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nodejs-backend-patterns

Build production-ready Node.js backend services with

coding
⭐1
# Node.js Backend Patterns Comprehensive guidance for building scalable, maintainable, and production-ready Node.js backend applications with modern frameworks, architectural patterns, and best practices. ## When to Use This Skill - Building REST APIs or GraphQL servers - Creating microservices with Node.js - Implementing authentication and authorization - Designing scalable backend architectures - Setting up middleware and error handling - Integrating databases (SQL and NoSQL) - Building real-time applications with WebSockets - Implementing background job processing ## Core Frameworks ### Express.js - Minimalist Framework **Basic Setup:** ```typescript import express, { Request, Response, NextFunction } from "express"; import helmet from "helmet"; import cors from "cors"; import compression from "compression"; const app = express(); // Security middleware app.use(helmet()); app.use(cors({ origin: process.env.ALLOWED_ORIGINS?.split(",") })); app.use(compression()); // Body parsing app.use(express.json({ limit: "10mb" })); app.use(express.urlencoded({ extended: true, limit: "10mb" })); // Request logging app.use((req: Request, res: Response, next: NextFunction) => { console.log(`${req.method} ${req.path}`); next(); }); const PORT = process.env.PORT || 3000; app.listen(PORT, () => { console.log(`Server running on port ${PORT}`); }); ``` ### Fastify - High Performance Framework **Basic Setup:** ```typescript import Fastify from "fastify"; import helmet from "@fastify/helmet"; import cors from "@fastify/cors"; import compress from "@fastify/compress"; const fastify = Fastify({ logger: { level: process.env.LOG_LEVEL || "info", transport: { target: "pino-pretty", options: { colorize: true }, }, }, }); // Plugins await fastify.register(helmet); await fastify.register(cors, { origin: true }); await fastify.register(compress); // Type-safe routes with schema validation fastify.post<{ Body: { name: string; email: string }; Reply: { id: string; name: string }; }>( "/users", { schema: { body: { type: "object", required: ["name", "email"], properties: { name: { type: "string", minLength: 1 }, email: { type: "string", format: "email" }, }, }, }, }, async (request, reply) => { const { name, email } = request.body; return { id: "123", name }; }, ); await fastify.listen({ port: 3000, host: "0.0.0.0" }); ``` ## Architectural Patterns ### Pattern 1: Layered Architecture **Structure:** ``` src/ ā”œā”€ā”€ controllers/ # Handle HTTP requests/responses ā”œā”€ā”€ services/ # Business logic ā”œā”€ā”€ repositories/ # Data access layer ā”œā”€ā”€ models/ # Data models ā”œā”€ā”€ middleware/ # Express/Fastify middleware ā”œā”€ā”€ routes/ # Route definitions ā”œā”€ā”€ utils/ # Helper functions ā”œā”€ā”€ config/ # Configuration └── types/ # TypeScript types ``` **Controller Layer:** ```typescript // controllers/user.controller.ts import { Request, Response, NextFunction } from "express"; import { UserService } from "../services/user.service"; import { CreateUserDTO, UpdateUserDTO } from "../types/user.types"; export class UserController { constructor(private userService: UserService) {} async createUser(req: Request, res: Response, next: NextFunction) { try { const userData: CreateUserDTO = req.body; const user = await this.userService.createUser(userData); res.status(201).json(user); } catch (error) { next(error); } } async getUser(req: Request, res: Response, next: NextFunction) { try { const { id } = req.params; const user = await this.userService.getUserById(id); res.json(user); } catch (error) { next(error); } } async updateUser(req: Request, res: Response, next: NextFunction) { try { const { id } = req.params; const updates: UpdateUserDTO = req.body; const user = await this.userService.updateUser(id, updates); res.json(user); } catch (error) { next(error); } } async deleteUser(req: Request, res: Response, next: NextFunction) { try { const { id } = req.params; await this.userService.deleteUser(id); res.status(204).send(); } catch (error) { next(error); } } } ``` **Service Layer:** ```typescript // services/user.service.ts import { UserRepository } from "../repositories/user.repository"; import { CreateUserDTO, UpdateUserDTO, User } from "../types/user.types"; import { NotFoundError, ValidationError } from "../utils/errors"; import bcrypt from "bcrypt"; export class UserService { constructor(private userRepository: UserRepository) {} async createUser(userData: CreateUserDTO): Promise<User> { // Validation const existingUser = await this.userRepository.findByEmail(userData.email); if (existingUser) { throw new ValidationError("Email already exists"); } // Hash password const hashedPassword = await bcrypt.hash(userData.password, 10); // Create user const user = await this.userRepository.create({ ...userData, password: hashedPassword, }); // Remove password from response const { password, ...userWithoutPassword } = user; return userWithoutPassword as User; } async getUserById(id: string): Promise<User> { const user = await this.userRepository.findById(id); if (!user) { throw new NotFoundError("User not found"); } const { password, ...userWithoutPassword } = user; return userWithoutPassword as User; } async updateUser(id: string, updates: UpdateUserDTO): Promise<User> { const user = await this.userRepository.update(id, updates); if (!user) { throw new NotFoundError("User not found"); } const { password, ...userWithoutPassword } = user; return userWithoutPassword as User; } async deleteUser(id: string): Promise<void> { const deleted = await this.userRepository.delete(id); if (!deleted) { throw new NotFoundError("User not found"); } } } ``` **Repository Layer:** ```typescript // repositories/user.repository.ts import { Pool } from "pg"; import { CreateUserDTO, UpdateUserDTO, UserEntity } from "../types/user.types"; export class UserRepository { constructor(private db: Pool) {} async create( userData: CreateUserDTO & { password: string }, ): Promise<UserEntity> { const query = ` INSERT INTO users (name, email, password) VALUES ($1, $2, $3) RETURNING id, name, email, password, created_at, updated_at `; const { rows } = await this.db.query(query, [ userData.name, userData.email, userData.password, ]); return rows[0]; } async findById(id: string): Promise<UserEntity | null> { const query = "SELECT * FROM users WHERE id = $1"; const { rows } = await this.db.query(query, [id]); return rows[0] || null; } async findByEmail(email: string): Promise<UserEntity | null> { const query = "SELECT * FROM users WHERE email = $1"; const { rows } = await this.db.query(query, [email]); return rows[0] || null; } async update(id: string, updates: UpdateUserDTO): Promise<UserEntity | null> { const fields = Object.keys(updates); const values = Object.values(updates); const setClause = fields .map((field, idx) => `${field} = $${idx + 2}`) .join(", "); const query = ` UPDATE users SET ${setClause}, updated_at = CURRENT_TIMESTAMP WHERE id = $1 RETURNING * `; const { rows } = await this.db.query(query, [id, ...values]); return rows[0] || null; } async delete(id: string): Promise<boolean> { const query = "DELETE FROM users WHERE id = $1"; const { rowCount } = await this.db.query(query, [id]); return rowCount > 0; } } ``` ### Pattern 2: Dependency Injection **DI Container:** ```typescript // di-container.ts import { Pool } from "pg"; import { UserRepository } from "./repositories/user.repository"; import { UserService } from "./services/user.service"; import { UserController } from "./controllers/user.controller"; import { AuthService } from "./services/auth.service"; class Container { private instances = new Map<string, any>(); register<T>(key: string, factory: () => T): void { this.instances.set(key, factory); } resolve<T>(key: string): T { const factory = this.instances.get(key); if (!factory) { throw new Error(`No factory registered for ${key}`); } return factory(); } singleton<T>(key: string, factory: () => T): void { let instance: T; this.instances.set(key, () => { if (!instance) { instance = factory(); } return instance; }); } } export const container = new Container(); // Register dependencies container.singleton( "db", () => new Pool({ host: process.env.DB_HOST, port: parseInt(process.env.DB_PORT || "5432"), database: process.env.DB_NAME, user: process.env.DB_USER, password: process.env.DB_PASSWORD, max: 20, idleTimeoutMillis: 30000, connectionTimeoutMillis: 2000, }), ); container.singleton( "userRepository", () => new UserRepository(container.resolve("db")), ); container.singleton( "userService", () => new UserService(container.resolve("userRepository")), ); container.register( "userController", () => new UserController(container.resolve("userService")), ); container.singleton( "authService", () => new AuthService(container.resolve("userRepository")), ); ``` ## Middleware Patterns ### Authentication Middleware ```typescript // middleware/auth.middleware.ts import { Request, Response, NextFunction } from "express"; import jwt from "jsonwebtoken"; import { UnauthorizedError } from "../utils/errors"; interface JWTPayload { userId: string; email: string; } declare global { namespace Express { interface Request { user?: JWTPayload; } } } export const authenticate = async ( req: Request, res: Response, next: NextFunction, ) => { try { const token = req.headers.authorization?.replace("Bearer ", ""); if (!token) { throw new UnauthorizedError("No token provided"); } const payload = jwt.verify(token, process.env.JWT_SECRET!) as JWTPayload; req.user = payload; next(); } catch (error) { next(new UnauthorizedError("Invalid token")); } }; export const authorize = (...roles: string[]) => { return async (req: Request, res: Response, next: NextFunction) => { if (!req.user) { return next(new UnauthorizedError("Not authenticated")); } // Check if user has required role const hasRole = roles.some((role) => req.user?.roles?.includes(role)); if (!hasRole) { return next(new UnauthorizedError("Insufficient permissions")); } next(); }; }; ``` ### Validation Middleware ```typescript // middleware/validation.middleware.ts import { Request, Response, NextFunction } from "express"; import { AnyZodObject, ZodError } from "zod"; import { ValidationError } from "../utils/errors"; export const validate = (schema: AnyZodObject) => { return async (req: Request, res: Response, next: NextFunction) => { try { await schema.parseAsync({ body: req.body, query: req.query, params: req.params, }); next(); } catch (error) { if (error instanceof ZodError) { const errors = error.errors.map((err) => ({ field: err.path.join("."), message: err.message, })); next(new ValidationError("Validation failed", errors)); } else { next(error); } } }; }; // Usage with Zod import { z } from "zod"; const createUserSchema = z.object({ body: z.object({ name: z.string().min(1), email: z.string().email(), password: z.string().min(8), }), }); router.post("/users", validate(createUserSchema), userController.createUser); ``` ### Rate Limiting Middleware ```typescript // middleware/rate-limit.middleware.ts import rateLimit from "express-rate-limit"; import RedisStore from "rate-limit-redis"; import Redis from "ioredis"; const redis = new Redis({ host: process.env.REDIS_HOST, port: parseInt(process.env.REDIS_PORT || "6379"), }); export const apiLimiter = rateLimit({ store: new RedisStore({ client: redis, prefix: "rl:", }), windowMs: 15 * 60 * 1000, // 15 minutes max: 100, // Limit each IP to 100 requests per windowMs message: "Too many requests from this IP, please try again later", standardHeaders: true, legacyHeaders: false, }); export const authLimiter = rateLimit({ store: new RedisStore({ client: redis, prefix: "rl:auth:", }), windowMs: 15 * 60 * 1000, max: 5, // Stricter limit for auth endpoints skipSuccessfulRequests: true, }); ``` ### Request Logging Middleware ```typescript // middleware/logger.middleware.ts import { Request, Response, NextFunction } from "express"; import pino from "pino"; const logger = pino({ level: process.env.LOG_LEVEL || "info", transport: { target: "pino-pretty", options: { colorize: true }, }, }); export const requestLogger = ( req: Request, res: Response, next: NextFunction, ) => { const start = Date.now(); // Log response when finished res.on("finish", () => { const duration = Date.now() - start; logger.info({ method: req.method, url: req.url, status: res.statusCode, duration: `${duration}ms`, userAgent: req.headers["user-agent"], ip: req.ip, }); }); next(); }; export { logger }; ``` ## Error Handling ### Custom Error Classes ```typescript // utils/errors.ts export class AppError extends Error { constructor( public message: string, public statusCode: number = 500, public isOperational: boolean = true, ) { super(message); Object.setPrototypeOf(this, AppError.prototype); Error.captureStackTrace(this, this.constructor); } } export class ValidationError extends AppError { constructor( message: string, public errors?: any[], ) { super(message, 400); } } export class NotFoundError extends AppError { constructor(message: string = "Resource not found") { super(message, 404); } } export class UnauthorizedError extends AppError { constructor(message: string = "Unauthorized") { super(message, 401); } } export class ForbiddenError extends AppError { constructor(message: string = "Forbidden") { super(message, 403); } } export class ConflictError extends AppError { constructor(message: string) { super(message, 409); } } ``` ### Global Error Handler ```typescript // middleware/error-handler.ts import { Request, Response, NextFunction } from "express"; import { AppError } from "../utils/errors"; import { logger } from "./logger.middleware"; export const errorHandler = ( err: Error, req: Request, res: Response, next: NextFunction, ) => { if (err instanceof AppError) { return res.status(err.statusCode).json({ status: "error", message: err.message, ...(err instanceof ValidationError && { errors: err.errors }), }); } // Log unexpected errors logger.error({ error: err.message, stack: err.stack, url: req.url, method: req.method, }); // Don't leak error details in production const message = process.env.NODE_ENV === "production" ? "Internal server error" : err.message; res.status(500).json({ status: "error", message, }); }; // Async error wrapper export const asyncHandler = ( fn: (req: Request, res: Response, next: NextFunction) => Promise<any>, ) => { return (req: Request, res: Response, next: NextFunction) => { Promise.resolve(fn(req, res, next)).catch(next); }; }; ``` ## Database Patterns ### PostgreSQL with Connection Pool ```typescript // config/database.ts import { Pool, PoolConfig } from "pg"; const poolConfig: PoolConfig = { host: process.env.DB_HOST, port: parseInt(process.env.DB_PORT || "5432"), database: process.env.DB_NAME, user: process.env.DB_USER, password: process.env.DB_PASSWORD, max: 20, idleTimeoutMillis: 30000, connectionTimeoutMillis: 2000, }; export const pool = new Pool(poolConfig); // Test connection pool.on("connect", () => { console.log("Database connected"); }); pool.on("error", (err) => { console.error("Unexpected database error", err); process.exit(-1); }); // Graceful shutdown export const closeDatabase = async () => { await pool.end(); console.log("Database connection closed"); }; ``` ### MongoDB with Mongoose ```typescript // config/mongoose.ts import mongoose from "mongoose"; const connectDB = async () => { try { await mongoose.connect(process.env.MONGODB_URI!, { maxPoolSize: 10, serverSelectionTimeoutMS: 5000, socketTimeoutMS: 45000, }); console.log("MongoDB connected"); } catch (error) { console.error("MongoDB connection error:", error); process.exit(1); } }; mongoose.connection.on("disconnected", () => { console.log("MongoDB disconnected"); }); mongoose.connection.on("error", (err) => { console.error("MongoDB error:", err); }); export { connectDB }; // Model example import { Schema, model, Document } from "mongoose"; interface IUser extends Document { name: string; email: string; password: string; createdAt: Date; updatedAt: Date; } const userSchema = new Schema<IUser>( { name: { type: String, required: true }, email: { type: String, required: true, unique: true }, password: { type: String, required: true }, }, { timestamps: true, }, ); // Indexes userSchema.index({ email: 1 }); export const User = model<IUser>("User", userSchema); ``` ### Transaction Pattern ```typescript // services/order.service.ts import { Pool } from "pg"; export class OrderService { constructor(private db: Pool) {} async createOrder(userId: string, items: any[]) { const client = await this.db.connect(); try { await client.query("BEGIN"); // Create order const orderResult = await client.query( "INSERT INTO orders (user_id, total) VALUES ($1, $2) RETURNING id", [userId, calculateTotal(items)], ); const orderId = orderResult.rows[0].id; // Create order items for (const item of items) { await client.query( "INSERT INTO order_items (order_id, product_id, quantity, price) VALUES ($1, $2, $3, $4)", [orderId, item.productId, item.quantity, item.price], ); // Update inventory await client.query( "UPDATE products SET stock = stock - $1 WHERE id = $2", [item.quantity, item.productId], ); } await client.query("COMMIT"); return orderId; } catch (error) { await client.query("ROLLBACK"); throw error; } finally { client.release(); } } } ``` ## Authentication & Authorization ### JWT Authentication ```typescript // services/auth.service.ts import jwt from
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šŸ¤–system prompt•7 months ago

gitops-workflow

Implement GitOps workflows with ArgoCD and Flux for automated,

architecture
⭐1
# GitOps Workflow Complete guide to implementing GitOps workflows with ArgoCD and Flux for automated Kubernetes deployments. ## Purpose Implement declarative, Git-based continuous delivery for Kubernetes using ArgoCD or Flux CD, following OpenGitOps principles. ## When to Use This Skill - Set up GitOps for Kubernetes clusters - Automate application deployments from Git - Implement progressive delivery strategies - Manage multi-cluster deployments - Configure automated sync policies - Set up secret management in GitOps ## OpenGitOps Principles 1. **Declarative** - Entire system described declaratively 2. **Versioned and Immutable** - Desired state stored in Git 3. **Pulled Automatically** - Software agents pull desired state 4. **Continuously Reconciled** - Agents reconcile actual vs desired state ## ArgoCD Setup ### 1. Installation ```bash # Create namespace kubectl create namespace argocd # Install ArgoCD kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml # Get admin password kubectl -n argocd get secret argocd-initial-admin-secret -o jsonpath="{.data.password}" | base64 -d ``` **Reference:** See `references/argocd-setup.md` for detailed setup ### 2. Repository Structure ``` gitops-repo/ ā”œā”€ā”€ apps/ │ ā”œā”€ā”€ production/ │ │ ā”œā”€ā”€ app1/ │ │ │ ā”œā”€ā”€ kustomization.yaml │ │ │ └── deployment.yaml │ │ └── app2/ │ └── staging/ ā”œā”€ā”€ infrastructure/ │ ā”œā”€ā”€ ingress-nginx/ │ ā”œā”€ā”€ cert-manager/ │ └── monitoring/ └── argocd/ ā”œā”€ā”€ applications/ └── projects/ ``` ### 3. Create Application ```yaml # argocd/applications/my-app.yaml apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: my-app namespace: argocd spec: project: default source: repoURL: https://github.com/org/gitops-repo targetRevision: main path: apps/production/my-app destination: server: https://kubernetes.default.svc namespace: production syncPolicy: automated: prune: true selfHeal: true syncOptions: - CreateNamespace=true ``` ### 4. App of Apps Pattern ```yaml apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: applications namespace: argocd spec: project: default source: repoURL: https://github.com/org/gitops-repo targetRevision: main path: argocd/applications destination: server: https://kubernetes.default.svc namespace: argocd syncPolicy: automated: {} ``` ## Flux CD Setup ### 1. Installation ```bash # Install Flux CLI curl -s https://fluxcd.io/install.sh | sudo bash # Bootstrap Flux flux bootstrap github \ --owner=org \ --repository=gitops-repo \ --branch=main \ --path=clusters/production \ --personal ``` ### 2. Create GitRepository ```yaml apiVersion: source.toolkit.fluxcd.io/v1 kind: GitRepository metadata: name: my-app namespace: flux-system spec: interval: 1m url: https://github.com/org/my-app ref: branch: main ``` ### 3. Create Kustomization ```yaml apiVersion: kustomize.toolkit.fluxcd.io/v1 kind: Kustomization metadata: name: my-app namespace: flux-system spec: interval: 5m path: ./deploy prune: true sourceRef: kind: GitRepository name: my-app ``` ## Sync Policies ### Auto-Sync Configuration **ArgoCD:** ```yaml syncPolicy: automated: prune: true # Delete resources not in Git selfHeal: true # Reconcile manual changes allowEmpty: false retry: limit: 5 backoff: duration: 5s factor: 2 maxDuration: 3m ``` **Flux:** ```yaml spec: interval: 1m prune: true wait: true timeout: 5m ``` **Reference:** See `references/sync-policies.md` ## Progressive Delivery ### Canary Deployment with ArgoCD Rollouts ```yaml apiVersion: argoproj.io/v1alpha1 kind: Rollout metadata: name: my-app spec: replicas: 5 strategy: canary: steps: - setWeight: 20 - pause: { duration: 1m } - setWeight: 50 - pause: { duration: 2m } - setWeight: 100 ``` ### Blue-Green Deployment ```yaml strategy: blueGreen: activeService: my-app previewService: my-app-preview autoPromotionEnabled: false ``` ## Secret Management ### External Secrets Operator ```yaml apiVersion: external-secrets.io/v1beta1 kind: ExternalSecret metadata: name: db-credentials spec: refreshInterval: 1h secretStoreRef: name: aws-secrets-manager kind: SecretStore target: name: db-credentials data: - secretKey: password remoteRef: key: prod/db/password ``` ### Sealed Secrets ```bash # Encrypt secret kubeseal --format yaml < secret.yaml > sealed-secret.yaml # Commit sealed-secret.yaml to Git ``` ## Best Practices 1. **Use separate repos or branches** for different environments 2. **Implement RBAC** for Git repositories 3. **Enable notifications** for sync failures 4. **Use health checks** for custom resources 5. **Implement approval gates** for production 6. **Keep secrets out of Git** (use External Secrets) 7. **Use App of Apps pattern** for organization 8. **Tag releases** for easy rollback 9. **Monitor sync status** with alerts 10. **Test changes** in staging first ## Troubleshooting **Sync failures:** ```bash argocd app get my-app argocd app sync my-app --prune ``` **Out of sync status:** ```bash argocd app diff my-app argocd app sync my-app --force ``` ## Related Skills - `k8s-manifest-generator` - For creating manifests - `helm-chart-scaffolding` - For packaging applications
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šŸ¤–system prompt•7 months ago

helm-chart-scaffolding

Design, organize, and manage Helm charts for templating and

architecture
⭐1
# Helm Chart Scaffolding Comprehensive guidance for creating, organizing, and managing Helm charts for packaging and deploying Kubernetes applications. ## Purpose This skill provides step-by-step instructions for building production-ready Helm charts, including chart structure, templating patterns, values management, and validation strategies. ## When to Use This Skill Use this skill when you need to: - Create new Helm charts from scratch - Package Kubernetes applications for distribution - Manage multi-environment deployments with Helm - Implement templating for reusable Kubernetes manifests - Set up Helm chart repositories - Follow Helm best practices and conventions ## Helm Overview **Helm** is the package manager for Kubernetes that: - Templates Kubernetes manifests for reusability - Manages application releases and rollbacks - Handles dependencies between charts - Provides version control for deployments - Simplifies configuration management across environments ## Step-by-Step Workflow ### 1. Initialize Chart Structure **Create new chart:** ```bash helm create my-app ``` **Standard chart structure:** ``` my-app/ ā”œā”€ā”€ Chart.yaml # Chart metadata ā”œā”€ā”€ values.yaml # Default configuration values ā”œā”€ā”€ charts/ # Chart dependencies ā”œā”€ā”€ templates/ # Kubernetes manifest templates │ ā”œā”€ā”€ NOTES.txt # Post-install notes │ ā”œā”€ā”€ _helpers.tpl # Template helpers │ ā”œā”€ā”€ deployment.yaml │ ā”œā”€ā”€ service.yaml │ ā”œā”€ā”€ ingress.yaml │ ā”œā”€ā”€ serviceaccount.yaml │ ā”œā”€ā”€ hpa.yaml │ └── tests/ │ └── test-connection.yaml └── .helmignore # Files to ignore ``` ### 2. Configure Chart.yaml **Chart metadata defines the package:** ```yaml apiVersion: v2 name: my-app description: A Helm chart for My Application type: application version: 1.0.0 # Chart version appVersion: "2.1.0" # Application version # Keywords for chart discovery keywords: - web - api - backend # Maintainer information maintainers: - name: DevOps Team email: devops@example.com url: https://github.com/example/my-app # Source code repository sources: - https://github.com/example/my-app # Homepage home: https://example.com # Chart icon icon: https://example.com/icon.png # Dependencies dependencies: - name: postgresql version: "12.0.0" repository: "https://charts.bitnami.com/bitnami" condition: postgresql.enabled - name: redis version: "17.0.0" repository: "https://charts.bitnami.com/bitnami" condition: redis.enabled ``` **Reference:** See `assets/Chart.yaml.template` for complete example ### 3. Design values.yaml Structure **Organize values hierarchically:** ```yaml # Image configuration image: repository: myapp tag: "1.0.0" pullPolicy: IfNotPresent # Number of replicas replicaCount: 3 # Service configuration service: type: ClusterIP port: 80 targetPort: 8080 # Ingress configuration ingress: enabled: false className: nginx hosts: - host: app.example.com paths: - path: / pathType: Prefix # Resources resources: requests: memory: "256Mi" cpu: "250m" limits: memory: "512Mi" cpu: "500m" # Autoscaling autoscaling: enabled: false minReplicas: 2 maxReplicas: 10 targetCPUUtilizationPercentage: 80 # Environment variables env: - name: LOG_LEVEL value: "info" # ConfigMap data configMap: data: APP_MODE: production # Dependencies postgresql: enabled: true auth: database: myapp username: myapp redis: enabled: false ``` **Reference:** See `assets/values.yaml.template` for complete structure ### 4. Create Template Files **Use Go templating with Helm functions:** **templates/deployment.yaml:** ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: {{ include "my-app.fullname" . }} labels: {{- include "my-app.labels" . | nindent 4 }} spec: {{- if not .Values.autoscaling.enabled }} replicas: {{ .Values.replicaCount }} {{- end }} selector: matchLabels: {{- include "my-app.selectorLabels" . | nindent 6 }} template: metadata: labels: {{- include "my-app.selectorLabels" . | nindent 8 }} spec: containers: - name: {{ .Chart.Name }} image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default .Chart.AppVersion }}" imagePullPolicy: {{ .Values.image.pullPolicy }} ports: - name: http containerPort: {{ .Values.service.targetPort }} resources: {{- toYaml .Values.resources | nindent 12 }} env: {{- toYaml .Values.env | nindent 12 }} ``` ### 5. Create Template Helpers **templates/\_helpers.tpl:** ```yaml {{/* Expand the name of the chart. */}} {{- define "my-app.name" -}} {{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" }} {{- end }} {{/* Create a default fully qualified app name. */}} {{- define "my-app.fullname" -}} {{- if .Values.fullnameOverride }} {{- .Values.fullnameOverride | trunc 63 | trimSuffix "-" }} {{- else }} {{- $name := default .Chart.Name .Values.nameOverride }} {{- if contains $name .Release.Name }} {{- .Release.Name | trunc 63 | trimSuffix "-" }} {{- else }} {{- printf "%s-%s" .Release.Name $name | trunc 63 | trimSuffix "-" }} {{- end }} {{- end }} {{- end }} {{/* Common labels */}} {{- define "my-app.labels" -}} helm.sh/chart: {{ include "my-app.chart" . }} {{ include "my-app.selectorLabels" . }} {{- if .Chart.AppVersion }} app.kubernetes.io/version: {{ .Chart.AppVersion | quote }} {{- end }} app.kubernetes.io/managed-by: {{ .Release.Service }} {{- end }} {{/* Selector labels */}} {{- define "my-app.selectorLabels" -}} app.kubernetes.io/name: {{ include "my-app.name" . }} app.kubernetes.io/instance: {{ .Release.Name }} {{- end }} ``` ### 6. Manage Dependencies **Add dependencies in Chart.yaml:** ```yaml dependencies: - name: postgresql version: "12.0.0" repository: "https://charts.bitnami.com/bitnami" condition: postgresql.enabled ``` **Update dependencies:** ```bash helm dependency update helm dependency build ``` **Override dependency values:** ```yaml # values.yaml postgresql: enabled: true auth: database: myapp username: myapp password: changeme primary: persistence: enabled: true size: 10Gi ``` ### 7. Test and Validate **Validation commands:** ```bash # Lint the chart helm lint my-app/ # Dry-run installation helm install my-app ./my-app --dry-run --debug # Template rendering helm template my-app ./my-app # Template with values helm template my-app ./my-app -f values-prod.yaml # Show computed values helm show values ./my-app ``` **Validation script:** ```bash #!/bin/bash set -e echo "Linting chart..." helm lint . echo "Testing template rendering..." helm template test-release . --dry-run echo "Checking for required values..." helm template test-release . --validate echo "All validations passed!" ``` **Reference:** See `scripts/validate-chart.sh` ### 8. Package and Distribute **Package the chart:** ```bash helm package my-app/ # Creates: my-app-1.0.0.tgz ``` **Create chart repository:** ```bash # Create index helm repo index . # Upload to repository # AWS S3 example aws s3 sync . s3://my-helm-charts/ --exclude "*" --include "*.tgz" --include "index.yaml" ``` **Use the chart:** ```bash helm repo add my-repo https://charts.example.com helm repo update helm install my-app my-repo/my-app ``` ### 9. Multi-Environment Configuration **Environment-specific values files:** ``` my-app/ ā”œā”€ā”€ values.yaml # Defaults ā”œā”€ā”€ values-dev.yaml # Development ā”œā”€ā”€ values-staging.yaml # Staging └── values-prod.yaml # Production ``` **values-prod.yaml:** ```yaml replicaCount: 5 image: tag: "2.1.0" resources: requests: memory: "512Mi" cpu: "500m" limits: memory: "1Gi" cpu: "1000m" autoscaling: enabled: true minReplicas: 3 maxReplicas: 20 ingress: enabled: true hosts: - host: app.example.com paths: - path: / pathType: Prefix postgresql: enabled: true primary: persistence: size: 100Gi ``` **Install with environment:** ```bash helm install my-app ./my-app -f values-prod.yaml --namespace production ``` ### 10. Implement Hooks and Tests **Pre-install hook:** ```yaml # templates/pre-install-job.yaml apiVersion: batch/v1 kind: Job metadata: name: {{ include "my-app.fullname" . }}-db-setup annotations: "helm.sh/hook": pre-install "helm.sh/hook-weight": "-5" "helm.sh/hook-delete-policy": hook-succeeded spec: template: spec: containers: - name: db-setup image: postgres:15 command: ["psql", "-c", "CREATE DATABASE myapp"] restartPolicy: Never ``` **Test connection:** ```yaml # templates/tests/test-connection.yaml apiVersion: v1 kind: Pod metadata: name: "{{ include "my-app.fullname" . }}-test-connection" annotations: "helm.sh/hook": test spec: containers: - name: wget image: busybox command: ['wget'] args: ['{{ include "my-app.fullname" . }}:{{ .Values.service.port }}'] restartPolicy: Never ``` **Run tests:** ```bash helm test my-app ``` ## Common Patterns ### Pattern 1: Conditional Resources ```yaml {{- if .Values.ingress.enabled }} apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: {{ include "my-app.fullname" . }} spec: # ... {{- end }} ``` ### Pattern 2: Iterating Over Lists ```yaml env: {{- range .Values.env }} - name: {{ .name }} value: {{ .value | quote }} {{- end }} ``` ### Pattern 3: Including Files ```yaml data: config.yaml: | {{- .Files.Get "config/application.yaml" | nindent 4 }} ``` ### Pattern 4: Global Values ```yaml global: imageRegistry: docker.io imagePullSecrets: - name: regcred # Use in templates: image: {{ .Values.global.imageRegistry }}/{{ .Values.image.repository }} ``` ## Best Practices 1. **Use semantic versioning** for chart and app versions 2. **Document all values** in values.yaml with comments 3. **Use template helpers** for repeated logic 4. **Validate charts** before packaging 5. **Pin dependency versions** explicitly 6. **Use conditions** for optional resources 7. **Follow naming conventions** (lowercase, hyphens) 8. **Include NOTES.txt** with usage instructions 9. **Add labels** consistently using helpers 10. **Test installations** in all environments ## Troubleshooting **Template rendering errors:** ```bash helm template my-app ./my-app --debug ``` **Dependency issues:** ```bash helm dependency update helm dependency list ``` **Installation failures:** ```bash helm install my-app ./my-app --dry-run --debug kubectl get events --sort-by='.lastTimestamp' ``` ## Reference Files - `assets/Chart.yaml.template` - Chart metadata template - `assets/values.yaml.template` - Values structure template - `scripts/validate-chart.sh` - Validation script - `references/chart-structure.md` - Detailed chart organization ## Related Skills - `k8s-manifest-generator` - For creating base Kubernetes manifests - `gitops-workflow` - For automated Helm chart deployments
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

k8s-manifest-generator

Create production-ready Kubernetes manifests for Deployments,

architecture
⭐1
# Kubernetes Manifest Generator Step-by-step guidance for creating production-ready Kubernetes manifests including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. ## Purpose This skill provides comprehensive guidance for generating well-structured, secure, and production-ready Kubernetes manifests following cloud-native best practices and Kubernetes conventions. ## When to Use This Skill Use this skill when you need to: - Create new Kubernetes Deployment manifests - Define Service resources for network connectivity - Generate ConfigMap and Secret resources for configuration management - Create PersistentVolumeClaim manifests for stateful workloads - Follow Kubernetes best practices and naming conventions - Implement resource limits, health checks, and security contexts - Design manifests for multi-environment deployments ## Step-by-Step Workflow ### 1. Gather Requirements **Understand the workload:** - Application type (stateless/stateful) - Container image and version - Environment variables and configuration needs - Storage requirements - Network exposure requirements (internal/external) - Resource requirements (CPU, memory) - Scaling requirements - Health check endpoints **Questions to ask:** - What is the application name and purpose? - What container image and tag will be used? - Does the application need persistent storage? - What ports does the application expose? - Are there any secrets or configuration files needed? - What are the CPU and memory requirements? - Does the application need to be exposed externally? ### 2. Create Deployment Manifest **Follow this structure:** ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: <app-name> namespace: <namespace> labels: app: <app-name> version: <version> spec: replicas: 3 selector: matchLabels: app: <app-name> template: metadata: labels: app: <app-name> version: <version> spec: containers: - name: <container-name> image: <image>:<tag> ports: - containerPort: <port> name: http resources: requests: memory: "256Mi" cpu: "250m" limits: memory: "512Mi" cpu: "500m" livenessProbe: httpGet: path: /health port: http initialDelaySeconds: 30 periodSeconds: 10 readinessProbe: httpGet: path: /ready port: http initialDelaySeconds: 5 periodSeconds: 5 env: - name: ENV_VAR value: "value" envFrom: - configMapRef: name: <app-name>-config - secretRef: name: <app-name>-secret ``` **Best practices to apply:** - Always set resource requests and limits - Implement both liveness and readiness probes - Use specific image tags (never `:latest`) - Apply security context for non-root users - Use labels for organization and selection - Set appropriate replica count based on availability needs **Reference:** See `references/deployment-spec.md` for detailed deployment options ### 3. Create Service Manifest **Choose the appropriate Service type:** **ClusterIP (internal only):** ```yaml apiVersion: v1 kind: Service metadata: name: <app-name> namespace: <namespace> labels: app: <app-name> spec: type: ClusterIP selector: app: <app-name> ports: - name: http port: 80 targetPort: 8080 protocol: TCP ``` **LoadBalancer (external access):** ```yaml apiVersion: v1 kind: Service metadata: name: <app-name> namespace: <namespace> labels: app: <app-name> annotations: service.beta.kubernetes.io/aws-load-balancer-type: nlb spec: type: LoadBalancer selector: app: <app-name> ports: - name: http port: 80 targetPort: 8080 protocol: TCP ``` **Reference:** See `references/service-spec.md` for service types and networking ### 4. Create ConfigMap **For application configuration:** ```yaml apiVersion: v1 kind: ConfigMap metadata: name: <app-name>-config namespace: <namespace> data: APP_MODE: production LOG_LEVEL: info DATABASE_HOST: db.example.com # For config files app.properties: | server.port=8080 server.host=0.0.0.0 logging.level=INFO ``` **Best practices:** - Use ConfigMaps for non-sensitive data only - Organize related configuration together - Use meaningful names for keys - Consider using one ConfigMap per component - Version ConfigMaps when making changes **Reference:** See `assets/configmap-template.yaml` for examples ### 5. Create Secret **For sensitive data:** ```yaml apiVersion: v1 kind: Secret metadata: name: <app-name>-secret namespace: <namespace> type: Opaque stringData: DATABASE_PASSWORD: "changeme" API_KEY: "secret-api-key" # For certificate files tls.crt: | -----BEGIN CERTIFICATE----- ... -----END CERTIFICATE----- tls.key: | -----BEGIN PRIVATE KEY----- ... -----END PRIVATE KEY----- ``` **Security considerations:** - Never commit secrets to Git in plain text - Use Sealed Secrets, External Secrets Operator, or Vault - Rotate secrets regularly - Use RBAC to limit secret access - Consider using Secret type: `kubernetes.io/tls` for TLS secrets ### 6. Create PersistentVolumeClaim (if needed) **For stateful applications:** ```yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: <app-name>-data namespace: <namespace> spec: accessModes: - ReadWriteOnce storageClassName: gp3 resources: requests: storage: 10Gi ``` **Mount in Deployment:** ```yaml spec: template: spec: containers: - name: app volumeMounts: - name: data mountPath: /var/lib/app volumes: - name: data persistentVolumeClaim: claimName: <app-name>-data ``` **Storage considerations:** - Choose appropriate StorageClass for performance needs - Use ReadWriteOnce for single-pod access - Use ReadWriteMany for multi-pod shared storage - Consider backup strategies - Set appropriate retention policies ### 7. Apply Security Best Practices **Add security context to Deployment:** ```yaml spec: template: spec: securityContext: runAsNonRoot: true runAsUser: 1000 fsGroup: 1000 seccompProfile: type: RuntimeDefault containers: - name: app securityContext: allowPrivilegeEscalation: false readOnlyRootFilesystem: true capabilities: drop: - ALL ``` **Security checklist:** - [ ] Run as non-root user - [ ] Drop all capabilities - [ ] Use read-only root filesystem - [ ] Disable privilege escalation - [ ] Set seccomp profile - [ ] Use Pod Security Standards ### 8. Add Labels and Annotations **Standard labels (recommended):** ```yaml metadata: labels: app.kubernetes.io/name: <app-name> app.kubernetes.io/instance: <instance-name> app.kubernetes.io/version: "1.0.0" app.kubernetes.io/component: backend app.kubernetes.io/part-of: <system-name> app.kubernetes.io/managed-by: kubectl ``` **Useful annotations:** ```yaml metadata: annotations: description: "Application description" contact: "team@example.com" prometheus.io/scrape: "true" prometheus.io/port: "9090" prometheus.io/path: "/metrics" ``` ### 9. Organize Multi-Resource Manifests **File organization options:** **Option 1: Single file with `---` separator** ```yaml # app-name.yaml --- apiVersion: v1 kind: ConfigMap ... --- apiVersion: v1 kind: Secret ... --- apiVersion: apps/v1 kind: Deployment ... --- apiVersion: v1 kind: Service ... ``` **Option 2: Separate files** ``` manifests/ ā”œā”€ā”€ configmap.yaml ā”œā”€ā”€ secret.yaml ā”œā”€ā”€ deployment.yaml ā”œā”€ā”€ service.yaml └── pvc.yaml ``` **Option 3: Kustomize structure** ``` base/ ā”œā”€ā”€ kustomization.yaml ā”œā”€ā”€ deployment.yaml ā”œā”€ā”€ service.yaml └── configmap.yaml overlays/ ā”œā”€ā”€ dev/ │ └── kustomization.yaml └── prod/ └── kustomization.yaml ``` ### 10. Validate and Test **Validation steps:** ```bash # Dry-run validation kubectl apply -f manifest.yaml --dry-run=client # Server-side validation kubectl apply -f manifest.yaml --dry-run=server # Validate with kubeval kubeval manifest.yaml # Validate with kube-score kube-score score manifest.yaml # Check with kube-linter kube-linter lint manifest.yaml ``` **Testing checklist:** - [ ] Manifest passes dry-run validation - [ ] All required fields are present - [ ] Resource limits are reasonable - [ ] Health checks are configured - [ ] Security context is set - [ ] Labels follow conventions - [ ] Namespace exists or is created ## Common Patterns ### Pattern 1: Simple Stateless Web Application **Use case:** Standard web API or microservice **Components needed:** - Deployment (3 replicas for HA) - ClusterIP Service - ConfigMap for configuration - Secret for API keys - HorizontalPodAutoscaler (optional) **Reference:** See `assets/deployment-template.yaml` ### Pattern 2: Stateful Database Application **Use case:** Database or persistent storage application **Components needed:** - StatefulSet (not Deployment) - Headless Service - PersistentVolumeClaim template - ConfigMap for DB configuration - Secret for credentials ### Pattern 3: Background Job or Cron **Use case:** Scheduled tasks or batch processing **Components needed:** - CronJob or Job - ConfigMap for job parameters - Secret for credentials - ServiceAccount with RBAC ### Pattern 4: Multi-Container Pod **Use case:** Application with sidecar containers **Components needed:** - Deployment with multiple containers - Shared volumes between containers - Init containers for setup - Service (if needed) ## Templates The following templates are available in the `assets/` directory: - `deployment-template.yaml` - Standard deployment with best practices - `service-template.yaml` - Service configurations (ClusterIP, LoadBalancer, NodePort) - `configmap-template.yaml` - ConfigMap examples with different data types - `secret-template.yaml` - Secret examples (to be generated, not committed) - `pvc-template.yaml` - PersistentVolumeClaim templates ## Reference Documentation - `references/deployment-spec.md` - Detailed Deployment specification - `references/service-spec.md` - Service types and networking details ## Best Practices Summary 1. **Always set resource requests and limits** - Prevents resource starvation 2. **Implement health checks** - Ensures Kubernetes can manage your application 3. **Use specific image tags** - Avoid unpredictable deployments 4. **Apply security contexts** - Run as non-root, drop capabilities 5. **Use ConfigMaps and Secrets** - Separate config from code 6. **Label everything** - Enables filtering and organization 7. **Follow naming conventions** - Use standard Kubernetes labels 8. **Validate before applying** - Use dry-run and validation tools 9. **Version your manifests** - Keep in Git with version control 10. **Document with annotations** - Add context for other developers ## Troubleshooting **Pods not starting:** - Check image pull errors: `kubectl describe pod <pod-name>` - Verify resource availability: `kubectl get nodes` - Check events: `kubectl get events --sort-by='.lastTimestamp'` **Service not accessible:** - Verify selector matches pod labels: `kubectl get endpoints <service-name>` - Check service type and port configuration - Test from within cluster: `kubectl run debug --rm -it --image=busybox -- sh` **ConfigMap/Secret not loading:** - Verify names match in Deployment - Check namespace - Ensure resources exist: `kubectl get configmap,secret` ## Next Steps After creating manifests: 1. Store in Git repository 2. Set up CI/CD pipeline for deployment 3. Consider using Helm or Kustomize for templating 4. Implement GitOps with ArgoCD or Flux 5. Add monitoring and observability ## Related Skills - `helm-chart-scaffolding` - For templating and packaging - `gitops-workflow` - For automated deployments - `k8s-security-policies` - For advanced security configurations
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šŸ¤– Auto-discovered
šŸ¤–system prompt•7 months ago

llm-evaluation

Implement comprehensive evaluation strategies for LLM applications

coding
⭐1
# LLM Evaluation Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing. ## When to Use This Skill - Measuring LLM application performance systematically - Comparing different models or prompts - Detecting performance regressions before deployment - Validating improvements from prompt changes - Building confidence in production systems - Establishing baselines and tracking progress over time - Debugging unexpected model behavior ## Core Evaluation Types ### 1. Automated Metrics Fast, repeatable, scalable evaluation using computed scores. **Text Generation:** - **BLEU**: N-gram overlap (translation) - **ROUGE**: Recall-oriented (summarization) - **METEOR**: Semantic similarity - **BERTScore**: Embedding-based similarity - **Perplexity**: Language model confidence **Classification:** - **Accuracy**: Percentage correct - **Precision/Recall/F1**: Class-specific performance - **Confusion Matrix**: Error patterns - **AUC-ROC**: Ranking quality **Retrieval (RAG):** - **MRR**: Mean Reciprocal Rank - **NDCG**: Normalized Discounted Cumulative Gain - **Precision@K**: Relevant in top K - **Recall@K**: Coverage in top K ### 2. Human Evaluation Manual assessment for quality aspects difficult to automate. **Dimensions:** - **Accuracy**: Factual correctness - **Coherence**: Logical flow - **Relevance**: Answers the question - **Fluency**: Natural language quality - **Safety**: No harmful content - **Helpfulness**: Useful to the user ### 3. LLM-as-Judge Use stronger LLMs to evaluate weaker model outputs. **Approaches:** - **Pointwise**: Score individual responses - **Pairwise**: Compare two responses - **Reference-based**: Compare to gold standard - **Reference-free**: Judge without ground truth ## Quick Start ```python from dataclasses import dataclass from typing import Callable import numpy as np @dataclass class Metric: name: str fn: Callable @staticmethod def accuracy(): return Metric("accuracy", calculate_accuracy) @staticmethod def bleu(): return Metric("bleu", calculate_bleu) @staticmethod def bertscore(): return Metric("bertscore", calculate_bertscore) @staticmethod def custom(name: str, fn: Callable): return Metric(name, fn) class EvaluationSuite: def __init__(self, metrics: list[Metric]): self.metrics = metrics async def evaluate(self, model, test_cases: list[dict]) -> dict: results = {m.name: [] for m in self.metrics} for test in test_cases: prediction = await model.predict(test["input"]) for metric in self.metrics: score = metric.fn( prediction=prediction, reference=test.get("expected"), context=test.get("context") ) results[metric.name].append(score) return { "metrics": {k: np.mean(v) for k, v in results.items()}, "raw_scores": results } # Usage suite = EvaluationSuite([ Metric.accuracy(), Metric.bleu(), Metric.bertscore(), Metric.custom("groundedness", check_groundedness) ]) test_cases = [ { "input": "What is the capital of France?", "expected": "Paris", "context": "France is a country in Europe. Paris is its capital." }, ] results = await suite.evaluate(model=your_model, test_cases=test_cases) ``` ## Automated Metrics Implementation ### BLEU Score ```python from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction def calculate_bleu(reference: str, hypothesis: str, **kwargs) -> float: """Calculate BLEU score between reference and hypothesis.""" smoothie = SmoothingFunction().method4 return sentence_bleu( [reference.split()], hypothesis.split(), smoothing_function=smoothie ) ``` ### ROUGE Score ```python from rouge_score import rouge_scorer def calculate_rouge(reference: str, hypothesis: str, **kwargs) -> dict: """Calculate ROUGE scores.""" scorer = rouge_scorer.RougeScorer( ['rouge1', 'rouge2', 'rougeL'], use_stemmer=True ) scores = scorer.score(reference, hypothesis) return { 'rouge1': scores['rouge1'].fmeasure, 'rouge2': scores['rouge2'].fmeasure, 'rougeL': scores['rougeL'].fmeasure } ``` ### BERTScore ```python from bert_score import score def calculate_bertscore( references: list[str], hypotheses: list[str], **kwargs ) -> dict: """Calculate BERTScore using pre-trained model.""" P, R, F1 = score( hypotheses, references, lang='en', model_type='microsoft/deberta-xlarge-mnli' ) return { 'precision': P.mean().item(), 'recall': R.mean().item(), 'f1': F1.mean().item() } ``` ### Custom Metrics ```python def calculate_groundedness(response: str, context: str, **kwargs) -> float: """Check if response is grounded in provided context.""" from transformers import pipeline nli = pipeline( "text-classification", model="microsoft/deberta-large-mnli" ) result = nli(f"{context} [SEP] {response}")[0] # Return confidence that response is entailed by context return result['score'] if result['label'] == 'ENTAILMENT' else 0.0 def calculate_toxicity(text: str, **kwargs) -> float: """Measure toxicity in generated text.""" from detoxify import Detoxify results = Detoxify('original').predict(text) return max(results.values()) # Return highest toxicity score def calculate_factuality(claim: str, sources: list[str], **kwargs) -> float: """Verify factual claims against sources.""" from transformers import pipeline nli = pipeline("text-classification", model="facebook/bart-large-mnli") scores = [] for source in sources: result = nli(f"{source}</s></s>{claim}")[0] if result['label'] == 'entailment': scores.append(result['score']) return max(scores) if scores else 0.0 ``` ## LLM-as-Judge Patterns ### Single Output Evaluation ```python from anthropic import Anthropic from pydantic import BaseModel, Field import json class QualityRating(BaseModel): accuracy: int = Field(ge=1, le=10, description="Factual correctness") helpfulness: int = Field(ge=1, le=10, description="Answers the question") clarity: int = Field(ge=1, le=10, description="Well-written and understandable") reasoning: str = Field(description="Brief explanation") async def llm_judge_quality( response: str, question: str, context: str = None ) -> QualityRating: """Use Claude to judge response quality.""" client = Anthropic() system = """You are an expert evaluator of AI responses. Rate responses on accuracy, helpfulness, and clarity (1-10 scale). Provide brief reasoning for your ratings.""" prompt = f"""Rate the following response: Question: {question} {f'Context: {context}' if context else ''} Response: {response} Provide ratings in JSON format: {{ "accuracy": <1-10>, "helpfulness": <1-10>, "clarity": <1-10>, "reasoning": "<brief explanation>" }}""" message = client.messages.create( model="claude-sonnet-4-6", max_tokens=500, system=system, messages=[{"role": "user", "content": prompt}] ) return QualityRating(**json.loads(message.content[0].text)) ``` ### Pairwise Comparison ```python from pydantic import BaseModel, Field from typing import Literal class ComparisonResult(BaseModel): winner: Literal["A", "B", "tie"] reasoning: str confidence: int = Field(ge=1, le=10) async def compare_responses( question: str, response_a: str, response_b: str ) -> ComparisonResult: """Compare two responses using LLM judge.""" client = Anthropic() prompt = f"""Compare these two responses and determine which is better. Question: {question} Response A: {response_a} Response B: {response_b} Consider accuracy, helpfulness, and clarity. Answer with JSON: {{ "winner": "A" or "B" or "tie", "reasoning": "<explanation>", "confidence": <1-10> }}""" message = client.messages.create( model="claude-sonnet-4-6", max_tokens=500, messages=[{"role": "user", "content": prompt}] ) return ComparisonResult(**json.loads(message.content[0].text)) ``` ### Reference-Based Evaluation ```python class ReferenceEvaluation(BaseModel): semantic_similarity: float = Field(ge=0, le=1) factual_accuracy: float = Field(ge=0, le=1) completeness: float = Field(ge=0, le=1) issues: list[str] async def evaluate_against_reference( response: str, reference: str, question: str ) -> ReferenceEvaluation: """Evaluate response against gold standard reference.""" client = Anthropic() prompt = f"""Compare the response to the reference answer. Question: {question} Reference Answer: {reference} Response to Evaluate: {response} Evaluate: 1. Semantic similarity (0-1): How similar is the meaning? 2. Factual accuracy (0-1): Are all facts correct? 3. Completeness (0-1): Does it cover all key points? 4. List any specific issues or errors. Respond in JSON: {{ "semantic_similarity": <0-1>, "factual_accuracy": <0-1>, "completeness": <0-1>, "issues": ["issue1", "issue2"] }}""" message = client.messages.create( model="claude-sonnet-4-6", max_tokens=500, messages=[{"role": "user", "content": prompt}] ) return ReferenceEvaluation(**json.loads(message.content[0].text)) ``` ## Human Evaluation Frameworks ### Annotation Guidelines ```python from dataclasses import dataclass, field from typing import Optional @dataclass class AnnotationTask: """Structure for human annotation task.""" response: str question: str context: Optional[str] = None def get_annotation_form(self) -> dict: return { "question": self.question, "context": self.context, "response": self.response, "ratings": { "accuracy": { "scale": "1-5", "description": "Is the response factually correct?" }, "relevance": { "scale": "1-5", "description": "Does it answer the question?" }, "coherence": { "scale": "1-5", "description": "Is it logically consistent?" } }, "issues": { "factual_error": False, "hallucination": False, "off_topic": False, "unsafe_content": False }, "feedback": "" } ``` ### Inter-Rater Agreement ```python from sklearn.metrics import cohen_kappa_score def calculate_agreement( rater1_scores: list[int], rater2_scores: list[int] ) -> dict: """Calculate inter-rater agreement.""" kappa = cohen_kappa_score(rater1_scores, rater2_scores) if kappa < 0: interpretation = "Poor" elif kappa < 0.2: interpretation = "Slight" elif kappa < 0.4: interpretation = "Fair" elif kappa < 0.6: interpretation = "Moderate" elif kappa < 0.8: interpretation = "Substantial" else: interpretation = "Almost Perfect" return { "kappa": kappa, "interpretation": interpretation } ``` ## A/B Testing ### Statistical Testing Framework ```python from scipy import stats import numpy as np from dataclasses import dataclass, field @dataclass class ABTest: variant_a_name: str = "A" variant_b_name: str = "B" variant_a_scores: list[float] = field(default_factory=list) variant_b_scores: list[float] = field(default_factory=list) def add_result(self, variant: str, score: float): """Add evaluation result for a variant.""" if variant == "A": self.variant_a_scores.append(score) else: self.variant_b_scores.append(score) def analyze(self, alpha: float = 0.05) -> dict: """Perform statistical analysis.""" a_scores = np.array(self.variant_a_scores) b_scores = np.array(self.variant_b_scores) # T-test t_stat, p_value = stats.ttest_ind(a_scores, b_scores) # Effect size (Cohen's d) pooled_std = np.sqrt((np.std(a_scores)**2 + np.std(b_scores)**2) / 2) cohens_d = (np.mean(b_scores) - np.mean(a_scores)) / pooled_std return { "variant_a_mean": np.mean(a_scores), "variant_b_mean": np.mean(b_scores), "difference": np.mean(b_scores) - np.mean(a_scores), "relative_improvement": (np.mean(b_scores) - np.mean(a_scores)) / np.mean(a_scores), "p_value": p_value, "statistically_significant": p_value < alpha, "cohens_d": cohens_d, "effect_size": self._interpret_cohens_d(cohens_d), "winner": self.variant_b_name if np.mean(b_scores) > np.mean(a_scores) else self.variant_a_name } @staticmethod def _interpret_cohens_d(d: float) -> str: """Interpret Cohen's d effect size.""" abs_d = abs(d) if abs_d < 0.2: return "negligible" elif abs_d < 0.5: return "small" elif abs_d < 0.8: return "medium" else: return "large" ``` ## Regression Testing ### Regression Detection ```python from dataclasses import dataclass @dataclass class RegressionResult: metric: str baseline: float current: float change: float is_regression: bool class RegressionDetector: def __init__(self, baseline_results: dict, threshold: float = 0.05): self.baseline = baseline_results self.threshold = threshold def check_for_regression(self, new_results: dict) -> dict: """Detect if new results show regression.""" regressions = [] for metric in self.baseline.keys(): baseline_score = self.baseline[metric] new_score = new_results.get(metric) if new_score is None: continue # Calculate relative change relative_change = (new_score - baseline_score) / baseline_score # Flag if significant decrease is_regression = relative_change < -self.threshold if is_regression: regressions.append(RegressionResult( metric=metric, baseline=baseline_score, current=new_score, change=relative_change, is_regression=True )) return { "has_regression": len(regressions) > 0, "regressions": regressions, "summary": f"{len(regressions)} metric(s) regressed" } ``` ## LangSmith Evaluation Integration ```python from langsmith import Client from langsmith.evaluation import evaluate, LangChainStringEvaluator # Initialize LangSmith client client = Client() # Create dataset dataset = client.create_dataset("qa_test_cases") client.create_examples( inputs=[{"question": q} for q in questions], outputs=[{"answer": a} for a in expected_answers], dataset_id=dataset.id ) # Define evaluators evaluators = [ LangChainStringEvaluator("qa"), # QA correctness LangChainStringEvaluator("context_qa"), # Context-grounded QA LangChainStringEvaluator("cot_qa"), # Chain-of-thought QA ] # Run evaluation async def target_function(inputs: dict) -> dict: result = await your_chain.ainvoke(inputs) return {"answer": result} experiment_results = await evaluate( target_function, data=dataset.name, evaluators=evaluators, experiment_prefix="v1.0.0", metadata={"model": "claude-sonnet-4-6", "version": "1.0.0"} ) print(f"Mean score: {experiment_results.aggregate_metrics['qa']['mean']}") ``` ## Benchmarking ### Running Benchmarks ```python from dataclasses import dataclass import numpy as np @dataclass class BenchmarkResult: metric: str mean: float std: float min: float max: float class BenchmarkRunner: def __init__(self, benchmark_dataset: list[dict]): self.dataset = benchmark_dataset async def run_benchmark( self, model, metrics: list[Metric] ) -> dict[str, BenchmarkResult]: """Run model on benchmark and calculate metrics.""" results = {metric.name: [] for metric in metrics} for example in self.dataset: # Generate prediction prediction = await model.predict(example["input"]) # Calculate each metric for metric in metrics: score = metric.fn( prediction=prediction, reference=example["reference"], context=example.get("context") ) results[metric.name].append(score) # Aggregate results return { metric: BenchmarkResult( metric=metric, mean=np.mean(scores), std=np.std(scores), min=min(scores), max=max(scores) ) for metric, scores in results.items() } ``` ## Resources - [LangSmith Evaluation Guide](https://docs.smith.langchain.com/evaluation) - [RAGAS Framework](https://docs.ragas.io/) - [DeepEval Library](https://docs.deepeval.com/) - [Arize Phoenix](https://docs.arize.com/phoenix/) - [HELM Benchmark](https://crfm.stanford.edu/helm/) ## Best Practices 1. **Multiple Metrics**: Use diverse metrics for comprehensive view 2. **Representative Data**: Test on real-world, diverse examples 3. **Baselines**: Always compare against baseline performance 4. **Statistical Rigor**: Use proper statistical tests for comparisons 5. **Continuous Evaluation**: Integrate into CI/CD pipeline 6. **Human Validation**: Combine automated metrics with human judgment 7. **Error Analysis**: Investigate failures to understand weaknesses 8. **Version Control**: Track evaluation results over time ## Common Pitfalls - **Single Metric Obsession**: Optimizing for one metric at the expense of others - **Small Sample Size**: Drawing conclusions from too few examples - **Data Contamination**: Testing on training data - **Ignoring Variance**: Not accounting for statistical uncertainty - **Metric Mismatch**: Using metrics not aligned with business goals - **Position Bias**: In pairwise evals, randomize order - **Overfitting Prompts**: Optimizing for test set instead of real use
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prometheus-configuration

Set up Prometheus for comprehensive metric collection, storage, and

coding
⭐1
# Prometheus Configuration Complete guide to Prometheus setup, metric collection, scrape configuration, and recording rules. ## Purpose Configure Prometheus for comprehensive metric collection, alerting, and monitoring of infrastructure and applications. ## When to Use - Set up Prometheus monitoring - Configure metric scraping - Create recording rules - Design alert rules - Implement service discovery ## Prometheus Architecture ``` ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Applications │ ← Instrumented with client libraries ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ /metrics endpoint ↓ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” │ Prometheus │ ← Scrapes metrics periodically │ Server │ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ │ ā”œā”€ā†’ AlertManager (alerts) ā”œā”€ā†’ Grafana (visualization) └─→ Long-term storage (Thanos/Cortex) ``` ## Installation ### Kubernetes with Helm ```bash helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo update helm install prometheus prometheus-community/kube-prometheus-stack \ --namespace monitoring \ --create-namespace \ --set prometheus.prometheusSpec.retention=30d \ --set prometheus.prometheusSpec.storageVolumeSize=50Gi ``` ### Docker Compose ```yaml version: "3.8" services: prometheus: image: prom/prometheus:latest ports: - "9090:9090" volumes: - ./prometheus.yml:/etc/prometheus/prometheus.yml - prometheus-data:/prometheus command: - "--config.file=/etc/prometheus/prometheus.yml" - "--storage.tsdb.path=/prometheus" - "--storage.tsdb.retention.time=30d" volumes: prometheus-data: ``` ## Configuration File **prometheus.yml:** ```yaml global: scrape_interval: 15s evaluation_interval: 15s external_labels: cluster: "production" region: "us-west-2" # Alertmanager configuration alerting: alertmanagers: - static_configs: - targets: - alertmanager:9093 # Load rules files rule_files: - /etc/prometheus/rules/*.yml # Scrape configurations scrape_configs: # Prometheus itself - job_name: "prometheus" static_configs: - targets: ["localhost:9090"] # Node exporters - job_name: "node-exporter" static_configs: - targets: - "node1:9100" - "node2:9100" - "node3:9100" relabel_configs: - source_labels: [__address__] target_label: instance regex: "([^:]+)(:[0-9]+)?" replacement: "${1}" # Kubernetes pods with annotations - job_name: "kubernetes-pods" kubernetes_sd_configs: - role: pod relabel_configs: - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape] action: keep regex: true - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path] action: replace target_label: __metrics_path__ regex: (.+) - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port] action: replace regex: ([^:]+)(?::\d+)?;(\d+) replacement: $1:$2 target_label: __address__ - source_labels: [__meta_kubernetes_namespace] action: replace target_label: namespace - source_labels: [__meta_kubernetes_pod_name] action: replace target_label: pod # Application metrics - job_name: "my-app" static_configs: - targets: - "app1.example.com:9090" - "app2.example.com:9090" metrics_path: "/metrics" scheme: "https" tls_config: ca_file: /etc/prometheus/ca.crt cert_file: /etc/prometheus/client.crt key_file: /etc/prometheus/client.key ``` **Reference:** See `assets/prometheus.yml.template` ## Scrape Configurations ### Static Targets ```yaml scrape_configs: - job_name: "static-targets" static_configs: - targets: ["host1:9100", "host2:9100"] labels: env: "production" region: "us-west-2" ``` ### File-based Service Discovery ```yaml scrape_configs: - job_name: "file-sd" file_sd_configs: - files: - /etc/prometheus/targets/*.json - /etc/prometheus/targets/*.yml refresh_interval: 5m ``` **targets/production.json:** ```json [ { "targets": ["app1:9090", "app2:9090"], "labels": { "env": "production", "service": "api" } } ] ``` ### Kubernetes Service Discovery ```yaml scrape_configs: - job_name: "kubernetes-services" kubernetes_sd_configs: - role: service relabel_configs: - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape] action: keep regex: true - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme] action: replace target_label: __scheme__ regex: (https?) - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path] action: replace target_label: __metrics_path__ regex: (.+) ``` **Reference:** See `references/scrape-configs.md` ## Recording Rules Create pre-computed metrics for frequently queried expressions: ```yaml # /etc/prometheus/rules/recording_rules.yml groups: - name: api_metrics interval: 15s rules: # HTTP request rate per service - record: job:http_requests:rate5m expr: sum by (job) (rate(http_requests_total[5m])) # Error rate percentage - record: job:http_requests_errors:rate5m expr: sum by (job) (rate(http_requests_total{status=~"5.."}[5m])) - record: job:http_requests_error_rate:percentage expr: | (job:http_requests_errors:rate5m / job:http_requests:rate5m) * 100 # P95 latency - record: job:http_request_duration:p95 expr: | histogram_quantile(0.95, sum by (job, le) (rate(http_request_duration_seconds_bucket[5m])) ) - name: resource_metrics interval: 30s rules: # CPU utilization percentage - record: instance:node_cpu:utilization expr: | 100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) # Memory utilization percentage - record: instance:node_memory:utilization expr: | 100 - ((node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100) # Disk usage percentage - record: instance:node_disk:utilization expr: | 100 - ((node_filesystem_avail_bytes / node_filesystem_size_bytes) * 100) ``` **Reference:** See `references/recording-rules.md` ## Alert Rules ```yaml # /etc/prometheus/rules/alert_rules.yml groups: - name: availability interval: 30s rules: - alert: ServiceDown expr: up{job="my-app"} == 0 for: 1m labels: severity: critical annotations: summary: "Service {{ $labels.instance }} is down" description: "{{ $labels.job }} has been down for more than 1 minute" - alert: HighErrorRate expr: job:http_requests_error_rate:percentage > 5 for: 5m labels: severity: warning annotations: summary: "High error rate for {{ $labels.job }}" description: "Error rate is {{ $value }}% (threshold: 5%)" - alert: HighLatency expr: job:http_request_duration:p95 > 1 for: 5m labels: severity: warning annotations: summary: "High latency for {{ $labels.job }}" description: "P95 latency is {{ $value }}s (threshold: 1s)" - name: resources interval: 1m rules: - alert: HighCPUUsage expr: instance:node_cpu:utilization > 80 for: 5m labels: severity: warning annotations: summary: "High CPU usage on {{ $labels.instance }}" description: "CPU usage is {{ $value }}%" - alert: HighMemoryUsage expr: instance:node_memory:utilization > 85 for: 5m labels: severity: warning annotations: summary: "High memory usage on {{ $labels.instance }}" description: "Memory usage is {{ $value }}%" - alert: DiskSpaceLow expr: instance:node_disk:utilization > 90 for: 5m labels: severity: critical annotations: summary: "Low disk space on {{ $labels.instance }}" description: "Disk usage is {{ $value }}%" ``` ## Validation ```bash # Validate configuration promtool check config prometheus.yml # Validate rules promtool check rules /etc/prometheus/rules/*.yml # Test query promtool query instant http://localhost:9090 'up' ``` **Reference:** See `scripts/validate-prometheus.sh` ## Best Practices 1. **Use consistent naming** for metrics (prefix_name_unit) 2. **Set appropriate scrape intervals** (15-60s typical) 3. **Use recording rules** for expensive queries 4. **Implement high availability** (multiple Prometheus instances) 5. **Configure retention** based on storage capacity 6. **Use relabeling** for metric cleanup 7. **Monitor Prometheus itself** 8. **Implement federation** for large deployments 9. **Use Thanos/Cortex** for long-term storage 10. **Document custom metrics** ## Troubleshooting **Check scrape targets:** ```bash curl http://localhost:9090/api/v1/targets ``` **Check configuration:** ```bash curl http://localhost:9090/api/v1/status/config ``` **Test query:** ```bash curl 'http://localhost:9090/api/v1/query?query=up' ``` ## Reference Files - `assets/prometheus.yml.template` - Complete configuration template - `references/scrape-configs.md` - Scrape configuration patterns - `references/recording-rules.md` - Recording rule examples - `scripts/validate-prometheus.sh` - Validation script ## Related Skills - `grafana-dashboards` - For visualization - `slo-implementation` - For SLO monitoring - `distributed-tracing` - For request tracing
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