SQLite and SQL: In-depth Understanding of Core Relational Database Technologies

1. SQLite Overview

SQLite is a lightweight, embedded database engine widely used across various operating systems and applications, particularly on mobile devices. It supports standard SQL language and offers excellent portability and reliability. One of SQLite's core strengths lies in its lightweight design, allowing easy integration into various applications without requiring a separate server setup.

2. Fundamentals of SQL Language

SQL (Structured Query Language) is a standard language for managing relational databases, designed to process and manipulate structured data stored in databases. SQL can be divided into four main parts:

  1. Data Query Language (DQL): Primarily uses the SELECT statement to retrieve data from the database.
  2. Data Manipulation Language (DML): Includes INSERT, UPDATE, and DELETE statements for adding, modifying, or deleting data.
  3. Data Definition Language (DDL): Uses commands like CREATE, ALTER, and DROP to create, modify, or delete database objects such as tables and views.
  4. Data Control Language (DCL): Manages transactions with COMMIT and ROLLBACK to ensure data consistency and integrity.

3. Creating Databases and Tables

  • Creating a Database: In SQLite, the database creation process is straightforward. By entering sqlite3 mydatabase.db in the command line, you can create a database file named mydatabase.db. Similarly, using the sqlite3_open() function with the database file name enables database creation in programming interfaces.

  • Creating Tables: Tables form the core of relational databases. In SQLite, a new table can be created using the CREATE TABLE command. Example:

CREATE TABLE Persons (
  Id_P INTEGER PRIMARY KEY,
  LastName TEXT NOT NULL,
  FirstName TEXT,
  Address TEXT,
  City TEXT
);

Here, Persons is the table name, and each field specifies a name and data type. The PRIMARY KEY designates the unique identifier column in the table.

4. Indexes

Indexes can significantly improve data retrieval speed. In particular, indexes enhance query performance in large databases, making data access more efficient.