PostgreSQL vs MySQL vs SQLite: Complete Database Comparison Guide

Compare PostgreSQL, MySQL, and SQLite to choose the right database for your project. Performance, features, use cases, and migration strategies included.

Introduction
Understanding SQL Databases
PostgreSQL: The Advanced Open-Source Database
MySQL: The Popular Workhorse
SQLite: The Embedded Database
Feature Comparison
Performance Comparison
Use Case Scenarios
Migration Strategies
Best Practices
Conclusion

Introduction

Choosing the right database is one of the most critical decisions in application development. PostgreSQL, MySQL, and SQLite are three of the most popular SQL databases, each with distinct strengths, use cases, and trade-offs. Understanding their differences will help you make an informed decision that aligns with your project’s requirements, scale, and constraints.

PostgreSQL has gained a reputation as the most advanced open-source relational database, offering extensive features and strict SQL compliance. MySQL remains the most widely used database globally, powering countless web applications with its simplicity and reliability. SQLite, while less visible, is the most deployed database engine in the world, embedded in billions of devices and applications.

This comprehensive guide compares PostgreSQL vs MySQL vs SQLite across performance, features, scalability, and real-world use cases. Whether you’re building a small web application, a large-scale enterprise system, or a mobile app, you’ll learn which database best fits your needs and how to leverage their unique capabilities.

Understanding SQL Databases

Before diving into specific databases, it’s essential to understand what makes SQL databases valuable and how they differ from NoSQL alternatives. SQL (Structured Query Language) databases store data in tables with predefined schemas, enforce relationships through foreign keys, and provide ACID (Atomicity, Consistency, Isolation, Durability) guarantees.

Relational Database Fundamentals

All three databases—PostgreSQL, MySQL, and SQLite—are relational database management systems (RDBMS) that:

Store data in tables with rows and columns
Enforce data integrity through constraints
Support SQL for querying and manipulation
Maintain relationships between tables
Provide transaction support

However, they differ significantly in their architecture, feature sets, and deployment models.

Database Architecture Types

Server-Based Databases (PostgreSQL, MySQL):

Run as separate server processes
Handle multiple concurrent connections
Require network access (even locally)
Support client-server architecture
Ideal for multi-user applications

Embedded Databases (SQLite):

Run as part of the application process
No separate server process needed
File-based storage
Zero configuration
Perfect for single-user or embedded systems

PostgreSQL: The Advanced Open-Source Database

PostgreSQL, often called “Postgres,” is an object-relational database system known for its advanced features, standards compliance, and extensibility. It’s developed by a global community and has been actively maintained for over 30 years.

Key Features

Advanced Data Types: PostgreSQL supports an extensive range of data types beyond standard SQL:

-- JSON and JSONB for document storage
CREATE TABLE products (
    id SERIAL PRIMARY KEY,
    name VARCHAR(100),
    metadata JSONB,  -- Binary JSON for better performance
    tags TEXT[]
);

-- Array types
INSERT INTO products (name, metadata, tags)
VALUES (
    'Laptop',
    '{"brand": "Dell", "specs": {"ram": "16GB"}}'::jsonb,
    ARRAY['electronics', 'computers']
);

-- Full-text search
SELECT * FROM products
WHERE to_tsvector('english', name || ' ' || metadata::text)
@@ to_tsquery('english', 'laptop');

Advanced Indexing: PostgreSQL offers multiple indexing strategies:

-- B-tree index (default)
CREATE INDEX idx_name ON products(name);

-- GIN index for JSONB and arrays
CREATE INDEX idx_metadata ON products USING GIN(metadata);

-- GiST index for full-text search
CREATE INDEX idx_search ON products USING GIST(to_tsvector('english', name));

-- Partial index for conditional indexing
CREATE INDEX idx_active_products ON products(name) WHERE active = true;

Extensibility: PostgreSQL’s extension system allows adding functionality:

-- Enable PostGIS for geographic data
CREATE EXTENSION postgis;

-- Enable UUID generation
CREATE EXTENSION "uuid-ossp";

-- Use UUID as primary key
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    email VARCHAR(255) UNIQUE NOT NULL
);

Strengths

✅ Standards Compliance: PostgreSQL closely follows SQL standards, making it easier to port applications

✅ Advanced Features: Supports complex queries, window functions, common table expressions (CTEs), and recursive queries

✅ Data Integrity: Strong type system and comprehensive constraint support

✅ Extensibility: Rich ecosystem of extensions (PostGIS, pg_trgm, hstore, etc.)

✅ Concurrent Access: Excellent handling of multiple concurrent connections

✅ JSON Support: Native JSON and JSONB types with indexing and querying capabilities

Limitations

❌ Memory Usage: Can be more memory-intensive than MySQL for simple workloads

❌ Learning Curve: Advanced features require deeper understanding

❌ Configuration Complexity: More configuration options can be overwhelming for beginners

Ideal Use Cases

Complex applications requiring advanced SQL features
Applications needing JSON/document storage alongside relational data
Geographic information systems (GIS) with PostGIS extension
Data warehousing and analytics
Applications requiring strict ACID compliance
Multi-tenant SaaS applications

MySQL: The Popular Workhorse

MySQL is the world’s most popular open-source database, powering millions of web applications from small blogs to large-scale platforms like Facebook, Twitter, and YouTube. Acquired by Oracle in 2010, MySQL continues to evolve with both open-source and commercial editions.

Key Features

Storage Engines: MySQL’s pluggable storage engine architecture allows choosing the right engine for your workload:

-- InnoDB (default, ACID-compliant)
CREATE TABLE orders (
    id INT AUTO_INCREMENT PRIMARY KEY,
    user_id INT,
    total DECIMAL(10, 2),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (user_id) REFERENCES users(id)
) ENGINE=InnoDB;

-- MyISAM (faster reads, no transactions)
CREATE TABLE logs (
    id INT AUTO_INCREMENT PRIMARY KEY,
    message TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
) ENGINE=MyISAM;

-- Memory (in-memory storage)
CREATE TABLE sessions (
    session_id VARCHAR(255) PRIMARY KEY,
    data TEXT,
    expires_at TIMESTAMP
) ENGINE=MEMORY;

Replication: MySQL offers robust replication capabilities:

-- Master server configuration (my.cnf)
[mysqld]
server-id = 1
log-bin = mysql-bin
binlog-format = ROW

-- Replica server configuration
[mysqld]
server-id = 2
relay-log = mysql-relay-bin
read-only = 1

Partitioning: MySQL supports table partitioning for large datasets:

-- Range partitioning by date
CREATE TABLE sales (
    id INT AUTO_INCREMENT,
    sale_date DATE,
    amount DECIMAL(10, 2),
    PRIMARY KEY (id, sale_date)
) PARTITION BY RANGE (YEAR(sale_date)) (
    PARTITION p2022 VALUES LESS THAN (2023),
    PARTITION p2023 VALUES LESS THAN (2024),
    PARTITION p2024 VALUES LESS THAN (2025),
    PARTITION p_future VALUES LESS THAN MAXVALUE
);

Strengths

✅ Performance: Excellent read performance, especially with MyISAM for read-heavy workloads

✅ Simplicity: Easy to set up and configure, great for beginners

✅ Ecosystem: Massive community, extensive documentation, and tooling support

✅ Replication: Mature replication features for high availability

✅ Compatibility: Works well with popular web frameworks (LAMP, LEMP stacks)

✅ Commercial Support: Available from Oracle and third-party vendors

Limitations

❌ Advanced Features: Fewer advanced SQL features compared to PostgreSQL

❌ JSON Support: JSON support is less mature than PostgreSQL’s JSONB

❌ Strict Mode: Default behavior can be less strict, leading to data integrity issues if not configured properly

❌ Storage Engine Choice: Need to understand different storage engines and their trade-offs

Ideal Use Cases

Web applications (especially PHP-based)
Content management systems (WordPress, Drupal, Joomla)
E-commerce platforms
Read-heavy applications
Applications requiring simple, straightforward database operations
Projects needing extensive community support and resources

SQLite: The Embedded Database

SQLite is a C-language library that provides a lightweight, disk-based database that doesn’t require a separate server process. It’s the most widely deployed database engine globally, embedded in everything from web browsers to mobile apps to operating systems.

Key Features

Zero Configuration: SQLite requires no setup—just include the library and start using it:

// Node.js example
const sqlite3 = require("sqlite3").verbose();
const db = new sqlite3.Database("./app.db");

db.serialize(() => {
  // Create table
  db.run(`CREATE TABLE IF NOT EXISTS users (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        email TEXT UNIQUE NOT NULL,
        name TEXT
    )`);

  // Insert data
  db.run(`INSERT INTO users (email, name) VALUES (?, ?)`, [
    "user@example.com",
    "John Doe",
  ]);

  // Query data
  db.each(`SELECT * FROM users`, (err, row) => {
    console.log(row);
  });
});

db.close();

File-Based Storage: All data is stored in a single file:

// JavaScript/Node.js example
const sqlite3 = require("sqlite3").verbose();
const db = new sqlite3.Database("./app.db");

// Create table
db.serialize(() => {
  db.run(`
        CREATE TABLE IF NOT EXISTS products (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL,
            price REAL,
            stock INTEGER DEFAULT 0
        )
    `);

  // Insert with transaction
  db.run("BEGIN TRANSACTION");

  const stmt = db.prepare(
    "INSERT INTO products (name, price, stock) VALUES (?, ?, ?)",
  );

  stmt.run("Laptop", 999.99, 10, (err) => {
    if (err) {
      db.run("ROLLBACK");
      throw err;
    }
  });

  stmt.run("Mouse", 29.99, 50, (err) => {
    if (err) {
      db.run("ROLLBACK");
      throw err;
    }
  });

  stmt.finalize((err) => {
    if (err) {
      db.run("ROLLBACK");
    } else {
      db.run("COMMIT");
    }
  });
});

db.close();

Full SQL Support: Despite its simplicity, SQLite supports most SQL features:

-- Window functions (SQLite 3.25+)
SELECT
    name,
    price,
    ROW_NUMBER() OVER (ORDER BY price DESC) as rank,
    AVG(price) OVER () as avg_price
FROM products;

-- Common Table Expressions (CTEs)
WITH RECURSIVE categories AS (
    SELECT id, name, parent_id, 0 as level
    FROM category
    WHERE parent_id IS NULL

    UNION ALL

    SELECT c.id, c.name, c.parent_id, cat.level + 1
    FROM category c
    JOIN categories cat ON c.parent_id = cat.id
)
SELECT * FROM categories;

-- JSON support (SQLite 3.9+)
SELECT
    json_extract(metadata, '$.brand') as brand,
    json_extract(metadata, '$.specs.ram') as ram
FROM products
WHERE json_extract(metadata, '$.category') = 'electronics';

Strengths

✅ Zero Configuration: No server setup, no configuration files

✅ Lightweight: Small footprint (~700KB library size)

✅ Fast: Excellent performance for single-user and read-heavy workloads

✅ Portable: Single file can be easily backed up, moved, or shared

✅ ACID Compliant: Full transaction support with ACID guarantees

✅ Embedded: Perfect for applications that need a database without external dependencies

Limitations

❌ Concurrent Writes: Limited write concurrency (database-level locking)

❌ No Network Access: Cannot be accessed over a network (file-based)

❌ Size Limitations: Practical limit around 140TB, but performance degrades with very large databases

❌ Limited User Management: No built-in user authentication or permissions

❌ No Stored Procedures: No support for stored procedures or functions

Ideal Use Cases

Mobile applications (iOS, Android)
Desktop applications
Embedded systems and IoT devices
Development and testing (quick prototyping)
Small to medium web applications with low traffic
Data analysis and reporting tools
Browser extensions
Configuration and caching

Feature Comparison

Let’s compare the three databases across key features:

Feature	PostgreSQL	MySQL	SQLite
Server Process	Required	Required	Not required
Concurrent Connections	Excellent	Good	Limited
ACID Compliance	Full	Full (InnoDB)	Full
JSON Support	Excellent (JSONB)	Good	Good
Full-Text Search	Excellent	Good	Basic
Window Functions	Full support	Full support	Full support
Stored Procedures	Yes (PL/pgSQL)	Yes	No
Triggers	Yes	Yes	Yes
Views	Yes (materialized)	Yes	Yes
Foreign Keys	Yes	Yes (InnoDB)	Yes
Transactions	Full	Full (InnoDB)	Full
Replication	Built-in	Built-in	No
Partitioning	Yes	Yes	No
Extensions	Extensive	Limited	No
Data Types	Extensive	Standard	Standard
Case Sensitivity	Case-sensitive	Configurable	Case-sensitive

Data Type Comparison

PostgreSQL offers the most extensive type system:

-- PostgreSQL: Advanced types
CREATE TABLE advanced_types (
    id SERIAL PRIMARY KEY,
    uuid_col UUID DEFAULT gen_random_uuid(),
    jsonb_col JSONB,
    array_col INTEGER[],
    range_col INT4RANGE,
    point_col POINT,
    polygon_col POLYGON,
    tsvector_col TSVECTOR,
    hstore_col HSTORE
);

MySQL provides standard types with some extensions:

-- MySQL: Standard types with extensions
CREATE TABLE standard_types (
    id INT AUTO_INCREMENT PRIMARY KEY,
    uuid_col CHAR(36),
    json_col JSON,
    enum_col ENUM('active', 'inactive', 'pending'),
    set_col SET('read', 'write', 'execute'),
    spatial_col POINT
);

SQLite uses dynamic typing with type affinity:

-- SQLite: Dynamic typing
CREATE TABLE flexible_types (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    -- SQLite uses type affinity, not strict types
    text_col TEXT,      -- Stores as TEXT
    numeric_col NUMERIC, -- Stores as NUMERIC
    blob_col BLOB,      -- Stores as BLOB
    real_col REAL       -- Stores as REAL
);

Performance Comparison

Performance characteristics vary significantly based on workload type, data size, and configuration. Here’s a practical comparison:

Read Performance

SQLite: Excellent for single-user read operations

Fast sequential reads
No network overhead
Ideal for read-heavy single-user applications

MySQL: Excellent for read-heavy multi-user applications

Optimized query cache
Efficient indexing with MyISAM
Great for web applications with many concurrent reads

PostgreSQL: Excellent for complex queries

Advanced query planner
Efficient handling of complex joins
Better for analytical workloads

Write Performance

SQLite: Limited by database-level locking

-- SQLite: Write operations block entire database
BEGIN TRANSACTION;
INSERT INTO orders (user_id, total) VALUES (1, 99.99);
INSERT INTO order_items (order_id, product_id, quantity) VALUES (1, 5, 2);
COMMIT; -- Blocks other writes during transaction

MySQL: Good write performance with InnoDB

-- MySQL: Row-level locking with InnoDB
BEGIN TRANSACTION;
INSERT INTO orders (user_id, total) VALUES (1, 99.99);
INSERT INTO order_items (order_id, product_id, quantity) VALUES (1, 5, 2);
COMMIT; -- Only locks affected rows

PostgreSQL: Excellent write performance with MVCC

-- PostgreSQL: Multi-Version Concurrency Control
BEGIN TRANSACTION;
INSERT INTO orders (user_id, total) VALUES (1, 99.99);
INSERT INTO order_items (order_id, product_id, quantity) VALUES (1, 5, 2);
COMMIT; -- Non-blocking reads, efficient writes

Benchmark Example

Here’s a simple benchmark comparing insert performance:

// Node.js benchmark example
const benchmark = async (db, name) => {
  const start = Date.now();

  await db.transaction(async (trx) => {
    for (let i = 0; i < 1000; i++) {
      await trx("users").insert({
        email: `user${i}@example.com`,
        name: `User ${i}`,
      });
    }
  });

  const duration = Date.now() - start;
  console.log(`${name}: ${duration}ms for 1000 inserts`);
};

// Typical results (varies by hardware and configuration):
// SQLite: ~50-100ms (single file, no network)
// MySQL: ~100-200ms (network overhead, but optimized)
// PostgreSQL: ~150-250ms (more overhead, but better concurrency)

⚠️ Important: Performance benchmarks are highly dependent on:

Hardware specifications
Database configuration
Data size and structure
Network latency (for server-based databases)
Concurrent load

Use Case Scenarios

Scenario 1: Small Web Application

Requirements: Blog or small business website, low traffic (< 1000 visitors/day), simple data structure

Recommendation: SQLite

// Express.js with SQLite
const express = require("express");
const sqlite3 = require("sqlite3").verbose();
const app = express();

const db = new sqlite3.Database("./blog.db");

// Simple blog post storage
db.serialize(() => {
  db.run(`CREATE TABLE IF NOT EXISTS posts (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        title TEXT NOT NULL,
        content TEXT,
        published_at DATETIME DEFAULT CURRENT_TIMESTAMP
    )`);
});

app.get("/posts", (req, res) => {
  db.all("SELECT * FROM posts ORDER BY published_at DESC", (err, rows) => {
    if (err) {
      res.status(500).json({ error: err.message });
      return;
    }
    res.json(rows);
  });
});

app.listen(3000);

Why SQLite: Zero configuration, perfect for small applications, easy deployment, no server management needed.

Scenario 2: E-commerce Platform

Requirements: Online store, moderate traffic, transactions, user accounts, product catalog

Recommendation: MySQL or PostgreSQL

// E-commerce with MySQL
const mysql = require("mysql2/promise");

const pool = mysql.createPool({
  host: "localhost",
  user: "root",
  password: "password",
  database: "ecommerce",
  waitForConnections: true,
  connectionLimit: 10,
  queueLimit: 0,
});

// Transaction example
async function createOrder(userId, items) {
  const connection = await pool.getConnection();

  try {
    await connection.beginTransaction();

    // Create order
    const [orderResult] = await connection.execute(
      "INSERT INTO orders (user_id, total, status) VALUES (?, ?, ?)",
      [userId, calculateTotal(items), "pending"],
    );

    const orderId = orderResult.insertId;

    // Add order items
    for (const item of items) {
      await connection.execute(
        "INSERT INTO order_items (order_id, product_id, quantity, price) VALUES (?, ?, ?, ?)",
        [orderId, item.productId, item.quantity, item.price],
      );

      // Update inventory
      await connection.execute(
        "UPDATE products SET stock = stock - ? WHERE id = ?",
        [item.quantity, item.productId],
      );
    }

    await connection.commit();
    return orderId;
  } catch (error) {
    await connection.rollback();
    throw error;
  } finally {
    connection.release();
  }
}

Why MySQL/PostgreSQL: Need concurrent access, transaction support, user management, and scalability.

Scenario 3: Mobile Application

Requirements: Mobile app (React Native), offline support, local data storage

Recommendation: SQLite

// React Native with react-native-sqlite-storage
import SQLite from "react-native-sqlite-storage";

class DatabaseHelper {
  static instance = null;
  static db = null;

  static getInstance() {
    if (!DatabaseHelper.instance) {
      DatabaseHelper.instance = new DatabaseHelper();
    }
    return DatabaseHelper.instance;
  }

  async initDatabase() {
    if (DatabaseHelper.db) {
      return DatabaseHelper.db;
    }

    DatabaseHelper.db = await SQLite.openDatabase({
      name: "app.db",
      location: "default",
    });

    await this.createTables();
    return DatabaseHelper.db;
  }

  async createTables() {
    return new Promise((resolve, reject) => {
      DatabaseHelper.db.transaction((tx) => {
        tx.executeSql(
          `CREATE TABLE IF NOT EXISTS notes (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            title TEXT NOT NULL,
            content TEXT,
            created_at INTEGER NOT NULL
          )`,
          [],
          () => resolve(),
          (error) => reject(error),
        );
      });
    });
  }

  async createNote(note) {
    const db = await this.initDatabase();
    return new Promise((resolve, reject) => {
      db.transaction((tx) => {
        tx.executeSql(
          "INSERT INTO notes (title, content, created_at) VALUES (?, ?, ?)",
          [note.title, note.content, Date.now()],
          (tx, results) => resolve(results.insertId),
          (error) => reject(error),
        );
      });
    });
  }

  async getAllNotes() {
    const db = await this.initDatabase();
    return new Promise((resolve, reject) => {
      db.transaction((tx) => {
        tx.executeSql(
          "SELECT * FROM notes ORDER BY created_at DESC",
          [],
          (tx, results) => {
            const notes = [];
            for (let i = 0; i < results.rows.length; i++) {
              notes.push(results.rows.item(i));
            }
            resolve(notes);
          },
          (error) => reject(error),
        );
      });
    });
  }
}

// Usage in React Native component
const db = DatabaseHelper.getInstance();

// Create a note
await db.createNote({
  title: "My Note",
  content: "Note content here",
});

// Get all notes
const notes = await db.getAllNotes();

Why SQLite: Native mobile support, offline-first, no network required, perfect for local storage. Works seamlessly with React Native and other JavaScript mobile frameworks.

Scenario 4: Data Analytics Platform

Requirements: Complex queries, aggregations, reporting, large datasets

Recommendation: PostgreSQL

-- Analytics queries with PostgreSQL
-- Window functions for rankings
SELECT
    product_id,
    SUM(quantity) as total_sold,
    RANK() OVER (ORDER BY SUM(quantity) DESC) as sales_rank,
    LAG(SUM(quantity)) OVER (ORDER BY date) as previous_day_sales,
    AVG(SUM(quantity)) OVER (
        ORDER BY date
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) as seven_day_avg
FROM sales
WHERE sale_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY product_id, date
ORDER BY total_sold DESC;

-- JSONB for flexible schema
SELECT
    user_id,
    jsonb_array_elements(metadata->'purchases') as purchase,
    (metadata->>'lifetime_value')::numeric as ltv
FROM users
WHERE metadata @> '{"premium": true}';

-- Full-text search
SELECT
    title,
    content,
    ts_rank_cd(
        to_tsvector('english', title || ' ' || content),
        plainto_tsquery('english', 'search query')
    ) as relevance
FROM articles
WHERE to_tsvector('english', title || ' ' || content)
    @@ plainto_tsquery('english', 'search query')
ORDER BY relevance DESC;

Why PostgreSQL: Advanced SQL features, excellent query planner, JSON support, extensibility for analytics.

Migration Strategies

Migrating from SQLite to PostgreSQL/MySQL

When your application outgrows SQLite, migration is straightforward:

// Migration script: SQLite to PostgreSQL
const sqlite3 = require("sqlite3").verbose();
const { Client } = require("pg");

async function migrateSQLiteToPostgreSQL(sqlitePath, pgConfig) {
  const sqliteDb = new sqlite3.Database(sqlitePath);
  const pgClient = new Client(pgConfig);

  await pgClient.connect();

  // Get all tables from SQLite
  sqliteDb.all(
    "SELECT name FROM sqlite_master WHERE type='table'",
    async (err, tables) => {
      for (const table of tables) {
        // Get table schema
        sqliteDb.all(
          `PRAGMA table_info(${table.name})`,
          async (err, columns) => {
            // Create table in PostgreSQL
            const createTableSQL = generateCreateTableSQL(table.name, columns);
            await pgClient.query(createTableSQL);

            // Migrate data
            sqliteDb.all(`SELECT * FROM ${table.name}`, async (err, rows) => {
              for (const row of rows) {
                const insertSQL = generateInsertSQL(table.name, row);
                await pgClient.query(insertSQL, Object.values(row));
              }
            });
          },
        );
      }
    },
  );
}

function generateCreateTableSQL(tableName, columns) {
  const columnDefs = columns
    .map((col) => {
      let type = mapSQLiteTypeToPostgreSQL(col.type);
      if (col.pk) type += " PRIMARY KEY";
      if (col.notnull && !col.pk) type += " NOT NULL";
      return `${col.name} ${type}`;
    })
    .join(", ");

  return `CREATE TABLE ${tableName} (${columnDefs})`;
}

function mapSQLiteTypeToPostgreSQL(sqliteType) {
  const typeMap = {
    INTEGER: "INTEGER",
    TEXT: "TEXT",
    REAL: "REAL",
    BLOB: "BYTEA",
    NUMERIC: "NUMERIC",
  };
  return typeMap[sqliteType.toUpperCase()] || "TEXT";
}

Migrating from MySQL to PostgreSQL

-- MySQL to PostgreSQL migration considerations

-- 1. AUTO_INCREMENT → SERIAL
-- MySQL:
CREATE TABLE users (
    id INT AUTO_INCREMENT PRIMARY KEY
);

-- PostgreSQL:
CREATE TABLE users (
    id SERIAL PRIMARY KEY
);

-- 2. ENUM types
-- MySQL:
CREATE TABLE status (
    value ENUM('active', 'inactive', 'pending')
);

-- PostgreSQL:
CREATE TYPE status_type AS ENUM ('active', 'inactive', 'pending');
CREATE TABLE status (
    value status_type
);

-- 3. String concatenation
-- MySQL:
SELECT CONCAT(first_name, ' ', last_name) as full_name FROM users;

-- PostgreSQL:
SELECT first_name || ' ' || last_name as full_name FROM users;

-- 4. Date functions
-- MySQL:
SELECT DATE_FORMAT(created_at, '%Y-%m-%d') FROM orders;

-- PostgreSQL:
SELECT TO_CHAR(created_at, 'YYYY-MM-DD') FROM orders;

-- 5. LIMIT/OFFSET syntax (same, but different behavior)
-- Both:
SELECT * FROM products LIMIT 10 OFFSET 20;

💡 Tip: Use migration tools like pgloader for automated MySQL to PostgreSQL migrations, or Flyway for version-controlled database migrations.

Best Practices

PostgreSQL Best Practices

-- 1. Use appropriate data types
CREATE TABLE users (
    id SERIAL PRIMARY KEY,  -- Not INT
    email VARCHAR(255) UNIQUE NOT NULL,  -- Not TEXT for emails
    created_at TIMESTAMPTZ DEFAULT NOW()  -- Use TIMESTAMPTZ, not TIMESTAMP
);

-- 2. Create indexes strategically
CREATE INDEX CONCURRENTLY idx_users_email ON users(email);
CREATE INDEX idx_users_created_at ON users(created_at DESC);

-- 3. Use EXPLAIN ANALYZE for query optimization
EXPLAIN ANALYZE
SELECT * FROM orders
WHERE user_id = 123
ORDER BY created_at DESC
LIMIT 10;

-- 4. Use connection pooling
-- In application code (Node.js example)
const { Pool } = require('pg');
const pool = new Pool({
    max: 20,  // Maximum connections
    idleTimeoutMillis: 30000,
    connectionTimeoutMillis: 2000,
});

-- 5. Use prepared statements
const query = {
    text: 'SELECT * FROM users WHERE email = $1',
    values: ['user@example.com']
};
await pool.query(query);

MySQL Best Practices

-- 1. Choose the right storage engine
CREATE TABLE orders (
    id INT AUTO_INCREMENT PRIMARY KEY,
    -- Use InnoDB for transactions
) ENGINE=InnoDB;

CREATE TABLE logs (
    id INT AUTO_INCREMENT PRIMARY KEY,
    -- Use MyISAM for read-heavy, no transactions
) ENGINE=MyISAM;

-- 2. Configure InnoDB properly
-- In my.cnf:
[mysqld]
innodb_buffer_pool_size = 1G  -- 70-80% of RAM
innodb_log_file_size = 256M
innodb_flush_log_at_trx_commit = 2  -- For better performance

-- 3. Use indexes effectively
CREATE INDEX idx_user_email ON users(email);
CREATE INDEX idx_order_user_date ON orders(user_id, created_at);

-- 4. Use connection pooling
-- In application code
const mysql = require('mysql2/promise');
const pool = mysql.createPool({
    host: 'localhost',
    user: 'root',
    password: 'password',
    database: 'app',
    waitForConnections: true,
    connectionLimit: 10,
    queueLimit: 0
});

-- 5. Enable query cache for read-heavy workloads
SET GLOBAL query_cache_size = 67108864;  -- 64MB
SET GLOBAL query_cache_type = 1;

SQLite Best Practices

// 1. Use WAL mode for better concurrency
const db = new sqlite3.Database("./app.db");
db.run("PRAGMA journal_mode = WAL"); // Write-Ahead Logging
db.run("PRAGMA synchronous = NORMAL"); // Balance safety and performance

// 2. Use transactions for bulk operations
db.serialize(() => {
  db.run("BEGIN TRANSACTION");

  const stmt = db.prepare("INSERT INTO users (email, name) VALUES (?, ?)");
  for (let i = 0; i < 1000; i++) {
    stmt.run(`user${i}@example.com`, `User ${i}`);
  }
  stmt.finalize();

  db.run("COMMIT");
});

// 3. Create indexes for frequently queried columns
db.run("CREATE INDEX IF NOT EXISTS idx_users_email ON users(email)");

// 4. Use prepared statements
const stmt = db.prepare("SELECT * FROM users WHERE email = ?");
stmt.get("user@example.com", (err, row) => {
  console.log(row);
});
stmt.finalize();

// 5. Vacuum periodically to reclaim space
db.run("VACUUM");

// 6. Set appropriate page size and cache size
db.run("PRAGMA page_size = 4096"); // Larger page size for better performance
db.run("PRAGMA cache_size = -64000"); // 64MB cache (negative = KB)

General Database Best Practices

✅ Always Use Transactions: Wrap related operations in transactions to maintain data integrity

✅ Use Parameterized Queries: Prevent SQL injection attacks

✅ Create Indexes Strategically: Index foreign keys and frequently queried columns

✅ Monitor Performance: Use EXPLAIN/EXPLAIN ANALYZE to understand query execution

✅ Backup Regularly: Implement automated backup strategies

✅ Use Connection Pooling: Manage database connections efficiently

✅ Normalize Data: Follow database normalization principles (but denormalize when needed for performance)

✅ Version Control Migrations: Use migration tools to version your database schema

Conclusion

Choosing between PostgreSQL, MySQL, and SQLite depends on your specific requirements, scale, and use case. Each database excels in different scenarios:

SQLite is perfect for embedded applications, mobile apps, small websites, and development/testing. Its zero-configuration approach and file-based storage make it ideal when you need a database without the overhead of a server process.
MySQL remains the go-to choice for web applications, especially those built with PHP or requiring simple, reliable database operations. Its massive ecosystem, excellent performance, and ease of use make it a solid choice for most web projects.
PostgreSQL shines when you need advanced features, complex queries, strict SQL compliance, or extensibility. It’s the best choice for applications requiring JSON/document storage, geographic data, or sophisticated data analysis.

Remember that you’re not locked into one choice forever. Many successful applications start with SQLite for prototyping, migrate to MySQL for initial production, and eventually move to PostgreSQL as they scale and require more advanced features. The key is understanding your current needs and choosing the database that best fits your requirements today, while keeping migration paths open for the future.

For related topics, check out our guides on GraphQL vs REST API for API design decisions, and Web Performance Optimization for optimizing database queries in web applications.

Whether you’re building a simple blog or a complex enterprise application, understanding these three databases will help you make informed decisions that set your project up for success.

Table of Contents

Introduction

Understanding SQL Databases

Relational Database Fundamentals

Database Architecture Types

PostgreSQL: The Advanced Open-Source Database

Key Features

Strengths

Limitations

Ideal Use Cases

MySQL: The Popular Workhorse

Key Features

Strengths

Limitations

Ideal Use Cases

SQLite: The Embedded Database

Key Features

Strengths

Limitations

Ideal Use Cases

Feature Comparison

Data Type Comparison

Performance Comparison

Read Performance

Write Performance

Benchmark Example

Use Case Scenarios

Scenario 1: Small Web Application

Scenario 2: E-commerce Platform

Scenario 3: Mobile Application

Scenario 4: Data Analytics Platform

Migration Strategies

Migrating from SQLite to PostgreSQL/MySQL

Migrating from MySQL to PostgreSQL

Best Practices

PostgreSQL Best Practices

MySQL Best Practices

SQLite Best Practices

General Database Best Practices

Conclusion