Full Stack Technology Stack Selection and Best Practices 2024

Introduction

Technology Stack Selection Principles

When choosing a technology stack, we need to consider the following dimensions:

1. Cost-Effectiveness and Efficiency

Cost-effectiveness and efficiency are the primary principles; any selection that deviates from these principles is irresponsible
Evaluate the learning curve and maintenance costs of the technology stack
Consider the ecosystem and community support

2. Technology Maturity

Choose mature technologies with active communities and comprehensive documentation
Avoid cutting-edge but unstable technologies
Ensure long-term maintenance and update plans for the technology stack

3. Team Compatibility

Prioritize technology stacks familiar to the team
Assess the alignment between technology stack and team skills
Consider the team's learning capacity and willingness to adopt new technologies

4. Avoid Over-Engineering

Here are some common over-engineering cases:

Multiple refactoring before product launch
Excessive focus on performance optimization before implementing features
Pursuing extreme scalability with a small user base

Technology Stack Overview

Frontend Technology Stack

graph TD
    A[Frontend Stack] --> B[Framework]
    A --> C[Build Tools]
    A --> D[UI Components]
    A --> E[State Management]
    
    B --> B1[React 18+]
    B --> B2[Next.js 14]
    
    C --> C1[Turbo]
    C --> C2[PNPM]
    C --> C3[TSUP]
    
    D --> D1[@radix-ui]
    D --> D2[Tailwind CSS]
    D --> D3[shadcn/ui]
    
    E --> E1[use-immer]
    E --> E2[Zustand]

Backend Technology Stack

graph TD
    A[Backend Stack] --> B[Service Framework]
    A --> C[Data Storage]
    A --> D[API Design]
    A --> E[Function Modules]
    
    B --> B1[Hono.js]
    B --> B2[Next.js API]
    
    C --> C1[Prisma ORM]
    C --> C2[Cloudflare D1]
    
    D --> D1[@hono/zod-openapi]
    D --> D2[Swagger UI]
    
    E --> E1[Email Service]
    E --> E2[Analytics]
    E --> E3[Authentication]

AI Technology Stack

graph TD
    A[AI Stack] --> B[Development Framework]
    A --> C[Model Services]
    A --> D[Vector Database]
    A --> E[SAAS Services]
    
    B --> B1[Vercel AI SDK]
    
    C --> C1[Azure OpenAI]
    C --> C2[Cloudflare AI]
    C --> C3[Alibaba Tongyi]
    
    D --> D1[Cloudflare Vector]
    D --> D2[Milvus]
    
    E --> E1[Dify]
    E --> E2[Flowise AI]

Database Technology Stack

graph TD
    A[Database Stack] --> B[Relational Database]
    A --> C[Document Database]
    A --> D[Graph Database]
    A --> E[Vector Database]
    
    B --> B1[MySQL]
    B --> B2[PostgreSQL]
    
    C --> C1[MongoDB]
    C --> C2[Cloudflare D1]
    
    D --> D1[Neo4j]
    
    E --> E1[Milvus]

Middleware Technology Stack

graph TD
    A[Middleware Stack] --> B[Message Queue]
    A --> C[Cache]
    A --> D[Search Engine]
    
    B --> B1[Kafka]
    B --> B2[Redis Stream]
    
    C --> C1[Redis]
    C --> C2[Memcached]
    
    D --> D1[Elasticsearch]
    D --> D2[Meilisearch]

DevOps Technology Stack

graph TD
    A[DevOps Stack] --> B[Deployment Platform]
    A --> C[Service Orchestration]
    A --> D[Network Services]
    
    B --> B1[Cloudflare Workers]
    B --> B2[Cloudflare Pages]
    
    C --> C1[Docker]
    C --> C2[PM2]
    
    D --> D1[Caddy]
    D --> D2[Cloudflare DNS]

Essential Utility Packages

Here are some indispensable utility packages for development:

zodjs: Powerful data validation and TypeScript type inference tool

import { z } from 'zod';

// Define data model
const UserSchema = z.object({
  id: z.string().uuid(),
  name: z.string().min(2).max(50),
  email: z.string().email(),
  age: z.number().min(0).max(120).optional(),
});

// Type inference
type User = z.infer<typeof UserSchema>;

// Data validation
function createUser(data: unknown): User {
  return UserSchema.parse(data);
}

tsx: Tool for directly running TypeScript files

# Run ts file directly
npx tsx src/script.ts

# Run in development mode (with hot reload)
npx tsx watch src/server.ts

vitest: Modern unit testing framework

import { describe, it, expect } from 'vitest';
import { sum } from './math';

describe('math utils', () => {
  it('should add two numbers correctly', () => {
    expect(sum(1, 2)).toBe(3);
  });
});

nanoid: Lightweight ID generation tool

import { nanoid } from 'nanoid';

// Generate unique ID
const id = nanoid(); // "V1StGXR8_Z5jdHi6B-myT"

// Specify length
const shortId = nanoid(10); // "IRFa-VaY2b"

SAAS Services Deep Dive

1. Dify - Open Source LLMOps Platform

Dify is a powerful LLMOps platform that provides a complete toolchain for large language model application development and operations:

Core Features

Prompt Management: Professional prompt orchestration and version management
Multi-Model Support: Supports OpenAI, Azure OpenAI, Alibaba Tongyi, and other model services
Application Orchestration: Visual conversation flow design
Data Security: Built-in data encryption and access control

Best Practices

// Using Dify API to build conversation applications
const DIFY_API_KEY = process.env.DIFY_API_KEY;
const DIFY_APP_ID = process.env.DIFY_APP_ID;

async function chatWithDify(message: string) {
  const response = await fetch(`https://api.dify.ai/v1/chat-messages`, {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${DIFY_API_KEY}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      app_id: DIFY_APP_ID,
      messages: [{ role: 'user', content: message }],
    }),
  });
  return response.json();
}

2. Umami - Open Source Web Analytics Tool

Umami is a privacy-focused web analytics tool that provides rich data analysis features:

Core Features

Cookie-less Tracking: Compliant with GDPR and other privacy regulations
Real-time Analysis: Real-time visitor data updates
Custom Events: Support for tracking custom behaviors
Multi-site Management: Single panel for managing multiple websites

Deployment Configuration

# docker-compose.yml
version: '3'
services:
  umami:
    image: ghcr.io/umami-software/umami:postgresql-latest
    ports:
      - "3000:3000"
    environment:
      DATABASE_URL: postgresql://umami:umami@db:5432/umami
      DATABASE_TYPE: postgresql
      APP_SECRET: your-secret-key

  db:
    image: postgres:12-alpine
    environment:
      POSTGRES_DB: umami
      POSTGRES_USER: umami
      POSTGRES_PASSWORD: umami
    volumes:
      - ./sql/schema.postgresql.sql:/docker-entrypoint-initdb.d/schema.postgresql.sql:ro
      - ./data:/var/lib/postgresql/data

3. Flowise - Visual AI Workflow Platform

Flowise is a powerful AI workflow design and deployment platform:

Key Advantages

Visual Orchestration: Drag-and-drop workflow design
Rich Components: Built-in AI and data processing components
API Integration: Automatic REST API generation
Version Control: Workflow version management

Deployment Example

# docker-compose.yml
version: '3.8'

services:
  flowise:
    image: flowiseai/flowise
    ports:
      - "3000:3000"
    environment:
      - DATABASE_PATH=/root/.flowise
      - APIKEY_PATH=/root/.flowise
      - SECRETKEY_PATH=/root/.flowise
    volumes:
      - ~/.flowise:/root/.flowise

Database Technology Stack Deep Dive

1. MySQL - Enterprise-Grade Relational Database

Performance Optimization Strategies

Index Optimization: Proper use of composite indexes, avoid over-indexing
Query Optimization: Use EXPLAIN to analyze execution plans
Partitioning Strategy: Choose partitioning scheme based on business characteristics
Master-Slave Architecture: Implement read-write separation for performance improvement

Best Practices

-- Index optimization example
CREATE TABLE users (
  id BIGINT PRIMARY KEY AUTO_INCREMENT,
  username VARCHAR(50) NOT NULL,
  email VARCHAR(100) NOT NULL,
  status TINYINT NOT NULL DEFAULT 1,
  created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
  -- Composite index
  INDEX idx_status_created (status, created_at),
  -- Unique index
  UNIQUE INDEX idx_email (email)
);

-- Partitioned table example
CREATE TABLE orders (
  id BIGINT NOT NULL,
  user_id BIGINT NOT NULL,
  amount DECIMAL(10,2) NOT NULL,
  created_at TIMESTAMP NOT NULL
)
PARTITION BY RANGE (UNIX_TIMESTAMP(created_at)) (
  PARTITION p_2023 VALUES LESS THAN (UNIX_TIMESTAMP('2024-01-01 00:00:00')),
  PARTITION p_2024 VALUES LESS THAN (UNIX_TIMESTAMP('2025-01-01 00:00:00'))
);

2. MongoDB - Distributed Document Database

Architecture Design Points

Schema Design: Optimize document structure based on query patterns
Indexing Strategy: Create composite indexes to support complex queries
Sharding Cluster: Horizontal scaling for high availability

Practice Examples

// Schema design example
const OrderSchema = new Schema({
  userId: { type: ObjectId, ref: 'User', index: true },
  items: [{
    productId: { type: ObjectId, ref: 'Product' },
    quantity: Number,
    price: Number
  }],
  status: { type: String, enum: ['pending', 'paid', 'shipped'], index: true },
  totalAmount: Number,
  // Composite index field
  createdAt: { type: Date, default: Date.now }
});

// Create composite index
OrderSchema.index({ userId: 1, status: 1, createdAt: -1 });

// Sharding configuration
db.adminCommand({
  shardCollection: "mydb.orders",
  key: { userId: "hashed" }
});

3. Neo4j - Graph Database

Application Scenarios

Social Networks: Relationship graph analysis
Recommendation Systems: Relationship-based personalized recommendations
Knowledge Graphs: Complex knowledge system construction

Query Examples

// Create user nodes and relationships
CREATE (u1:User {id: 1, name: 'Alice'})
CREATE (u2:User {id: 2, name: 'Bob'})
CREATE (u1)-[:FOLLOWS]->(u2)

// Find second-degree relationships
MATCH (user:User {name: 'Alice'})-[:FOLLOWS*2]->(follower)
RETURN follower.name;

// Shortest path query
MATCH p=shortestPath(
  (user1:User {name: 'Alice'})-[:FOLLOWS*]-(user2:User {name: 'Charlie'})
)
RETURN p;

Middleware Technology Stack Deep Dive

1. Kafka - Distributed Message Queue

Architectural Features

High Throughput: Parallel processing with partitions
Reliability: Multi-replica mechanism
Scalability: Support for horizontal scaling
Persistence: Message persistence storage

Best Practices

// Kafka producer example
import { Kafka } from 'kafkajs';

const kafka = new Kafka({
  clientId: 'my-app',
  brokers: ['localhost:9092']
});

const producer = kafka.producer();

async function sendMessage() {
  await producer.connect();
  await producer.send({
    topic: 'test-topic',
    messages: [
      { key: 'key1', value: JSON.stringify({ hello: 'world' }) }
    ],
  });
}

// Kafka consumer example
const consumer = kafka.consumer({ groupId: 'test-group' });

async function startConsumer() {
  await consumer.connect();
  await consumer.subscribe({ topic: 'test-topic', fromBeginning: true });

  await consumer.run({
    eachMessage: async ({ topic, partition, message }) => {
      console.log({
        value: message.value.toString(),
      });
    },
  });
}

2. Redis - High-Performance Cache

Use Cases

Cache Layer: Reduce database pressure
Session Storage: Distributed session management
Message Queue: Lightweight message queue
Leaderboard: Real-time ranking statistics

Practice Examples

import { Redis } from 'ioredis';

const redis = new Redis({
  host: 'localhost',
  port: 6379,
  // Cluster configuration
  cluster: true,
  nodes: [
    { host: 'node1', port: 6379 },
    { host: 'node2', port: 6379 },
    { host: 'node3', port: 6379 }
  ]
});

// Caching example
async function cacheUser(userId: string, userData: any) {
  await redis.set(`user:${userId}`, JSON.stringify(userData), 'EX', 3600);
}

// Distributed lock
async function acquireLock(lockKey: string, timeout: number) {
  const token = Math.random().toString(36);
  const acquired = await redis.set(lockKey, token, 'NX', 'EX', timeout);
  return acquired ? token : null;
}

// Leaderboard implementation
async function updateLeaderboard(userId: string, score: number) {
  await redis.zadd('leaderboard', score, userId);
  // Get top 10
  const top10 = await redis.zrevrange('leaderboard', 0, 9, 'WITHSCORES');
  return top10;
}

Frontend Technology Stack Deep Dive

1. Project Build - Turbo + PNPM + Next.js

Using Turbo and PNPM to set up monorepo project structure:

# Initialize project
pnpm dlx create-turbo@latest my-turborepo
cd my-turborepo

# Install dependencies
pnpm install

# Create new app or package
pnpm turbo gen workspace

Project structure example:

.
├── apps/
│   ├── web/           # Next.js main application
│   └── docs/          # Documentation site
├── packages/
│   ├── ui/           # Shared UI components
│   ├── config/       # Shared configuration
│   └── utils/        # Utility functions
├── turbo.json        # Turbo configuration
└── package.json      # Workspace configuration

turbo.json configuration example:

{
  "$schema": "https://turbo.build/schema.json",
  "globalDependencies": ["**/.env.*local"],
  "pipeline": {
    "build": {
      "dependsOn": ["^build"],
      "outputs": [".next/**", "!.next/cache/**"]
    },
    "lint": {},
    "dev": {
      "cache": false,
      "persistent": true
    }
  }
}

2. UI Component Library Best Practices

In modern frontend development, we recommend using the combination of @radix-ui + Tailwind CSS + shadcn/ui:

@radix-ui Basic Components

import * as Dialog from '@radix-ui/react-dialog';

function ConfirmDialog() {
  return (
    <Dialog.Root>
      <Dialog.Trigger>
        <button>Open Dialog</button>
      </Dialog.Trigger>
      <Dialog.Portal>
        <Dialog.Overlay className="fixed inset-0 bg-black/50" />
        <Dialog.Content className="fixed top-1/2 left-1/2 -translate-x-1/2 -translate-y-1/2 bg-white p-6 rounded-lg">
          <Dialog.Title>Confirm Action</Dialog.Title>
          <Dialog.Description>
            Are you sure you want to perform this action?
          </Dialog.Description>
          <div className="mt-4 flex justify-end gap-2">
            <Dialog.Close>
              <button>Cancel</button>
            </Dialog.Close>
            <button onClick={() => console.log('Confirmed')}>Confirm</button>
          </div>
        </Dialog.Content>
      </Dialog.Portal>
    </Dialog.Root>
  );
}

shadcn/ui Advanced Components

import {
  Card,
  CardContent,
  CardDescription,
  CardFooter,
  CardHeader,
  CardTitle,
} from "@/components/ui/card";

function ProductCard() {
  return (
    <Card>
      <CardHeader>
        <CardTitle>Product Name</CardTitle>
        <CardDescription>Product Description</CardDescription>
      </CardHeader>
      <CardContent>
        <p>Product details...</p>
      </CardContent>
      <CardFooter>
        <button>Buy Now</button>
      </CardFooter>
    </Card>
  );
}

3. State Management Best Practices

In Next.js applications, we recommend using lightweight use-immer for state management:

Simple Component State

import { useImmer } from 'use-immer';

function TodoList() {
  const [todos, updateTodos] = useImmer([
    { id: 1, text: 'Learn React', done: false },
    { id: 2, text: 'Learn Next.js', done: false },
  ]);

  const toggleTodo = (id: number) => {
    updateTodos(draft => {
      const todo = draft.find(t => t.id === id);
      if (todo) todo.done = !todo.done;
    });
  };

  return (
    <ul>
      {todos.map(todo => (
        <li key={todo.id} onClick={() => toggleTodo(todo.id)}>
          {todo.text} {todo.done ? '✓' : ''}
        </li>
      ))}
    </ul>
  );
}

Complex Application State

import { createStore } from 'zustand';
import { immer } from 'zustand/middleware/immer';

interface Post {
  id: number;
  title: string;
  content: string;
}

interface PostStore {
  posts: Post[];
  loading: boolean;
  addPost: (post: Post) => void;
  fetchPosts: () => Promise<void>;
}

const usePostStore = createStore(
  immer<PostStore>((set) => ({
    posts: [],
    loading: false,
    addPost: (post) =>
      set((state) => {
        state.posts.push(post);
      }),
    fetchPosts: async () => {
      set((state) => { state.loading = true });
      try {
        const response = await fetch('/api/posts');
        const posts = await response.json();
        set((state) => {
          state.posts = posts;
          state.loading = false;
        });
      } catch (error) {
        set((state) => { state.loading = false });
        console.error('Failed to fetch posts:', error);
      }
    },
  }))
);

Backend Technology Stack Deep Dive

1. API Interface Design - OpenAPI Specification

Using Hono.js + Zod to build type-safe API interfaces:

import { OpenAPIHono } from '@hono/zod-openapi';
import { z } from 'zod';

const app = new OpenAPIHono();

// Define request and response Schema
const UserSchema = z.object({
  id: z.string().uuid(),
  name: z.string().min(2),
  email: z.string().email(),
});

const CreateUserSchema = UserSchema.omit({ id: true });

// Define API routes
app.openapi(
  '/users',
  {
    method: 'post',
    request: {
      body: {
        content: {
          'application/json': {
            schema: CreateUserSchema,
          },
        },
      },
    },
    responses: {
      200: {
        content: {
          'application/json': {
            schema: UserSchema,
          },
        },
      },
    },
  },
  async (c) => {
    const data = c.req.valid('json');
    const user = await createUser(data);
    return c.json(user);
  }
);

// Generate OpenAPI documentation
app.doc('/docs', {
  openapi: '3.0.0',
  info: {
    title: 'User API',
    version: '1.0.0',
  },
});

2. Database Operations - Prisma ORM

Best practices for database operations using Prisma:

// schema.prisma
datasource db {
  provider = "postgresql"
  url      = env("DATABASE_URL")
}

model User {
  id        String    @id @default(uuid())
  email     String    @unique
  name      String
  posts     Post[]
  createdAt DateTime  @default(now())
  updatedAt DateTime  @updatedAt
}

model Post {
  id        String    @id @default(uuid())
  title     String
  content   String
  published Boolean   @default(false)
  author    User      @relation(fields: [authorId], references: [id])
  authorId  String
  createdAt DateTime  @default(now())
  updatedAt DateTime  @updatedAt
}

Database operation examples:

import { PrismaClient } from '@prisma/client';

const prisma = new PrismaClient();

// Create user and posts
async function createUserWithPosts() {
  const user = await prisma.user.create({
    data: {
      email: 'alice@example.com',
      name: 'Alice',
      posts: {
        create: [
          {
            title: 'Hello World',
            content: 'This is my first post!',
            published: true,
          },
        ],
      },
    },
    include: {
      posts: true,
    },
  });
  return user;
}

// Query paginated post list
async function getPublishedPosts(page: number, pageSize: number) {
  const posts = await prisma.post.findMany({
    where: {
      published: true,
    },
    include: {
      author: {
        select: {
          name: true,
          email: true,
        },
      },
    },
    skip: (page - 1) * pageSize,
    take: pageSize,
    orderBy: {
      createdAt: 'desc',
    },
  });
  return posts;
}

3. Email Service Integration

Building modern email services using React Email + Nodemailer:

// email-templates/WelcomeEmail.tsx
import { Html, Head, Body, Container, Text, Button } from '@react-email/components';

export function WelcomeEmail({ username }: { username: string }) {
  return (
    <Html>
      <Head />
      <Body style={{
        backgroundColor: '#ffffff',
        margin: '0 auto',
        fontFamily: 'system-ui'
      }}>
        <Container>
          <Text>Welcome {username} to our community!</Text>
          <Button
            href="https://example.com/get-started"
            style={{
              backgroundColor: '#000000',
              color: '#ffffff',
              padding: '12px 20px',
            }}
          >
            Get Started
          </Button>
        </Container>
      </Body>
    </Html>
  );
}

// email-service.ts
import nodemailer from 'nodemailer';
import { render } from '@react-email/render';
import { WelcomeEmail } from './email-templates/WelcomeEmail';

const transporter = nodemailer.createTransport({
  host: process.env.SMTP_HOST,
  port: Number(process.env.SMTP_PORT),
  secure: true,
  auth: {
    user: process.env.SMTP_USER,
    pass: process.env.SMTP_PASS,
  },
});

export async function sendWelcomeEmail(to: string, username: string) {
  const html = render(WelcomeEmail({ username }));
  
  await transporter.sendMail({
    from: '"Team" <team@example.com>',
    to,
    subject: 'Welcome to Our Community',
    html,
  });
}

AI Technology Stack Deep Dive

1. Vercel AI SDK Integration

Implementing AI features using Vercel AI SDK:

// app/api/chat/route.ts
import { Configuration, OpenAIApi } from 'openai-edge';
import { OpenAIStream, StreamingTextResponse } from 'ai';

const config = new Configuration({
  apiKey: process.env.OPENAI_API_KEY,
});

const openai = new OpenAIApi(config);

export async function POST(req: Request) {
  const { messages } = await req.json();

  const response = await openai.createChatCompletion({
    model: 'gpt-3.5-turbo',
    stream: true,
    messages: messages.map((message: any) => ({
      content: message.content,
      role: message.role,
    })),
  });

  const stream = OpenAIStream(response);
  return new StreamingTextResponse(stream);
}