SQL typing tipsdatabase query typingSQL syntax practice

SQL Typing Tips: Master Database Query Syntax for Faster Development

Name: DevType
Author: DevType

Learn essential tips to type SQL queries faster. From SELECT statements and JOINs to subqueries and aggregations, improve your SQL typing speed and accuracy.

SQL (Structured Query Language) is the standard language for managing and manipulating relational databases. Whether you're a backend developer, data analyst, or database administrator, mastering SQL typing can significantly boost your productivity.

Why SQL Typing Skills Matter

Database work often involves writing complex queries with specific syntax patterns. Being able to type SQL quickly and accurately means less time on query construction and more time on data analysis and optimization.

Essential SQL Keywords to Master

SELECT / FROM / WHERE

The foundation of every query. Practice typing these until they're automatic.

JOIN / ON

Essential for combining data from multiple tables.

GROUP BY / HAVING

Critical for aggregation queries.

ORDER BY / LIMIT

Common for sorting and pagination.

INSERT / UPDATE / DELETE

Core DML operations.

Basic SELECT Patterns

Practice these fundamental query patterns:

sql

SELECT * FROM users;

sql

SELECT id, name, email FROM users WHERE active = 1;

sql

SELECT name, COUNT(*) FROM orders GROUP BY name;

JOIN Patterns

JOINs are essential for relational data. Master these patterns:

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SELECT u.name, o.total
FROM users u
INNER JOIN orders o ON u.id = o.user_id;

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SELECT a.name, b.value
FROM table_a a
LEFT JOIN table_b b ON a.id = b.a_id;

Subquery Patterns

Subqueries are powerful for complex data retrieval:

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SELECT * FROM users
WHERE id IN (SELECT user_id FROM orders WHERE total > 100);

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SELECT name, (SELECT COUNT(*) FROM orders o WHERE o.user_id = u.id) as order_count
FROM users u;

Aggregation Patterns

Master aggregation functions and grouping:

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SELECT category, SUM(amount), AVG(price)
FROM products
GROUP BY category
HAVING SUM(amount) > 1000;

Window Function Patterns

Window functions are essential for advanced analytics:

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SELECT name, salary,
  ROW_NUMBER() OVER (ORDER BY salary DESC) as rank
FROM employees;

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SELECT department, name, salary,
  RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank,
  AVG(salary) OVER (PARTITION BY department) as dept_avg
FROM employees;

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SELECT date, revenue,
  LAG(revenue, 1) OVER (ORDER BY date) as prev_day,
  LEAD(revenue, 1) OVER (ORDER BY date) as next_day
FROM sales;

CTE (Common Table Expression) Patterns

CTEs make complex queries readable and maintainable:

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WITH active_users AS (
  SELECT * FROM users WHERE status = 'active'
)
SELECT * FROM active_users WHERE created_at > '2024-01-01';

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WITH RECURSIVE hierarchy AS (
  SELECT id, name, parent_id, 1 as level
  FROM categories WHERE parent_id IS NULL
  UNION ALL
  SELECT c.id, c.name, c.parent_id, h.level + 1
  FROM categories c
  INNER JOIN hierarchy h ON c.parent_id = h.id
)
SELECT * FROM hierarchy;

CASE WHEN Patterns

Conditional logic within queries:

sql

SELECT name,
  CASE
    WHEN score >= 90 THEN 'A'
    WHEN score >= 80 THEN 'B'
    WHEN score >= 70 THEN 'C'
    ELSE 'F'
  END as grade
FROM students;

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SELECT
  SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) as completed,
  SUM(CASE WHEN status = 'pending' THEN 1 ELSE 0 END) as pending
FROM orders;

UNION and Set Operations

Combine results from multiple queries:

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SELECT name, email FROM customers
UNION
SELECT name, email FROM suppliers;

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SELECT id FROM table_a
INTERSECT
SELECT id FROM table_b;

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SELECT id FROM table_a
EXCEPT
SELECT id FROM table_b;

UPDATE and DELETE with JOIN

Advanced data manipulation patterns:

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UPDATE orders o
SET status = 'archived'
FROM customers c
WHERE o.customer_id = c.id AND c.deleted_at IS NOT NULL;

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DELETE FROM orders
WHERE customer_id IN (
  SELECT id FROM customers WHERE status = 'inactive'
);

Transaction Patterns

Essential for data integrity:

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BEGIN;
UPDATE accounts SET balance = balance - 100 WHERE id = 1;
UPDATE accounts SET balance = balance + 100 WHERE id = 2;
COMMIT;

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BEGIN;
INSERT INTO orders (user_id, total) VALUES (1, 99.99);
INSERT INTO order_items (order_id, product_id) VALUES (LASTVAL(), 5);
COMMIT;

NULL Handling and COALESCE

Handle NULL values effectively:

sql

SELECT name, COALESCE(phone, email, 'No contact') as contact
FROM users;

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SELECT * FROM users WHERE deleted_at IS NULL;

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SELECT NULLIF(status, 'unknown') as valid_status FROM items;

EXISTS and NOT EXISTS

Check for existence efficiently:

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SELECT * FROM customers c
WHERE EXISTS (
  SELECT 1 FROM orders o WHERE o.customer_id = c.id
);

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SELECT * FROM products p
WHERE NOT EXISTS (
  SELECT 1 FROM order_items oi WHERE oi.product_id = p.id
);

DDL Patterns (Data Definition)

Create and modify database structures:

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CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name VARCHAR(255) NOT NULL,
  email VARCHAR(255) UNIQUE,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

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CREATE INDEX idx_users_email ON users(email);

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ALTER TABLE users ADD COLUMN phone VARCHAR(20);

Common SQL Symbols

•

Semicolon (;) - Terminates every statement

•

Asterisk (*) - Selects all columns

•

Equals (=) - Comparison operator

•

Single quotes ('') - String literals

•

Parentheses (()) - Grouping and subqueries

•

Comma (,) - Separates columns and values

Practice Tips

1. Start with simple SELECT statements

2. Progress to JOINs and subqueries

3. Practice typing table aliases (u, o, a, b)

4. Master the AS keyword for column aliases

5. Get comfortable with NULL handling (IS NULL, IS NOT NULL)

Regular practice with DevType's SQL exercises will help you internalize these patterns and type queries with confidence.

Put these tips into practice!

Use DevType to type real code and improve your typing skills.

Start Practicing