A cursor acts as a bridge between Python code and the MySQL database. It executes SQL queries and retrieves results row by row.

Cursors maintain query state and allow fetching single or multiple records. Without a cursor, SQL commands cannot be executed. Cursors are essential for controlled data access in Python applications.

A SELECT query is written in Python as a string and passed to the cursor’s execute() method. MySQL processes the query and returns results.

The cursor fetches the data using fetchone(), fetchall(), or fetchmany(). Python then converts rows into tuples or dictionaries. This flow enables dynamic data retrieval in backend systems.

MySQL manages table relationships using primary and foreign keys. Foreign keys link rows between tables, ensuring referential integrity. This prevents invalid data entries.

Python applications rely on these relationships when joining tables. Proper relationships improve data consistency and simplify complex queries.

MySQL is preferred for real-world backend applications, while SQLite is used for lightweight testing.

executemany() is commonly used when inserting large datasets from Python.

Choosing the right type improves storage and performance.

DELETE is safer for controlled row removal in applications.

MySQL is an open-source relational database system. Python uses MySQL for storing application data persistently. It supports structured data and relationships. MySQL integrates well with Flask and Django. It is widely used in backend development.

CRUD stands for Create, Read, Update, and Delete. These operations manage database records.

Python applications perform CRUD using SQL queries. CRUD forms the foundation of all database-driven applications. Almost every interview expects this concept.

Data is fetched using cursor methods like fetchone(), fetchall(), or fetchmany(). These methods return rows as tuples or dictionaries. Fetching data allows Python to process results. Proper fetching avoids memory issues. It is essential for APIs and reports.

commit() saves changes permanently to the database. rollback() undoes changes if an error occurs. They ensure transaction safety. Python calls these methods on the connection object. They prevent partial data updates.

A primary key uniquely identifies each row in a table. It prevents duplicate records. Python relies on primary keys for updates and joins. Every table should have one. It improves query performance.

A foreign key links one table to another. It ensures data consistency. Python joins tables using foreign keys. They prevent orphan records. Foreign keys support relational modeling.

Indexing improves query speed by reducing data scans. Indexes are created on frequently searched columns. Python applications benefit from faster lookups. Too many indexes slow inserts. Balanced indexing is important.

SQL Injection is prevented using parameterized queries. Placeholders are used instead of string formatting. MySQL drivers handle escaping automatically. This protects databases from attacks. Security questions are common in interviews.

Normalization organizes data to reduce redundancy. It splits data into related tables. Normalized databases are easier to maintain. Python works efficiently with normalized schemas. It improves data integrity.

JOIN combines data from multiple tables. Common types include INNER, LEFT, and RIGHT JOIN. Python applications use JOINs to fetch related data. JOINs reduce query count. They are essential for relational databases.

LIMIT restricts the number of rows returned. It improves performance for large datasets. Python uses LIMIT for pagination. It prevents memory overload. LIMIT is very common in APIs.

MySQL is fast, reliable, and scalable. It supports large datasets and multiple users. Python integrates easily with MySQL. It is open-source and widely supported. This makes it a popular backend choice.