sg)

# Setup target database
engine = create_engine("postgresql://user:pass@localhost/warehouse")
sg_orm.Base.metadata.create_all(engine)
session = Session(engine)

# Fetch all shots from Shotgrid
Shot = sg_orm["Shot"]
shots_data = sg.find("Shot", [], ["id", "code", "description", "project"])

# Insert into warehouse database
for shot_data in shots_data:
    shot = Shot()
    shot.id = shot_data["id"]
    shot.code = shot_data["code"]
    shot.description = shot_data["description"]

    # Handle entity relationships
    if shot_data.get("project"):
        shot.project_id = shot_data["project"]["id"]
        shot.project_type = shot_data["project"]["type"]

    session.add(shot)

session.commit()
session.close()

Common Pitfalls & Solutions

1. Primary Key Conflicts

Problem: Attempting to use auto-increment IDs when Shotgrid uses specific IDs.

# ❌ Wrong - SQLAlchemy tries to auto-increment
shot = Shot()
# id is not set, but SQLAlchemy expects it

# ✅ Correct - Always set the ID explicitly
shot = Shot()
shot.id = 1234  # Use the actual Shotgrid ID

Solution: Always explicitly set the id field to match Shotgrid's ID. This tool intentionally disables auto-increment to preserve Shotgrid IDs.

2. Entity Field Confusion

Problem: Trying to set entity relationships as objects instead of ID/type pairs.

# ❌ Wrong - Shotgrid entity fields aren't relationships
shot.project = project_object

# ✅ Correct - Set _id and _type separately
shot.project_id = 1
shot.project_type = "Project"

Solution: Entity fields become two columns: {field}_id (integer) and {field}_type (string).

3. Metadata Field Name Clash

Problem: Shotgrid's metadata field conflicts with SQLAlchemy's metadata.

Solution: This is automatically handled - the field is renamed to _metadata in the ORM.

# Access Shotgrid's metadata field
shot._metadata = {"custom": "data"}

4. Schema Not Found

Problem: FileNotFoundError when loading schema from JSON.

# ❌ Wrong - Relative path might not work
sg_orm = SGORM(SchemaType.JSON_FILE, "schema.json")

# ✅ Correct - Use absolute path or ensure working directory
import os
schema_path = os.path.join(os.getcwd(), "schema.json")
sg_orm = SGORM(SchemaType.JSON_FILE, schema_path)

5. Missing Shotgun API Package

Problem: ImportError when trying to connect to live Shotgrid.

Solution: Install the Shotgrid API:

pip install shotgun-api3

6. NULL vs Empty String

Problem: Shotgrid treats empty strings differently than NULL.

# Be explicit about NULL vs empty string
shot.description = None  # NULL in database
shot.description = ""     # Empty string

FAQ

How do I get my Shotgrid schema as JSON?

Use the Shotgrid API to export your schema:

import shotgun_api3
import json

sg = shotgun_api3.Shotgun(url, script, key)
schema = {}

entities = sg.schema_entity_read()
for entity_name in entities:
    entity = entities[entity_name]
    fields = sg.schema_field_read(entity_name)
    entity["fields"] = fields
    schema[entity_name] = entity

with open("schema.json", "w") as f:
    json.dump(schema, f, indent=2)

Can I add foreign key constraints manually?

Yes! While they're not generated automatically, you can add them:

from sqlalchemy import ForeignKey

# After generating the ORM, modify the class
Shot = sg_orm["Shot"]
# Add ForeignKey to project_id if you want referential integrity
# Note: This requires manual modification of generated code

Does this work with custom Shotgrid fields?

Yes! Custom fields are automatically included in the generated ORM. The tool reads the complete schema including all custom fields.

Can I use this for real-time syncing?

This tool is designed for one-way data transfer (Shotgrid → Database) for analytics/reporting. For real-time bidirectional sync, consider using Shotgrid's event framework or webhooks.

What database engines are supported?

Any database supported by SQLAlchemy 2.0:

• PostgreSQL (recommended for production)
• MySQL/MariaDB
• SQLite (great for development)
• Oracle
• Microsoft SQL Server

How do I handle schema changes?

Use Alembic for migrations:

# Generate migration after Shotgrid schema changes
alembic revision --autogenerate -m "Shotgrid schema update"
alembic upgrade head

Or regenerate the ORM and recreate tables (loses data):

sg_orm.Base.metadata.drop_all(engine)
sg_orm.Base.metadata.create_all(engine)

Can I filter which entities are included?

Yes, use the ignored_tables parameter:

sg_orm = SGORM(
    sg_schema_type=SchemaType.JSON_FILE,
    sg_schema_source="schema.json",
    ignored_tables=["Banner", "AppWelcome", "PageSetting"]
)

How do I access classes dynamically?

Use dictionary notation or the CLASSES dict:

# From SGORM instance
Shot = sg_orm["Shot"]
Shot = sg_orm.classes["Shot"]

# From generated script
import sgmodel
Shot = sgmodel.CLASSES["Shot"]

Is this an official Autodesk tool?

No, this is a community-developed tool. It uses the official Shotgrid Python API but is not affiliated with or supported by Autodesk.

What about multi-entity fields?

Multi-entity fields create {field}_ids (String) and {field}_type (String) columns on the source table, mirroring the same convention as single-entity fields.

Environment Variables

For live Shotgrid connections, set these environment variables:

export SG_URL="https://your-studio.shotgunstudio.com"
export SG_SCRIPT="your_script_name"
export SG_API_KEY="your_api_key_here"

Known Limitations

• Foreign Keys: ForeignKey constraints are intentionally not generated to allow flexibility in data transfer. You can add them manually if needed.
• Entity Relationships: Entity and multi-entity types are stored as integer IDs and strings rather than full SQLAlchemy relationship() objects.
• Complex Types: Serializable fields (dicts/JSON) and URL fields are stored as strings. Consider JSON serialization for complex use cases.
• Read-Only Fields: Some Shotgrid field types (like image) are read-only and stored as strings.

Use Cases

• Data Warehousing: Extract Shotgrid data into analytical databases (PostgreSQL, MySQL, etc.)
• Reporting & BI: Build custom reports using standard SQL tools
• Data Migration: Transfer Shotgrid data between environments while preserving IDs
• Backup & Archival: Create local copies of Shotgrid data with full schema preservation
• Custom Integrations: Build applications that work with Shotgrid data offline

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with SQLAlchemy
• Schema code generation powered by sqlacodegen-v2
• Shotgrid API integration via shotgun-api3

Links

• PyPI: https://pypi.org/project/shotgrid_orm/
• GitHub: https://github.com/johnetran/shotgrid_orm
• Issues: https://github.com/johnetran/shotgrid_orm/issues
• Autodesk Flow Production Tracking: https://www.autodesk.com/products/flow-production-tracking/