Sql

You have data. You need to get answers from it. SQL is the language that does this. Every app you use — social media, banking, shopping — stores data in a database. SQL is how developers read, filter, and organize that data. It has been around since the 1970s, and it is still the most important skill for working with data. What Is SQL? SQL stands for Structured Query Language. It is a language for talking to databases. You write a query, and the database gives you the answer. ...

In the previous tutorials, we used SchemaUtils.create() to create database tables. This works for development, but it has a big problem: it cannot handle schema changes. What happens when you need to add a column? Rename a table? Change a data type? You cannot just drop the database and recreate it — production data would be lost. This is where database migrations come in. Why Migrations Matter Without migrations, you have these problems: ...

A real application has connected data. Users own notes. Notes have tags. Orders belong to customers. These connections are called relationships. In this tutorial, you will add relationships between tables, write JOIN queries, and build advanced filtering, sorting, and pagination. Types of Relationships Type Example Implementation One-to-Many One user has many notes Foreign key on notes table Many-to-Many Notes have many tags, tags belong to many notes Join table (note_tags) One-to-One One user has one profile Foreign key with unique constraint One-to-Many: Users → Notes A user can have many notes. Each note belongs to one user (or no user). ...

Our API works, but the data lives in memory. Restart the server and everything is gone. Time to add a real database. In this tutorial, you will connect your Ktor API to a database using Exposed — JetBrains’ SQL library for Kotlin. We will use H2 for development and explain how to switch to PostgreSQL for production. What is Exposed? Exposed is a SQL library made by JetBrains. It gives you two ways to work with databases: ...

Bookmark this page. Use Ctrl+F (or Cmd+F on Mac) to find what you need. This cheat sheet covers SQL from basic queries to window functions and performance. Try examples at db-fiddle.com. Last updated: March 2026 SELECT Basics SELECT * FROM users; -- all columns SELECT name, email FROM users; -- specific columns SELECT DISTINCT city FROM users; -- unique values SELECT name AS full_name FROM users; -- column alias SELECT * FROM users LIMIT 10; -- first 10 rows SELECT * FROM users LIMIT 10 OFFSET 20; -- rows 21-30 (pagination) WHERE — Filtering Rows SELECT * FROM users WHERE age > 18; SELECT * FROM users WHERE city = 'Berlin'; SELECT * FROM users WHERE age BETWEEN 18 AND 30; SELECT * FROM users WHERE city IN ('Berlin', 'Munich', 'Hamburg'); SELECT * FROM users WHERE name LIKE 'A%'; -- starts with A SELECT * FROM users WHERE name LIKE '%son'; -- ends with son SELECT * FROM users WHERE name LIKE '%alex%'; -- contains alex SELECT * FROM users WHERE email IS NULL; -- null check SELECT * FROM users WHERE email IS NOT NULL; SELECT * FROM users WHERE age > 18 AND city = 'Berlin'; SELECT * FROM users WHERE age > 65 OR age < 18; SELECT * FROM users WHERE NOT city = 'Berlin'; ORDER BY and LIMIT SELECT * FROM users ORDER BY name; -- ascending (default) SELECT * FROM users ORDER BY age DESC; -- descending SELECT * FROM users ORDER BY city, name; -- multiple columns SELECT * FROM users ORDER BY age DESC LIMIT 5; -- top 5 oldest INSERT, UPDATE, DELETE -- Insert one row INSERT INTO users (name, email, age) VALUES ('Alex', 'alex@example.com', 25); -- Insert multiple rows INSERT INTO users (name, email, age) VALUES ('Sam', 'sam@example.com', 30), ('Jordan', 'jordan@example.com', 22); -- Update UPDATE users SET age = 26 WHERE name = 'Alex'; -- Update multiple columns UPDATE users SET city = 'Munich', age = 27 WHERE id = 1; -- Delete DELETE FROM users WHERE id = 5; -- Delete all rows (use with caution!) DELETE FROM users; -- Faster delete all (resets table) TRUNCATE TABLE users; -- UPSERT — insert or update if exists (PostgreSQL) INSERT INTO users (email, name) VALUES ('alex@example.com', 'Alex') ON CONFLICT (email) DO UPDATE SET name = EXCLUDED.name; -- MySQL equivalent INSERT INTO users (email, name) VALUES ('alex@example.com', 'Alex') ON DUPLICATE KEY UPDATE name = VALUES(name); Warning: Always use WHERE with UPDATE and DELETE. Without it, every row is affected. ...