Databricks Python Application

Build Python-based Databricks applications. For full examples and recipes, see the Databricks Apps Cookbook.

Critical Rules (always follow)

MUST confirm framework choice or use Framework Selection below
MUST use SDK Config() for authentication (never hardcode tokens)
MUST use app.yaml valueFrom for resources (never hardcode resource IDs)
MUST use dash-bootstrap-components for Dash app layout and styling
MUST use @st.cache_resource for Streamlit database connections
MUST deploy Flask with Gunicorn, FastAPI with uvicorn (not dev servers)

Required Steps

Copy this checklist and verify each item:

- [ ] Framework selected
- [ ] Auth strategy decided: app auth, user auth, or both
- [ ] App resources identified (SQL warehouse, Lakebase, serving endpoint, etc.)
- [ ] Backend data strategy decided (SQL warehouse, Lakebase, or SDK)
- [ ] Deployment method: CLI or DABs

Framework Selection

| Framework | Best For | app.yaml Command | |-----------|----------|------------------| | Dash | Production dashboards, BI tools, complex interactivity | ["python", "app.py"] | | Streamlit | Rapid prototyping, data science apps, internal tools | ["streamlit", "run", "app.py"] | | Gradio | ML demos, model interfaces, chat UIs | ["python", "app.py"] | | Flask | Custom REST APIs, lightweight apps, webhooks | ["gunicorn", "app:app", "-w", "4", "-b", "0.0.0.0:8000"] | | FastAPI | Async APIs, auto-generated OpenAPI docs | ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"] | | Reflex | Full-stack Python apps without JavaScript | ["reflex", "run", "--env", "prod"] |

Default: Recommend Streamlit for prototypes, Dash for production dashboards, FastAPI for APIs, Gradio for ML demos.

Quick Reference

| Concept | Details | |---------|---------| | Runtime | Python 3.11, Ubuntu 22.04, 2 vCPU, 6 GB RAM | | Pre-installed | Dash 2.18.1, Streamlit 1.38.0, Gradio 4.44.0, Flask 3.0.3, FastAPI 0.115.0 | | Auth (app) | Service principal via Config() — auto-injected DATABRICKS_CLIENT_ID/DATABRICKS_CLIENT_SECRET | | Auth (user) | x-forwarded-access-token header — see 1-authorization.md | | Resources | valueFrom in app.yaml — see 2-app-resources.md | | Cookbook | https://apps-cookbook.dev/ | | Docs | https://docs.databricks.com/aws/en/dev-tools/databricks-apps/ |

Detailed Guides

Authorization: Use 1-authorization.md when configuring app or user authorization — covers service principal auth, on-behalf-of user tokens, OAuth scopes, and per-framework code examples. (Keywords: OAuth, service principal, user auth, on-behalf-of, access token, scopes)

App resources: Use 2-app-resources.md when connecting your app to Databricks resources — covers SQL warehouses, Lakebase, model serving, secrets, volumes, and the valueFrom pattern. (Keywords: resources, valueFrom, SQL warehouse, model serving, secrets, volumes, connections)

Frameworks: See 3-frameworks.md for Databricks-specific patterns per framework — covers Dash, Streamlit, Gradio, Flask, FastAPI, and Reflex with auth integration, deployment commands, and Cookbook links. (Keywords: Dash, Streamlit, Gradio, Flask, FastAPI, Reflex, framework selection)

Deployment: Use 4-deployment.md when deploying your app — covers Databricks CLI, Asset Bundles (DABs), app.yaml configuration, and post-deployment verification. (Keywords: deploy, CLI, DABs, asset bundles, app.yaml, logs)

Lakebase: Use 5-lakebase.md when using Lakebase (PostgreSQL) as your app's data layer — covers auto-injected env vars, psycopg2/asyncpg patterns, and when to choose Lakebase vs SQL warehouse. (Keywords: Lakebase, PostgreSQL, psycopg2, asyncpg, transactional, PGHOST)

MCP tools: Use 6-mcp-approach.md for managing app lifecycle via MCP tools — covers creating, deploying, monitoring, and deleting apps programmatically. (Keywords: MCP, create app, deploy app, app logs)

Foundation Models: See examples/llm_config.py for calling Databricks foundation model APIs — covers OAuth M2M auth, OpenAI-compatible client wiring, and token caching. (Keywords: foundation model, LLM, OpenAI client, chat completions)

Workflow

Determine the task type:

New app from scratch? → Use Framework Selection, then read 3-frameworks.md Setting up authorization? → Read 1-authorization.md Connecting to data/resources? → Read 2-app-resources.md Using Lakebase (PostgreSQL)? → Read 5-lakebase.md Deploying to Databricks? → Read 4-deployment.md Using MCP tools? → Read 6-mcp-approach.md Calling foundation model/LLM APIs? → See examples/llm_config.py
Follow the instructions in the relevant guide
For full code examples, browse https://apps-cookbook.dev/

Core Architecture

All Python Databricks apps follow this pattern:

app-directory/
├── app.py                 # Main application (or framework-specific name)
├── models.py              # Pydantic data models
├── backend.py             # Data access layer
├── requirements.txt       # Additional Python dependencies
├── app.yaml               # Databricks Apps configuration
└── README.md

Backend Toggle Pattern

import os
from databricks.sdk.core import Config

USE_MOCK = os.getenv("USE_MOCK_BACKEND", "true").lower() == "true"

if USE_MOCK:
    from backend_mock import MockBackend as Backend
else:
    from backend_real import RealBackend as Backend

backend = Backend()

SQL Warehouse Connection (shared across all frameworks)

from databricks.sdk.core import Config
from databricks import sql

cfg = Config()  # Auto-detects credentials from environment
conn = sql.connect(
    server_hostname=cfg.host,
    http_path=f"/sql/1.0/warehouses/{os.getenv('DATABRICKS_WAREHOUSE_ID')}",
    credentials_provider=lambda: cfg.authenticate,
)

Pydantic Models

from pydantic import BaseModel, Field
from datetime import datetime
from enum import Enum

class Status(str, Enum):
    ACTIVE = "active"
    PENDING = "pending"

class EntityOut(BaseModel):
    id: str
    name: str
    status: Status
    created_at: datetime

class EntityIn(BaseModel):
    name: str = Field(..., min_length=1)
    status: Status = Status.PENDING

Common Issues

| Issue | Solution | |-------|----------| | Connection exhausted | Use @st.cache_resource (Streamlit) or connection pooling | | Auth token not found | Check x-forwarded-access-token header — only available when deployed, not locally | | App won't start | Check app.yaml command matches framework; check databricks apps logs <name> | | Resource not accessible | Add resource via UI, verify SP has permissions, use valueFrom in app.yaml | | Import error on deploy | Add missing packages to requirements.txt (pre-installed packages don't need listing) | | Lakebase app crashes on start | psycopg2/asyncpg are NOT pre-installed — MUST add to requirements.txt | | Port conflict | Apps must bind to DATABRICKS_APP_PORT env var (defaults to 8000). Never use 8080. Streamlit is auto-configured; for others, read the env var in code or use 8000 in app.yaml command | | Streamlit: set_page_config error | st.set_page_config() must be the first Streamlit command | | Dash: unstyled layout | Add dash-bootstrap-components; use dbc.themes.BOOTSTRAP | | Slow queries | Use Lakebase for transactional/low-latency; SQL warehouse for analytical queries |

Platform Constraints

| Constraint | Details | |------------|---------| | Runtime | Python 3.11, Ubuntu 22.04 LTS | | Compute | 2 vCPUs, 6 GB memory (default) | | Pre-installed frameworks | Dash, Streamlit, Gradio, Flask, FastAPI, Shiny | | Custom packages | Add to requirements.txt in app root | | Network | Apps can reach Databricks APIs; external access depends on workspace config | | User auth | Public Preview — workspace admin must enable before adding scopes |

Official Documentation

Databricks Apps Overview — main docs hub
Apps Cookbook — ready-to-use code snippets (Streamlit, Dash, Reflex, FastAPI)
Authorization — app auth and user auth
Resources — SQL warehouse, Lakebase, serving, secrets
app.yaml Reference — command and env config
System Environment — pre-installed packages, runtime details

Related Skills

databricks-app-apx - full-stack apps with FastAPI + React
databricks-bundles - deploying apps via DABs
databricks-python-sdk - backend SDK integration
databricks-lakebase-provisioned - adding persistent PostgreSQL state
databricks-model-serving - serving ML models for app integration