배포 및 운영

# app.yaml — Databricks Apps 배포 설정
command:
  - "streamlit"
  - "run"
  - "app.py"
  - "--server.port=8501"
  - "--server.address=0.0.0.0"

# 리소스 선언 — Databricks가 자동으로 자격증명을 주입합니다
resources:
  - name: "lakebase-instance"
    description: "운영 Lakebase PostgreSQL 인스턴스"
    databricks_resource_type: "databricks-lakebase-database"
    # Lakebase 인스턴스 이름 (Databricks Console에서 확인)
    instance_id: "${var.lakebase_instance_id}"

  - name: "sql-warehouse"
    description: "분석용 SQL Warehouse"
    databricks_resource_type: "databricks-sql-warehouse"
    warehouse_id: "${var.warehouse_id}"

# 환경 변수 설정
env:
  - name: LAKEBASE_HOST
    valueFrom:
      resourceRef:
        resourceName: "lakebase-instance"
        fieldPath: "host"
  - name: LAKEBASE_PORT
    value: "5432"
  - name: LAKEBASE_DBNAME
    value: "shop_db"
  - name: APP_ENV
    value: "production"
  - name: LOG_LEVEL
    value: "INFO"
  # 시크릿은 Databricks Secrets에서 참조합니다
  - name: APP_SECRET_KEY
    valueFrom:
      secretRef:
        secretName: "app-secrets"
        key: "secret_key"

# app.yaml — FastAPI 배포
command:
  - "uvicorn"
  - "main:app"
  - "--host=0.0.0.0"
  - "--port=8000"
  - "--workers=2"

resources:
  - name: "lakebase-instance"
    databricks_resource_type: "databricks-lakebase-database"
    instance_id: "${var.lakebase_instance_id}"

env:
  - name: DATABASE_URL
    value: "postgresql://${LAKEBASE_USER}:${LAKEBASE_PASSWORD}@${LAKEBASE_HOST}:5432/app_db"
  - name: POOL_SIZE
    value: "10"
  - name: MAX_OVERFLOW
    value: "20"

# Databricks CLI 설치
pip install databricks-cli

# 워크스페이스 인증 설정
databricks configure --token

# 앱 생성 (최초 1회)
databricks apps create my-lakebase-app \
  --description "Lakebase 기반 쇼핑몰 앱"

# 앱 배포 (소스 디렉터리 기준)
databricks apps deploy my-lakebase-app \
  --source-code-path ./my-app

# 배포 상태 확인
databricks apps get my-lakebase-app

# 앱 로그 실시간 확인
databricks apps logs my-lakebase-app --follow

# databricks.yml — Asset Bundle 설정
bundle:
  name: lakebase-app-bundle

variables:
  lakebase_instance_id:
    description: "Lakebase 인스턴스 ID"
  warehouse_id:
    description: "SQL Warehouse ID"

targets:
  dev:
    mode: development
    workspace:
      host: https://adb-xxxx.azuredatabricks.net
    variables:
      lakebase_instance_id: "dev-instance-id"
      warehouse_id: "dev-warehouse-id"

  prod:
    mode: production
    workspace:
      host: https://adb-yyyy.azuredatabricks.net
    variables:
      lakebase_instance_id: "prod-instance-id"
      warehouse_id: "prod-warehouse-id"

resources:
  apps:
    lakebase_app:
      name: "my-lakebase-app"
      description: "Lakebase 기반 운영 앱"
      source_code_path: ./app

# 개발 환경 배포
databricks bundle deploy --target dev

# 프로덕션 환경 배포
databricks bundle deploy --target prod

# .github/workflows/deploy.yml
name: Deploy Databricks App

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Install Databricks CLI
        run: pip install databricks-cli

      - name: Deploy to Production
        env:
          DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
          DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
        run: |
          databricks bundle deploy --target prod

# config.py — 환경별 DB 연결 설정
import os

APP_ENV = os.getenv("APP_ENV", "dev")

DB_CONFIG = {
    "dev": {
        "host": os.getenv("LAKEBASE_DEV_HOST"),
        "dbname": "app_dev",
        "pool_size": 3,
        "max_overflow": 5,
    },
    "staging": {
        "host": os.getenv("LAKEBASE_STAGING_HOST"),
        "dbname": "app_staging",
        "pool_size": 5,
        "max_overflow": 10,
    },
    "production": {
        "host": os.getenv("LAKEBASE_PROD_HOST"),
        "dbname": "app_prod",
        "pool_size": 20,
        "max_overflow": 40,
    },
}

current_config = DB_CONFIG[APP_ENV]

# database.py — SQLAlchemy 연결 풀 설정
from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool
import os

def get_engine():
    db_url = (
        f"postgresql+psycopg2://{os.getenv('LAKEBASE_USER')}:"
        f"{os.getenv('LAKEBASE_PASSWORD')}@"
        f"{os.getenv('LAKEBASE_HOST')}:5432/"
        f"{os.getenv('LAKEBASE_DBNAME')}"
    )
    return create_engine(
        db_url,
        poolclass=QueuePool,
        pool_size=int(os.getenv("POOL_SIZE", 10)),      # 기본 유지 연결 수
        max_overflow=int(os.getenv("MAX_OVERFLOW", 20)), # 최대 초과 허용 연결 수
        pool_timeout=30,       # 풀에서 연결을 기다리는 최대 시간 (초)
        pool_recycle=1800,     # 연결 재활용 주기 (초) — 장시간 유휴 연결 방지
        pool_pre_ping=True,    # 연결 유효성 사전 확인 (Dead Connection 방지)
        connect_args={
            "connect_timeout": 10,
            "application_name": "databricks-app",
            "sslmode": "require",  # SSL 필수 (보안)
        },
    )

# 앱 시작 시 엔진 1회 초기화
engine = get_engine()