The Data Mapper pattern is a design pattern used by software developers in development to separate the database or data storage logic from the application. It helps to map data between the application’s domain model (objects representing the application’s data) and the database, hiding the database-specific implementation details from the application’s core logic. This separation allows for greater flexibility and maintainability when dealing with data persistence.
Here’s an explanation of the Data Mapper pattern in Python, along with an example:
Key Components of the Data Mapper Pattern:
- Domain Model: This represents the objects that make up the application’s data structure. These objects are typically independent of the data storage mechanism.
- Data Mapper: The Data Mapper is responsible for translating data between the domain model and the database. It performs CRUD (Create, Read, Update, Delete) operations on the database and maps database records to domain objects.
- Database: This is the data storage where the application’s data is stored.
Example in Python:
Let’s consider a simple example involving a User domain model, a Data Mapper, and an SQLite database in Python:
Python
import sqlite3
# Domain Model
class User:
__tablename__ = ’user’
User_id = Column(INTEGER, primary_key=True, index=True)
Username = Column(VARCHAR(100), nullable=False)
Email = Column(VARCHAR(250), nullable=False, unique=True)
def __init__(self, user_id, username, email):
self.user_id = user_id
self.username = username
self.email = email
# Data Mapper
class UserMapper:
def __init__(self, db_file):
self.db_file = db_file
def save(self, user):
with sqlite3.connect(self.db_file) as conn:
cursor = conn.cursor()
cursor.execute(
"INSERT INTO user (user_id, username, email) VALUES (?, ?, ?)",
(user.user_id, user.username, user.email)
)
conn.commit()
def find(self, user_id):
with sqlite3.connect(self.db_file) as conn:
cursor = conn.cursor()
cursor.execute("SELECT * FROM user WHERE user_id = ?", (user_id,))
row = cursor.fetchone()
if row:
user_id, username, email = row
return User(user_id, username, email)
return None
def update(self, user):
with sqlite3.connect(self.db_file) as conn:
cursor = conn.cursor()
cursor.execute(
"UPDATE user SET username = ?, email = ? WHERE user_id = ?",
(user.username, user.email, user.user_id)
)
conn.commit()
def delete(self, user_id):
with sqlite3.connect(self.db_file) as conn:
cursor = conn.cursor()
cursor.execute("DELETE FROM user WHERE user_id = ?", (user_id,))
conn.commit()
if __name__ == "__main__":
db_file = "user_data.db"
user_mapper = UserMapper(db_file)
# Create a new user
new_user = User(1, "john_doe", "john@example.com")
user_mapper.save(new_user)
# Find and update a user
retrieved_user = user_mapper.find(1)
if retrieved_user:
retrieved_user.username = "jane_doe"
user_mapper.update(retrieved_user)
# Delete a user
user_mapper.delete(1)
In this example, we have a User domain model representing a user with user_id, username, and email attributes. The UserMapper class handles the interaction with the SQLite database, including CRUD operations. This separation allows for easy management of data storage, keeping the domain model independent of the database details.
The Data Mapper pattern is commonly used in real-world applications, especially in larger and more complex systems, to maintain a clean separation between the application’s data structures and the underlying data storage mechanisms.
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