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šŸ“text•3 hours ago

E2E Test: Hello World Prompt

Test submission for pipeline verification

coding
⭐1
Write a function that prints Hello World in Python. This is a simple coding prompt for testing the end-to-end moderation pipeline.
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šŸ“text•6 months ago

Docker-Backed Jupyter Kernel Operator

Operate containerized Jupyter kernels with enablement, smoke checks, cleanup, and failure handling baked into the runbook.

devops
⭐1
# Docker-Backed Jupyter Kernel Operator Imported from curated first-party documentation sources. ## What this covers Use this runbook when you need isolated notebook execution, clear container lifecycle management, and troubleshooting guidance. ## Use this when - Enabling containerized notebook workflows - Cleaning up kernel containers safely - Troubleshooting notebook execution environments ## Expected outcomes - Kernel startup and cleanup steps are documented end-to-end - Failure modes are easier to diagnose - Notebook execution can be repeated without manual guesswork ## Source synthesis - AGENT33/docs/runbooks/jupyter-kernel-containers.md (https://github.com/mattmre/AGENT33/blob/main/docs/runbooks/jupyter-kernel-containers.md) ## Dedupe notes Uses the AGENT33 Jupyter container runbook as a focused operations import without duplicating broader walkthroughs. ## Source excerpts ### AGENT33/docs/runbooks/jupyter-kernel-containers.md ## Purpose Operate the Docker-backed Jupyter kernel adapter introduced for Phase 38 Stage 3 / Phase 42 follow-on work. ## Enablement Set: - `JUPYTER_KERNEL_ENABLED=true` - `JUPYTER_KERNEL_MODE=docker` Optional settings: - `JUPYTER_KERNEL_DOCKER_IMAGE` - `JUPYTER_KERNEL_ALLOWED_IMAGES` - `JUPYTER_KERNEL_NETWORK_ENABLED` - `JUPYTER_KERNEL_MOUNT_WORKDIR` - `JUPYTER_KERNEL_CONTAINER_WORKDIR` ## Operational Notes - Docker mode publishes kernel ports to the host and mounts a per-session runtime directory containing the Jupyter connection file. - When `JUPYTER_KERNEL_NETWORK_ENABLED=false`, the adapter starts containers with `--network none`. - Working-directory mounting is opt-in and should only point at paths already approved by workflow / execution policy. - The adapter enforces an image allowlist when one is configured. ## Failure Modes - `jupyter_client not installed`: install with `pip install agent33[jupyter]` - `docker executable not found`: install Docker and ensure `docker` is on `PATH` - `Docker image ... is not permitted`: align the requested image with `JUPYTER_KERNEL_ALLOWED_IMAGES` - kernel startup timeout: inspect Docker logs for the session container and verify the image includes `ipykernel` ## Cleanup - One-shot sessions are removed after execution. - Stateful sessions are removed explicitly or via adapter shutdown. - Forced cleanup uses `docker rm -f <container>` and deletes the runtime connection directory. ## Quick Smoke Workflow Register a minimal workflow that exercises the Docker-backed `code-interpreter` tool: ```bash curl -X POST http://localhost:8000/v1/workflows/ \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "name": "docker-kernel-smoke", "version": "1.0.0", "description": "Validate Docker-backed Jupyter execution", "triggers": {"manual": true}, "inputs": {}, "outputs": { "result": {"type": "object"} }, "steps": [ { "id": "run-notebook-code", "action": "execute-code", "inputs": { "tool_id": "code-interpreter", "language": "python", "code": "print(6 * 7)" } } ], "execution": {"mode": "sequential"} }' ``` Then execute it: ```bash curl -X POST http://localhost:8000/v1/workflows/docker-kernel-smoke/execute \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"inputs": {}}' ```
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docs
šŸ¤–system prompt•7 months ago

fastapi-templates

Create production-ready FastAPI projects with async patterns,

coding
⭐1
# FastAPI Project Templates Production-ready FastAPI project structures with async patterns, dependency injection, middleware, and best practices for building high-performance APIs. ## When to Use This Skill - Starting new FastAPI projects from scratch - Implementing async REST APIs with Python - Building high-performance web services and microservices - Creating async applications with PostgreSQL, MongoDB - Setting up API projects with proper structure and testing ## Core Concepts ### 1. Project Structure **Recommended Layout:** ``` app/ ā”œā”€ā”€ api/ # API routes │ ā”œā”€ā”€ v1/ │ │ ā”œā”€ā”€ endpoints/ │ │ │ ā”œā”€ā”€ users.py │ │ │ ā”œā”€ā”€ auth.py │ │ │ └── items.py │ │ └── router.py │ └── dependencies.py # Shared dependencies ā”œā”€ā”€ core/ # Core configuration │ ā”œā”€ā”€ config.py │ ā”œā”€ā”€ security.py │ └── database.py ā”œā”€ā”€ models/ # Database models │ ā”œā”€ā”€ user.py │ └── item.py ā”œā”€ā”€ schemas/ # Pydantic schemas │ ā”œā”€ā”€ user.py │ └── item.py ā”œā”€ā”€ services/ # Business logic │ ā”œā”€ā”€ user_service.py │ └── auth_service.py ā”œā”€ā”€ repositories/ # Data access │ ā”œā”€ā”€ user_repository.py │ └── item_repository.py └── main.py # Application entry ``` ### 2. Dependency Injection FastAPI's built-in DI system using `Depends`: - Database session management - Authentication/authorization - Shared business logic - Configuration injection ### 3. Async Patterns Proper async/await usage: - Async route handlers - Async database operations - Async background tasks - Async middleware ## Implementation Patterns ### Pattern 1: Complete FastAPI Application ```python # main.py from fastapi import FastAPI, Depends from fastapi.middleware.cors import CORSMiddleware from contextlib import asynccontextmanager @asynccontextmanager async def lifespan(app: FastAPI): """Application lifespan events.""" # Startup await database.connect() yield # Shutdown await database.disconnect() app = FastAPI( title="API Template", version="1.0.0", lifespan=lifespan ) # CORS middleware app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Include routers from app.api.v1.router import api_router app.include_router(api_router, prefix="/api/v1") # core/config.py from pydantic_settings import BaseSettings from functools import lru_cache class Settings(BaseSettings): """Application settings.""" DATABASE_URL: str SECRET_KEY: str ACCESS_TOKEN_EXPIRE_MINUTES: int = 30 API_V1_STR: str = "/api/v1" class Config: env_file = ".env" @lru_cache() def get_settings() -> Settings: return Settings() # core/database.py from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker from app.core.config import get_settings settings = get_settings() engine = create_async_engine( settings.DATABASE_URL, echo=True, future=True ) AsyncSessionLocal = sessionmaker( engine, class_=AsyncSession, expire_on_commit=False ) Base = declarative_base() async def get_db() -> AsyncSession: """Dependency for database session.""" async with AsyncSessionLocal() as session: try: yield session await session.commit() except Exception: await session.rollback() raise finally: await session.close() ``` ### Pattern 2: CRUD Repository Pattern ```python # repositories/base_repository.py from typing import Generic, TypeVar, Type, Optional, List from sqlalchemy.ext.asyncio import AsyncSession from sqlalchemy import select from pydantic import BaseModel ModelType = TypeVar("ModelType") CreateSchemaType = TypeVar("CreateSchemaType", bound=BaseModel) UpdateSchemaType = TypeVar("UpdateSchemaType", bound=BaseModel) class BaseRepository(Generic[ModelType, CreateSchemaType, UpdateSchemaType]): """Base repository for CRUD operations.""" def __init__(self, model: Type[ModelType]): self.model = model async def get(self, db: AsyncSession, id: int) -> Optional[ModelType]: """Get by ID.""" result = await db.execute( select(self.model).where(self.model.id == id) ) return result.scalars().first() async def get_multi( self, db: AsyncSession, skip: int = 0, limit: int = 100 ) -> List[ModelType]: """Get multiple records.""" result = await db.execute( select(self.model).offset(skip).limit(limit) ) return result.scalars().all() async def create( self, db: AsyncSession, obj_in: CreateSchemaType ) -> ModelType: """Create new record.""" db_obj = self.model(**obj_in.dict()) db.add(db_obj) await db.flush() await db.refresh(db_obj) return db_obj async def update( self, db: AsyncSession, db_obj: ModelType, obj_in: UpdateSchemaType ) -> ModelType: """Update record.""" update_data = obj_in.dict(exclude_unset=True) for field, value in update_data.items(): setattr(db_obj, field, value) await db.flush() await db.refresh(db_obj) return db_obj async def delete(self, db: AsyncSession, id: int) -> bool: """Delete record.""" obj = await self.get(db, id) if obj: await db.delete(obj) return True return False # repositories/user_repository.py from app.repositories.base_repository import BaseRepository from app.models.user import User from app.schemas.user import UserCreate, UserUpdate class UserRepository(BaseRepository[User, UserCreate, UserUpdate]): """User-specific repository.""" async def get_by_email(self, db: AsyncSession, email: str) -> Optional[User]: """Get user by email.""" result = await db.execute( select(User).where(User.email == email) ) return result.scalars().first() async def is_active(self, db: AsyncSession, user_id: int) -> bool: """Check if user is active.""" user = await self.get(db, user_id) return user.is_active if user else False user_repository = UserRepository(User) ``` ### Pattern 3: Service Layer ```python # services/user_service.py from typing import Optional from sqlalchemy.ext.asyncio import AsyncSession from app.repositories.user_repository import user_repository from app.schemas.user import UserCreate, UserUpdate, User from app.core.security import get_password_hash, verify_password class UserService: """Business logic for users.""" def __init__(self): self.repository = user_repository async def create_user( self, db: AsyncSession, user_in: UserCreate ) -> User: """Create new user with hashed password.""" # Check if email exists existing = await self.repository.get_by_email(db, user_in.email) if existing: raise ValueError("Email already registered") # Hash password user_in_dict = user_in.dict() user_in_dict["hashed_password"] = get_password_hash(user_in_dict.pop("password")) # Create user user = await self.repository.create(db, UserCreate(**user_in_dict)) return user async def authenticate( self, db: AsyncSession, email: str, password: str ) -> Optional[User]: """Authenticate user.""" user = await self.repository.get_by_email(db, email) if not user: return None if not verify_password(password, user.hashed_password): return None return user async def update_user( self, db: AsyncSession, user_id: int, user_in: UserUpdate ) -> Optional[User]: """Update user.""" user = await self.repository.get(db, user_id) if not user: return None if user_in.password: user_in_dict = user_in.dict(exclude_unset=True) user_in_dict["hashed_password"] = get_password_hash( user_in_dict.pop("password") ) user_in = UserUpdate(**user_in_dict) return await self.repository.update(db, user, user_in) user_service = UserService() ``` ### Pattern 4: API Endpoints with Dependencies ```python # api/v1/endpoints/users.py from fastapi import APIRouter, Depends, HTTPException, status from sqlalchemy.ext.asyncio import AsyncSession from typing import List from app.core.database import get_db from app.schemas.user import User, UserCreate, UserUpdate from app.services.user_service import user_service from app.api.dependencies import get_current_user router = APIRouter() @router.post("/", response_model=User, status_code=status.HTTP_201_CREATED) async def create_user( user_in: UserCreate, db: AsyncSession = Depends(get_db) ): """Create new user.""" try: user = await user_service.create_user(db, user_in) return user except ValueError as e: raise HTTPException(status_code=400, detail=str(e)) @router.get("/me", response_model=User) async def read_current_user( current_user: User = Depends(get_current_user) ): """Get current user.""" return current_user @router.get("/{user_id}", response_model=User) async def read_user( user_id: int, db: AsyncSession = Depends(get_db), current_user: User = Depends(get_current_user) ): """Get user by ID.""" user = await user_service.repository.get(db, user_id) if not user: raise HTTPException(status_code=404, detail="User not found") return user @router.patch("/{user_id}", response_model=User) async def update_user( user_id: int, user_in: UserUpdate, db: AsyncSession = Depends(get_db), current_user: User = Depends(get_current_user) ): """Update user.""" if current_user.id != user_id: raise HTTPException(status_code=403, detail="Not authorized") user = await user_service.update_user(db, user_id, user_in) if not user: raise HTTPException(status_code=404, detail="User not found") return user @router.delete("/{user_id}", status_code=status.HTTP_204_NO_CONTENT) async def delete_user( user_id: int, db: AsyncSession = Depends(get_db), current_user: User = Depends(get_current_user) ): """Delete user.""" if current_user.id != user_id: raise HTTPException(status_code=403, detail="Not authorized") deleted = await user_service.repository.delete(db, user_id) if not deleted: raise HTTPException(status_code=404, detail="User not found") ``` ### Pattern 5: Authentication & Authorization ```python # core/security.py from datetime import datetime, timedelta from typing import Optional from jose import JWTError, jwt from passlib.context import CryptContext from app.core.config import get_settings settings = get_settings() pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") ALGORITHM = "HS256" def create_access_token(data: dict, expires_delta: Optional[timedelta] = None): """Create JWT access token.""" to_encode = data.copy() if expires_delta: expire = datetime.utcnow() + expires_delta else: expire = datetime.utcnow() + timedelta(minutes=15) to_encode.update({"exp": expire}) encoded_jwt = jwt.encode(to_encode, settings.SECRET_KEY, algorithm=ALGORITHM) return encoded_jwt def verify_password(plain_password: str, hashed_password: str) -> bool: """Verify password against hash.""" return pwd_context.verify(plain_password, hashed_password) def get_password_hash(password: str) -> str: """Hash password.""" return pwd_context.hash(password) # api/dependencies.py from fastapi import Depends, HTTPException, status from fastapi.security import OAuth2PasswordBearer from jose import JWTError, jwt from sqlalchemy.ext.asyncio import AsyncSession from app.core.database import get_db from app.core.security import ALGORITHM from app.core.config import get_settings from app.repositories.user_repository import user_repository oauth2_scheme = OAuth2PasswordBearer(tokenUrl=f"{settings.API_V1_STR}/auth/login") async def get_current_user( db: AsyncSession = Depends(get_db), token: str = Depends(oauth2_scheme) ): """Get current authenticated user.""" credentials_exception = HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Could not validate credentials", headers={"WWW-Authenticate": "Bearer"}, ) try: payload = jwt.decode(token, settings.SECRET_KEY, algorithms=[ALGORITHM]) user_id: int = payload.get("sub") if user_id is None: raise credentials_exception except JWTError: raise credentials_exception user = await user_repository.get(db, user_id) if user is None: raise credentials_exception return user ``` ## Testing ```python # tests/conftest.py import pytest import asyncio from httpx import AsyncClient from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession from sqlalchemy.orm import sessionmaker from app.main import app from app.core.database import get_db, Base TEST_DATABASE_URL = "sqlite+aiosqlite:///:memory:" @pytest.fixture(scope="session") def event_loop(): loop = asyncio.get_event_loop_policy().new_event_loop() yield loop loop.close() @pytest.fixture async def db_session(): engine = create_async_engine(TEST_DATABASE_URL, echo=True) async with engine.begin() as conn: await conn.run_sync(Base.metadata.create_all) AsyncSessionLocal = sessionmaker( engine, class_=AsyncSession, expire_on_commit=False ) async with AsyncSessionLocal() as session: yield session @pytest.fixture async def client(db_session): async def override_get_db(): yield db_session app.dependency_overrides[get_db] = override_get_db async with AsyncClient(app=app, base_url="http://test") as client: yield client # tests/test_users.py import pytest @pytest.mark.asyncio async def test_create_user(client): response = await client.post( "/api/v1/users/", json={ "email": "test@example.com", "password": "testpass123", "name": "Test User" } ) assert response.status_code == 201 data = response.json() assert data["email"] == "test@example.com" assert "id" in data ``` ## Resources - **references/fastapi-architecture.md**: Detailed architecture guide - **references/async-best-practices.md**: Async/await patterns - **references/testing-strategies.md**: Comprehensive testing guide - **assets/project-template/**: Complete FastAPI project - **assets/docker-compose.yml**: Development environment setup ## Best Practices 1. **Async All The Way**: Use async for database, external APIs 2. **Dependency Injection**: Leverage FastAPI's DI system 3. **Repository Pattern**: Separate data access from business logic 4. **Service Layer**: Keep business logic out of routes 5. **Pydantic Schemas**: Strong typing for request/response 6. **Error Handling**: Consistent error responses 7. **Testing**: Test all layers independently ## Common Pitfalls - **Blocking Code in Async**: Using synchronous database drivers - **No Service Layer**: Business logic in route handlers - **Missing Type Hints**: Loses FastAPI's benefits - **Ignoring Sessions**: Not properly managing database sessions - **No Testing**: Skipping integration tests - **Tight Coupling**: Direct database access in routes
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šŸ¤–system prompt•7 months ago

api-design-principles

Master REST and GraphQL API design principles to build intuitive,

coding
⭐1
# API Design Principles Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers and stand the test of time. ## When to Use This Skill - Designing new REST or GraphQL APIs - Refactoring existing APIs for better usability - Establishing API design standards for your team - Reviewing API specifications before implementation - Migrating between API paradigms (REST to GraphQL, etc.) - Creating developer-friendly API documentation - Optimizing APIs for specific use cases (mobile, third-party integrations) ## Core Concepts ### 1. RESTful Design Principles **Resource-Oriented Architecture** - Resources are nouns (users, orders, products), not verbs - Use HTTP methods for actions (GET, POST, PUT, PATCH, DELETE) - URLs represent resource hierarchies - Consistent naming conventions **HTTP Methods Semantics:** - `GET`: Retrieve resources (idempotent, safe) - `POST`: Create new resources - `PUT`: Replace entire resource (idempotent) - `PATCH`: Partial resource updates - `DELETE`: Remove resources (idempotent) ### 2. GraphQL Design Principles **Schema-First Development** - Types define your domain model - Queries for reading data - Mutations for modifying data - Subscriptions for real-time updates **Query Structure:** - Clients request exactly what they need - Single endpoint, multiple operations - Strongly typed schema - Introspection built-in ### 3. API Versioning Strategies **URL Versioning:** ``` /api/v1/users /api/v2/users ``` **Header Versioning:** ``` Accept: application/vnd.api+json; version=1 ``` **Query Parameter Versioning:** ``` /api/users?version=1 ``` ## REST API Design Patterns ### Pattern 1: Resource Collection Design ```python # Good: Resource-oriented endpoints GET /api/users # List users (with pagination) POST /api/users # Create user GET /api/users/{id} # Get specific user PUT /api/users/{id} # Replace user PATCH /api/users/{id} # Update user fields DELETE /api/users/{id} # Delete user # Nested resources GET /api/users/{id}/orders # Get user's orders POST /api/users/{id}/orders # Create order for user # Bad: Action-oriented endpoints (avoid) POST /api/createUser POST /api/getUserById POST /api/deleteUser ``` ### Pattern 2: Pagination and Filtering ```python from typing import List, Optional from pydantic import BaseModel, Field class PaginationParams(BaseModel): page: int = Field(1, ge=1, description="Page number") page_size: int = Field(20, ge=1, le=100, description="Items per page") class FilterParams(BaseModel): status: Optional[str] = None created_after: Optional[str] = None search: Optional[str] = None class PaginatedResponse(BaseModel): items: List[dict] total: int page: int page_size: int pages: int @property def has_next(self) -> bool: return self.page < self.pages @property def has_prev(self) -> bool: return self.page > 1 # FastAPI endpoint example from fastapi import FastAPI, Query, Depends app = FastAPI() @app.get("/api/users", response_model=PaginatedResponse) async def list_users( page: int = Query(1, ge=1), page_size: int = Query(20, ge=1, le=100), status: Optional[str] = Query(None), search: Optional[str] = Query(None) ): # Apply filters query = build_query(status=status, search=search) # Count total total = await count_users(query) # Fetch page offset = (page - 1) * page_size users = await fetch_users(query, limit=page_size, offset=offset) return PaginatedResponse( items=users, total=total, page=page, page_size=page_size, pages=(total + page_size - 1) // page_size ) ``` ### Pattern 3: Error Handling and Status Codes ```python from fastapi import HTTPException, status from pydantic import BaseModel class ErrorResponse(BaseModel): error: str message: str details: Optional[dict] = None timestamp: str path: str class ValidationErrorDetail(BaseModel): field: str message: str value: Any # Consistent error responses STATUS_CODES = { "success": 200, "created": 201, "no_content": 204, "bad_request": 400, "unauthorized": 401, "forbidden": 403, "not_found": 404, "conflict": 409, "unprocessable": 422, "internal_error": 500 } def raise_not_found(resource: str, id: str): raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, detail={ "error": "NotFound", "message": f"{resource} not found", "details": {"id": id} } ) def raise_validation_error(errors: List[ValidationErrorDetail]): raise HTTPException( status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, detail={ "error": "ValidationError", "message": "Request validation failed", "details": {"errors": [e.dict() for e in errors]} } ) # Example usage @app.get("/api/users/{user_id}") async def get_user(user_id: str): user = await fetch_user(user_id) if not user: raise_not_found("User", user_id) return user ``` ### Pattern 4: HATEOAS (Hypermedia as the Engine of Application State) ```python class UserResponse(BaseModel): id: str name: str email: str _links: dict @classmethod def from_user(cls, user: User, base_url: str): return cls( id=user.id, name=user.name, email=user.email, _links={ "self": {"href": f"{base_url}/api/users/{user.id}"}, "orders": {"href": f"{base_url}/api/users/{user.id}/orders"}, "update": { "href": f"{base_url}/api/users/{user.id}", "method": "PATCH" }, "delete": { "href": f"{base_url}/api/users/{user.id}", "method": "DELETE" } } ) ``` ## GraphQL Design Patterns ### Pattern 1: Schema Design ```graphql # schema.graphql # Clear type definitions type User { id: ID! email: String! name: String! createdAt: DateTime! # Relationships orders(first: Int = 20, after: String, status: OrderStatus): OrderConnection! profile: UserProfile } type Order { id: ID! status: OrderStatus! total: Money! items: [OrderItem!]! createdAt: DateTime! # Back-reference user: User! } # Pagination pattern (Relay-style) type OrderConnection { edges: [OrderEdge!]! pageInfo: PageInfo! totalCount: Int! } type OrderEdge { node: Order! cursor: String! } type PageInfo { hasNextPage: Boolean! hasPreviousPage: Boolean! startCursor: String endCursor: String } # Enums for type safety enum OrderStatus { PENDING CONFIRMED SHIPPED DELIVERED CANCELLED } # Custom scalars scalar DateTime scalar Money # Query root type Query { user(id: ID!): User users(first: Int = 20, after: String, search: String): UserConnection! order(id: ID!): Order } # Mutation root type Mutation { createUser(input: CreateUserInput!): CreateUserPayload! updateUser(input: UpdateUserInput!): UpdateUserPayload! deleteUser(id: ID!): DeleteUserPayload! createOrder(input: CreateOrderInput!): CreateOrderPayload! } # Input types for mutations input CreateUserInput { email: String! name: String! password: String! } # Payload types for mutations type CreateUserPayload { user: User errors: [Error!] } type Error { field: String message: String! } ``` ### Pattern 2: Resolver Design ```python from typing import Optional, List from ariadne import QueryType, MutationType, ObjectType from dataclasses import dataclass query = QueryType() mutation = MutationType() user_type = ObjectType("User") @query.field("user") async def resolve_user(obj, info, id: str) -> Optional[dict]: """Resolve single user by ID.""" return await fetch_user_by_id(id) @query.field("users") async def resolve_users( obj, info, first: int = 20, after: Optional[str] = None, search: Optional[str] = None ) -> dict: """Resolve paginated user list.""" # Decode cursor offset = decode_cursor(after) if after else 0 # Fetch users users = await fetch_users( limit=first + 1, # Fetch one extra to check hasNextPage offset=offset, search=search ) # Pagination has_next = len(users) > first if has_next: users = users[:first] edges = [ { "node": user, "cursor": encode_cursor(offset + i) } for i, user in enumerate(users) ] return { "edges": edges, "pageInfo": { "hasNextPage": has_next, "hasPreviousPage": offset > 0, "startCursor": edges[0]["cursor"] if edges else None, "endCursor": edges[-1]["cursor"] if edges else None }, "totalCount": await count_users(search=search) } @user_type.field("orders") async def resolve_user_orders(user: dict, info, first: int = 20) -> dict: """Resolve user's orders (N+1 prevention with DataLoader).""" # Use DataLoader to batch requests loader = info.context["loaders"]["orders_by_user"] orders = await loader.load(user["id"]) return paginate_orders(orders, first) @mutation.field("createUser") async def resolve_create_user(obj, info, input: dict) -> dict: """Create new user.""" try: # Validate input validate_user_input(input) # Create user user = await create_user( email=input["email"], name=input["name"], password=hash_password(input["password"]) ) return { "user": user, "errors": [] } except ValidationError as e: return { "user": None, "errors": [{"field": e.field, "message": e.message}] } ``` ### Pattern 3: DataLoader (N+1 Problem Prevention) ```python from aiodataloader import DataLoader from typing import List, Optional class UserLoader(DataLoader): """Batch load users by ID.""" async def batch_load_fn(self, user_ids: List[str]) -> List[Optional[dict]]: """Load multiple users in single query.""" users = await fetch_users_by_ids(user_ids) # Map results back to input order user_map = {user["id"]: user for user in users} return [user_map.get(user_id) for user_id in user_ids] class OrdersByUserLoader(DataLoader): """Batch load orders by user ID.""" async def batch_load_fn(self, user_ids: List[str]) -> List[List[dict]]: """Load orders for multiple users in single query.""" orders = await fetch_orders_by_user_ids(user_ids) # Group orders by user_id orders_by_user = {} for order in orders: user_id = order["user_id"] if user_id not in orders_by_user: orders_by_user[user_id] = [] orders_by_user[user_id].append(order) # Return in input order return [orders_by_user.get(user_id, []) for user_id in user_ids] # Context setup def create_context(): return { "loaders": { "user": UserLoader(), "orders_by_user": OrdersByUserLoader() } } ``` ## Best Practices ### REST APIs 1. **Consistent Naming**: Use plural nouns for collections (`/users`, not `/user`) 2. **Stateless**: Each request contains all necessary information 3. **Use HTTP Status Codes Correctly**: 2xx success, 4xx client errors, 5xx server errors 4. **Version Your API**: Plan for breaking changes from day one 5. **Pagination**: Always paginate large collections 6. **Rate Limiting**: Protect your API with rate limits 7. **Documentation**: Use OpenAPI/Swagger for interactive docs ### GraphQL APIs 1. **Schema First**: Design schema before writing resolvers 2. **Avoid N+1**: Use DataLoaders for efficient data fetching 3. **Input Validation**: Validate at schema and resolver levels 4. **Error Handling**: Return structured errors in mutation payloads 5. **Pagination**: Use cursor-based pagination (Relay spec) 6. **Deprecation**: Use `@deprecated` directive for gradual migration 7. **Monitoring**: Track query complexity and execution time ## Common Pitfalls - **Over-fetching/Under-fetching (REST)**: Fixed in GraphQL but requires DataLoaders - **Breaking Changes**: Version APIs or use deprecation strategies - **Inconsistent Error Formats**: Standardize error responses - **Missing Rate Limits**: APIs without limits are vulnerable to abuse - **Poor Documentation**: Undocumented APIs frustrate developers - **Ignoring HTTP Semantics**: POST for idempotent operations breaks expectations - **Tight Coupling**: API structure shouldn't mirror database schema ## Resources - **references/rest-best-practices.md**: Comprehensive REST API design guide - **references/graphql-schema-design.md**: GraphQL schema patterns and anti-patterns - **references/api-versioning-strategies.md**: Versioning approaches and migration paths - **assets/rest-api-template.py**: FastAPI REST API template - **assets/graphql-schema-template.graphql**: Complete GraphQL schema example - **assets/api-design-checklist.md**: Pre-implementation review checklist - **scripts/openapi-generator.py**: Generate OpenAPI specs from code
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

architecture-patterns

Implement proven backend architecture patterns including Clean

coding
⭐1
# Architecture Patterns Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems. ## When to Use This Skill - Designing new backend systems from scratch - Refactoring monolithic applications for better maintainability - Establishing architecture standards for your team - Migrating from tightly coupled to loosely coupled architectures - Implementing domain-driven design principles - Creating testable and mockable codebases - Planning microservices decomposition ## Core Concepts ### 1. Clean Architecture (Uncle Bob) **Layers (dependency flows inward):** - **Entities**: Core business models - **Use Cases**: Application business rules - **Interface Adapters**: Controllers, presenters, gateways - **Frameworks & Drivers**: UI, database, external services **Key Principles:** - Dependencies point inward - Inner layers know nothing about outer layers - Business logic independent of frameworks - Testable without UI, database, or external services ### 2. Hexagonal Architecture (Ports and Adapters) **Components:** - **Domain Core**: Business logic - **Ports**: Interfaces defining interactions - **Adapters**: Implementations of ports (database, REST, message queue) **Benefits:** - Swap implementations easily (mock for testing) - Technology-agnostic core - Clear separation of concerns ### 3. Domain-Driven Design (DDD) **Strategic Patterns:** - **Bounded Contexts**: Separate models for different domains - **Context Mapping**: How contexts relate - **Ubiquitous Language**: Shared terminology **Tactical Patterns:** - **Entities**: Objects with identity - **Value Objects**: Immutable objects defined by attributes - **Aggregates**: Consistency boundaries - **Repositories**: Data access abstraction - **Domain Events**: Things that happened ## Clean Architecture Pattern ### Directory Structure ``` app/ ā”œā”€ā”€ domain/ # Entities & business rules │ ā”œā”€ā”€ entities/ │ │ ā”œā”€ā”€ user.py │ │ └── order.py │ ā”œā”€ā”€ value_objects/ │ │ ā”œā”€ā”€ email.py │ │ └── money.py │ └── interfaces/ # Abstract interfaces │ ā”œā”€ā”€ user_repository.py │ └── payment_gateway.py ā”œā”€ā”€ use_cases/ # Application business rules │ ā”œā”€ā”€ create_user.py │ ā”œā”€ā”€ process_order.py │ └── send_notification.py ā”œā”€ā”€ adapters/ # Interface implementations │ ā”œā”€ā”€ repositories/ │ │ ā”œā”€ā”€ postgres_user_repository.py │ │ └── redis_cache_repository.py │ ā”œā”€ā”€ controllers/ │ │ └── user_controller.py │ └── gateways/ │ ā”œā”€ā”€ stripe_payment_gateway.py │ └── sendgrid_email_gateway.py └── infrastructure/ # Framework & external concerns ā”œā”€ā”€ database.py ā”œā”€ā”€ config.py └── logging.py ``` ### Implementation Example ```python # domain/entities/user.py from dataclasses import dataclass from datetime import datetime from typing import Optional @dataclass class User: """Core user entity - no framework dependencies.""" id: str email: str name: str created_at: datetime is_active: bool = True def deactivate(self): """Business rule: deactivating user.""" self.is_active = False def can_place_order(self) -> bool: """Business rule: active users can order.""" return self.is_active # domain/interfaces/user_repository.py from abc import ABC, abstractmethod from typing import Optional, List from domain.entities.user import User class IUserRepository(ABC): """Port: defines contract, no implementation.""" @abstractmethod async def find_by_id(self, user_id: str) -> Optional[User]: pass @abstractmethod async def find_by_email(self, email: str) -> Optional[User]: pass @abstractmethod async def save(self, user: User) -> User: pass @abstractmethod async def delete(self, user_id: str) -> bool: pass # use_cases/create_user.py from domain.entities.user import User from domain.interfaces.user_repository import IUserRepository from dataclasses import dataclass from datetime import datetime import uuid @dataclass class CreateUserRequest: email: str name: str @dataclass class CreateUserResponse: user: User success: bool error: Optional[str] = None class CreateUserUseCase: """Use case: orchestrates business logic.""" def __init__(self, user_repository: IUserRepository): self.user_repository = user_repository async def execute(self, request: CreateUserRequest) -> CreateUserResponse: # Business validation existing = await self.user_repository.find_by_email(request.email) if existing: return CreateUserResponse( user=None, success=False, error="Email already exists" ) # Create entity user = User( id=str(uuid.uuid4()), email=request.email, name=request.name, created_at=datetime.now(), is_active=True ) # Persist saved_user = await self.user_repository.save(user) return CreateUserResponse( user=saved_user, success=True ) # adapters/repositories/postgres_user_repository.py from domain.interfaces.user_repository import IUserRepository from domain.entities.user import User from typing import Optional import asyncpg class PostgresUserRepository(IUserRepository): """Adapter: PostgreSQL implementation.""" def __init__(self, pool: asyncpg.Pool): self.pool = pool async def find_by_id(self, user_id: str) -> Optional[User]: async with self.pool.acquire() as conn: row = await conn.fetchrow( "SELECT * FROM users WHERE id = $1", user_id ) return self._to_entity(row) if row else None async def find_by_email(self, email: str) -> Optional[User]: async with self.pool.acquire() as conn: row = await conn.fetchrow( "SELECT * FROM users WHERE email = $1", email ) return self._to_entity(row) if row else None async def save(self, user: User) -> User: async with self.pool.acquire() as conn: await conn.execute( """ INSERT INTO users (id, email, name, created_at, is_active) VALUES ($1, $2, $3, $4, $5) ON CONFLICT (id) DO UPDATE SET email = $2, name = $3, is_active = $5 """, user.id, user.email, user.name, user.created_at, user.is_active ) return user async def delete(self, user_id: str) -> bool: async with self.pool.acquire() as conn: result = await conn.execute( "DELETE FROM users WHERE id = $1", user_id ) return result == "DELETE 1" def _to_entity(self, row) -> User: """Map database row to entity.""" return User( id=row["id"], email=row["email"], name=row["name"], created_at=row["created_at"], is_active=row["is_active"] ) # adapters/controllers/user_controller.py from fastapi import APIRouter, Depends, HTTPException from use_cases.create_user import CreateUserUseCase, CreateUserRequest from pydantic import BaseModel router = APIRouter() class CreateUserDTO(BaseModel): email: str name: str @router.post("/users") async def create_user( dto: CreateUserDTO, use_case: CreateUserUseCase = Depends(get_create_user_use_case) ): """Controller: handles HTTP concerns only.""" request = CreateUserRequest(email=dto.email, name=dto.name) response = await use_case.execute(request) if not response.success: raise HTTPException(status_code=400, detail=response.error) return {"user": response.user} ``` ## Hexagonal Architecture Pattern ```python # Core domain (hexagon center) class OrderService: """Domain service - no infrastructure dependencies.""" def __init__( self, order_repository: OrderRepositoryPort, payment_gateway: PaymentGatewayPort, notification_service: NotificationPort ): self.orders = order_repository self.payments = payment_gateway self.notifications = notification_service async def place_order(self, order: Order) -> OrderResult: # Business logic if not order.is_valid(): return OrderResult(success=False, error="Invalid order") # Use ports (interfaces) payment = await self.payments.charge( amount=order.total, customer=order.customer_id ) if not payment.success: return OrderResult(success=False, error="Payment failed") order.mark_as_paid() saved_order = await self.orders.save(order) await self.notifications.send( to=order.customer_email, subject="Order confirmed", body=f"Order {order.id} confirmed" ) return OrderResult(success=True, order=saved_order) # Ports (interfaces) class OrderRepositoryPort(ABC): @abstractmethod async def save(self, order: Order) -> Order: pass class PaymentGatewayPort(ABC): @abstractmethod async def charge(self, amount: Money, customer: str) -> PaymentResult: pass class NotificationPort(ABC): @abstractmethod async def send(self, to: str, subject: str, body: str): pass # Adapters (implementations) class StripePaymentAdapter(PaymentGatewayPort): """Primary adapter: connects to Stripe API.""" def __init__(self, api_key: str): self.stripe = stripe self.stripe.api_key = api_key async def charge(self, amount: Money, customer: str) -> PaymentResult: try: charge = self.stripe.Charge.create( amount=amount.cents, currency=amount.currency, customer=customer ) return PaymentResult(success=True, transaction_id=charge.id) except stripe.error.CardError as e: return PaymentResult(success=False, error=str(e)) class MockPaymentAdapter(PaymentGatewayPort): """Test adapter: no external dependencies.""" async def charge(self, amount: Money, customer: str) -> PaymentResult: return PaymentResult(success=True, transaction_id="mock-123") ``` ## Domain-Driven Design Pattern ```python # Value Objects (immutable) from dataclasses import dataclass from typing import Optional @dataclass(frozen=True) class Email: """Value object: validated email.""" value: str def __post_init__(self): if "@" not in self.value: raise ValueError("Invalid email") @dataclass(frozen=True) class Money: """Value object: amount with currency.""" amount: int # cents currency: str def add(self, other: "Money") -> "Money": if self.currency != other.currency: raise ValueError("Currency mismatch") return Money(self.amount + other.amount, self.currency) # Entities (with identity) class Order: """Entity: has identity, mutable state.""" def __init__(self, id: str, customer: Customer): self.id = id self.customer = customer self.items: List[OrderItem] = [] self.status = OrderStatus.PENDING self._events: List[DomainEvent] = [] def add_item(self, product: Product, quantity: int): """Business logic in entity.""" item = OrderItem(product, quantity) self.items.append(item) self._events.append(ItemAddedEvent(self.id, item)) def total(self) -> Money: """Calculated property.""" return sum(item.subtotal() for item in self.items) def submit(self): """State transition with business rules.""" if not self.items: raise ValueError("Cannot submit empty order") if self.status != OrderStatus.PENDING: raise ValueError("Order already submitted") self.status = OrderStatus.SUBMITTED self._events.append(OrderSubmittedEvent(self.id)) # Aggregates (consistency boundary) class Customer: """Aggregate root: controls access to entities.""" def __init__(self, id: str, email: Email): self.id = id self.email = email self._addresses: List[Address] = [] self._orders: List[str] = [] # Order IDs, not full objects def add_address(self, address: Address): """Aggregate enforces invariants.""" if len(self._addresses) >= 5: raise ValueError("Maximum 5 addresses allowed") self._addresses.append(address) @property def primary_address(self) -> Optional[Address]: return next((a for a in self._addresses if a.is_primary), None) # Domain Events @dataclass class OrderSubmittedEvent: order_id: str occurred_at: datetime = field(default_factory=datetime.now) # Repository (aggregate persistence) class OrderRepository: """Repository: persist/retrieve aggregates.""" async def find_by_id(self, order_id: str) -> Optional[Order]: """Reconstitute aggregate from storage.""" pass async def save(self, order: Order): """Persist aggregate and publish events.""" await self._persist(order) await self._publish_events(order._events) order._events.clear() ``` ## Resources - **references/clean-architecture-guide.md**: Detailed layer breakdown - **references/hexagonal-architecture-guide.md**: Ports and adapters patterns - **references/ddd-tactical-patterns.md**: Entities, value objects, aggregates - **assets/clean-architecture-template/**: Complete project structure - **assets/ddd-examples/**: Domain modeling examples ## Best Practices 1. **Dependency Rule**: Dependencies always point inward 2. **Interface Segregation**: Small, focused interfaces 3. **Business Logic in Domain**: Keep frameworks out of core 4. **Test Independence**: Core testable without infrastructure 5. **Bounded Contexts**: Clear domain boundaries 6. **Ubiquitous Language**: Consistent terminology 7. **Thin Controllers**: Delegate to use cases 8. **Rich Domain Models**: Behavior with data ## Common Pitfalls - **Anemic Domain**: Entities with only data, no behavior - **Framework Coupling**: Business logic depends on frameworks - **Fat Controllers**: Business logic in controllers - **Repository Leakage**: Exposing ORM objects - **Missing Abstractions**: Concrete dependencies in core - **Over-Engineering**: Clean architecture for simple CRUD
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

cqrs-implementation

Implement Command Query Responsibility Segregation for scalable

coding
⭐1
# CQRS Implementation Comprehensive guide to implementing CQRS (Command Query Responsibility Segregation) patterns. ## When to Use This Skill - Separating read and write concerns - Scaling reads independently from writes - Building event-sourced systems - Optimizing complex query scenarios - Different read/write data models needed - High-performance reporting requirements ## Core Concepts ### 1. CQRS Architecture ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Client │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ ā–¼ ā–¼ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Commands │ │ Queries │ │ API │ │ API │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ ā–¼ ā–¼ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Command │ │ Query │ │ Handlers │ │ Handlers │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ ā–¼ ā–¼ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Write │─────────►│ Read │ │ Model │ Events │ Model │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ### 2. Key Components | Component | Responsibility | | ------------------- | ------------------------------- | | **Command** | Intent to change state | | **Command Handler** | Validates and executes commands | | **Event** | Record of state change | | **Query** | Request for data | | **Query Handler** | Retrieves data from read model | | **Projector** | Updates read model from events | ## Templates ### Template 1: Command Infrastructure ```python from abc import ABC, abstractmethod from dataclasses import dataclass from typing import TypeVar, Generic, Dict, Any, Type from datetime import datetime import uuid # Command base @dataclass class Command: command_id: str = None timestamp: datetime = None def __post_init__(self): self.command_id = self.command_id or str(uuid.uuid4()) self.timestamp = self.timestamp or datetime.utcnow() # Concrete commands @dataclass class CreateOrder(Command): customer_id: str items: list shipping_address: dict @dataclass class AddOrderItem(Command): order_id: str product_id: str quantity: int price: float @dataclass class CancelOrder(Command): order_id: str reason: str # Command handler base T = TypeVar('T', bound=Command) class CommandHandler(ABC, Generic[T]): @abstractmethod async def handle(self, command: T) -> Any: pass # Command bus class CommandBus: def __init__(self): self._handlers: Dict[Type[Command], CommandHandler] = {} def register(self, command_type: Type[Command], handler: CommandHandler): self._handlers[command_type] = handler async def dispatch(self, command: Command) -> Any: handler = self._handlers.get(type(command)) if not handler: raise ValueError(f"No handler for {type(command).__name__}") return await handler.handle(command) # Command handler implementation class CreateOrderHandler(CommandHandler[CreateOrder]): def __init__(self, order_repository, event_store): self.order_repository = order_repository self.event_store = event_store async def handle(self, command: CreateOrder) -> str: # Validate if not command.items: raise ValueError("Order must have at least one item") # Create aggregate order = Order.create( customer_id=command.customer_id, items=command.items, shipping_address=command.shipping_address ) # Persist events await self.event_store.append_events( stream_id=f"Order-{order.id}", stream_type="Order", events=order.uncommitted_events ) return order.id ``` ### Template 2: Query Infrastructure ```python from abc import ABC, abstractmethod from dataclasses import dataclass from typing import TypeVar, Generic, List, Optional # Query base @dataclass class Query: pass # Concrete queries @dataclass class GetOrderById(Query): order_id: str @dataclass class GetCustomerOrders(Query): customer_id: str status: Optional[str] = None page: int = 1 page_size: int = 20 @dataclass class SearchOrders(Query): query: str filters: dict = None sort_by: str = "created_at" sort_order: str = "desc" # Query result types @dataclass class OrderView: order_id: str customer_id: str status: str total_amount: float item_count: int created_at: datetime shipped_at: Optional[datetime] = None @dataclass class PaginatedResult(Generic[T]): items: List[T] total: int page: int page_size: int @property def total_pages(self) -> int: return (self.total + self.page_size - 1) // self.page_size # Query handler base T = TypeVar('T', bound=Query) R = TypeVar('R') class QueryHandler(ABC, Generic[T, R]): @abstractmethod async def handle(self, query: T) -> R: pass # Query bus class QueryBus: def __init__(self): self._handlers: Dict[Type[Query], QueryHandler] = {} def register(self, query_type: Type[Query], handler: QueryHandler): self._handlers[query_type] = handler async def dispatch(self, query: Query) -> Any: handler = self._handlers.get(type(query)) if not handler: raise ValueError(f"No handler for {type(query).__name__}") return await handler.handle(query) # Query handler implementation class GetOrderByIdHandler(QueryHandler[GetOrderById, Optional[OrderView]]): def __init__(self, read_db): self.read_db = read_db async def handle(self, query: GetOrderById) -> Optional[OrderView]: async with self.read_db.acquire() as conn: row = await conn.fetchrow( """ SELECT order_id, customer_id, status, total_amount, item_count, created_at, shipped_at FROM order_views WHERE order_id = $1 """, query.order_id ) if row: return OrderView(**dict(row)) return None class GetCustomerOrdersHandler(QueryHandler[GetCustomerOrders, PaginatedResult[OrderView]]): def __init__(self, read_db): self.read_db = read_db async def handle(self, query: GetCustomerOrders) -> PaginatedResult[OrderView]: async with self.read_db.acquire() as conn: # Build query with optional status filter where_clause = "customer_id = $1" params = [query.customer_id] if query.status: where_clause += " AND status = $2" params.append(query.status) # Get total count total = await conn.fetchval( f"SELECT COUNT(*) FROM order_views WHERE {where_clause}", *params ) # Get paginated results offset = (query.page - 1) * query.page_size rows = await conn.fetch( f""" SELECT order_id, customer_id, status, total_amount, item_count, created_at, shipped_at FROM order_views WHERE {where_clause} ORDER BY created_at DESC LIMIT ${len(params) + 1} OFFSET ${len(params) + 2} """, *params, query.page_size, offset ) return PaginatedResult( items=[OrderView(**dict(row)) for row in rows], total=total, page=query.page, page_size=query.page_size ) ``` ### Template 3: FastAPI CQRS Application ```python from fastapi import FastAPI, HTTPException, Depends from pydantic import BaseModel from typing import List, Optional app = FastAPI() # Request/Response models class CreateOrderRequest(BaseModel): customer_id: str items: List[dict] shipping_address: dict class OrderResponse(BaseModel): order_id: str customer_id: str status: str total_amount: float item_count: int created_at: datetime # Dependency injection def get_command_bus() -> CommandBus: return app.state.command_bus def get_query_bus() -> QueryBus: return app.state.query_bus # Command endpoints (POST, PUT, DELETE) @app.post("/orders", response_model=dict) async def create_order( request: CreateOrderRequest, command_bus: CommandBus = Depends(get_command_bus) ): command = CreateOrder( customer_id=request.customer_id, items=request.items, shipping_address=request.shipping_address ) order_id = await command_bus.dispatch(command) return {"order_id": order_id} @app.post("/orders/{order_id}/items") async def add_item( order_id: str, product_id: str, quantity: int, price: float, command_bus: CommandBus = Depends(get_command_bus) ): command = AddOrderItem( order_id=order_id, product_id=product_id, quantity=quantity, price=price ) await command_bus.dispatch(command) return {"status": "item_added"} @app.delete("/orders/{order_id}") async def cancel_order( order_id: str, reason: str, command_bus: CommandBus = Depends(get_command_bus) ): command = CancelOrder(order_id=order_id, reason=reason) await command_bus.dispatch(command) return {"status": "cancelled"} # Query endpoints (GET) @app.get("/orders/{order_id}", response_model=OrderResponse) async def get_order( order_id: str, query_bus: QueryBus = Depends(get_query_bus) ): query = GetOrderById(order_id=order_id) result = await query_bus.dispatch(query) if not result: raise HTTPException(status_code=404, detail="Order not found") return result @app.get("/customers/{customer_id}/orders") async def get_customer_orders( customer_id: str, status: Optional[str] = None, page: int = 1, page_size: int = 20, query_bus: QueryBus = Depends(get_query_bus) ): query = GetCustomerOrders( customer_id=customer_id, status=status, page=page, page_size=page_size ) return await query_bus.dispatch(query) @app.get("/orders/search") async def search_orders( q: str, sort_by: str = "created_at", query_bus: QueryBus = Depends(get_query_bus) ): query = SearchOrders(query=q, sort_by=sort_by) return await query_bus.dispatch(query) ``` ### Template 4: Read Model Synchronization ```python class ReadModelSynchronizer: """Keeps read models in sync with events.""" def __init__(self, event_store, read_db, projections: List[Projection]): self.event_store = event_store self.read_db = read_db self.projections = {p.name: p for p in projections} async def run(self): """Continuously sync read models.""" while True: for name, projection in self.projections.items(): await self._sync_projection(projection) await asyncio.sleep(0.1) async def _sync_projection(self, projection: Projection): checkpoint = await self._get_checkpoint(projection.name) events = await self.event_store.read_all( from_position=checkpoint, limit=100 ) for event in events: if event.event_type in projection.handles(): try: await projection.apply(event) except Exception as e: # Log error, possibly retry or skip logger.error(f"Projection error: {e}") continue await self._save_checkpoint(projection.name, event.global_position) async def rebuild_projection(self, projection_name: str): """Rebuild a projection from scratch.""" projection = self.projections[projection_name] # Clear existing data await projection.clear() # Reset checkpoint await self._save_checkpoint(projection_name, 0) # Rebuild while True: checkpoint = await self._get_checkpoint(projection_name) events = await self.event_store.read_all(checkpoint, 1000) if not events: break for event in events: if event.event_type in projection.handles(): await projection.apply(event) await self._save_checkpoint( projection_name, events[-1].global_position ) ``` ### Template 5: Eventual Consistency Handling ```python class ConsistentQueryHandler: """Query handler that can wait for consistency.""" def __init__(self, read_db, event_store): self.read_db = read_db self.event_store = event_store async def query_after_command( self, query: Query, expected_version: int, stream_id: str, timeout: float = 5.0 ): """ Execute query, ensuring read model is at expected version. Used for read-your-writes consistency. """ start_time = time.time() while time.time() - start_time < timeout: # Check if read model is caught up projection_version = await self._get_projection_version(stream_id) if projection_version >= expected_version: return await self.execute_query(query) # Wait a bit and retry await asyncio.sleep(0.1) # Timeout - return stale data with warning return { "data": await self.execute_query(query), "_warning": "Data may be stale" } async def _get_projection_version(self, stream_id: str) -> int: """Get the last processed event version for a stream.""" async with self.read_db.acquire() as conn: return await conn.fetchval( "SELECT last_event_version FROM projection_state WHERE stream_id = $1", stream_id ) or 0 ``` ## Best Practices ### Do's - **Separate command and query models** - Different needs - **Use eventual consistency** - Accept propagation delay - **Validate in command handlers** - Before state change - **Denormalize read models** - Optimize for queries - **Version your events** - For schema evolution ### Don'ts - **Don't query in commands** - Use only for writes - **Don't couple read/write schemas** - Independent evolution - **Don't over-engineer** - Start simple - **Don't ignore consistency SLAs** - Define acceptable lag ## Resources - [CQRS Pattern](https://martinfowler.com/bliki/CQRS.html) - [Microsoft CQRS Guidance](https://docs.microsoft.com/en-us/azure/architecture/patterns/cqrs)
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event-store-design

Design and implement event stores for event-sourced systems. Use

coding
⭐1
# Event Store Design Comprehensive guide to designing event stores for event-sourced applications. ## When to Use This Skill - Designing event sourcing infrastructure - Choosing between event store technologies - Implementing custom event stores - Optimizing event storage and retrieval - Setting up event store schemas - Planning for event store scaling ## Core Concepts ### 1. Event Store Architecture ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Event Store │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ │ Stream 1 │ │ Stream 2 │ │ Stream 3 │ │ │ │ (Aggregate) │ │ (Aggregate) │ │ (Aggregate) │ │ │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ │ │ Event 1 │ │ Event 1 │ │ Event 1 │ │ │ │ Event 2 │ │ Event 2 │ │ Event 2 │ │ │ │ Event 3 │ │ ... │ │ Event 3 │ │ │ │ ... │ │ │ │ Event 4 │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ Global Position: 1 → 2 → 3 → 4 → 5 → 6 → ... │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ### 2. Event Store Requirements | Requirement | Description | | ----------------- | ---------------------------------- | | **Append-only** | Events are immutable, only appends | | **Ordered** | Per-stream and global ordering | | **Versioned** | Optimistic concurrency control | | **Subscriptions** | Real-time event notifications | | **Idempotent** | Handle duplicate writes safely | ## Technology Comparison | Technology | Best For | Limitations | | ---------------- | ------------------------- | -------------------------------- | | **EventStoreDB** | Pure event sourcing | Single-purpose | | **PostgreSQL** | Existing Postgres stack | Manual implementation | | **Kafka** | High-throughput streaming | Not ideal for per-stream queries | | **DynamoDB** | Serverless, AWS-native | Query limitations | | **Marten** | .NET ecosystems | .NET specific | ## Templates ### Template 1: PostgreSQL Event Store Schema ```sql -- Events table CREATE TABLE events ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), stream_id VARCHAR(255) NOT NULL, stream_type VARCHAR(255) NOT NULL, event_type VARCHAR(255) NOT NULL, event_data JSONB NOT NULL, metadata JSONB DEFAULT '{}', version BIGINT NOT NULL, global_position BIGSERIAL, created_at TIMESTAMPTZ DEFAULT NOW(), CONSTRAINT unique_stream_version UNIQUE (stream_id, version) ); -- Index for stream queries CREATE INDEX idx_events_stream_id ON events(stream_id, version); -- Index for global subscription CREATE INDEX idx_events_global_position ON events(global_position); -- Index for event type queries CREATE INDEX idx_events_event_type ON events(event_type); -- Index for time-based queries CREATE INDEX idx_events_created_at ON events(created_at); -- Snapshots table CREATE TABLE snapshots ( stream_id VARCHAR(255) PRIMARY KEY, stream_type VARCHAR(255) NOT NULL, snapshot_data JSONB NOT NULL, version BIGINT NOT NULL, created_at TIMESTAMPTZ DEFAULT NOW() ); -- Subscriptions checkpoint table CREATE TABLE subscription_checkpoints ( subscription_id VARCHAR(255) PRIMARY KEY, last_position BIGINT NOT NULL DEFAULT 0, updated_at TIMESTAMPTZ DEFAULT NOW() ); ``` ### Template 2: Python Event Store Implementation ```python from dataclasses import dataclass, field from datetime import datetime from typing import Any, Optional, List from uuid import UUID, uuid4 import json import asyncpg @dataclass class Event: stream_id: str event_type: str data: dict metadata: dict = field(default_factory=dict) event_id: UUID = field(default_factory=uuid4) version: Optional[int] = None global_position: Optional[int] = None created_at: datetime = field(default_factory=datetime.utcnow) class EventStore: def __init__(self, pool: asyncpg.Pool): self.pool = pool async def append_events( self, stream_id: str, stream_type: str, events: List[Event], expected_version: Optional[int] = None ) -> List[Event]: """Append events to a stream with optimistic concurrency.""" async with self.pool.acquire() as conn: async with conn.transaction(): # Check expected version if expected_version is not None: current = await conn.fetchval( "SELECT MAX(version) FROM events WHERE stream_id = $1", stream_id ) current = current or 0 if current != expected_version: raise ConcurrencyError( f"Expected version {expected_version}, got {current}" ) # Get starting version start_version = await conn.fetchval( "SELECT COALESCE(MAX(version), 0) + 1 FROM events WHERE stream_id = $1", stream_id ) # Insert events saved_events = [] for i, event in enumerate(events): event.version = start_version + i row = await conn.fetchrow( """ INSERT INTO events (id, stream_id, stream_type, event_type, event_data, metadata, version, created_at) VALUES ($1, $2, $3, $4, $5, $6, $7, $8) RETURNING global_position """, event.event_id, stream_id, stream_type, event.event_type, json.dumps(event.data), json.dumps(event.metadata), event.version, event.created_at ) event.global_position = row['global_position'] saved_events.append(event) return saved_events async def read_stream( self, stream_id: str, from_version: int = 0, limit: int = 1000 ) -> List[Event]: """Read events from a stream.""" async with self.pool.acquire() as conn: rows = await conn.fetch( """ SELECT id, stream_id, event_type, event_data, metadata, version, global_position, created_at FROM events WHERE stream_id = $1 AND version >= $2 ORDER BY version LIMIT $3 """, stream_id, from_version, limit ) return [self._row_to_event(row) for row in rows] async def read_all( self, from_position: int = 0, limit: int = 1000 ) -> List[Event]: """Read all events globally.""" async with self.pool.acquire() as conn: rows = await conn.fetch( """ SELECT id, stream_id, event_type, event_data, metadata, version, global_position, created_at FROM events WHERE global_position > $1 ORDER BY global_position LIMIT $2 """, from_position, limit ) return [self._row_to_event(row) for row in rows] async def subscribe( self, subscription_id: str, handler, from_position: int = 0, batch_size: int = 100 ): """Subscribe to all events from a position.""" # Get checkpoint async with self.pool.acquire() as conn: checkpoint = await conn.fetchval( """ SELECT last_position FROM subscription_checkpoints WHERE subscription_id = $1 """, subscription_id ) position = checkpoint or from_position while True: events = await self.read_all(position, batch_size) if not events: await asyncio.sleep(1) # Poll interval continue for event in events: await handler(event) position = event.global_position # Save checkpoint async with self.pool.acquire() as conn: await conn.execute( """ INSERT INTO subscription_checkpoints (subscription_id, last_position) VALUES ($1, $2) ON CONFLICT (subscription_id) DO UPDATE SET last_position = $2, updated_at = NOW() """, subscription_id, position ) def _row_to_event(self, row) -> Event: return Event( event_id=row['id'], stream_id=row['stream_id'], event_type=row['event_type'], data=json.loads(row['event_data']), metadata=json.loads(row['metadata']), version=row['version'], global_position=row['global_position'], created_at=row['created_at'] ) class ConcurrencyError(Exception): """Raised when optimistic concurrency check fails.""" pass ``` ### Template 3: EventStoreDB Usage ```python from esdbclient import EventStoreDBClient, NewEvent, StreamState import json # Connect client = EventStoreDBClient(uri="esdb://localhost:2113?tls=false") # Append events def append_events(stream_name: str, events: list, expected_revision=None): new_events = [ NewEvent( type=event['type'], data=json.dumps(event['data']).encode(), metadata=json.dumps(event.get('metadata', {})).encode() ) for event in events ] if expected_revision is None: state = StreamState.ANY elif expected_revision == -1: state = StreamState.NO_STREAM else: state = expected_revision return client.append_to_stream( stream_name=stream_name, events=new_events, current_version=state ) # Read stream def read_stream(stream_name: str, from_revision: int = 0): events = client.get_stream( stream_name=stream_name, stream_position=from_revision ) return [ { 'type': event.type, 'data': json.loads(event.data), 'metadata': json.loads(event.metadata) if event.metadata else {}, 'stream_position': event.stream_position, 'commit_position': event.commit_position } for event in events ] # Subscribe to all async def subscribe_to_all(handler, from_position: int = 0): subscription = client.subscribe_to_all(commit_position=from_position) async for event in subscription: await handler({ 'type': event.type, 'data': json.loads(event.data), 'stream_id': event.stream_name, 'position': event.commit_position }) # Category projection ($ce-Category) def read_category(category: str): """Read all events for a category using system projection.""" return read_stream(f"$ce-{category}") ``` ### Template 4: DynamoDB Event Store ```python import boto3 from boto3.dynamodb.conditions import Key from datetime import datetime import json import uuid class DynamoEventStore: def __init__(self, table_name: str): self.dynamodb = boto3.resource('dynamodb') self.table = self.dynamodb.Table(table_name) def append_events(self, stream_id: str, events: list, expected_version: int = None): """Append events with conditional write for concurrency.""" with self.table.batch_writer() as batch: for i, event in enumerate(events): version = (expected_version or 0) + i + 1 item = { 'PK': f"STREAM#{stream_id}", 'SK': f"VERSION#{version:020d}", 'GSI1PK': 'EVENTS', 'GSI1SK': datetime.utcnow().isoformat(), 'event_id': str(uuid.uuid4()), 'stream_id': stream_id, 'event_type': event['type'], 'event_data': json.dumps(event['data']), 'version': version, 'created_at': datetime.utcnow().isoformat() } batch.put_item(Item=item) return events def read_stream(self, stream_id: str, from_version: int = 0): """Read events from a stream.""" response = self.table.query( KeyConditionExpression=Key('PK').eq(f"STREAM#{stream_id}") & Key('SK').gte(f"VERSION#{from_version:020d}") ) return [ { 'event_type': item['event_type'], 'data': json.loads(item['event_data']), 'version': item['version'] } for item in response['Items'] ] # Table definition (CloudFormation/Terraform) """ DynamoDB Table: - PK (Partition Key): String - SK (Sort Key): String - GSI1PK, GSI1SK for global ordering Capacity: On-demand or provisioned based on throughput needs """ ``` ## Best Practices ### Do's - **Use stream IDs that include aggregate type** - `Order-{uuid}` - **Include correlation/causation IDs** - For tracing - **Version events from day one** - Plan for schema evolution - **Implement idempotency** - Use event IDs for deduplication - **Index appropriately** - For your query patterns ### Don'ts - **Don't update or delete events** - They're immutable facts - **Don't store large payloads** - Keep events small - **Don't skip optimistic concurrency** - Prevents data corruption - **Don't ignore backpressure** - Handle slow consumers ## Resources - [EventStoreDB](https://www.eventstore.com/) - [Marten Events](https://martendb.io/events/) - [Event Sourcing Pattern](https://docs.microsoft.com/en-us/azure/architecture/patterns/event-sourcing)
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microservices-patterns

Design microservices architectures with service boundaries,

coding
⭐1
# Microservices Patterns Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems. ## When to Use This Skill - Decomposing monoliths into microservices - Designing service boundaries and contracts - Implementing inter-service communication - Managing distributed data and transactions - Building resilient distributed systems - Implementing service discovery and load balancing - Designing event-driven architectures ## Core Concepts ### 1. Service Decomposition Strategies **By Business Capability** - Organize services around business functions - Each service owns its domain - Example: OrderService, PaymentService, InventoryService **By Subdomain (DDD)** - Core domain, supporting subdomains - Bounded contexts map to services - Clear ownership and responsibility **Strangler Fig Pattern** - Gradually extract from monolith - New functionality as microservices - Proxy routes to old/new systems ### 2. Communication Patterns **Synchronous (Request/Response)** - REST APIs - gRPC - GraphQL **Asynchronous (Events/Messages)** - Event streaming (Kafka) - Message queues (RabbitMQ, SQS) - Pub/Sub patterns ### 3. Data Management **Database Per Service** - Each service owns its data - No shared databases - Loose coupling **Saga Pattern** - Distributed transactions - Compensating actions - Eventual consistency ### 4. Resilience Patterns **Circuit Breaker** - Fail fast on repeated errors - Prevent cascade failures **Retry with Backoff** - Transient fault handling - Exponential backoff **Bulkhead** - Isolate resources - Limit impact of failures ## Service Decomposition Patterns ### Pattern 1: By Business Capability ```python # E-commerce example # Order Service class OrderService: """Handles order lifecycle.""" async def create_order(self, order_data: dict) -> Order: order = Order.create(order_data) # Publish event for other services await self.event_bus.publish( OrderCreatedEvent( order_id=order.id, customer_id=order.customer_id, items=order.items, total=order.total ) ) return order # Payment Service (separate service) class PaymentService: """Handles payment processing.""" async def process_payment(self, payment_request: PaymentRequest) -> PaymentResult: # Process payment result = await self.payment_gateway.charge( amount=payment_request.amount, customer=payment_request.customer_id ) if result.success: await self.event_bus.publish( PaymentCompletedEvent( order_id=payment_request.order_id, transaction_id=result.transaction_id ) ) return result # Inventory Service (separate service) class InventoryService: """Handles inventory management.""" async def reserve_items(self, order_id: str, items: List[OrderItem]) -> ReservationResult: # Check availability for item in items: available = await self.inventory_repo.get_available(item.product_id) if available < item.quantity: return ReservationResult( success=False, error=f"Insufficient inventory for {item.product_id}" ) # Reserve items reservation = await self.create_reservation(order_id, items) await self.event_bus.publish( InventoryReservedEvent( order_id=order_id, reservation_id=reservation.id ) ) return ReservationResult(success=True, reservation=reservation) ``` ### Pattern 2: API Gateway ```python from fastapi import FastAPI, HTTPException, Depends import httpx from circuitbreaker import circuit app = FastAPI() class APIGateway: """Central entry point for all client requests.""" def __init__(self): self.order_service_url = "http://order-service:8000" self.payment_service_url = "http://payment-service:8001" self.inventory_service_url = "http://inventory-service:8002" self.http_client = httpx.AsyncClient(timeout=5.0) @circuit(failure_threshold=5, recovery_timeout=30) async def call_order_service(self, path: str, method: str = "GET", **kwargs): """Call order service with circuit breaker.""" response = await self.http_client.request( method, f"{self.order_service_url}{path}", **kwargs ) response.raise_for_status() return response.json() async def create_order_aggregate(self, order_id: str) -> dict: """Aggregate data from multiple services.""" # Parallel requests order, payment, inventory = await asyncio.gather( self.call_order_service(f"/orders/{order_id}"), self.call_payment_service(f"/payments/order/{order_id}"), self.call_inventory_service(f"/reservations/order/{order_id}"), return_exceptions=True ) # Handle partial failures result = {"order": order} if not isinstance(payment, Exception): result["payment"] = payment if not isinstance(inventory, Exception): result["inventory"] = inventory return result @app.post("/api/orders") async def create_order( order_data: dict, gateway: APIGateway = Depends() ): """API Gateway endpoint.""" try: # Route to order service order = await gateway.call_order_service( "/orders", method="POST", json=order_data ) return {"order": order} except httpx.HTTPError as e: raise HTTPException(status_code=503, detail="Order service unavailable") ``` ## Communication Patterns ### Pattern 1: Synchronous REST Communication ```python # Service A calls Service B import httpx from tenacity import retry, stop_after_attempt, wait_exponential class ServiceClient: """HTTP client with retries and timeout.""" def __init__(self, base_url: str): self.base_url = base_url self.client = httpx.AsyncClient( timeout=httpx.Timeout(5.0, connect=2.0), limits=httpx.Limits(max_keepalive_connections=20) ) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10) ) async def get(self, path: str, **kwargs): """GET with automatic retries.""" response = await self.client.get(f"{self.base_url}{path}", **kwargs) response.raise_for_status() return response.json() async def post(self, path: str, **kwargs): """POST request.""" response = await self.client.post(f"{self.base_url}{path}", **kwargs) response.raise_for_status() return response.json() # Usage payment_client = ServiceClient("http://payment-service:8001") result = await payment_client.post("/payments", json=payment_data) ``` ### Pattern 2: Asynchronous Event-Driven ```python # Event-driven communication with Kafka from aiokafka import AIOKafkaProducer, AIOKafkaConsumer import json from dataclasses import dataclass, asdict from datetime import datetime @dataclass class DomainEvent: event_id: str event_type: str aggregate_id: str occurred_at: datetime data: dict class EventBus: """Event publishing and subscription.""" def __init__(self, bootstrap_servers: List[str]): self.bootstrap_servers = bootstrap_servers self.producer = None async def start(self): self.producer = AIOKafkaProducer( bootstrap_servers=self.bootstrap_servers, value_serializer=lambda v: json.dumps(v).encode() ) await self.producer.start() async def publish(self, event: DomainEvent): """Publish event to Kafka topic.""" topic = event.event_type await self.producer.send_and_wait( topic, value=asdict(event), key=event.aggregate_id.encode() ) async def subscribe(self, topic: str, handler: callable): """Subscribe to events.""" consumer = AIOKafkaConsumer( topic, bootstrap_servers=self.bootstrap_servers, value_deserializer=lambda v: json.loads(v.decode()), group_id="my-service" ) await consumer.start() try: async for message in consumer: event_data = message.value await handler(event_data) finally: await consumer.stop() # Order Service publishes event async def create_order(order_data: dict): order = await save_order(order_data) event = DomainEvent( event_id=str(uuid.uuid4()), event_type="OrderCreated", aggregate_id=order.id, occurred_at=datetime.now(), data={ "order_id": order.id, "customer_id": order.customer_id, "total": order.total } ) await event_bus.publish(event) # Inventory Service listens for OrderCreated async def handle_order_created(event_data: dict): """React to order creation.""" order_id = event_data["data"]["order_id"] items = event_data["data"]["items"] # Reserve inventory await reserve_inventory(order_id, items) ``` ### Pattern 3: Saga Pattern (Distributed Transactions) ```python # Saga orchestration for order fulfillment from enum import Enum from typing import List, Callable class SagaStep: """Single step in saga.""" def __init__( self, name: str, action: Callable, compensation: Callable ): self.name = name self.action = action self.compensation = compensation class SagaStatus(Enum): PENDING = "pending" COMPLETED = "completed" COMPENSATING = "compensating" FAILED = "failed" class OrderFulfillmentSaga: """Orchestrated saga for order fulfillment.""" def __init__(self): self.steps: List[SagaStep] = [ SagaStep( "create_order", action=self.create_order, compensation=self.cancel_order ), SagaStep( "reserve_inventory", action=self.reserve_inventory, compensation=self.release_inventory ), SagaStep( "process_payment", action=self.process_payment, compensation=self.refund_payment ), SagaStep( "confirm_order", action=self.confirm_order, compensation=self.cancel_order_confirmation ) ] async def execute(self, order_data: dict) -> SagaResult: """Execute saga steps.""" completed_steps = [] context = {"order_data": order_data} try: for step in self.steps: # Execute step result = await step.action(context) if not result.success: # Compensate await self.compensate(completed_steps, context) return SagaResult( status=SagaStatus.FAILED, error=result.error ) completed_steps.append(step) context.update(result.data) return SagaResult(status=SagaStatus.COMPLETED, data=context) except Exception as e: # Compensate on error await self.compensate(completed_steps, context) return SagaResult(status=SagaStatus.FAILED, error=str(e)) async def compensate(self, completed_steps: List[SagaStep], context: dict): """Execute compensating actions in reverse order.""" for step in reversed(completed_steps): try: await step.compensation(context) except Exception as e: # Log compensation failure print(f"Compensation failed for {step.name}: {e}") # Step implementations async def create_order(self, context: dict) -> StepResult: order = await order_service.create(context["order_data"]) return StepResult(success=True, data={"order_id": order.id}) async def cancel_order(self, context: dict): await order_service.cancel(context["order_id"]) async def reserve_inventory(self, context: dict) -> StepResult: result = await inventory_service.reserve( context["order_id"], context["order_data"]["items"] ) return StepResult( success=result.success, data={"reservation_id": result.reservation_id} ) async def release_inventory(self, context: dict): await inventory_service.release(context["reservation_id"]) async def process_payment(self, context: dict) -> StepResult: result = await payment_service.charge( context["order_id"], context["order_data"]["total"] ) return StepResult( success=result.success, data={"transaction_id": result.transaction_id}, error=result.error ) async def refund_payment(self, context: dict): await payment_service.refund(context["transaction_id"]) ``` ## Resilience Patterns ### Circuit Breaker Pattern ```python from enum import Enum from datetime import datetime, timedelta from typing import Callable, Any class CircuitState(Enum): CLOSED = "closed" # Normal operation OPEN = "open" # Failing, reject requests HALF_OPEN = "half_open" # Testing if recovered class CircuitBreaker: """Circuit breaker for service calls.""" def __init__( self, failure_threshold: int = 5, recovery_timeout: int = 30, success_threshold: int = 2 ): self.failure_threshold = failure_threshold self.recovery_timeout = recovery_timeout self.success_threshold = success_threshold self.failure_count = 0 self.success_count = 0 self.state = CircuitState.CLOSED self.opened_at = None async def call(self, func: Callable, *args, **kwargs) -> Any: """Execute function with circuit breaker.""" if self.state == CircuitState.OPEN: if self._should_attempt_reset(): self.state = CircuitState.HALF_OPEN else: raise CircuitBreakerOpenError("Circuit breaker is open") try: result = await func(*args, **kwargs) self._on_success() return result except Exception as e: self._on_failure() raise def _on_success(self): """Handle successful call.""" self.failure_count = 0 if self.state == CircuitState.HALF_OPEN: self.success_count += 1 if self.success_count >= self.success_threshold: self.state = CircuitState.CLOSED self.success_count = 0 def _on_failure(self): """Handle failed call.""" self.failure_count += 1 if self.failure_count >= self.failure_threshold: self.state = CircuitState.OPEN self.opened_at = datetime.now() if self.state == CircuitState.HALF_OPEN: self.state = CircuitState.OPEN self.opened_at = datetime.now() def _should_attempt_reset(self) -> bool: """Check if enough time passed to try again.""" return ( datetime.now() - self.opened_at > timedelta(seconds=self.recovery_timeout) ) # Usage breaker = CircuitBreaker(failure_threshold=5, recovery_timeout=30) async def call_payment_service(payment_data: dict): return await breaker.call( payment_client.process_payment, payment_data ) ``` ## Resources - **references/service-decomposition-guide.md**: Breaking down monoliths - **references/communication-patterns.md**: Sync vs async patterns - **references/saga-implementation.md**: Distributed transactions - **assets/circuit-breaker.py**: Production circuit breaker - **assets/event-bus-template.py**: Kafka event bus implementation - **assets/api-gateway-template.py**: Complete API gateway ## Best Practices 1. **Service Boundaries**: Align with business capabilities 2. **Database Per Service**: No shared databases 3. **API Contracts**: Versioned, backward compatible 4. **Async When Possible**: Events over direct calls 5. **Circuit Breakers**: Fail fast on service failures 6. **Distributed Tracing**: Track requests across services 7. **Service Registry**: Dynamic service discovery 8. **Health Checks**: Liveness and readiness probes ## Common Pitfalls - **Distributed Monolith**: Tightly coupled services - **Chatty Services**: Too many inter-service calls - **Shared Databases**: Tight coupling through data - **No Circuit Breakers**: Cascade failures - **Synchronous Everything**: Tight coupling, poor resilience - **Premature Microservices**: Starting with microservices - **Ignoring Network Failures**: Assuming reliable network - **No Compensation Logic**: Can't undo failed transactions
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

projection-patterns

Build read models and projections from event streams. Use when

coding
⭐1
# Projection Patterns Comprehensive guide to building projections and read models for event-sourced systems. ## When to Use This Skill - Building CQRS read models - Creating materialized views from events - Optimizing query performance - Implementing real-time dashboards - Building search indexes from events - Aggregating data across streams ## Core Concepts ### 1. Projection Architecture ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Event Store │────►│ Projector │────►│ Read Model │ │ │ │ │ │ (Database) │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ │ Events │ │ │ │ Handler │ │ │ │ Tables │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ │ Logic │ │ │ │ Views │ │ │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ │ Cache │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ### 2. Projection Types | Type | Description | Use Case | | -------------- | --------------------------- | ---------------------- | | **Live** | Real-time from subscription | Current state queries | | **Catchup** | Process historical events | Rebuilding read models | | **Persistent** | Stores checkpoint | Resume after restart | | **Inline** | Same transaction as write | Strong consistency | ## Templates ### Template 1: Basic Projector ```python from abc import ABC, abstractmethod from dataclasses import dataclass from typing import Dict, Any, Callable, List import asyncpg @dataclass class Event: stream_id: str event_type: str data: dict version: int global_position: int class Projection(ABC): """Base class for projections.""" @property @abstractmethod def name(self) -> str: """Unique projection name for checkpointing.""" pass @abstractmethod def handles(self) -> List[str]: """List of event types this projection handles.""" pass @abstractmethod async def apply(self, event: Event) -> None: """Apply event to the read model.""" pass class Projector: """Runs projections from event store.""" def __init__(self, event_store, checkpoint_store): self.event_store = event_store self.checkpoint_store = checkpoint_store self.projections: List[Projection] = [] def register(self, projection: Projection): self.projections.append(projection) async def run(self, batch_size: int = 100): """Run all projections continuously.""" while True: for projection in self.projections: await self._run_projection(projection, batch_size) await asyncio.sleep(0.1) async def _run_projection(self, projection: Projection, batch_size: int): checkpoint = await self.checkpoint_store.get(projection.name) position = checkpoint or 0 events = await self.event_store.read_all(position, batch_size) for event in events: if event.event_type in projection.handles(): await projection.apply(event) await self.checkpoint_store.save( projection.name, event.global_position ) async def rebuild(self, projection: Projection): """Rebuild a projection from scratch.""" await self.checkpoint_store.delete(projection.name) # Optionally clear read model tables await self._run_projection(projection, batch_size=1000) ``` ### Template 2: Order Summary Projection ```python class OrderSummaryProjection(Projection): """Projects order events to a summary read model.""" def __init__(self, db_pool: asyncpg.Pool): self.pool = db_pool @property def name(self) -> str: return "order_summary" def handles(self) -> List[str]: return [ "OrderCreated", "OrderItemAdded", "OrderItemRemoved", "OrderShipped", "OrderCompleted", "OrderCancelled" ] async def apply(self, event: Event) -> None: handlers = { "OrderCreated": self._handle_created, "OrderItemAdded": self._handle_item_added, "OrderItemRemoved": self._handle_item_removed, "OrderShipped": self._handle_shipped, "OrderCompleted": self._handle_completed, "OrderCancelled": self._handle_cancelled, } handler = handlers.get(event.event_type) if handler: await handler(event) async def _handle_created(self, event: Event): async with self.pool.acquire() as conn: await conn.execute( """ INSERT INTO order_summaries (order_id, customer_id, status, total_amount, item_count, created_at) VALUES ($1, $2, $3, $4, $5, $6) """, event.data['order_id'], event.data['customer_id'], 'pending', 0, 0, event.data['created_at'] ) async def _handle_item_added(self, event: Event): async with self.pool.acquire() as conn: await conn.execute( """ UPDATE order_summaries SET total_amount = total_amount + $2, item_count = item_count + 1, updated_at = NOW() WHERE order_id = $1 """, event.data['order_id'], event.data['price'] * event.data['quantity'] ) async def _handle_item_removed(self, event: Event): async with self.pool.acquire() as conn: await conn.execute( """ UPDATE order_summaries SET total_amount = total_amount - $2, item_count = item_count - 1, updated_at = NOW() WHERE order_id = $1 """, event.data['order_id'], event.data['price'] * event.data['quantity'] ) async def _handle_shipped(self, event: Event): async with self.pool.acquire() as conn: await conn.execute( """ UPDATE order_summaries SET status = 'shipped', shipped_at = $2, updated_at = NOW() WHERE order_id = $1 """, event.data['order_id'], event.data['shipped_at'] ) async def _handle_completed(self, event: Event): async with self.pool.acquire() as conn: await conn.execute( """ UPDATE order_summaries SET status = 'completed', completed_at = $2, updated_at = NOW() WHERE order_id = $1 """, event.data['order_id'], event.data['completed_at'] ) async def _handle_cancelled(self, event: Event): async with self.pool.acquire() as conn: await conn.execute( """ UPDATE order_summaries SET status = 'cancelled', cancelled_at = $2, cancellation_reason = $3, updated_at = NOW() WHERE order_id = $1 """, event.data['order_id'], event.data['cancelled_at'], event.data.get('reason') ) ``` ### Template 3: Elasticsearch Search Projection ```python from elasticsearch import AsyncElasticsearch class ProductSearchProjection(Projection): """Projects product events to Elasticsearch for full-text search.""" def __init__(self, es_client: AsyncElasticsearch): self.es = es_client self.index = "products" @property def name(self) -> str: return "product_search" def handles(self) -> List[str]: return [ "ProductCreated", "ProductUpdated", "ProductPriceChanged", "ProductDeleted" ] async def apply(self, event: Event) -> None: if event.event_type == "ProductCreated": await self.es.index( index=self.index, id=event.data['product_id'], document={ 'name': event.data['name'], 'description': event.data['description'], 'category': event.data['category'], 'price': event.data['price'], 'tags': event.data.get('tags', []), 'created_at': event.data['created_at'] } ) elif event.event_type == "ProductUpdated": await self.es.update( index=self.index, id=event.data['product_id'], doc={ 'name': event.data['name'], 'description': event.data['description'], 'category': event.data['category'], 'tags': event.data.get('tags', []), 'updated_at': event.data['updated_at'] } ) elif event.event_type == "ProductPriceChanged": await self.es.update( index=self.index, id=event.data['product_id'], doc={ 'price': event.data['new_price'], 'price_updated_at': event.data['changed_at'] } ) elif event.event_type == "ProductDeleted": await self.es.delete( index=self.index, id=event.data['product_id'] ) ``` ### Template 4: Aggregating Projection ```python class DailySalesProjection(Projection): """Aggregates sales data by day for reporting.""" def __init__(self, db_pool: asyncpg.Pool): self.pool = db_pool @property def name(self) -> str: return "daily_sales" def handles(self) -> List[str]: return ["OrderCompleted", "OrderRefunded"] async def apply(self, event: Event) -> None: if event.event_type == "OrderCompleted": await self._increment_sales(event) elif event.event_type == "OrderRefunded": await self._decrement_sales(event) async def _increment_sales(self, event: Event): date = event.data['completed_at'][:10] # YYYY-MM-DD async with self.pool.acquire() as conn: await conn.execute( """ INSERT INTO daily_sales (date, total_orders, total_revenue, total_items) VALUES ($1, 1, $2, $3) ON CONFLICT (date) DO UPDATE SET total_orders = daily_sales.total_orders + 1, total_revenue = daily_sales.total_revenue + $2, total_items = daily_sales.total_items + $3, updated_at = NOW() """, date, event.data['total_amount'], event.data['item_count'] ) async def _decrement_sales(self, event: Event): date = event.data['original_completed_at'][:10] async with self.pool.acquire() as conn: await conn.execute( """ UPDATE daily_sales SET total_orders = total_orders - 1, total_revenue = total_revenue - $2, total_refunds = total_refunds + $2, updated_at = NOW() WHERE date = $1 """, date, event.data['refund_amount'] ) ``` ### Template 5: Multi-Table Projection ```python class CustomerActivityProjection(Projection): """Projects customer activity across multiple tables.""" def __init__(self, db_pool: asyncpg.Pool): self.pool = db_pool @property def name(self) -> str: return "customer_activity" def handles(self) -> List[str]: return [ "CustomerCreated", "OrderCompleted", "ReviewSubmitted", "CustomerTierChanged" ] async def apply(self, event: Event) -> None: async with self.pool.acquire() as conn: async with conn.transaction(): if event.event_type == "CustomerCreated": # Insert into customers table await conn.execute( """ INSERT INTO customers (customer_id, email, name, tier, created_at) VALUES ($1, $2, $3, 'bronze', $4) """, event.data['customer_id'], event.data['email'], event.data['name'], event.data['created_at'] ) # Initialize activity summary await conn.execute( """ INSERT INTO customer_activity_summary (customer_id, total_orders, total_spent, total_reviews) VALUES ($1, 0, 0, 0) """, event.data['customer_id'] ) elif event.event_type == "OrderCompleted": # Update activity summary await conn.execute( """ UPDATE customer_activity_summary SET total_orders = total_orders + 1, total_spent = total_spent + $2, last_order_at = $3 WHERE customer_id = $1 """, event.data['customer_id'], event.data['total_amount'], event.data['completed_at'] ) # Insert into order history await conn.execute( """ INSERT INTO customer_order_history (customer_id, order_id, amount, completed_at) VALUES ($1, $2, $3, $4) """, event.data['customer_id'], event.data['order_id'], event.data['total_amount'], event.data['completed_at'] ) elif event.event_type == "ReviewSubmitted": await conn.execute( """ UPDATE customer_activity_summary SET total_reviews = total_reviews + 1, last_review_at = $2 WHERE customer_id = $1 """, event.data['customer_id'], event.data['submitted_at'] ) elif event.event_type == "CustomerTierChanged": await conn.execute( """ UPDATE customers SET tier = $2, updated_at = NOW() WHERE customer_id = $1 """, event.data['customer_id'], event.data['new_tier'] ) ``` ## Best Practices ### Do's - **Make projections idempotent** - Safe to replay - **Use transactions** - For multi-table updates - **Store checkpoints** - Resume after failures - **Monitor lag** - Alert on projection delays - **Plan for rebuilds** - Design for reconstruction ### Don'ts - **Don't couple projections** - Each is independent - **Don't skip error handling** - Log and alert on failures - **Don't ignore ordering** - Events must be processed in order - **Don't over-normalize** - Denormalize for query patterns ## Resources - [CQRS Pattern](https://docs.microsoft.com/en-us/azure/architecture/patterns/cqrs) - [Projection Building Blocks](https://zimarev.com/blog/event-sourcing/projections/)
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šŸ¤–system prompt•7 months ago

saga-orchestration

Implement saga patterns for distributed transactions and

coding
⭐1
# Saga Orchestration Patterns for managing distributed transactions and long-running business processes. ## When to Use This Skill - Coordinating multi-service transactions - Implementing compensating transactions - Managing long-running business workflows - Handling failures in distributed systems - Building order fulfillment processes - Implementing approval workflows ## Core Concepts ### 1. Saga Types ``` Choreography Orchestration ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │Svc A│─►│Svc B│─►│Svc C│ │ Orchestrator│ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ │ │ ā–¼ ā–¼ ā–¼ ā”Œā”€ā”€ā”€ā”€ā”€ā”¼ā”€ā”€ā”€ā”€ā”€ā” Event Event Event ā–¼ ā–¼ ā–¼ ā”Œā”€ā”€ā”€ā”€ā”ā”Œā”€ā”€ā”€ā”€ā”ā”Œā”€ā”€ā”€ā”€ā” │Svc1││Svc2││Svc3│ ā””ā”€ā”€ā”€ā”€ā”˜ā””ā”€ā”€ā”€ā”€ā”˜ā””ā”€ā”€ā”€ā”€ā”˜ ``` ### 2. Saga Execution States | State | Description | | ---------------- | ------------------------------ | | **Started** | Saga initiated | | **Pending** | Waiting for step completion | | **Compensating** | Rolling back due to failure | | **Completed** | All steps succeeded | | **Failed** | Saga failed after compensation | ## Templates ### Template 1: Saga Orchestrator Base ```python from abc import ABC, abstractmethod from dataclasses import dataclass, field from enum import Enum from typing import List, Dict, Any, Optional from datetime import datetime import uuid class SagaState(Enum): STARTED = "started" PENDING = "pending" COMPENSATING = "compensating" COMPLETED = "completed" FAILED = "failed" @dataclass class SagaStep: name: str action: str compensation: str status: str = "pending" result: Optional[Dict] = None error: Optional[str] = None executed_at: Optional[datetime] = None compensated_at: Optional[datetime] = None @dataclass class Saga: saga_id: str saga_type: str state: SagaState data: Dict[str, Any] steps: List[SagaStep] current_step: int = 0 created_at: datetime = field(default_factory=datetime.utcnow) updated_at: datetime = field(default_factory=datetime.utcnow) class SagaOrchestrator(ABC): """Base class for saga orchestrators.""" def __init__(self, saga_store, event_publisher): self.saga_store = saga_store self.event_publisher = event_publisher @abstractmethod def define_steps(self, data: Dict) -> List[SagaStep]: """Define the saga steps.""" pass @property @abstractmethod def saga_type(self) -> str: """Unique saga type identifier.""" pass async def start(self, data: Dict) -> Saga: """Start a new saga.""" saga = Saga( saga_id=str(uuid.uuid4()), saga_type=self.saga_type, state=SagaState.STARTED, data=data, steps=self.define_steps(data) ) await self.saga_store.save(saga) await self._execute_next_step(saga) return saga async def handle_step_completed(self, saga_id: str, step_name: str, result: Dict): """Handle successful step completion.""" saga = await self.saga_store.get(saga_id) # Update step for step in saga.steps: if step.name == step_name: step.status = "completed" step.result = result step.executed_at = datetime.utcnow() break saga.current_step += 1 saga.updated_at = datetime.utcnow() # Check if saga is complete if saga.current_step >= len(saga.steps): saga.state = SagaState.COMPLETED await self.saga_store.save(saga) await self._on_saga_completed(saga) else: saga.state = SagaState.PENDING await self.saga_store.save(saga) await self._execute_next_step(saga) async def handle_step_failed(self, saga_id: str, step_name: str, error: str): """Handle step failure - start compensation.""" saga = await self.saga_store.get(saga_id) # Mark step as failed for step in saga.steps: if step.name == step_name: step.status = "failed" step.error = error break saga.state = SagaState.COMPENSATING saga.updated_at = datetime.utcnow() await self.saga_store.save(saga) # Start compensation from current step backwards await self._compensate(saga) async def _execute_next_step(self, saga: Saga): """Execute the next step in the saga.""" if saga.current_step >= len(saga.steps): return step = saga.steps[saga.current_step] step.status = "executing" await self.saga_store.save(saga) # Publish command to execute step await self.event_publisher.publish( step.action, { "saga_id": saga.saga_id, "step_name": step.name, **saga.data } ) async def _compensate(self, saga: Saga): """Execute compensation for completed steps.""" # Compensate in reverse order for i in range(saga.current_step - 1, -1, -1): step = saga.steps[i] if step.status == "completed": step.status = "compensating" await self.saga_store.save(saga) await self.event_publisher.publish( step.compensation, { "saga_id": saga.saga_id, "step_name": step.name, "original_result": step.result, **saga.data } ) async def handle_compensation_completed(self, saga_id: str, step_name: str): """Handle compensation completion.""" saga = await self.saga_store.get(saga_id) for step in saga.steps: if step.name == step_name: step.status = "compensated" step.compensated_at = datetime.utcnow() break # Check if all compensations complete all_compensated = all( s.status in ("compensated", "pending", "failed") for s in saga.steps ) if all_compensated: saga.state = SagaState.FAILED await self._on_saga_failed(saga) await self.saga_store.save(saga) async def _on_saga_completed(self, saga: Saga): """Called when saga completes successfully.""" await self.event_publisher.publish( f"{self.saga_type}Completed", {"saga_id": saga.saga_id, **saga.data} ) async def _on_saga_failed(self, saga: Saga): """Called when saga fails after compensation.""" await self.event_publisher.publish( f"{self.saga_type}Failed", {"saga_id": saga.saga_id, "error": "Saga failed", **saga.data} ) ``` ### Template 2: Order Fulfillment Saga ```python class OrderFulfillmentSaga(SagaOrchestrator): """Orchestrates order fulfillment across services.""" @property def saga_type(self) -> str: return "OrderFulfillment" def define_steps(self, data: Dict) -> List[SagaStep]: return [ SagaStep( name="reserve_inventory", action="InventoryService.ReserveItems", compensation="InventoryService.ReleaseReservation" ), SagaStep( name="process_payment", action="PaymentService.ProcessPayment", compensation="PaymentService.RefundPayment" ), SagaStep( name="create_shipment", action="ShippingService.CreateShipment", compensation="ShippingService.CancelShipment" ), SagaStep( name="send_confirmation", action="NotificationService.SendOrderConfirmation", compensation="NotificationService.SendCancellationNotice" ) ] # Usage async def create_order(order_data: Dict): saga = OrderFulfillmentSaga(saga_store, event_publisher) return await saga.start({ "order_id": order_data["order_id"], "customer_id": order_data["customer_id"], "items": order_data["items"], "payment_method": order_data["payment_method"], "shipping_address": order_data["shipping_address"] }) # Event handlers in each service class InventoryService: async def handle_reserve_items(self, command: Dict): try: # Reserve inventory reservation = await self.reserve( command["items"], command["order_id"] ) # Report success await self.event_publisher.publish( "SagaStepCompleted", { "saga_id": command["saga_id"], "step_name": "reserve_inventory", "result": {"reservation_id": reservation.id} } ) except InsufficientInventoryError as e: await self.event_publisher.publish( "SagaStepFailed", { "saga_id": command["saga_id"], "step_name": "reserve_inventory", "error": str(e) } ) async def handle_release_reservation(self, command: Dict): # Compensating action await self.release_reservation( command["original_result"]["reservation_id"] ) await self.event_publisher.publish( "SagaCompensationCompleted", { "saga_id": command["saga_id"], "step_name": "reserve_inventory" } ) ``` ### Template 3: Choreography-Based Saga ```python from dataclasses import dataclass from typing import Dict, Any import asyncio @dataclass class SagaContext: """Passed through choreographed saga events.""" saga_id: str step: int data: Dict[str, Any] completed_steps: list class OrderChoreographySaga: """Choreography-based saga using events.""" def __init__(self, event_bus): self.event_bus = event_bus self._register_handlers() def _register_handlers(self): self.event_bus.subscribe("OrderCreated", self._on_order_created) self.event_bus.subscribe("InventoryReserved", self._on_inventory_reserved) self.event_bus.subscribe("PaymentProcessed", self._on_payment_processed) self.event_bus.subscribe("ShipmentCreated", self._on_shipment_created) # Compensation handlers self.event_bus.subscribe("PaymentFailed", self._on_payment_failed) self.event_bus.subscribe("ShipmentFailed", self._on_shipment_failed) async def _on_order_created(self, event: Dict): """Step 1: Order created, reserve inventory.""" await self.event_bus.publish("ReserveInventory", { "saga_id": event["order_id"], "order_id": event["order_id"], "items": event["items"] }) async def _on_inventory_reserved(self, event: Dict): """Step 2: Inventory reserved, process payment.""" await self.event_bus.publish("ProcessPayment", { "saga_id": event["saga_id"], "order_id": event["order_id"], "amount": event["total_amount"], "reservation_id": event["reservation_id"] }) async def _on_payment_processed(self, event: Dict): """Step 3: Payment done, create shipment.""" await self.event_bus.publish("CreateShipment", { "saga_id": event["saga_id"], "order_id": event["order_id"], "payment_id": event["payment_id"] }) async def _on_shipment_created(self, event: Dict): """Step 4: Complete - send confirmation.""" await self.event_bus.publish("OrderFulfilled", { "saga_id": event["saga_id"], "order_id": event["order_id"], "tracking_number": event["tracking_number"] }) # Compensation handlers async def _on_payment_failed(self, event: Dict): """Payment failed - release inventory.""" await self.event_bus.publish("ReleaseInventory", { "saga_id": event["saga_id"], "reservation_id": event["reservation_id"] }) await self.event_bus.publish("OrderFailed", { "order_id": event["order_id"], "reason": "Payment failed" }) async def _on_shipment_failed(self, event: Dict): """Shipment failed - refund payment and release inventory.""" await self.event_bus.publish("RefundPayment", { "saga_id": event["saga_id"], "payment_id": event["payment_id"] }) await self.event_bus.publish("ReleaseInventory", { "saga_id": event["saga_id"], "reservation_id": event["reservation_id"] }) ``` ### Template 4: Saga with Timeouts ```python class TimeoutSagaOrchestrator(SagaOrchestrator): """Saga orchestrator with step timeouts.""" def __init__(self, saga_store, event_publisher, scheduler): super().__init__(saga_store, event_publisher) self.scheduler = scheduler async def _execute_next_step(self, saga: Saga): if saga.current_step >= len(saga.steps): return step = saga.steps[saga.current_step] step.status = "executing" step.timeout_at = datetime.utcnow() + timedelta(minutes=5) await self.saga_store.save(saga) # Schedule timeout check await self.scheduler.schedule( f"saga_timeout_{saga.saga_id}_{step.name}", self._check_timeout, {"saga_id": saga.saga_id, "step_name": step.name}, run_at=step.timeout_at ) await self.event_publisher.publish( step.action, {"saga_id": saga.saga_id, "step_name": step.name, **saga.data} ) async def _check_timeout(self, data: Dict): """Check if step has timed out.""" saga = await self.saga_store.get(data["saga_id"]) step = next(s for s in saga.steps if s.name == data["step_name"]) if step.status == "executing": # Step timed out - fail it await self.handle_step_failed( data["saga_id"], data["step_name"], "Step timed out" ) ``` ## Best Practices ### Do's - **Make steps idempotent** - Safe to retry - **Design compensations carefully** - They must work - **Use correlation IDs** - For tracing across services - **Implement timeouts** - Don't wait forever - **Log everything** - For debugging failures ### Don'ts - **Don't assume instant completion** - Sagas take time - **Don't skip compensation testing** - Most critical part - **Don't couple services** - Use async messaging - **Don't ignore partial failures** - Handle gracefully ## Resources - [Saga Pattern](https://microservices.io/patterns/data/saga.html) - [Designing Data-Intensive Applications](https://dataintensive.net/)
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šŸ¤–system prompt•7 months ago

temporal-python-testing

Test Temporal workflows with pytest, time-skipping, and mocking

coding
⭐1
# Temporal Python Testing Strategies Comprehensive testing approaches for Temporal workflows using pytest, progressive disclosure resources for specific testing scenarios. ## When to Use This Skill - **Unit testing workflows** - Fast tests with time-skipping - **Integration testing** - Workflows with mocked activities - **Replay testing** - Validate determinism against production histories - **Local development** - Set up Temporal server and pytest - **CI/CD integration** - Automated testing pipelines - **Coverage strategies** - Achieve ≄80% test coverage ## Testing Philosophy **Recommended Approach** (Source: docs.temporal.io/develop/python/testing-suite): - Write majority as integration tests - Use pytest with async fixtures - Time-skipping enables fast feedback (month-long workflows → seconds) - Mock activities to isolate workflow logic - Validate determinism with replay testing **Three Test Types**: 1. **Unit**: Workflows with time-skipping, activities with ActivityEnvironment 2. **Integration**: Workers with mocked activities 3. **End-to-end**: Full Temporal server with real activities (use sparingly) ## Available Resources This skill provides detailed guidance through progressive disclosure. Load specific resources based on your testing needs: ### Unit Testing Resources **File**: `resources/unit-testing.md` **When to load**: Testing individual workflows or activities in isolation **Contains**: - WorkflowEnvironment with time-skipping - ActivityEnvironment for activity testing - Fast execution of long-running workflows - Manual time advancement patterns - pytest fixtures and patterns ### Integration Testing Resources **File**: `resources/integration-testing.md` **When to load**: Testing workflows with mocked external dependencies **Contains**: - Activity mocking strategies - Error injection patterns - Multi-activity workflow testing - Signal and query testing - Coverage strategies ### Replay Testing Resources **File**: `resources/replay-testing.md` **When to load**: Validating determinism or deploying workflow changes **Contains**: - Determinism validation - Production history replay - CI/CD integration patterns - Version compatibility testing ### Local Development Resources **File**: `resources/local-setup.md` **When to load**: Setting up development environment **Contains**: - Docker Compose configuration - pytest setup and configuration - Coverage tool integration - Development workflow ## Quick Start Guide ### Basic Workflow Test ```python import pytest from temporalio.testing import WorkflowEnvironment from temporalio.worker import Worker @pytest.fixture async def workflow_env(): env = await WorkflowEnvironment.start_time_skipping() yield env await env.shutdown() @pytest.mark.asyncio async def test_workflow(workflow_env): async with Worker( workflow_env.client, task_queue="test-queue", workflows=[YourWorkflow], activities=[your_activity], ): result = await workflow_env.client.execute_workflow( YourWorkflow.run, args, id="test-wf-id", task_queue="test-queue", ) assert result == expected ``` ### Basic Activity Test ```python from temporalio.testing import ActivityEnvironment async def test_activity(): env = ActivityEnvironment() result = await env.run(your_activity, "test-input") assert result == expected_output ``` ## Coverage Targets **Recommended Coverage** (Source: docs.temporal.io best practices): - **Workflows**: ≄80% logic coverage - **Activities**: ≄80% logic coverage - **Integration**: Critical paths with mocked activities - **Replay**: All workflow versions before deployment ## Key Testing Principles 1. **Time-Skipping** - Month-long workflows test in seconds 2. **Mock Activities** - Isolate workflow logic from external dependencies 3. **Replay Testing** - Validate determinism before deployment 4. **High Coverage** - ≄80% target for production workflows 5. **Fast Feedback** - Unit tests run in milliseconds ## How to Use Resources **Load specific resource when needed**: - "Show me unit testing patterns" → Load `resources/unit-testing.md` - "How do I mock activities?" → Load `resources/integration-testing.md` - "Setup local Temporal server" → Load `resources/local-setup.md` - "Validate determinism" → Load `resources/replay-testing.md` ## Additional References - Python SDK Testing: docs.temporal.io/develop/python/testing-suite - Testing Patterns: github.com/temporalio/temporal/blob/main/docs/development/testing.md - Python Samples: github.com/temporalio/samples-python
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šŸ¤–system prompt•7 months ago

data-storytelling

Transform data into compelling narratives using visualization,

data
⭐1
# Data Storytelling Transform raw data into compelling narratives that drive decisions and inspire action. ## When to Use This Skill - Presenting analytics to executives - Creating quarterly business reviews - Building investor presentations - Writing data-driven reports - Communicating insights to non-technical audiences - Making recommendations based on data ## Core Concepts ### 1. Story Structure ``` Setup → Conflict → Resolution Setup: Context and baseline Conflict: The problem or opportunity Resolution: Insights and recommendations ``` ### 2. Narrative Arc ``` 1. Hook: Grab attention with surprising insight 2. Context: Establish the baseline 3. Rising Action: Build through data points 4. Climax: The key insight 5. Resolution: Recommendations 6. Call to Action: Next steps ``` ### 3. Three Pillars | Pillar | Purpose | Components | | ------------- | -------- | -------------------------------- | | **Data** | Evidence | Numbers, trends, comparisons | | **Narrative** | Meaning | Context, causation, implications | | **Visuals** | Clarity | Charts, diagrams, highlights | ## Story Frameworks ### Framework 1: The Problem-Solution Story ```markdown # Customer Churn Analysis ## The Hook "We're losing $2.4M annually to preventable churn." ## The Context - Current churn rate: 8.5% (industry average: 5%) - Average customer lifetime value: $4,800 - 500 customers churned last quarter ## The Problem Analysis of churned customers reveals a pattern: - 73% churned within first 90 days - Common factor: < 3 support interactions - Low feature adoption in first month ## The Insight [Show engagement curve visualization] Customers who don't engage in the first 14 days are 4x more likely to churn. ## The Solution 1. Implement 14-day onboarding sequence 2. Proactive outreach at day 7 3. Feature adoption tracking ## Expected Impact - Reduce early churn by 40% - Save $960K annually - Payback period: 3 months ## Call to Action Approve $50K budget for onboarding automation. ``` ### Framework 2: The Trend Story ```markdown # Q4 Performance Analysis ## Where We Started Q3 ended with $1.2M MRR, 15% below target. Team morale was low after missed goals. ## What Changed [Timeline visualization] - Oct: Launched self-serve pricing - Nov: Reduced friction in signup - Dec: Added customer success calls ## The Transformation [Before/after comparison chart] | Metric | Q3 | Q4 | Change | |----------------|--------|--------|--------| | Trial → Paid | 8% | 15% | +87% | | Time to Value | 14 days| 5 days | -64% | | Expansion Rate | 2% | 8% | +300% | ## Key Insight Self-serve + high-touch creates compound growth. Customers who self-serve AND get a success call have 3x higher expansion rate. ## Going Forward Double down on hybrid model. Target: $1.8M MRR by Q2. ``` ### Framework 3: The Comparison Story ```markdown # Market Opportunity Analysis ## The Question Should we expand into EMEA or APAC first? ## The Comparison [Side-by-side market analysis] ### EMEA - Market size: $4.2B - Growth rate: 8% - Competition: High - Regulatory: Complex (GDPR) - Language: Multiple ### APAC - Market size: $3.8B - Growth rate: 15% - Competition: Moderate - Regulatory: Varied - Language: Multiple ## The Analysis [Weighted scoring matrix visualization] | Factor | Weight | EMEA Score | APAC Score | | ----------- | ------ | ---------- | ---------- | | Market Size | 25% | 5 | 4 | | Growth | 30% | 3 | 5 | | Competition | 20% | 2 | 4 | | Ease | 25% | 2 | 3 | | **Total** | | **2.9** | **4.1** | ## The Recommendation APAC first. Higher growth, less competition. Start with Singapore hub (English, business-friendly). Enter EMEA in Year 2 with localization ready. ## Risk Mitigation - Timezone coverage: Hire 24/7 support - Cultural fit: Local partnerships - Payment: Multi-currency from day 1 ``` ## Visualization Techniques ### Technique 1: Progressive Reveal ```markdown Start simple, add layers: Slide 1: "Revenue is growing" [single line chart] Slide 2: "But growth is slowing" [add growth rate overlay] Slide 3: "Driven by one segment" [add segment breakdown] Slide 4: "Which is saturating" [add market share] Slide 5: "We need new segments" [add opportunity zones] ``` ### Technique 2: Contrast and Compare ```markdown Before/After: ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ BEFORE │ AFTER │ │ │ │ │ Process: 5 days│ Process: 1 day │ │ Errors: 15% │ Errors: 2% │ │ Cost: $50/unit │ Cost: $20/unit │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ This/That (emphasize difference): ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ CUSTOMER A vs B │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ │ ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ │ │ ā–ˆā–ˆ │ │ │ │ $45,000 │ │ $8,000 │ │ │ │ LTV │ │ LTV │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ Onboarded No onboarding │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ### Technique 3: Annotation and Highlight ```python import matplotlib.pyplot as plt import pandas as pd fig, ax = plt.subplots(figsize=(12, 6)) # Plot the main data ax.plot(dates, revenue, linewidth=2, color='#2E86AB') # Add annotation for key events ax.annotate( 'Product Launch\n+32% spike', xy=(launch_date, launch_revenue), xytext=(launch_date, launch_revenue * 1.2), fontsize=10, arrowprops=dict(arrowstyle='->', color='#E63946'), color='#E63946' ) # Highlight a region ax.axvspan(growth_start, growth_end, alpha=0.2, color='green', label='Growth Period') # Add threshold line ax.axhline(y=target, color='gray', linestyle='--', label=f'Target: ${target:,.0f}') ax.set_title('Revenue Growth Story', fontsize=14, fontweight='bold') ax.legend() ``` ## Presentation Templates ### Template 1: Executive Summary Slide ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ KEY INSIGHT │ │ ══════════════════════════════════════════════════════════│ │ │ │ "Customers who complete onboarding in week 1 │ │ have 3x higher lifetime value" │ │ │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ │ │ │ THE DATA │ THE IMPLICATION │ │ │ │ │ Week 1 completers: │ āœ“ Prioritize onboarding UX │ │ • LTV: $4,500 │ āœ“ Add day-1 success milestones │ │ • Retention: 85% │ āœ“ Proactive week-1 outreach │ │ • NPS: 72 │ │ │ │ Investment: $75K │ │ Others: │ Expected ROI: 8x │ │ • LTV: $1,500 │ │ │ • Retention: 45% │ │ │ • NPS: 34 │ │ │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ### Template 2: Data Story Flow ``` Slide 1: THE HEADLINE "We can grow 40% faster by fixing onboarding" Slide 2: THE CONTEXT Current state metrics Industry benchmarks Gap analysis Slide 3: THE DISCOVERY What the data revealed Surprising finding Pattern identification Slide 4: THE DEEP DIVE Root cause analysis Segment breakdowns Statistical significance Slide 5: THE RECOMMENDATION Proposed actions Resource requirements Timeline Slide 6: THE IMPACT Expected outcomes ROI calculation Risk assessment Slide 7: THE ASK Specific request Decision needed Next steps ``` ### Template 3: One-Page Dashboard Story ```markdown # Monthly Business Review: January 2024 ## THE HEADLINE Revenue up 15% but CAC increasing faster than LTV ## KEY METRICS AT A GLANCE ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ MRR │ NRR │ CAC │ LTV │ │ $125K │ 108% │ $450 │ $2,200 │ │ ā–²15% │ ā–²3% │ ā–²22% │ ā–²8% │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ## WHAT'S WORKING āœ“ Enterprise segment growing 25% MoM āœ“ Referral program driving 30% of new logos āœ“ Support satisfaction at all-time high (94%) ## WHAT NEEDS ATTENTION āœ— SMB acquisition cost up 40% āœ— Trial conversion down 5 points āœ— Time-to-value increased by 3 days ## ROOT CAUSE [Mini chart showing SMB vs Enterprise CAC trend] SMB paid ads becoming less efficient. CPC up 35% while conversion flat. ## RECOMMENDATION 1. Shift $20K/mo from paid to content 2. Launch SMB self-serve trial 3. A/B test shorter onboarding ## NEXT MONTH'S FOCUS - Launch content marketing pilot - Complete self-serve MVP - Reduce time-to-value to < 7 days ``` ## Writing Techniques ### Headlines That Work ```markdown BAD: "Q4 Sales Analysis" GOOD: "Q4 Sales Beat Target by 23% - Here's Why" BAD: "Customer Churn Report" GOOD: "We're Losing $2.4M to Preventable Churn" BAD: "Marketing Performance" GOOD: "Content Marketing Delivers 4x ROI vs. Paid" Formula: [Specific Number] + [Business Impact] + [Actionable Context] ``` ### Transition Phrases ```markdown Building the narrative: • "This leads us to ask..." • "When we dig deeper..." • "The pattern becomes clear when..." • "Contrast this with..." Introducing insights: • "The data reveals..." • "What surprised us was..." • "The inflection point came when..." • "The key finding is..." Moving to action: • "This insight suggests..." • "Based on this analysis..." • "The implication is clear..." • "Our recommendation is..." ``` ### Handling Uncertainty ```markdown Acknowledge limitations: • "With 95% confidence, we can say..." • "The sample size of 500 shows..." • "While correlation is strong, causation requires..." • "This trend holds for [segment], though [caveat]..." Present ranges: • "Impact estimate: $400K-$600K" • "Confidence interval: 15-20% improvement" • "Best case: X, Conservative: Y" ``` ## Best Practices ### Do's - **Start with the "so what"** - Lead with insight - **Use the rule of three** - Three points, three comparisons - **Show, don't tell** - Let data speak - **Make it personal** - Connect to audience goals - **End with action** - Clear next steps ### Don'ts - **Don't data dump** - Curate ruthlessly - **Don't bury the insight** - Front-load key findings - **Don't use jargon** - Match audience vocabulary - **Don't show methodology first** - Context, then method - **Don't forget the narrative** - Numbers need meaning ## Resources - [Storytelling with Data (Cole Nussbaumer)](https://www.storytellingwithdata.com/) - [The Pyramid Principle (Barbara Minto)](https://www.amazon.com/Pyramid-Principle-Logic-Writing-Thinking/dp/0273710516) - [Resonate (Nancy Duarte)](https://www.duarte.com/resonate/)
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kpi-dashboard-design

Design effective KPI dashboards with metrics selection,

coding
⭐1
# KPI Dashboard Design Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions. ## When to Use This Skill - Designing executive dashboards - Selecting meaningful KPIs - Building real-time monitoring displays - Creating department-specific metrics views - Improving existing dashboard layouts - Establishing metric governance ## Core Concepts ### 1. KPI Framework | Level | Focus | Update Frequency | Audience | | --------------- | ---------------- | ----------------- | ---------- | | **Strategic** | Long-term goals | Monthly/Quarterly | Executives | | **Tactical** | Department goals | Weekly/Monthly | Managers | | **Operational** | Day-to-day | Real-time/Daily | Teams | ### 2. SMART KPIs ``` Specific: Clear definition Measurable: Quantifiable Achievable: Realistic targets Relevant: Aligned to goals Time-bound: Defined period ``` ### 3. Dashboard Hierarchy ``` ā”œā”€ā”€ Executive Summary (1 page) │ ā”œā”€ā”€ 4-6 headline KPIs │ ā”œā”€ā”€ Trend indicators │ └── Key alerts ā”œā”€ā”€ Department Views │ ā”œā”€ā”€ Sales Dashboard │ ā”œā”€ā”€ Marketing Dashboard │ ā”œā”€ā”€ Operations Dashboard │ └── Finance Dashboard └── Detailed Drilldowns ā”œā”€ā”€ Individual metrics └── Root cause analysis ``` ## Common KPIs by Department ### Sales KPIs ```yaml Revenue Metrics: - Monthly Recurring Revenue (MRR) - Annual Recurring Revenue (ARR) - Average Revenue Per User (ARPU) - Revenue Growth Rate Pipeline Metrics: - Sales Pipeline Value - Win Rate - Average Deal Size - Sales Cycle Length Activity Metrics: - Calls/Emails per Rep - Demos Scheduled - Proposals Sent - Close Rate ``` ### Marketing KPIs ```yaml Acquisition: - Cost Per Acquisition (CPA) - Customer Acquisition Cost (CAC) - Lead Volume - Marketing Qualified Leads (MQL) Engagement: - Website Traffic - Conversion Rate - Email Open/Click Rate - Social Engagement ROI: - Marketing ROI - Campaign Performance - Channel Attribution - CAC Payback Period ``` ### Product KPIs ```yaml Usage: - Daily/Monthly Active Users (DAU/MAU) - Session Duration - Feature Adoption Rate - Stickiness (DAU/MAU) Quality: - Net Promoter Score (NPS) - Customer Satisfaction (CSAT) - Bug/Issue Count - Time to Resolution Growth: - User Growth Rate - Activation Rate - Retention Rate - Churn Rate ``` ### Finance KPIs ```yaml Profitability: - Gross Margin - Net Profit Margin - EBITDA - Operating Margin Liquidity: - Current Ratio - Quick Ratio - Cash Flow - Working Capital Efficiency: - Revenue per Employee - Operating Expense Ratio - Days Sales Outstanding - Inventory Turnover ``` ## Dashboard Layout Patterns ### Pattern 1: Executive Summary ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ EXECUTIVE DASHBOARD [Date Range ā–¼] │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ REVENUE │ PROFIT │ CUSTOMERS │ NPS SCORE │ │ $2.4M │ $450K │ 12,450 │ 72 │ │ ā–² 12% │ ā–² 8% │ ā–² 15% │ ā–² 5pts │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ │ │ Revenue Trend │ Revenue by Product │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ │ /\ /\ │ │ │ ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ 45% │ │ │ │ / \ / \ /\ │ │ │ ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ 32% │ │ │ │ / \/ \ / \ │ │ │ ā–ˆā–ˆā–ˆā–ˆ 18% │ │ │ │ / \/ \ │ │ │ ā–ˆā–ˆ 5% │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ šŸ”“ Alert: Churn rate exceeded threshold (>5%) │ │ 🟔 Warning: Support ticket volume 20% above average │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ### Pattern 2: SaaS Metrics Dashboard ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ SAAS METRICS Jan 2024 [Monthly ā–¼] │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ MRR GROWTH │ │ │ MRR │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ │ $125,000 │ │ │ /── │ │ │ │ ā–² 8% │ │ │ /────/ │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ /────/ │ │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ /────/ │ │ │ │ ARR │ │ │ /────/ │ │ │ │ $1,500,000 │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ │ ā–² 15% │ │ J F M A M J J A S O N D │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¼ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ UNIT ECONOMICS │ COHORT RETENTION │ │ │ │ │ CAC: $450 │ Month 1: ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ 100% │ │ LTV: $2,700 │ Month 3: ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ 85% │ │ LTV/CAC: 6.0x │ Month 6: ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ 80% │ │ │ Month 12: ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ 72% │ │ Payback: 4 months │ │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ CHURN ANALYSIS │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ │ Gross │ Net │ Logo │ Expansion │ │ │ │ 4.2% │ 1.8% │ 3.1% │ 2.4% │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ### Pattern 3: Real-time Operations ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ OPERATIONS CENTER Live ā— Last: 10:42:15 │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ SYSTEM HEALTH │ SERVICE STATUS │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ │ │ CPU MEM DISK │ │ ā— API Gateway Healthy │ │ │ 45% 72% 58% │ │ ā— User Service Healthy │ │ │ ā–ˆā–ˆā–ˆ ā–ˆā–ˆā–ˆā–ˆ ā–ˆā–ˆā–ˆ │ │ ā— Payment Service Degraded │ │ │ ā–ˆā–ˆā–ˆ ā–ˆā–ˆā–ˆā–ˆ ā–ˆā–ˆā–ˆ │ │ ā— Database Healthy │ │ │ ā–ˆā–ˆā–ˆ ā–ˆā–ˆā–ˆā–ˆ ā–ˆā–ˆā–ˆ │ │ ā— Cache Healthy │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¼ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ REQUEST THROUGHPUT │ ERROR RATE │ │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ │ │ ā–ā–‚ā–ƒā–„ā–…ā–†ā–‡ā–ˆā–‡ā–†ā–…ā–„ā–ƒā–‚ā–ā–‚ā–ƒā–„ā–… │ │ │ ▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁ │ │ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ │ Current: 12,450 req/s │ Current: 0.02% │ │ Peak: 18,200 req/s │ Threshold: 1.0% │ ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ │ RECENT ALERTS │ │ 10:40 🟔 High latency on payment-service (p99 > 500ms) │ │ 10:35 🟢 Resolved: Database connection pool recovered │ │ 10:22 šŸ”“ Payment service circuit breaker tripped │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ## Implementation Patterns ### SQL for KPI Calculations ```sql -- Monthly Recurring Revenue (MRR) WITH mrr_calculation AS ( SELECT DATE_TRUNC('month', billing_date) AS month, SUM( CASE subscription_interval WHEN 'monthly' THEN amount WHEN 'yearly' THEN amount / 12 WHEN 'quarterly' THEN amount / 3 END ) AS mrr FROM subscriptions WHERE status = 'active' GROUP BY DATE_TRUNC('month', billing_date) ) SELECT month, mrr, LAG(mrr) OVER (ORDER BY month) AS prev_mrr, (mrr - LAG(mrr) OVER (ORDER BY month)) / LAG(mrr) OVER (ORDER BY month) * 100 AS growth_pct FROM mrr_calculation; -- Cohort Retention WITH cohorts AS ( SELECT user_id, DATE_TRUNC('month', created_at) AS cohort_month FROM users ), activity AS ( SELECT user_id, DATE_TRUNC('month', event_date) AS activity_month FROM user_events WHERE event_type = 'active_session' ) SELECT c.cohort_month, EXTRACT(MONTH FROM age(a.activity_month, c.cohort_month)) AS months_since_signup, COUNT(DISTINCT a.user_id) AS active_users, COUNT(DISTINCT a.user_id)::FLOAT / COUNT(DISTINCT c.user_id) * 100 AS retention_rate FROM cohorts c LEFT JOIN activity a ON c.user_id = a.user_id AND a.activity_month >= c.cohort_month GROUP BY c.cohort_month, EXTRACT(MONTH FROM age(a.activity_month, c.cohort_month)) ORDER BY c.cohort_month, months_since_signup; -- Customer Acquisition Cost (CAC) SELECT DATE_TRUNC('month', acquired_date) AS month, SUM(marketing_spend) / NULLIF(COUNT(new_customers), 0) AS cac, SUM(marketing_spend) AS total_spend, COUNT(new_customers) AS customers_acquired FROM ( SELECT DATE_TRUNC('month', u.created_at) AS acquired_date, u.id AS new_customers, m.spend AS marketing_spend FROM users u JOIN marketing_spend m ON DATE_TRUNC('month', u.created_at) = m.month WHERE u.source = 'marketing' ) acquisition GROUP BY DATE_TRUNC('month', acquired_date); ``` ### Python Dashboard Code (Streamlit) ```python import streamlit as st import pandas as pd import plotly.express as px import plotly.graph_objects as go st.set_page_config(page_title="KPI Dashboard", layout="wide") # Header with date filter col1, col2 = st.columns([3, 1]) with col1: st.title("Executive Dashboard") with col2: date_range = st.selectbox( "Period", ["Last 7 Days", "Last 30 Days", "Last Quarter", "YTD"] ) # KPI Cards def metric_card(label, value, delta, prefix="", suffix=""): delta_color = "green" if delta >= 0 else "red" delta_arrow = "ā–²" if delta >= 0 else "ā–¼" st.metric( label=label, value=f"{prefix}{value:,.0f}{suffix}", delta=f"{delta_arrow} {abs(delta):.1f}%" ) col1, col2, col3, col4 = st.columns(4) with col1: metric_card("Revenue", 2400000, 12.5, prefix="$") with col2: metric_card("Customers", 12450, 15.2) with col3: metric_card("NPS Score", 72, 5.0) with col4: metric_card("Churn Rate", 4.2, -0.8, suffix="%") # Charts col1, col2 = st.columns(2) with col1: st.subheader("Revenue Trend") revenue_data = pd.DataFrame({ 'Month': pd.date_range('2024-01-01', periods=12, freq='M'), 'Revenue': [180000, 195000, 210000, 225000, 240000, 255000, 270000, 285000, 300000, 315000, 330000, 345000] }) fig = px.line(revenue_data, x='Month', y='Revenue', line_shape='spline', markers=True) fig.update_layout(height=300) st.plotly_chart(fig, use_container_width=True) with col2: st.subheader("Revenue by Product") product_data = pd.DataFrame({ 'Product': ['Enterprise', 'Professional', 'Starter', 'Other'], 'Revenue': [45, 32, 18, 5] }) fig = px.pie(product_data, values='Revenue', names='Product', hole=0.4) fig.update_layout(height=300) st.plotly_chart(fig, use_container_width=True) # Cohort Heatmap st.subheader("Cohort Retention") cohort_data = pd.DataFrame({ 'Cohort': ['Jan', 'Feb', 'Mar', 'Apr', 'May'], 'M0': [100, 100, 100, 100, 100], 'M1': [85, 87, 84, 86, 88], 'M2': [78, 80, 76, 79, None], 'M3': [72, 74, 70, None, None], 'M4': [68, 70, None, None, None], }) fig = go.Figure(data=go.Heatmap( z=cohort_data.iloc[:, 1:].values, x=['M0', 'M1', 'M2', 'M3', 'M4'], y=cohort_data['Cohort'], colorscale='Blues', text=cohort_data.iloc[:, 1:].values, texttemplate='%{text}%', textfont={"size": 12}, )) fig.update_layout(height=250) st.plotly_chart(fig, use_container_width=True) # Alerts Section st.subheader("Alerts") alerts = [ {"level": "error", "message": "Churn rate exceeded threshold (>5%)"}, {"level": "warning", "message": "Support ticket volume 20% above average"}, ] for alert in alerts: if alert["level"] == "error": st.error(f"šŸ”“ {alert['message']}") elif alert["level"] == "warning": st.warning(f"🟔 {alert['message']}") ``` ## Best Practices ### Do's - **Limit to 5-7 KPIs** - Focus on what matters - **Show context** - Comparisons, trends, targets - **Use consistent colors** - Red=bad, green=good - **Enable drilldown** - From summary to detail - **Update appropriately** - Match metric frequency ### Don'ts - **Don't show vanity metrics** - Focus on actionable data - **Don't overcrowd** - White space aids comprehension - **Don't use 3D charts** - They distort perception - **Don't hide methodology** - Document calculations - **Don't ignore mobile** - Ensure responsive design ## Resources - [Stephen Few's Dashboard Design](https://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf) - [Edward Tufte's Principles](https://www.edwardtufte.com/tufte/) - [Google Data Studio Gallery](https://datastudio.google.com/gallery)
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

deployment-pipeline-design

Design multi-stage CI/CD pipelines with approval gates, security

coding
⭐1
# Deployment Pipeline Design Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies. ## Purpose Design robust, secure deployment pipelines that balance speed with safety through proper stage organization and approval workflows. ## When to Use - Design CI/CD architecture - Implement deployment gates - Configure multi-environment pipelines - Establish deployment best practices - Implement progressive delivery ## Pipeline Stages ### Standard Pipeline Flow ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Build │ → │ Test │ → │ Staging │ → │ Approve│ → │Production│ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ``` ### Detailed Stage Breakdown 1. **Source** - Code checkout 2. **Build** - Compile, package, containerize 3. **Test** - Unit, integration, security scans 4. **Staging Deploy** - Deploy to staging environment 5. **Integration Tests** - E2E, smoke tests 6. **Approval Gate** - Manual approval required 7. **Production Deploy** - Canary, blue-green, rolling 8. **Verification** - Health checks, monitoring 9. **Rollback** - Automated rollback on failure ## Approval Gate Patterns ### Pattern 1: Manual Approval ```yaml # GitHub Actions production-deploy: needs: staging-deploy environment: name: production url: https://app.example.com runs-on: ubuntu-latest steps: - name: Deploy to production run: | # Deployment commands ``` ### Pattern 2: Time-Based Approval ```yaml # GitLab CI deploy:production: stage: deploy script: - deploy.sh production environment: name: production when: delayed start_in: 30 minutes only: - main ``` ### Pattern 3: Multi-Approver ```yaml # Azure Pipelines stages: - stage: Production dependsOn: Staging jobs: - deployment: Deploy environment: name: production resourceType: Kubernetes strategy: runOnce: preDeploy: steps: - task: ManualValidation@0 inputs: notifyUsers: "team-leads@example.com" instructions: "Review staging metrics before approving" ``` **Reference:** See `assets/approval-gate-template.yml` ## Deployment Strategies ### 1. Rolling Deployment ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: my-app spec: replicas: 10 strategy: type: RollingUpdate rollingUpdate: maxSurge: 2 maxUnavailable: 1 ``` **Characteristics:** - Gradual rollout - Zero downtime - Easy rollback - Best for most applications ### 2. Blue-Green Deployment ```yaml # Blue (current) kubectl apply -f blue-deployment.yaml kubectl label service my-app version=blue # Green (new) kubectl apply -f green-deployment.yaml # Test green environment kubectl label service my-app version=green # Rollback if needed kubectl label service my-app version=blue ``` **Characteristics:** - Instant switchover - Easy rollback - Doubles infrastructure cost temporarily - Good for high-risk deployments ### 3. Canary Deployment ```yaml apiVersion: argoproj.io/v1alpha1 kind: Rollout metadata: name: my-app spec: replicas: 10 strategy: canary: steps: - setWeight: 10 - pause: { duration: 5m } - setWeight: 25 - pause: { duration: 5m } - setWeight: 50 - pause: { duration: 5m } - setWeight: 100 ``` **Characteristics:** - Gradual traffic shift - Risk mitigation - Real user testing - Requires service mesh or similar ### 4. Feature Flags ```python from flagsmith import Flagsmith flagsmith = Flagsmith(environment_key="API_KEY") if flagsmith.has_feature("new_checkout_flow"): # New code path process_checkout_v2() else: # Existing code path process_checkout_v1() ``` **Characteristics:** - Deploy without releasing - A/B testing - Instant rollback - Granular control ## Pipeline Orchestration ### Multi-Stage Pipeline Example ```yaml name: Production Pipeline on: push: branches: [main] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Build application run: make build - name: Build Docker image run: docker build -t myapp:${{ github.sha }} . - name: Push to registry run: docker push myapp:${{ github.sha }} test: needs: build runs-on: ubuntu-latest steps: - name: Unit tests run: make test - name: Security scan run: trivy image myapp:${{ github.sha }} deploy-staging: needs: test runs-on: ubuntu-latest environment: name: staging steps: - name: Deploy to staging run: kubectl apply -f k8s/staging/ integration-test: needs: deploy-staging runs-on: ubuntu-latest steps: - name: Run E2E tests run: npm run test:e2e deploy-production: needs: integration-test runs-on: ubuntu-latest environment: name: production steps: - name: Canary deployment run: | kubectl apply -f k8s/production/ kubectl argo rollouts promote my-app verify: needs: deploy-production runs-on: ubuntu-latest steps: - name: Health check run: curl -f https://app.example.com/health - name: Notify team run: | curl -X POST ${{ secrets.SLACK_WEBHOOK }} \ -d '{"text":"Production deployment successful!"}' ``` ## Pipeline Best Practices 1. **Fail fast** - Run quick tests first 2. **Parallel execution** - Run independent jobs concurrently 3. **Caching** - Cache dependencies between runs 4. **Artifact management** - Store build artifacts 5. **Environment parity** - Keep environments consistent 6. **Secrets management** - Use secret stores (Vault, etc.) 7. **Deployment windows** - Schedule deployments appropriately 8. **Monitoring integration** - Track deployment metrics 9. **Rollback automation** - Auto-rollback on failures 10. **Documentation** - Document pipeline stages ## Rollback Strategies ### Automated Rollback ```yaml deploy-and-verify: steps: - name: Deploy new version run: kubectl apply -f k8s/ - name: Wait for rollout run: kubectl rollout status deployment/my-app - name: Health check id: health run: | for i in {1..10}; do if curl -sf https://app.example.com/health; then exit 0 fi sleep 10 done exit 1 - name: Rollback on failure if: failure() run: kubectl rollout undo deployment/my-app ``` ### Manual Rollback ```bash # List revision history kubectl rollout history deployment/my-app # Rollback to previous version kubectl rollout undo deployment/my-app # Rollback to specific revision kubectl rollout undo deployment/my-app --to-revision=3 ``` ## Monitoring and Metrics ### Key Pipeline Metrics - **Deployment Frequency** - How often deployments occur - **Lead Time** - Time from commit to production - **Change Failure Rate** - Percentage of failed deployments - **Mean Time to Recovery (MTTR)** - Time to recover from failure - **Pipeline Success Rate** - Percentage of successful runs - **Average Pipeline Duration** - Time to complete pipeline ### Integration with Monitoring ```yaml - name: Post-deployment verification run: | # Wait for metrics stabilization sleep 60 # Check error rate ERROR_RATE=$(curl -s "$PROMETHEUS_URL/api/v1/query?query=rate(http_errors_total[5m])" | jq '.data.result[0].value[1]') if (( $(echo "$ERROR_RATE > 0.01" | bc -l) )); then echo "Error rate too high: $ERROR_RATE" exit 1 fi ``` ## Reference Files - `references/pipeline-orchestration.md` - Complex pipeline patterns - `assets/approval-gate-template.yml` - Approval workflow templates ## Related Skills - `github-actions-templates` - For GitHub Actions implementation - `gitlab-ci-patterns` - For GitLab CI implementation - `secrets-management` - For secrets handling
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

github-actions-templates

Create production-ready GitHub Actions workflows for automated

coding
⭐1
# GitHub Actions Templates Production-ready GitHub Actions workflow patterns for testing, building, and deploying applications. ## Purpose Create efficient, secure GitHub Actions workflows for continuous integration and deployment across various tech stacks. ## When to Use - Automate testing and deployment - Build Docker images and push to registries - Deploy to Kubernetes clusters - Run security scans - Implement matrix builds for multiple environments ## Common Workflow Patterns ### Pattern 1: Test Workflow ```yaml name: Test on: push: branches: [main, develop] pull_request: branches: [main] jobs: test: runs-on: ubuntu-latest strategy: matrix: node-version: [18.x, 20.x] steps: - uses: actions/checkout@v4 - name: Use Node.js ${{ matrix.node-version }} uses: actions/setup-node@v4 with: node-version: ${{ matrix.node-version }} cache: "npm" - name: Install dependencies run: npm ci - name: Run linter run: npm run lint - name: Run tests run: npm test - name: Upload coverage uses: codecov/codecov-action@v3 with: files: ./coverage/lcov.info ``` **Reference:** See `assets/test-workflow.yml` ### Pattern 2: Build and Push Docker Image ```yaml name: Build and Push on: push: branches: [main] tags: ["v*"] env: REGISTRY: ghcr.io IMAGE_NAME: ${{ github.repository }} jobs: build: runs-on: ubuntu-latest permissions: contents: read packages: write steps: - uses: actions/checkout@v4 - name: Log in to Container Registry uses: docker/login-action@v3 with: registry: ${{ env.REGISTRY }} username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - name: Extract metadata id: meta uses: docker/metadata-action@v5 with: images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} tags: | type=ref,event=branch type=ref,event=pr type=semver,pattern={{version}} type=semver,pattern={{major}}.{{minor}} - name: Build and push uses: docker/build-push-action@v5 with: context: . push: true tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} cache-from: type=gha cache-to: type=gha,mode=max ``` **Reference:** See `assets/deploy-workflow.yml` ### Pattern 3: Deploy to Kubernetes ```yaml name: Deploy to Kubernetes on: push: branches: [main] jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Configure AWS credentials uses: aws-actions/configure-aws-credentials@v4 with: aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} aws-region: us-west-2 - name: Update kubeconfig run: | aws eks update-kubeconfig --name production-cluster --region us-west-2 - name: Deploy to Kubernetes run: | kubectl apply -f k8s/ kubectl rollout status deployment/my-app -n production kubectl get services -n production - name: Verify deployment run: | kubectl get pods -n production kubectl describe deployment my-app -n production ``` ### Pattern 4: Matrix Build ```yaml name: Matrix Build on: [push, pull_request] jobs: build: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] python-version: ["3.9", "3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt - name: Run tests run: pytest ``` **Reference:** See `assets/matrix-build.yml` ## Workflow Best Practices 1. **Use specific action versions** (@v4, not @latest) 2. **Cache dependencies** to speed up builds 3. **Use secrets** for sensitive data 4. **Implement status checks** on PRs 5. **Use matrix builds** for multi-version testing 6. **Set appropriate permissions** 7. **Use reusable workflows** for common patterns 8. **Implement approval gates** for production 9. **Add notification steps** for failures 10. **Use self-hosted runners** for sensitive workloads ## Reusable Workflows ```yaml # .github/workflows/reusable-test.yml name: Reusable Test Workflow on: workflow_call: inputs: node-version: required: true type: string secrets: NPM_TOKEN: required: true jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-node@v4 with: node-version: ${{ inputs.node-version }} - run: npm ci - run: npm test ``` **Use reusable workflow:** ```yaml jobs: call-test: uses: ./.github/workflows/reusable-test.yml with: node-version: "20.x" secrets: NPM_TOKEN: ${{ secrets.NPM_TOKEN }} ``` ## Security Scanning ```yaml name: Security Scan on: push: branches: [main] pull_request: branches: [main] jobs: security: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Run Trivy vulnerability scanner uses: aquasecurity/trivy-action@master with: scan-type: "fs" scan-ref: "." format: "sarif" output: "trivy-results.sarif" - name: Upload Trivy results to GitHub Security uses: github/codeql-action/upload-sarif@v2 with: sarif_file: "trivy-results.sarif" - name: Run Snyk Security Scan uses: snyk/actions/node@master env: SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }} ``` ## Deployment with Approvals ```yaml name: Deploy to Production on: push: tags: ["v*"] jobs: deploy: runs-on: ubuntu-latest environment: name: production url: https://app.example.com steps: - uses: actions/checkout@v4 - name: Deploy application run: | echo "Deploying to production..." # Deployment commands here - name: Notify Slack if: success() uses: slackapi/slack-github-action@v1 with: webhook-url: ${{ secrets.SLACK_WEBHOOK }} payload: | { "text": "Deployment to production completed successfully!" } ``` ## Reference Files - `assets/test-workflow.yml` - Testing workflow template - `assets/deploy-workflow.yml` - Deployment workflow template - `assets/matrix-build.yml` - Matrix build template - `references/common-workflows.md` - Common workflow patterns ## Related Skills - `gitlab-ci-patterns` - For GitLab CI workflows - `deployment-pipeline-design` - For pipeline architecture - `secrets-management` - For secrets handling
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

gitlab-ci-patterns

Build GitLab CI/CD pipelines with multi-stage workflows, caching,

coding
⭐1
# GitLab CI Patterns Comprehensive GitLab CI/CD pipeline patterns for automated testing, building, and deployment. ## Purpose Create efficient GitLab CI pipelines with proper stage organization, caching, and deployment strategies. ## When to Use - Automate GitLab-based CI/CD - Implement multi-stage pipelines - Configure GitLab Runners - Deploy to Kubernetes from GitLab - Implement GitOps workflows ## Basic Pipeline Structure ```yaml stages: - build - test - deploy variables: DOCKER_DRIVER: overlay2 DOCKER_TLS_CERTDIR: "/certs" build: stage: build image: node:20 script: - npm ci - npm run build artifacts: paths: - dist/ expire_in: 1 hour cache: key: ${CI_COMMIT_REF_SLUG} paths: - node_modules/ test: stage: test image: node:20 script: - npm ci - npm run lint - npm test coverage: '/Lines\s*:\s*(\d+\.\d+)%/' artifacts: reports: coverage_report: coverage_format: cobertura path: coverage/cobertura-coverage.xml deploy: stage: deploy image: bitnami/kubectl:latest script: - kubectl apply -f k8s/ - kubectl rollout status deployment/my-app only: - main environment: name: production url: https://app.example.com ``` ## Docker Build and Push ```yaml build-docker: stage: build image: docker:24 services: - docker:24-dind before_script: - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY script: - docker build -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA . - docker build -t $CI_REGISTRY_IMAGE:latest . - docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA - docker push $CI_REGISTRY_IMAGE:latest only: - main - tags ``` ## Multi-Environment Deployment ```yaml .deploy_template: &deploy_template image: bitnami/kubectl:latest before_script: - kubectl config set-cluster k8s --server="$KUBE_URL" --insecure-skip-tls-verify=true - kubectl config set-credentials admin --token="$KUBE_TOKEN" - kubectl config set-context default --cluster=k8s --user=admin - kubectl config use-context default deploy:staging: <<: *deploy_template stage: deploy script: - kubectl apply -f k8s/ -n staging - kubectl rollout status deployment/my-app -n staging environment: name: staging url: https://staging.example.com only: - develop deploy:production: <<: *deploy_template stage: deploy script: - kubectl apply -f k8s/ -n production - kubectl rollout status deployment/my-app -n production environment: name: production url: https://app.example.com when: manual only: - main ``` ## Terraform Pipeline ```yaml stages: - validate - plan - apply variables: TF_ROOT: ${CI_PROJECT_DIR}/terraform TF_VERSION: "1.6.0" before_script: - cd ${TF_ROOT} - terraform --version validate: stage: validate image: hashicorp/terraform:${TF_VERSION} script: - terraform init -backend=false - terraform validate - terraform fmt -check plan: stage: plan image: hashicorp/terraform:${TF_VERSION} script: - terraform init - terraform plan -out=tfplan artifacts: paths: - ${TF_ROOT}/tfplan expire_in: 1 day apply: stage: apply image: hashicorp/terraform:${TF_VERSION} script: - terraform init - terraform apply -auto-approve tfplan dependencies: - plan when: manual only: - main ``` ## Security Scanning ```yaml include: - template: Security/SAST.gitlab-ci.yml - template: Security/Dependency-Scanning.gitlab-ci.yml - template: Security/Container-Scanning.gitlab-ci.yml trivy-scan: stage: test image: aquasec/trivy:latest script: - trivy image --exit-code 1 --severity HIGH,CRITICAL $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA allow_failure: true ``` ## Caching Strategies ```yaml # Cache node_modules build: cache: key: ${CI_COMMIT_REF_SLUG} paths: - node_modules/ policy: pull-push # Global cache cache: key: ${CI_COMMIT_REF_SLUG} paths: - .cache/ - vendor/ # Separate cache per job job1: cache: key: job1-cache paths: - build/ job2: cache: key: job2-cache paths: - dist/ ``` ## Dynamic Child Pipelines ```yaml generate-pipeline: stage: build script: - python generate_pipeline.py > child-pipeline.yml artifacts: paths: - child-pipeline.yml trigger-child: stage: deploy trigger: include: - artifact: child-pipeline.yml job: generate-pipeline strategy: depend ``` ## Reference Files - `assets/gitlab-ci.yml.template` - Complete pipeline template - `references/pipeline-stages.md` - Stage organization patterns ## Best Practices 1. **Use specific image tags** (node:20, not node:latest) 2. **Cache dependencies** appropriately 3. **Use artifacts** for build outputs 4. **Implement manual gates** for production 5. **Use environments** for deployment tracking 6. **Enable merge request pipelines** 7. **Use pipeline schedules** for recurring jobs 8. **Implement security scanning** 9. **Use CI/CD variables** for secrets 10. **Monitor pipeline performance** ## Related Skills - `github-actions-templates` - For GitHub Actions - `deployment-pipeline-design` - For architecture - `secrets-management` - For secrets handling
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

secrets-management

Implement secure secrets management for CI/CD pipelines using

coding
⭐1
# Secrets Management Secure secrets management practices for CI/CD pipelines using Vault, AWS Secrets Manager, and other tools. ## Purpose Implement secure secrets management in CI/CD pipelines without hardcoding sensitive information. ## When to Use - Store API keys and credentials - Manage database passwords - Handle TLS certificates - Rotate secrets automatically - Implement least-privilege access ## Secrets Management Tools ### HashiCorp Vault - Centralized secrets management - Dynamic secrets generation - Secret rotation - Audit logging - Fine-grained access control ### AWS Secrets Manager - AWS-native solution - Automatic rotation - Integration with RDS - CloudFormation support ### Azure Key Vault - Azure-native solution - HSM-backed keys - Certificate management - RBAC integration ### Google Secret Manager - GCP-native solution - Versioning - IAM integration ## HashiCorp Vault Integration ### Setup Vault ```bash # Start Vault dev server vault server -dev # Set environment export VAULT_ADDR='http://127.0.0.1:8200' export VAULT_TOKEN='root' # Enable secrets engine vault secrets enable -path=secret kv-v2 # Store secret vault kv put secret/database/config username=admin password=secret ``` ### GitHub Actions with Vault ```yaml name: Deploy with Vault Secrets on: [push] jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Import Secrets from Vault uses: hashicorp/vault-action@v2 with: url: https://vault.example.com:8200 token: ${{ secrets.VAULT_TOKEN }} secrets: | secret/data/database username | DB_USERNAME ; secret/data/database password | DB_PASSWORD ; secret/data/api key | API_KEY - name: Use secrets run: | echo "Connecting to database as $DB_USERNAME" # Use $DB_PASSWORD, $API_KEY ``` ### GitLab CI with Vault ```yaml deploy: image: vault:latest before_script: - export VAULT_ADDR=https://vault.example.com:8200 - export VAULT_TOKEN=$VAULT_TOKEN - apk add curl jq script: - | DB_PASSWORD=$(vault kv get -field=password secret/database/config) API_KEY=$(vault kv get -field=key secret/api/credentials) echo "Deploying with secrets..." # Use $DB_PASSWORD, $API_KEY ``` **Reference:** See `references/vault-setup.md` ## AWS Secrets Manager ### Store Secret ```bash aws secretsmanager create-secret \ --name production/database/password \ --secret-string "super-secret-password" ``` ### Retrieve in GitHub Actions ```yaml - name: Configure AWS credentials uses: aws-actions/configure-aws-credentials@v4 with: aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} aws-region: us-west-2 - name: Get secret from AWS run: | SECRET=$(aws secretsmanager get-secret-value \ --secret-id production/database/password \ --query SecretString \ --output text) echo "::add-mask::$SECRET" echo "DB_PASSWORD=$SECRET" >> $GITHUB_ENV - name: Use secret run: | # Use $DB_PASSWORD ./deploy.sh ``` ### Terraform with AWS Secrets Manager ```hcl data "aws_secretsmanager_secret_version" "db_password" { secret_id = "production/database/password" } resource "aws_db_instance" "main" { allocated_storage = 100 engine = "postgres" instance_class = "db.t3.large" username = "admin" password = jsondecode(data.aws_secretsmanager_secret_version.db_password.secret_string)["password"] } ``` ## GitHub Secrets ### Organization/Repository Secrets ```yaml - name: Use GitHub secret run: | echo "API Key: ${{ secrets.API_KEY }}" echo "Database URL: ${{ secrets.DATABASE_URL }}" ``` ### Environment Secrets ```yaml deploy: runs-on: ubuntu-latest environment: production steps: - name: Deploy run: | echo "Deploying with ${{ secrets.PROD_API_KEY }}" ``` **Reference:** See `references/github-secrets.md` ## GitLab CI/CD Variables ### Project Variables ```yaml deploy: script: - echo "Deploying with $API_KEY" - echo "Database: $DATABASE_URL" ``` ### Protected and Masked Variables - Protected: Only available in protected branches - Masked: Hidden in job logs - File type: Stored as file ## Best Practices 1. **Never commit secrets** to Git 2. **Use different secrets** per environment 3. **Rotate secrets regularly** 4. **Implement least-privilege access** 5. **Enable audit logging** 6. **Use secret scanning** (GitGuardian, TruffleHog) 7. **Mask secrets in logs** 8. **Encrypt secrets at rest** 9. **Use short-lived tokens** when possible 10. **Document secret requirements** ## Secret Rotation ### Automated Rotation with AWS ```python import boto3 import json def lambda_handler(event, context): client = boto3.client('secretsmanager') # Get current secret response = client.get_secret_value(SecretId='my-secret') current_secret = json.loads(response['SecretString']) # Generate new password new_password = generate_strong_password() # Update database password update_database_password(new_password) # Update secret client.put_secret_value( SecretId='my-secret', SecretString=json.dumps({ 'username': current_secret['username'], 'password': new_password }) ) return {'statusCode': 200} ``` ### Manual Rotation Process 1. Generate new secret 2. Update secret in secret store 3. Update applications to use new secret 4. Verify functionality 5. Revoke old secret ## External Secrets Operator ### Kubernetes Integration ```yaml apiVersion: external-secrets.io/v1beta1 kind: SecretStore metadata: name: vault-backend namespace: production spec: provider: vault: server: "https://vault.example.com:8200" path: "secret" version: "v2" auth: kubernetes: mountPath: "kubernetes" role: "production" --- apiVersion: external-secrets.io/v1beta1 kind: ExternalSecret metadata: name: database-credentials namespace: production spec: refreshInterval: 1h secretStoreRef: name: vault-backend kind: SecretStore target: name: database-credentials creationPolicy: Owner data: - secretKey: username remoteRef: key: database/config property: username - secretKey: password remoteRef: key: database/config property: password ``` ## Secret Scanning ### Pre-commit Hook ```bash #!/bin/bash # .git/hooks/pre-commit # Check for secrets with TruffleHog docker run --rm -v "$(pwd):/repo" \ trufflesecurity/trufflehog:latest \ filesystem --directory=/repo if [ $? -ne 0 ]; then echo "āŒ Secret detected! Commit blocked." exit 1 fi ``` ### CI/CD Secret Scanning ```yaml secret-scan: stage: security image: trufflesecurity/trufflehog:latest script: - trufflehog filesystem . allow_failure: false ``` ## Reference Files - `references/vault-setup.md` - HashiCorp Vault configuration - `references/github-secrets.md` - GitHub Secrets best practices ## Related Skills - `github-actions-templates` - For GitHub Actions integration - `gitlab-ci-patterns` - For GitLab CI integration - `deployment-pipeline-design` - For pipeline architecture
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

context-driven-development

Creates and maintains project context artifacts (product.md,

coding
⭐1
# Context-Driven Development Guide for implementing and maintaining context as a managed artifact alongside code, enabling consistent AI interactions and team alignment through structured project documentation. ## When to Use This Skill - Setting up new projects with Conductor - Understanding the relationship between context artifacts - Maintaining consistency across AI-assisted development sessions - Onboarding team members to an existing Conductor project - Deciding when to update context documents - Managing greenfield vs brownfield project contexts ## Core Philosophy Context-Driven Development treats project context as a first-class artifact managed alongside code. Instead of relying on ad-hoc prompts or scattered documentation, establish a persistent, structured foundation that informs all AI interactions. Key principles: 1. **Context precedes code**: Define what you're building and how before implementation 2. **Living documentation**: Context artifacts evolve with the project 3. **Single source of truth**: One canonical location for each type of information 4. **AI alignment**: Consistent context produces consistent AI behavior ## The Workflow Follow the **Context → Spec & Plan → Implement** workflow: 1. **Context Phase**: Establish or verify project context artifacts exist and are current 2. **Specification Phase**: Define requirements and acceptance criteria for work units 3. **Planning Phase**: Break specifications into phased, actionable tasks 4. **Implementation Phase**: Execute tasks following established workflow patterns ## Artifact Relationships ### product.md - Defines WHAT and WHY Purpose: Captures product vision, goals, target users, and business context. Contents: - Product name and one-line description - Problem statement and solution approach - Target user personas - Core features and capabilities - Success metrics and KPIs - Product roadmap (high-level) Update when: - Product vision or goals change - New major features are planned - Target audience shifts - Business priorities evolve ### product-guidelines.md - Defines HOW to Communicate Purpose: Establishes brand voice, messaging standards, and communication patterns. Contents: - Brand voice and tone guidelines - Terminology and glossary - Error message conventions - User-facing copy standards - Documentation style Update when: - Brand guidelines change - New terminology is introduced - Communication patterns need refinement ### tech-stack.md - Defines WITH WHAT Purpose: Documents technology choices, dependencies, and architectural decisions. Contents: - Primary languages and frameworks - Key dependencies with versions - Infrastructure and deployment targets - Development tools and environment - Testing frameworks - Code quality tools Update when: - Adding new dependencies - Upgrading major versions - Changing infrastructure - Adopting new tools or patterns ### workflow.md - Defines HOW to Work Purpose: Establishes development practices, quality gates, and team workflows. Contents: - Development methodology (TDD, etc.) - Git workflow and commit conventions - Code review requirements - Testing requirements and coverage targets - Quality assurance gates - Deployment procedures Update when: - Team practices evolve - Quality standards change - New workflow patterns are adopted ### tracks.md - Tracks WHAT'S HAPPENING Purpose: Registry of all work units with status and metadata. Contents: - Active tracks with current status - Completed tracks with completion dates - Track metadata (type, priority, assignee) - Links to individual track directories Update when: - New tracks are created - Track status changes - Tracks are completed or archived See [references/artifact-templates.md](references/artifact-templates.md) for copy-paste starter templates. ## Context Maintenance Principles ### Keep Artifacts Synchronized Ensure changes in one artifact reflect in related documents: - New feature in product.md → Update tech-stack.md if new dependencies needed - Completed track → Update product.md to reflect new capabilities - Workflow change → Update all affected track plans ### Update tech-stack.md When Adding Dependencies Before adding any new dependency: 1. Check if existing dependencies solve the need 2. Document the rationale for new dependencies 3. Add version constraints 4. Note any configuration requirements ### Update product.md When Features Complete After completing a feature track: 1. Move feature from "planned" to "implemented" in product.md 2. Update any affected success metrics 3. Document any scope changes from original plan ### Verify Context Before Implementation Before starting any track: 1. Read all context artifacts 2. Flag any outdated information 3. Propose updates before proceeding 4. Confirm context accuracy with stakeholders ## Greenfield vs Brownfield Handling ### Greenfield Projects (New) For new projects: 1. Run `/conductor:setup` to create all artifacts interactively 2. Answer questions about product vision, tech preferences, and workflow 3. Generate initial style guides for chosen languages 4. Create empty tracks registry Characteristics: - Full control over context structure - Define standards before code exists - Establish patterns early ### Brownfield Projects (Existing) For existing codebases: 1. Run `/conductor:setup` with existing codebase detection 2. System analyzes existing code, configs, and documentation 3. Pre-populate artifacts based on discovered patterns 4. Review and refine generated context Characteristics: - Extract implicit context from existing code - Reconcile existing patterns with desired patterns - Document technical debt and modernization plans - Preserve working patterns while establishing standards ## Benefits ### Team Alignment - New team members onboard faster with explicit context - Consistent terminology and conventions across the team - Shared understanding of product goals and technical decisions ### AI Consistency - AI assistants produce aligned outputs across sessions - Reduced need to re-explain context in each interaction - Predictable behavior based on documented standards ### Institutional Memory - Decisions and rationale are preserved - Context survives team changes - Historical context informs future decisions ### Quality Assurance - Standards are explicit and verifiable - Deviations from context are detectable - Quality gates are documented and enforceable ## Directory Structure ``` conductor/ ā”œā”€ā”€ index.md # Navigation hub linking all artifacts ā”œā”€ā”€ product.md # Product vision and goals ā”œā”€ā”€ product-guidelines.md # Communication standards ā”œā”€ā”€ tech-stack.md # Technology preferences ā”œā”€ā”€ workflow.md # Development practices ā”œā”€ā”€ tracks.md # Work unit registry ā”œā”€ā”€ setup_state.json # Resumable setup state ā”œā”€ā”€ code_styleguides/ # Language-specific conventions │ ā”œā”€ā”€ python.md │ ā”œā”€ā”€ typescript.md │ └── ... └── tracks/ └── <track-id>/ ā”œā”€ā”€ spec.md ā”œā”€ā”€ plan.md ā”œā”€ā”€ metadata.json └── index.md ``` ## Context Lifecycle 1. **Creation**: Initial setup via `/conductor:setup` 2. **Validation**: Verify before each track 3. **Evolution**: Update as project grows 4. **Synchronization**: Keep artifacts aligned 5. **Archival**: Document historical decisions ## Context Validation Checklist Before starting implementation on any track, validate context: ### Product Context - [ ] product.md reflects current product vision - [ ] Target users are accurately described - [ ] Feature list is up to date - [ ] Success metrics are defined ### Technical Context - [ ] tech-stack.md lists all current dependencies - [ ] Version numbers are accurate - [ ] Infrastructure targets are correct - [ ] Development tools are documented ### Workflow Context - [ ] workflow.md describes current practices - [ ] Quality gates are defined - [ ] Coverage targets are specified - [ ] Commit conventions are documented ### Track Context - [ ] tracks.md shows all active work - [ ] No stale or abandoned tracks - [ ] Dependencies between tracks are noted ## Common Anti-Patterns Avoid these context management mistakes: ### Stale Context Problem: Context documents become outdated and misleading. Solution: Update context as part of each track's completion process. ### Context Sprawl Problem: Information scattered across multiple locations. Solution: Use the defined artifact structure; resist creating new document types. ### Implicit Context Problem: Relying on knowledge not captured in artifacts. Solution: If you reference something repeatedly, add it to the appropriate artifact. ### Context Hoarding Problem: One person maintains context without team input. Solution: Review context artifacts in pull requests; make updates collaborative. ### Over-Specification Problem: Context becomes so detailed it's impossible to maintain. Solution: Keep artifacts focused on decisions that affect AI behavior and team alignment. ## Integration with Development Tools ### IDE Integration Configure your IDE to display context files prominently: - Pin conductor/product.md for quick reference - Add tech-stack.md to project notes - Create snippets for common patterns from style guides ### Git Hooks Consider pre-commit hooks that: - Warn when dependencies change without tech-stack.md update - Remind to update product.md when feature branches merge - Validate context artifact syntax ### CI/CD Integration Include context validation in pipelines: - Check tech-stack.md matches actual dependencies - Verify links in context documents resolve - Ensure tracks.md status matches git branch state ## Session Continuity Conductor supports multi-session development through context persistence: ### Starting a New Session 1. Read index.md to orient yourself 2. Check tracks.md for active work 3. Review relevant track's plan.md for current task 4. Verify context artifacts are current ### Ending a Session 1. Update plan.md with current progress 2. Note any blockers or decisions made 3. Commit in-progress work with clear status 4. Update tracks.md if status changed ### Handling Interruptions If interrupted mid-task: 1. Mark task as `[~]` with note about stopping point 2. Commit work-in-progress to feature branch 3. Document any uncommitted decisions in plan.md ## Best Practices 1. **Read context first**: Always read relevant artifacts before starting work 2. **Small updates**: Make incremental context changes, not massive rewrites 3. **Link decisions**: Reference context when making implementation choices 4. **Version context**: Commit context changes alongside code changes 5. **Review context**: Include context artifact reviews in code reviews 6. **Validate regularly**: Run context validation checklist before major work 7. **Communicate changes**: Notify team when context artifacts change significantly 8. **Preserve history**: Use git to track context evolution over time 9. **Question staleness**: If context feels wrong, investigate and update 10. **Keep it actionable**: Every context item should inform a decision or behavior
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

workflow-patterns

Use this skill when implementing tasks according to Conductor's TDD

coding
⭐1
# Workflow Patterns Guide for implementing tasks using Conductor's TDD workflow, managing phase checkpoints, handling git commits, and executing the verification protocol that ensures quality throughout implementation. ## When to Use This Skill - Implementing tasks from a track's plan.md - Following TDD red-green-refactor cycle - Completing phase checkpoints - Managing git commits and notes - Understanding quality assurance gates - Handling verification protocols - Recording progress in plan files ## TDD Task Lifecycle Follow these 11 steps for each task: ### Step 1: Select Next Task Read plan.md and identify the next pending `[ ]` task. Select tasks in order within the current phase. Do not skip ahead to later phases. ### Step 2: Mark as In Progress Update plan.md to mark the task as `[~]`: ```markdown - [~] **Task 2.1**: Implement user validation ``` Commit this status change separately from implementation. ### Step 3: RED - Write Failing Tests Write tests that define the expected behavior before writing implementation: - Create test file if needed - Write test cases covering happy path - Write test cases covering edge cases - Write test cases covering error conditions - Run tests - they should FAIL Example: ```python def test_validate_user_email_valid(): user = User(email="test@example.com") assert user.validate_email() is True def test_validate_user_email_invalid(): user = User(email="invalid") assert user.validate_email() is False ``` ### Step 4: GREEN - Implement Minimum Code Write the minimum code necessary to make tests pass: - Focus on making tests green, not perfection - Avoid premature optimization - Keep implementation simple - Run tests - they should PASS ### Step 5: REFACTOR - Improve Clarity With green tests, improve the code: - Extract common patterns - Improve naming - Remove duplication - Simplify logic - Run tests after each change - they should remain GREEN ### Step 6: Verify Coverage Check test coverage meets the 80% target: ```bash pytest --cov=module --cov-report=term-missing ``` If coverage is below 80%: - Identify uncovered lines - Add tests for missing paths - Re-run coverage check ### Step 7: Document Deviations If implementation deviated from plan or introduced new dependencies: - Update tech-stack.md with new dependencies - Note deviations in plan.md task comments - Update spec.md if requirements changed ### Step 8: Commit Implementation Create a focused commit for the task: ```bash git add -A git commit -m "feat(user): implement email validation - Add validate_email method to User class - Handle empty and malformed emails - Add comprehensive test coverage Task: 2.1 Track: user-auth_20250115" ``` Commit message format: - Type: feat, fix, refactor, test, docs, chore - Scope: affected module or component - Summary: imperative, present tense - Body: bullet points of changes - Footer: task and track references ### Step 9: Attach Git Notes Add rich task summary as git note: ```bash git notes add -m "Task 2.1: Implement user validation Summary: - Added email validation using regex pattern - Handles edge cases: empty, no @, no domain - Coverage: 94% on validation module Files changed: - src/models/user.py (modified) - tests/test_user.py (modified) Decisions: - Used simple regex over email-validator library - Reason: No external dependency for basic validation" ``` ### Step 10: Update Plan with SHA Update plan.md to mark task complete with commit SHA: ```markdown - [x] **Task 2.1**: Implement user validation `abc1234` ``` ### Step 11: Commit Plan Update Commit the plan status update: ```bash git add conductor/tracks/*/plan.md git commit -m "docs: update plan - task 2.1 complete Track: user-auth_20250115" ``` ## Phase Completion Protocol When all tasks in a phase are complete, execute the verification protocol: ### Identify Changed Files List all files modified since the last checkpoint: ```bash git diff --name-only <last-checkpoint-sha>..HEAD ``` ### Ensure Test Coverage For each modified file: 1. Identify corresponding test file 2. Verify tests exist for new/changed code 3. Run coverage for modified modules 4. Add tests if coverage < 80% ### Run Full Test Suite Execute complete test suite: ```bash pytest -v --tb=short ``` All tests must pass before proceeding. ### Generate Manual Verification Steps Create checklist of manual verifications: ```markdown ## Phase 1 Verification Checklist - [ ] User can register with valid email - [ ] Invalid email shows appropriate error - [ ] Database stores user correctly - [ ] API returns expected response codes ``` ### WAIT for User Approval Present verification checklist to user: ``` Phase 1 complete. Please verify: 1. [ ] Test suite passes (automated) 2. [ ] Coverage meets target (automated) 3. [ ] Manual verification items (requires human) Respond with 'approved' to continue, or note issues. ``` Do NOT proceed without explicit approval. ### Create Checkpoint Commit After approval, create checkpoint commit: ```bash git add -A git commit -m "checkpoint: phase 1 complete - user-auth_20250115 Verified: - All tests passing - Coverage: 87% - Manual verification approved Phase 1 tasks: - [x] Task 1.1: Setup database schema - [x] Task 1.2: Implement user model - [x] Task 1.3: Add validation logic" ``` ### Record Checkpoint SHA Update plan.md checkpoints table: ```markdown ## Checkpoints | Phase | Checkpoint SHA | Date | Status | | ------- | -------------- | ---------- | -------- | | Phase 1 | def5678 | 2025-01-15 | verified | | Phase 2 | | | pending | ``` ## Quality Assurance Gates Before marking any task complete, verify these gates: ### Passing Tests - All existing tests pass - New tests pass - No test regressions ### Coverage >= 80% - New code has 80%+ coverage - Overall project coverage maintained - Critical paths fully covered ### Style Compliance - Code follows style guides - Linting passes - Formatting correct ### Documentation - Public APIs documented - Complex logic explained - README updated if needed ### Type Safety - Type hints present (if applicable) - Type checker passes - No type: ignore without reason ### No Linting Errors - Zero linter errors - Warnings addressed or justified - Static analysis clean ### Mobile Compatibility If applicable: - Responsive design verified - Touch interactions work - Performance acceptable ### Security Audit - No secrets in code - Input validation present - Authentication/authorization correct - Dependencies vulnerability-free ## Git Integration ### Commit Message Format ``` <type>(<scope>): <subject> <body> <footer> ``` Types: - `feat`: New feature - `fix`: Bug fix - `refactor`: Code change without feature/fix - `test`: Adding tests - `docs`: Documentation - `chore`: Maintenance ### Git Notes for Rich Summaries Attach detailed notes to commits: ```bash git notes add -m "<detailed summary>" ``` View notes: ```bash git log --show-notes ``` Benefits: - Preserves context without cluttering commit message - Enables semantic queries across commits - Supports track-based operations ### SHA Recording in plan.md Always record the commit SHA when completing tasks: ```markdown - [x] **Task 1.1**: Setup schema `abc1234` - [x] **Task 1.2**: Add model `def5678` ``` This enables: - Traceability from plan to code - Semantic revert operations - Progress auditing ## Verification Checkpoints ### Why Checkpoints Matter Checkpoints create restore points for semantic reversion: - Revert to end of any phase - Maintain logical code state - Enable safe experimentation ### When to Create Checkpoints Create checkpoint after: - All phase tasks complete - All phase verifications pass - User approval received ### Checkpoint Commit Content Include in checkpoint commit: - All uncommitted changes - Updated plan.md - Updated metadata.json - Any documentation updates ### How to Use Checkpoints For reverting: ```bash # Revert to end of Phase 1 git revert --no-commit <phase-2-commits>... git commit -m "revert: rollback to phase 1 checkpoint" ``` For review: ```bash # See what changed in Phase 2 git diff <phase-1-sha>..<phase-2-sha> ``` ## Handling Deviations During implementation, deviations from the plan may occur. Handle them systematically: ### Types of Deviations **Scope Addition** Discovered requirement not in original spec. - Document in spec.md as new requirement - Add tasks to plan.md - Note addition in task comments **Scope Reduction** Feature deemed unnecessary during implementation. - Mark tasks as `[-]` (skipped) with reason - Update spec.md scope section - Document decision rationale **Technical Deviation** Different implementation approach than planned. - Note deviation in task completion comment - Update tech-stack.md if dependencies changed - Document why original approach was unsuitable **Requirement Change** Understanding of requirement changes during work. - Update spec.md with corrected requirement - Adjust plan.md tasks if needed - Re-verify acceptance criteria ### Deviation Documentation Format When completing a task with deviation: ```markdown - [x] **Task 2.1**: Implement validation `abc1234` - DEVIATION: Used library instead of custom code - Reason: Better edge case handling - Impact: Added email-validator to dependencies ``` ## Error Recovery ### Failed Tests After GREEN If tests fail after reaching GREEN: 1. Do NOT proceed to REFACTOR 2. Identify which test started failing 3. Check if refactoring broke something 4. Revert to last known GREEN state 5. Re-approach the implementation ### Checkpoint Rejection If user rejects a checkpoint: 1. Note rejection reason in plan.md 2. Create tasks to address issues 3. Complete remediation tasks 4. Request checkpoint approval again ### Blocked by Dependency If task cannot proceed: 1. Mark task as `[!]` with blocker description 2. Check if other tasks can proceed 3. Document expected resolution timeline 4. Consider creating dependency resolution track ## TDD Variations by Task Type ### Data Model Tasks ``` RED: Write test for model creation and validation GREEN: Implement model class with fields REFACTOR: Add computed properties, improve types ``` ### API Endpoint Tasks ``` RED: Write test for request/response contract GREEN: Implement endpoint handler REFACTOR: Extract validation, improve error handling ``` ### Integration Tasks ``` RED: Write test for component interaction GREEN: Wire components together REFACTOR: Improve error propagation, add logging ``` ### Refactoring Tasks ``` RED: Add characterization tests for current behavior GREEN: Apply refactoring (tests should stay green) REFACTOR: Clean up any introduced complexity ``` ## Working with Existing Tests When modifying code with existing tests: ### Extend, Don't Replace - Keep existing tests passing - Add new tests for new behavior - Update tests only when requirements change ### Test Migration When refactoring changes test structure: 1. Run existing tests (should pass) 2. Add new tests for refactored code 3. Migrate test cases to new structure 4. Remove old tests only after new tests pass ### Regression Prevention After any change: 1. Run full test suite 2. Check for unexpected failures 3. Investigate any new failures 4. Fix regressions before proceeding ## Checkpoint Verification Details ### Automated Verification Run before requesting approval: ```bash # Test suite pytest -v --tb=short # Coverage pytest --cov=src --cov-report=term-missing # Linting ruff check src/ tests/ # Type checking (if applicable) mypy src/ ``` ### Manual Verification Guidance For manual items, provide specific instructions: ```markdown ## Manual Verification Steps ### User Registration 1. Navigate to /register 2. Enter valid email: test@example.com 3. Enter password meeting requirements 4. Click Submit 5. Verify success message appears 6. Verify user appears in database ### Error Handling 1. Enter invalid email: "notanemail" 2. Verify error message shows 3. Verify form retains other entered data ``` ## Performance Considerations ### Test Suite Performance Keep test suite fast: - Use fixtures to avoid redundant setup - Mock slow external calls - Run subset during development, full suite at checkpoints ### Commit Performance Keep commits atomic: - One logical change per commit - Complete thought, not work-in-progress - Tests should pass after every commit ## Best Practices 1. **Never skip RED**: Always write failing tests first 2. **Small commits**: One logical change per commit 3. **Immediate updates**: Update plan.md right after task completion 4. **Wait for approval**: Never skip checkpoint verification 5. **Rich git notes**: Include context that helps future understanding 6. **Coverage discipline**: Don't accept coverage below target 7. **Quality gates**: Check all gates before marking complete 8. **Sequential phases**: Complete phases in order 9. **Document deviations**: Note any changes from original plan 10. **Clean state**: Each commit should leave code in working state 11. **Fast feedback**: Run relevant tests frequently during development 12. **Clear blockers**: Address blockers promptly, don't work around them
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airflow-dag-patterns

Build production Apache Airflow DAGs with best practices for

data
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# Apache Airflow DAG Patterns Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies. ## When to Use This Skill - Creating data pipeline orchestration with Airflow - Designing DAG structures and dependencies - Implementing custom operators and sensors - Testing Airflow DAGs locally - Setting up Airflow in production - Debugging failed DAG runs ## Core Concepts ### 1. DAG Design Principles | Principle | Description | | --------------- | ----------------------------------- | | **Idempotent** | Running twice produces same result | | **Atomic** | Tasks succeed or fail completely | | **Incremental** | Process only new/changed data | | **Observable** | Logs, metrics, alerts at every step | ### 2. Task Dependencies ```python # Linear task1 >> task2 >> task3 # Fan-out task1 >> [task2, task3, task4] # Fan-in [task1, task2, task3] >> task4 # Complex task1 >> task2 >> task4 task1 >> task3 >> task4 ``` ## Quick Start ```python # dags/example_dag.py from datetime import datetime, timedelta from airflow import DAG from airflow.operators.python import PythonOperator from airflow.operators.empty import EmptyOperator default_args = { 'owner': 'data-team', 'depends_on_past': False, 'email_on_failure': True, 'email_on_retry': False, 'retries': 3, 'retry_delay': timedelta(minutes=5), 'retry_exponential_backoff': True, 'max_retry_delay': timedelta(hours=1), } with DAG( dag_id='example_etl', default_args=default_args, description='Example ETL pipeline', schedule='0 6 * * *', # Daily at 6 AM start_date=datetime(2024, 1, 1), catchup=False, tags=['etl', 'example'], max_active_runs=1, ) as dag: start = EmptyOperator(task_id='start') def extract_data(**context): execution_date = context['ds'] # Extract logic here return {'records': 1000} extract = PythonOperator( task_id='extract', python_callable=extract_data, ) end = EmptyOperator(task_id='end') start >> extract >> end ``` ## Patterns ### Pattern 1: TaskFlow API (Airflow 2.0+) ```python # dags/taskflow_example.py from datetime import datetime from airflow.decorators import dag, task from airflow.models import Variable @dag( dag_id='taskflow_etl', schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False, tags=['etl', 'taskflow'], ) def taskflow_etl(): """ETL pipeline using TaskFlow API""" @task() def extract(source: str) -> dict: """Extract data from source""" import pandas as pd df = pd.read_csv(f's3://bucket/{source}/{{ ds }}.csv') return {'data': df.to_dict(), 'rows': len(df)} @task() def transform(extracted: dict) -> dict: """Transform extracted data""" import pandas as pd df = pd.DataFrame(extracted['data']) df['processed_at'] = datetime.now() df = df.dropna() return {'data': df.to_dict(), 'rows': len(df)} @task() def load(transformed: dict, target: str): """Load data to target""" import pandas as pd df = pd.DataFrame(transformed['data']) df.to_parquet(f's3://bucket/{target}/{{ ds }}.parquet') return transformed['rows'] @task() def notify(rows_loaded: int): """Send notification""" print(f'Loaded {rows_loaded} rows') # Define dependencies with XCom passing extracted = extract(source='raw_data') transformed = transform(extracted) loaded = load(transformed, target='processed_data') notify(loaded) # Instantiate the DAG taskflow_etl() ``` ### Pattern 2: Dynamic DAG Generation ```python # dags/dynamic_dag_factory.py from datetime import datetime, timedelta from airflow import DAG from airflow.operators.python import PythonOperator from airflow.models import Variable import json # Configuration for multiple similar pipelines PIPELINE_CONFIGS = [ {'name': 'customers', 'schedule': '@daily', 'source': 's3://raw/customers'}, {'name': 'orders', 'schedule': '@hourly', 'source': 's3://raw/orders'}, {'name': 'products', 'schedule': '@weekly', 'source': 's3://raw/products'}, ] def create_dag(config: dict) -> DAG: """Factory function to create DAGs from config""" dag_id = f"etl_{config['name']}" default_args = { 'owner': 'data-team', 'retries': 3, 'retry_delay': timedelta(minutes=5), } dag = DAG( dag_id=dag_id, default_args=default_args, schedule=config['schedule'], start_date=datetime(2024, 1, 1), catchup=False, tags=['etl', 'dynamic', config['name']], ) with dag: def extract_fn(source, **context): print(f"Extracting from {source} for {context['ds']}") def transform_fn(**context): print(f"Transforming data for {context['ds']}") def load_fn(table_name, **context): print(f"Loading to {table_name} for {context['ds']}") extract = PythonOperator( task_id='extract', python_callable=extract_fn, op_kwargs={'source': config['source']}, ) transform = PythonOperator( task_id='transform', python_callable=transform_fn, ) load = PythonOperator( task_id='load', python_callable=load_fn, op_kwargs={'table_name': config['name']}, ) extract >> transform >> load return dag # Generate DAGs for config in PIPELINE_CONFIGS: globals()[f"dag_{config['name']}"] = create_dag(config) ``` ### Pattern 3: Branching and Conditional Logic ```python # dags/branching_example.py from airflow.decorators import dag, task from airflow.operators.python import BranchPythonOperator from airflow.operators.empty import EmptyOperator from airflow.utils.trigger_rule import TriggerRule @dag( dag_id='branching_pipeline', schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False, ) def branching_pipeline(): @task() def check_data_quality() -> dict: """Check data quality and return metrics""" quality_score = 0.95 # Simulated return {'score': quality_score, 'rows': 10000} def choose_branch(**context) -> str: """Determine which branch to execute""" ti = context['ti'] metrics = ti.xcom_pull(task_ids='check_data_quality') if metrics['score'] >= 0.9: return 'high_quality_path' elif metrics['score'] >= 0.7: return 'medium_quality_path' else: return 'low_quality_path' quality_check = check_data_quality() branch = BranchPythonOperator( task_id='branch', python_callable=choose_branch, ) high_quality = EmptyOperator(task_id='high_quality_path') medium_quality = EmptyOperator(task_id='medium_quality_path') low_quality = EmptyOperator(task_id='low_quality_path') # Join point - runs after any branch completes join = EmptyOperator( task_id='join', trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS, ) quality_check >> branch >> [high_quality, medium_quality, low_quality] >> join branching_pipeline() ``` ### Pattern 4: Sensors and External Dependencies ```python # dags/sensor_patterns.py from datetime import datetime, timedelta from airflow import DAG from airflow.sensors.filesystem import FileSensor from airflow.providers.amazon.aws.sensors.s3 import S3KeySensor from airflow.sensors.external_task import ExternalTaskSensor from airflow.operators.python import PythonOperator with DAG( dag_id='sensor_example', schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False, ) as dag: # Wait for file on S3 wait_for_file = S3KeySensor( task_id='wait_for_s3_file', bucket_name='data-lake', bucket_key='raw/{{ ds }}/data.parquet', aws_conn_id='aws_default', timeout=60 * 60 * 2, # 2 hours poke_interval=60 * 5, # Check every 5 minutes mode='reschedule', # Free up worker slot while waiting ) # Wait for another DAG to complete wait_for_upstream = ExternalTaskSensor( task_id='wait_for_upstream_dag', external_dag_id='upstream_etl', external_task_id='final_task', execution_date_fn=lambda dt: dt, # Same execution date timeout=60 * 60 * 3, mode='reschedule', ) # Custom sensor using @task.sensor decorator @task.sensor(poke_interval=60, timeout=3600, mode='reschedule') def wait_for_api() -> PokeReturnValue: """Custom sensor for API availability""" import requests response = requests.get('https://api.example.com/health') is_done = response.status_code == 200 return PokeReturnValue(is_done=is_done, xcom_value=response.json()) api_ready = wait_for_api() def process_data(**context): api_result = context['ti'].xcom_pull(task_ids='wait_for_api') print(f"API returned: {api_result}") process = PythonOperator( task_id='process', python_callable=process_data, ) [wait_for_file, wait_for_upstream, api_ready] >> process ``` ### Pattern 5: Error Handling and Alerts ```python # dags/error_handling.py from datetime import datetime, timedelta from airflow import DAG from airflow.operators.python import PythonOperator from airflow.utils.trigger_rule import TriggerRule from airflow.models import Variable def task_failure_callback(context): """Callback on task failure""" task_instance = context['task_instance'] exception = context.get('exception') # Send to Slack/PagerDuty/etc message = f""" Task Failed! DAG: {task_instance.dag_id} Task: {task_instance.task_id} Execution Date: {context['ds']} Error: {exception} Log URL: {task_instance.log_url} """ # send_slack_alert(message) print(message) def dag_failure_callback(context): """Callback on DAG failure""" # Aggregate failures, send summary pass with DAG( dag_id='error_handling_example', schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False, on_failure_callback=dag_failure_callback, default_args={ 'on_failure_callback': task_failure_callback, 'retries': 3, 'retry_delay': timedelta(minutes=5), }, ) as dag: def might_fail(**context): import random if random.random() < 0.3: raise ValueError("Random failure!") return "Success" risky_task = PythonOperator( task_id='risky_task', python_callable=might_fail, ) def cleanup(**context): """Cleanup runs regardless of upstream failures""" print("Cleaning up...") cleanup_task = PythonOperator( task_id='cleanup', python_callable=cleanup, trigger_rule=TriggerRule.ALL_DONE, # Run even if upstream fails ) def notify_success(**context): """Only runs if all upstream succeeded""" print("All tasks succeeded!") success_notification = PythonOperator( task_id='notify_success', python_callable=notify_success, trigger_rule=TriggerRule.ALL_SUCCESS, ) risky_task >> [cleanup_task, success_notification] ``` ### Pattern 6: Testing DAGs ```python # tests/test_dags.py import pytest from datetime import datetime from airflow.models import DagBag @pytest.fixture def dagbag(): return DagBag(dag_folder='dags/', include_examples=False) def test_dag_loaded(dagbag): """Test that all DAGs load without errors""" assert len(dagbag.import_errors) == 0, f"DAG import errors: {dagbag.import_errors}" def test_dag_structure(dagbag): """Test specific DAG structure""" dag = dagbag.get_dag('example_etl') assert dag is not None assert len(dag.tasks) == 3 assert dag.schedule_interval == '0 6 * * *' def test_task_dependencies(dagbag): """Test task dependencies are correct""" dag = dagbag.get_dag('example_etl') extract_task = dag.get_task('extract') assert 'start' in [t.task_id for t in extract_task.upstream_list] assert 'end' in [t.task_id for t in extract_task.downstream_list] def test_dag_integrity(dagbag): """Test DAG has no cycles and is valid""" for dag_id, dag in dagbag.dags.items(): assert dag.test_cycle() is None, f"Cycle detected in {dag_id}" # Test individual task logic def test_extract_function(): """Unit test for extract function""" from dags.example_dag import extract_data result = extract_data(ds='2024-01-01') assert 'records' in result assert isinstance(result['records'], int) ``` ## Project Structure ``` airflow/ ā”œā”€ā”€ dags/ │ ā”œā”€ā”€ __init__.py │ ā”œā”€ā”€ common/ │ │ ā”œā”€ā”€ __init__.py │ │ ā”œā”€ā”€ operators.py # Custom operators │ │ ā”œā”€ā”€ sensors.py # Custom sensors │ │ └── callbacks.py # Alert callbacks │ ā”œā”€ā”€ etl/ │ │ ā”œā”€ā”€ customers.py │ │ └── orders.py │ └── ml/ │ └── training.py ā”œā”€ā”€ plugins/ │ └── custom_plugin.py ā”œā”€ā”€ tests/ │ ā”œā”€ā”€ __init__.py │ ā”œā”€ā”€ test_dags.py │ └── test_operators.py ā”œā”€ā”€ docker-compose.yml └── requirements.txt ``` ## Best Practices ### Do's - **Use TaskFlow API** - Cleaner code, automatic XCom - **Set timeouts** - Prevent zombie tasks - **Use `mode='reschedule'`** - For sensors, free up workers - **Test DAGs** - Unit tests and integration tests - **Idempotent tasks** - Safe to retry ### Don'ts - **Don't use `depends_on_past=True`** - Creates bottlenecks - **Don't hardcode dates** - Use `{{ ds }}` macros - **Don't use global state** - Tasks should be stateless - **Don't skip catchup blindly** - Understand implications - **Don't put heavy logic in DAG file** - Import from modules ## Resources - [Airflow Documentation](https://airflow.apache.org/docs/) - [Astronomer Guides](https://docs.astronomer.io/learn) - [TaskFlow API](https://airflow.apache.org/docs/apache-airflow/stable/tutorial/taskflow.html)
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