What is DML

DML (Data Manipulation Language) refers to SQL statements used to work with the actual data in a database—namely SELECT, INSERT, UPDATE, and DELETE.

How It Works

DML commands let you retrieve, add, change, or remove rows from tables in a relational database. SELECT queries can filter, group, and combine data. INSERT statements add new rows. UPDATE changes existing rows. DELETE removes rows. Since these operations modify data, they often run in transactions to ensure consistency—either fully committing changes or rolling them back if something goes wrong.

Technical Details

Many databases offer flexibility in these commands—like multi-table UPDATEs or the ability to use subqueries in INSERT or DELETE statements. DML often goes hand in hand with DDL (Data Definition Language) and DCL (Data Control Language) to manage the full lifecycle of data. Performance can largely depend on indexing strategies, query optimization, and transaction handling.

How to Write It

Basic Syntax

-- Common DML statements in SQL

-- 1. SELECT: Query data
SELECT name, department
FROM employees
WHERE salary > 50000;

-- 2. INSERT: Add new data
INSERT INTO employees (name, department, salary)
VALUES ('John Doe', 'Engineering', 75000);

-- 3. UPDATE: Modify existing data
UPDATE employees
SET salary = salary * 1.1
WHERE department = 'Engineering';

-- 4. DELETE: Remove data
DELETE FROM employees
WHERE department = 'Deprecated';

Learn More

Best Practices

Use transactions when applying multiple related DML operations, ensuring data consistency.
Always try your data manipulation queries on a small subset or a test environment first.
Optimize DML statements with appropriate indexing and query plans, especially for large datasets.
Avoid SELECT * in production queries; specify columns to reduce overhead and future-proof your code.

Common Pitfalls

Forgetting the WHERE clause in an UPDATE or DELETE can affect all rows in the table.
Using transactions too broadly can lead to lock contention and hurt concurrency.
Not handling errors or rollbacks might leave inconsistent data if something fails mid-transaction.
Failing to consider indexing for frequent UPDATE or DELETE queries can degrade performance.

Advanced Tips

Use parameterized queries to prevent SQL injection and improve performance via caching.
Leverage window functions or CTEs (common table expressions) in SELECT queries for complex data transformations.
Explore database-specific features like MERGE (upsert) for combining INSERT and UPDATE logic.
Monitor and analyze query execution plans (EXPLAIN, EXPLAIN ANALYZE) to refine performance.