SQLite is a self-contained, serverless, zero-configuration, transactional SQL database engine. It is widely used for embedded systems, desktop applications, and small-scale web applications due to its simplicity, reliability, and efficiency.

Key Features of SQLite:

1. Self-contained: SQLite is a single library that integrates directly into the application.
2. Serverless: SQLite does not require a separate server process or system to operate.
3. Zero-configuration: No setup or administration is necessary to create an SQLite database.
4. Transactional: SQLite transactions comply with ACID properties (Atomicity, Consistency, Isolation, Durability).

Creating a Database in SQLite

A database in SQLite is simply a file on disk. You can create a new database and tables, insert data, and query it using SQL commands.

Example:

1. Creating a Database: To create a database, you simply open a new SQLite connection to a database file. If the file does not exist, SQLite will create it.

2. Creating a Table:

   CREATE TABLE students (
       id INTEGER PRIMARY KEY,
       name TEXT NOT NULL,
       age INTEGER,
       grade TEXT
   );
   

3. Inserting Data:

   INSERT INTO students (name, age, grade) VALUES ('John Doe', 20, 'A');
   INSERT INTO students (name, age, grade) VALUES ('Jane Smith', 22, 'B');
   

4. Querying Data:

   SELECT * FROM students;
   

Detailed Steps with Outputs:

1. Creating the Database:

In Python, you can use the sqlite3 module to interact with SQLite.

import sqlite3

# Connect to a database (or create it if it doesn't exist)
conn = sqlite3.connect('example.db')

# Create a cursor object
cursor = conn.cursor()

# Close the connection (for now, we will reopen it later)
conn.close()

2. Creating a Table:

# Reconnect to the database
conn = sqlite3.connect('example.db')
cursor = conn.cursor()

# Create a table
cursor.execute('''
CREATE TABLE students (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    age INTEGER,
    grade TEXT
)
''')

# Commit the changes and close the connection
conn.commit()
conn.close()

3. Inserting Data:

# Reconnect to the database
conn = sqlite3.connect('example.db')
cursor = conn.cursor()

# Insert data
cursor.execute("INSERT INTO students (name, age, grade) VALUES ('John Doe', 20, 'A')")
cursor.execute("INSERT INTO students (name, age, grade) VALUES ('Jane Smith', 22, 'B')")

# Commit the changes and close the connection
conn.commit()
conn.close()

4. Querying Data:

# Reconnect to the database
conn = sqlite3.connect('example.db')
cursor = conn.cursor()

# Query data
cursor.execute("SELECT * FROM students")
rows = cursor.fetchall()

# Print the results
for row in rows:
    print(row)

# Close the connection
conn.close()

Example Output:

When you run the query to fetch all records from the students table, the output will look like this:

(1, 'John Doe', 20, 'A')
(2, 'Jane Smith', 22, 'B')

Each tuple represents a row in the table, with columns corresponding to the id, name, age, and grade fields.

Summary

SQLite databases are lightweight and easy to use. With simple SQL commands, you can create databases, tables, insert data, and run queries. This makes SQLite an excellent choice for many applications, especially those requiring a local database that is easy to manage.

Types of Databases in SQLite Database

SQLite is a lightweight, file-based database management system that supports various types of databases or data storage models. Unlike more complex DBMSs like Oracle, SQLite primarily focuses on the relational model, but it also supports handling non-relational data in a structured way. Here, we will discuss the types of databases and data models that SQLite supports with examples and outputs.

1. Relational Database

The primary type of database supported by SQLite is the relational database, which organizes data into tables consisting of rows and columns. It adheres to SQL standards and supports features such as primary keys, foreign keys, constraints, and indexes.

Example: Creating and Querying a Relational Table

-- Create a table
CREATE TABLE employees (
    employee_id INTEGER PRIMARY KEY,
    first_name TEXT NOT NULL,
    last_name TEXT NOT NULL,
    email TEXT UNIQUE,
    hire_date TEXT,
    salary REAL
);

-- Insert data into the table
INSERT INTO employees (first_name, last_name, email, hire_date, salary)
VALUES ('Alice', 'Johnson', 'alice.johnson@example.com', '2024-01-15', 75000.00);

INSERT INTO employees (first_name, last_name, email, hire_date, salary)
VALUES ('Bob', 'Smith', 'bob.smith@example.com', '2024-02-20', 80000.00);

-- Query the table
SELECT employee_id, first_name, last_name, email, hire_date, salary FROM employees;

Output:

2. Key-Value Storage

SQLite allows the storage and retrieval of data in a key-value format using the key and value columns within a single table. This approach can be useful for certain types of applications, such as configuration storage or caching mechanisms.

Example: Key-Value Storage

-- Create a table for key-value storage
CREATE TABLE kv_store (
    key TEXT PRIMARY KEY,
    value TEXT
);

-- Insert data into the key-value table
INSERT INTO kv_store (key, value) VALUES ('site_name', 'ExampleSite');
INSERT INTO kv_store (key, value) VALUES ('max_users', '1000');

-- Query the key-value table
SELECT key, value FROM kv_store;

Output:

3. JSON Data Handling

While SQLite does not have a native JSON data type, it supports storing JSON data as text and provides a set of JSON functions to manipulate JSON data. This allows SQLite to handle semi-structured data.

Example: Storing and Querying JSON Data

-- Create a table with a JSON column
CREATE TABLE orders (
    order_id INTEGER PRIMARY KEY,
    order_details TEXT
);

-- Insert JSON data into the table
INSERT INTO orders (order_id, order_details)
VALUES (1, '{"customer": "Alice", "items": [{"product": "Laptop", "price": 1000}]}');

INSERT INTO orders (order_id, order_details)
VALUES (2, '{"customer": "Bob", "items": [{"product": "Phone", "price": 500}]}');

-- Query the JSON data
SELECT order_id,
       json_extract(order_details, '$.customer') AS customer,
       json_extract(order_details, '$.items[0].product') AS product,
       json_extract(order_details, '$.items[0].price') AS price
FROM orders;

Output:

4. Full-Text Search (FTS)

SQLite provides an extension for full-text search (FTS), allowing efficient text searches within a database. This is particularly useful for applications requiring search functionality over large text data.

Example: Creating and Using a Full-Text Search Table

-- Create an FTS table
CREATE VIRTUAL TABLE documents USING fts5(content);

-- Insert data into the FTS table
INSERT INTO documents (content) VALUES ('This is a sample document about SQLite.');
INSERT INTO documents (content) VALUES ('Another document that mentions SQL and databases.');

-- Perform a full-text search
SELECT rowid, content FROM documents WHERE content MATCH 'SQLite';

Output:

Conclusion

SQLite is a versatile DBMS that supports various types of databases and data models, primarily focusing on the relational model but also extending capabilities to key-value storage, JSON data handling, and full-text search. This makes SQLite suitable for a wide range of applications, from simple configuration storage to complex data-driven applications requiring efficient search functionality.

Introduction to SQLite Database

SQLite is a self-contained, serverless, and lightweight database management system. Unlike traditional database management systems (DBMS) that rely on a client-server architecture, SQLite operates directly on the disk as a single file. This makes SQLite an ideal choice for embedded applications, mobile devices, and small-scale data management tasks.

Key Features of SQLite

1. Self-Contained: SQLite is a single library that implements a full SQL database engine. It requires no external dependencies.
2. Serverless: There is no need for a separate server process or configuration. The database is simply a file on disk.
3. Zero-Configuration: No setup or administration is required. Simply link the SQLite library into your application and start using it.
4. Cross-Platform: SQLite is available on almost all platforms, including Windows, macOS, Linux, iOS, and Android.
5. Compact Size: The entire library with all features enabled can be less than 600 KiB in size.
6. ACID Compliance: SQLite transactions are fully ACID (Atomicity, Consistency, Isolation, Durability) compliant, even after a system crash.

Example: Using SQLite

Let's explore a simple example of creating and querying a database using SQLite.

Step 1: Create a Database

SQLite databases are created and managed using SQL commands. A database is created simply by opening a connection to a file. If the file does not exist, SQLite creates it.

sqlite3 example.db

This command opens the SQLite command-line interface and creates a file named example.db.

Step 2: Create a Table

Next, create a table within this database.

CREATE TABLE employees (
    employee_id INTEGER PRIMARY KEY,
    first_name TEXT NOT NULL,
    last_name TEXT NOT NULL,
    email TEXT UNIQUE,
    hire_date TEXT,
    salary REAL
);

Output:

This command will create an empty table named employees.

Step 3: Insert Data

Insert some data into the employees table.

INSERT INTO employees (first_name, last_name, email, hire_date, salary)
VALUES ('Alice', 'Johnson', 'alice.johnson@example.com', '2024-01-15', 75000.00);

INSERT INTO employees (first_name, last_name, email, hire_date, salary)
VALUES ('Bob', 'Smith', 'bob.smith@example.com', '2024-02-20', 80000.00);

Output:

Two rows of data will be inserted into the employees table.

Step 4: Query Data

Retrieve data from the table using a simple SQL query.

SELECT employee_id, first_name, last_name, email, hire_date, salary FROM employees;

Output:

Step 5: Update Data

Update existing data in the table.

UPDATE employees SET salary = 85000.00 WHERE employee_id = 2;

Output:

The salary of the employee with employee_id 2 will be updated to 85000.00.

Step 6: Delete Data

Delete a row from the table.

DELETE FROM employees WHERE employee_id = 1;

Output:

The row with employee_id 1 will be deleted from the employees table.

Conclusion

SQLite is an efficient and user-friendly database management system, suitable for a wide range of applications, from embedded systems to mobile apps and small to medium-sized websites. Its serverless nature, ease of use, and robust SQL support make it a powerful tool for developers who need a simple yet effective database solution.

SQLite is a lightweight, self-contained, serverless SQL database engine. Here are its key features and capabilities, along with detailed explanations and examples with outputs:

1. Serverless Architecture

SQLite does not require a separate server process to operate. The entire database system is contained in a single library that is linked into the application. This simplifies deployment and eliminates the need for a separate database server.

2. Self-Contained

SQLite is self-contained, meaning it has no external dependencies. It reads and writes directly to ordinary disk files. A complete SQLite database is stored in a single cross-platform disk file.

3. Zero-Configuration

SQLite does not need to be installed and configured. There are no setup procedures, and the database engine can be used directly out of the box.

4. Transactional

SQLite is ACID-compliant, supporting transactions with atomic commit and rollback. This ensures the integrity of the database in the event of a crash.

5. Compact and Efficient

SQLite is very compact, with the library size typically being less than 500 KB. Despite its small size, it is highly efficient and can handle databases up to 140 terabytes in size.

6. Cross-Platform

SQLite is cross-platform and can be used on various operating systems, including Windows, Linux, and macOS. The database files are also portable across different systems.

7. Extensive SQL Support

SQLite supports most of the SQL92 standard, including complex queries, joins, indexes, and triggers. However, it has some limitations compared to full-fledged database systems like MySQL or PostgreSQL.

8. Full-Text Search (FTS)

SQLite includes a full-text search engine, which allows for efficient querying of text-heavy databases.

9. Backup and Restore

SQLite provides an API to create backups of the database, which can be used for creating database snapshots or copying data between different databases.

10. Concurrency

SQLite supports database-level locking for concurrency. While this can be a limitation for highly concurrent applications, it is often sufficient for applications with moderate concurrency requirements.

Example and Output

Let's go through an example of using SQLite in Python, demonstrating some of its capabilities.

Step 1: Create a Database and Table

import sqlite3

# Connect to SQLite database (or create it if it doesn't exist)
conn = sqlite3.connect('example.db')

# Create a cursor object to interact with the database
cursor = conn.cursor()

# Create a table
cursor.execute('''
CREATE TABLE IF NOT EXISTS users (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    email TEXT UNIQUE NOT NULL,
    age INTEGER
)
''')

# Commit the changes and close the connection
conn.commit()

Step 2: Insert Data

# Insert data into the table
cursor.execute('''
INSERT INTO users (name, email, age) VALUES
('Alice', 'alice@example.com', 30),
('Bob', 'bob@example.com', 25),
('Charlie', 'charlie@example.com', 35)
''')

# Commit the changes
conn.commit()

Step 3: Query Data

# Query data from the table
cursor.execute('SELECT * FROM users')

# Fetch all rows from the executed query
rows = cursor.fetchall()

# Print the rows
for row in rows:
    print(row)

Output

(1, 'Alice', 'alice@example.com', 30)
(2, 'Bob', 'bob@example.com', 25)
(3, 'Charlie', 'charlie@example.com', 35)

Step 4: Update Data

# Update data in the table
cursor.execute('''
UPDATE users
SET age = 31
WHERE name = 'Alice'
''')

# Commit the changes
conn.commit()

Step 5: Delete Data

# Delete data from the table
cursor.execute('''
DELETE FROM users
WHERE name = 'Bob'
''')

# Commit the changes
conn.commit()

Step 6: Backup Database

# Create a backup of the database
with sqlite3.connect('example_backup.db') as backup_conn:
    conn.backup(backup_conn)

Full-Text Search Example

Step 1: Create a Table with FTS

# Create a table with full-text search
cursor.execute('''
CREATE VIRTUAL TABLE IF NOT EXISTS articles USING fts5(
    title,
    body
)
''')

# Commit the changes
conn.commit()

Step 2: Insert Data

# Insert data into the FTS table
cursor.execute('''
INSERT INTO articles (title, body) VALUES
('SQLite Tutorial', 'Learn how to use SQLite with examples.'),
('Python and SQLite', 'Integrate SQLite into your Python applications.'),
('Full-Text Search', 'Implement full-text search with SQLite.')
''')

# Commit the changes
conn.commit()

Step 3: Query Data with Full-Text Search

# Query data using full-text search
cursor.execute("SELECT rowid, title FROM articles WHERE body MATCH 'SQLite'")

# Fetch all rows from the executed query
fts_rows = cursor.fetchall()

# Print the rows
for row in fts_rows:
    print(row)

Output

(1, 'SQLite Tutorial')
(2, 'Python and SQLite')

This example demonstrates SQLite's key features and capabilities, showing how easy it is to create, manage, and query a database using SQLite.

Definition of a Database

A database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system (DBMS). Together, the data and the DBMS, along with the applications associated with them, are referred to as a database system, often shortened to just "database."

Importance of Databases

Databases are crucial for several reasons:

1. Data Management: Databases allow for efficient storage, retrieval, and management of data.
2. Data Integrity: They enforce data integrity and consistency through constraints and transactions.
3. Data Security: Databases provide mechanisms to ensure data security and user access control.
4. Concurrency: They allow multiple users to access data concurrently without conflicts.
5. Scalability: Databases can handle large volumes of data and can be scaled to meet increasing data needs.
6. Data Analysis: They support complex queries, reporting, and data analysis.
7. Automation: Databases automate repetitive tasks and complex data operations.
8. Backup and Recovery: Databases provide tools for data backup and recovery in case of data loss.

SQLite Database: Definition and Importance

SQLite is a relational database management system contained in a C library. It is a lightweight, self-contained, serverless, zero-configuration, and transactional SQL database engine.

Importance of SQLite:

1. Simplicity and Ease of Use: SQLite is easy to set up and use, requiring no configuration or server setup.
2. Portability: The entire database is stored in a single file, making it highly portable across different platforms.
3. Performance: For many applications, SQLite provides sufficient performance and is highly efficient.
4. Reliability: SQLite is ACID-compliant, ensuring reliable transactions and data integrity.
5. Small Footprint: The library is very small, making it ideal for embedded systems and applications with limited resources.
6. Cross-Platform: SQLite works on various operating systems without modification.

Example and Output

Let's walk through an example that demonstrates the definition and importance of SQLite by creating, managing, and querying a database.

Step 1: Create a Database and Table

import sqlite3

# Connect to SQLite database (or create it if it doesn't exist)
conn = sqlite3.connect('example.db')

# Create a cursor object to interact with the database
cursor = conn.cursor()

# Create a table
cursor.execute('''
CREATE TABLE IF NOT EXISTS users (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    email TEXT UNIQUE NOT NULL,
    age INTEGER
)
''')

# Commit the changes
conn.commit()

Step 2: Insert Data

# Insert data into the table
cursor.execute('''
INSERT INTO users (name, email, age) VALUES
('Alice', 'alice@example.com', 30),
('Bob', 'bob@example.com', 25),
('Charlie', 'charlie@example.com', 35)
''')

# Commit the changes
conn.commit()

Step 3: Query Data

# Query data from the table
cursor.execute('SELECT * FROM users')

# Fetch all rows from the executed query
rows = cursor.fetchall()

# Print the rows
for row in rows:
    print(row)

Output

(1, 'Alice', 'alice@example.com', 30)
(2, 'Bob', 'bob@example.com', 25)
(3, 'Charlie', 'charlie@example.com', 35)

Step 4: Update Data

# Update data in the table
cursor.execute('''
UPDATE users
SET age = 31
WHERE name = 'Alice'
''')

# Commit the changes
conn.commit()

Step 5: Delete Data

# Delete data from the table
cursor.execute('''
DELETE FROM users
WHERE name = 'Bob'
''')

# Commit the changes
conn.commit()

Full-Text Search Example

Step 1: Create a Table with FTS

# Create a table with full-text search
cursor.execute('''
CREATE VIRTUAL TABLE IF NOT EXISTS articles USING fts5(
    title,
    body
)
''')

# Commit the changes
conn.commit()

Step 2: Insert Data

# Insert data into the FTS table
cursor.execute('''
INSERT INTO articles (title, body) VALUES
('SQLite Tutorial', 'Learn how to use SQLite with examples.'),
('Python and SQLite', 'Integrate SQLite into your Python applications.'),
('Full-Text Search', 'Implement full-text search with SQLite.')
''')

# Commit the changes
conn.commit()

Step 3: Query Data with Full-Text Search

# Query data using full-text search
cursor.execute("SELECT rowid, title FROM articles WHERE body MATCH 'SQLite'")

# Fetch all rows from the executed query
fts_rows = cursor.fetchall()

# Print the rows
for row in fts_rows:
    print(row)

Output

(1, 'SQLite Tutorial')
(2, 'Python and SQLite')

Conclusion

This example demonstrates the importance of databases in organizing and managing data effectively. SQLite, in particular, showcases how a lightweight, serverless database can be powerful yet easy to use, making it suitable for various applications, from embedded systems to small to medium-sized applications.

Historical Development of Database Systems

The development of database systems has gone through several stages, each marked by significant advancements and innovations. Here's a brief overview of the key stages in the historical development of database systems, culminating in the emergence of SQLite:

1. Early File-Based Systems (1950s-1960s)

   • File Systems: Early computers used flat file systems to store data. Data was stored in simple text files or binary files, and accessing or managing this data was cumbersome.
   • Limitations: Lack of standardization, redundancy, and difficulty in data retrieval and management.

2. Hierarchical and Network Databases (1960s-1970s)

   • Hierarchical Databases: Introduced by IBM with systems like IMS (Information Management System). Data was organized in a tree-like structure.
   • Network Databases: Codasyl model, which allowed more complex relationships among data than hierarchical databases.
   • Limitations: Complex structure and rigid schema design.

3. Relational Databases (1970s-1980s)

   • Relational Model: Proposed by E.F. Codd in 1970, this model organizes data into tables (relations) and uses SQL (Structured Query Language) for data manipulation.
   • Major Systems: IBM's System R, Oracle, MySQL, PostgreSQL.
   • Advantages: Flexibility, simplicity, and powerful query capabilities.

4. Object-Oriented Databases (1980s-1990s)

   • Object-Oriented DBMS: Integrated database capabilities with object-oriented programming languages. Examples include ObjectDB and db4o.
   • Limitations: Complexity and niche adoption.

5. NoSQL Databases (2000s-Present)

   • NoSQL Movement: Emergence of non-relational databases designed to handle unstructured data and scale horizontally. Examples include MongoDB, Cassandra, and Redis.
   • Advantages: Flexibility, scalability, and performance for specific use cases.

6. SQLite (2000-Present)

   • Introduction: Developed by D. Richard Hipp in 2000, SQLite is a lightweight, serverless, self-contained SQL database engine.
   • Significance: Designed for simplicity and ease of use, making it suitable for embedded systems, mobile devices, and applications with moderate concurrency.

SQLite Development and Capabilities

SQLite stands out due to its unique features and historical significance:

• 2000: SQLite is released as an open-source project.
• 2004: Introduction of transactions, making SQLite ACID-compliant.
• 2005: Full-text search (FTS) module added.
• 2010: Release of SQLite 3.7 with Write-Ahead Logging (WAL) for improved concurrency.
• 2018: Support for JSON1 extension, enabling JSON handling within SQLite.

Example and Output

Let's demonstrate SQLite's features with an example, focusing on its development capabilities.

Step 1: Create a Database and Table

import sqlite3

# Connect to SQLite database (or create it if it doesn't exist)
conn = sqlite3.connect('example.db')

# Create a cursor object to interact with the database
cursor = conn.cursor()

# Create a table
cursor.execute('''
CREATE TABLE IF NOT EXISTS users (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    email TEXT UNIQUE NOT NULL,
    age INTEGER
)
''')

# Commit the changes
conn.commit()

Step 2: Insert Data

# Insert data into the table
cursor.execute('''
INSERT INTO users (name, email, age) VALUES
('Alice', 'alice@example.com', 30),
('Bob', 'bob@example.com', 25),
('Charlie', 'charlie@example.com', 35)
''')

# Commit the changes
conn.commit()

Step 3: Query Data

# Query data from the table
cursor.execute('SELECT * FROM users')

# Fetch all rows from the executed query
rows = cursor.fetchall()

# Print the rows
for row in rows:
    print(row)

Output

(1, 'Alice', 'alice@example.com', 30)
(2, 'Bob', 'bob@example.com', 25)
(3, 'Charlie', 'charlie@example.com', 35)

Step 4: Full-Text Search Example

Create a Table with FTS

# Create a table with full-text search
cursor.execute('''
CREATE VIRTUAL TABLE IF NOT EXISTS articles USING fts5(
    title,
    body
)
''')

# Commit the changes
conn.commit()

Insert Data

# Insert data into the FTS table
cursor.execute('''
INSERT INTO articles (title, body) VALUES
('SQLite Tutorial', 'Learn how to use SQLite with examples.'),
('Python and SQLite', 'Integrate SQLite into your Python applications.'),
('Full-Text Search', 'Implement full-text search with SQLite.')
''')

# Commit the changes
conn.commit()

Query Data with Full-Text Search

# Query data using full-text search
cursor.execute("SELECT rowid, title FROM articles WHERE body MATCH 'SQLite'")

# Fetch all rows from the executed query
fts_rows = cursor.fetchall()

# Print the rows
for row in fts_rows:
    print(row)

Output

(1, 'SQLite Tutorial')
(2, 'Python and SQLite')

Conclusion

The development of database systems has evolved from simple file-based systems to sophisticated relational and NoSQL databases. SQLite represents a significant milestone in this evolution, offering a lightweight, self-contained, and highly efficient database engine suitable for various applications. Its ease of use, portability, and robust feature set make it a popular choice for developers worldwide.

Types of Database Models

Database models define the logical structure of a database and determine how data is stored, organized, and manipulated. The major types of database models include Relational, NoSQL, NewSQL, and others. Here's an overview of these models, with details on their characteristics and an example of how SQLite fits into these models.

1. Relational Database Model

Relational databases use a table-based structure to represent data and their relationships. Each table, known as a relation, consists of rows and columns, with each row representing a record and each column representing a field.

• Characteristics:

  • Structured data.
  • Uses SQL (Structured Query Language) for data manipulation.
  • Supports ACID (Atomicity, Consistency, Isolation, Durability) properties.
  • Ensures data integrity and consistency through constraints and relationships.

• Examples: MySQL, PostgreSQL, Oracle, SQLite.

Example with SQLite:

Creating and querying a relational database in SQLite:

import sqlite3

# Connect to SQLite database (or create it if it doesn't exist)
conn = sqlite3.connect('relational_example.db')

# Create a cursor object to interact with the database
cursor = conn.cursor()

# Create a table
cursor.execute('''
CREATE TABLE IF NOT EXISTS employees (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    position TEXT NOT NULL,
    salary REAL
)
''')

# Insert data into the table
cursor.execute('''
INSERT INTO employees (name, position, salary) VALUES
('Alice', 'Manager', 70000),
('Bob', 'Developer', 60000),
('Charlie', 'Analyst', 50000)
''')

# Commit the changes
conn.commit()

# Query data from the table
cursor.execute('SELECT * FROM employees')

# Fetch all rows from the executed query
rows = cursor.fetchall()

# Print the rows
for row in rows:
    print(row)

# Output:
# (1, 'Alice', 'Manager', 70000.0)
# (2, 'Bob', 'Developer', 60000.0)
# (3, 'Charlie', 'Analyst', 50000.0)

# Close the connection
conn.close()

2. NoSQL Database Model

NoSQL databases are designed to handle unstructured or semi-structured data. They are scalable and flexible, making them suitable for big data applications and real-time web applications.

• Characteristics:

  • Schema-less or flexible schema.
  • Horizontal scalability.
  • Different data models like document, key-value, column-family, and graph.
  • Suitable for handling large volumes of diverse data types.

• Examples: MongoDB (document), Redis (key-value), Cassandra (column-family), Neo4j (graph).

Example (Conceptual) with SQLite:

While SQLite is primarily a relational database, it can handle JSON data, which is common in NoSQL document stores.

# Insert JSON data into an SQLite table
cursor.execute('''
CREATE TABLE IF NOT EXISTS json_data (
    id INTEGER PRIMARY KEY,
    data TEXT
)
''')

# Insert a JSON document
cursor.execute('''
INSERT INTO json_data (data) VALUES
('{"name": "Alice", "role": "Manager", "projects": ["project1", "project2"]}')
''')

# Commit the changes
conn.commit()

# Query JSON data
cursor.execute('SELECT data FROM json_data WHERE id = 1')

# Fetch the result
json_result = cursor.fetchone()[0]
print(json_result)

# Output:
# {"name": "Alice", "role": "Manager", "projects": ["project1", "project2"]}

# Close the connection
conn.close()

3. NewSQL Database Model

NewSQL databases aim to combine the best features of traditional relational databases and NoSQL databases. They offer the scalability and performance of NoSQL systems while maintaining the ACID properties and SQL-based querying of relational databases.

• Characteristics:

  • High scalability and performance.
  • ACID compliance.
  • SQL-based querying.
  • Suitable for large-scale transactional applications.

• Examples: Google Spanner, CockroachDB, NuoDB.

Conclusion

SQLite primarily fits into the relational database model, but it has features that allow it to handle semi-structured data, like JSON, making it versatile for various applications. Here's a summary of the examples discussed:

1. Relational Database Example:

   • Creation and querying of a relational table.
   • Structured data and SQL-based querying.

2. NoSQL-like Example with JSON:

   • Inserting and querying JSON data.
   • Flexible handling of semi-structured data within a relational framework.

SQLite's simplicity, ease of use, and self-contained nature make it an excellent choice for applications requiring a lightweight and reliable database solution.

Overview of Database Management Systems (DBMS)

A Database Management System (DBMS) is a software system designed to create, manage, and manipulate databases. It provides an interface for users to interact with databases, ensuring data is organized and easily accessible while maintaining data integrity, security, and consistency. DBMS can be categorized based on the data models they support, such as relational, NoSQL, NewSQL, etc.

Key Features of a DBMS

1. Data Definition: Allows the creation, modification, and deletion of database schemas.
2. Data Storage, Retrieval, and Update: Facilitates efficient data storage, querying, and updating.
3. User Interfaces: Provides various user interfaces, such as command-line interfaces, graphical user interfaces, and application programming interfaces (APIs).
4. Data Integrity and Security: Ensures data accuracy and protection through constraints, authentication, and authorization.
5. Transaction Management: Supports ACID properties (Atomicity, Consistency, Isolation, Durability) to ensure reliable transactions.
6. Concurrency Control: Manages concurrent data access to maintain consistency and prevent conflicts.
7. Backup and Recovery: Provides mechanisms for data backup and recovery to prevent data loss.

SQLite as a DBMS

SQLite is a relational DBMS that is embedded, serverless, self-contained, and highly portable. It is widely used in various applications, from small embedded systems to large-scale data management systems, due to its simplicity and efficiency.

Key Features of SQLite

1. Serverless: No server setup or administration required; SQLite databases are self-contained.
2. Zero-Configuration: No configuration or installation required; databases can be created and managed with minimal setup.
3. Lightweight: Small footprint (library size is less than 500 KB).
4. Transactional: Fully ACID-compliant, ensuring reliable transactions.
5. Cross-Platform: Works on various operating systems and platforms.
6. Compact and Portable: Entire database stored in a single file, making it easy to share and transfer.
7. SQL Compliance: Supports most of the SQL92 standard.

Example and Output

Let's demonstrate some DBMS features using SQLite.

Step 1: Create a Database and Table

import sqlite3

# Connect to SQLite database (or create it if it doesn't exist)
conn = sqlite3.connect('example.db')

# Create a cursor object to interact with the database
cursor = conn.cursor()

# Create a table
cursor.execute('''
CREATE TABLE IF NOT EXISTS employees (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    position TEXT NOT NULL,
    salary REAL
)
''')

# Commit the changes
conn.commit()

Step 2: Insert Data

# Insert data into the table
cursor.execute('''
INSERT INTO employees (name, position, salary) VALUES
('Alice', 'Manager', 70000),
('Bob', 'Developer', 60000),
('Charlie', 'Analyst', 50000)
''')

# Commit the changes
conn.commit()

Step 3: Query Data

# Query data from the table
cursor.execute('SELECT * FROM employees')

# Fetch all rows from the executed query
rows = cursor.fetchall()

# Print the rows
for row in rows:
    print(row)

Output

(1, 'Alice', 'Manager', 70000.0)
(2, 'Bob', 'Developer', 60000.0)
(3, 'Charlie', 'Analyst', 50000.0)

Step 4: Update Data

# Update data in the table
cursor.execute('''
UPDATE employees
SET salary = 75000
WHERE name = 'Alice'
''')

# Commit the changes
conn.commit()

Step 5: Delete Data

# Delete data from the table
cursor.execute('''
DELETE FROM employees
WHERE name = 'Bob'
''')

# Commit the changes
conn.commit()

Step 6: Transaction Management

try:
    # Start a transaction
    conn.execute('BEGIN TRANSACTION')
    
    # Perform multiple operations
    cursor.execute("INSERT INTO employees (name, position, salary) VALUES ('David', 'Designer', 55000)")
    cursor.execute("UPDATE employees SET salary = 50000 WHERE name = 'Charlie'")
    
    # Commit the transaction
    conn.commit()
except sqlite3.Error as e:
    # Rollback the transaction in case of error
    conn.rollback()
    print("Transaction failed:", e)

Conclusion

SQLite demonstrates the core capabilities of a DBMS within a lightweight and easy-to-use package. It provides essential database management features such as data definition, storage, retrieval, integrity, security, transaction management, and concurrency control. This makes it a versatile choice for applications requiring an embedded or serverless database solution.