pgconnect 0.1.5

A PostgreSQL connection and ORM library

PgConnect

PgConnect is a PostgreSQL connection and ORM library for Python. It provides an easy-to-use interface for connecting to PostgreSQL databases and performing common database operations.

Features

• Easy connection to PostgreSQL databases
• ORM-like interface for defining and interacting with database tables
• Support for various PostgreSQL data types
• Caching mechanism for improved performance

Installation

You can install PgConnect using pip:

pip install pgconnect

Caching

Caching in PgConnect is designed to improve performance by reducing the number of database queries for frequently accessed data. When caching is enabled, the library stores query results in memory (or another caching backend) to serve future requests faster.

Benefits of Caching

1. Reduced Latency: Queries can be served directly from the cache, reducing the time spent waiting for a response from the database.
2. Lower Database Load: Cached results reduce the number of queries sent to the database, freeing up resources for other operations.
3. Improved Scalability: Applications with high read-heavy traffic can benefit from caching to handle a larger number of requests efficiently.

When to Use Caching

• For frequently read data that does not change often.
• To improve the performance of applications with high traffic and concurrent requests.
• To optimize expensive database queries.

Caching Example

Here’s an example showing how caching can improve performance:

# Enable caching for the table with a cache key
users = pgconnect.Table(
    name="users",
    connection=connection,
    columns=[
        pgconnect.Column(name="id", type=pgconnect.DataType.SERIAL().primary_key().not_null()),
        pgconnect.Column(name="email", type=pgconnect.DataType.VARCHAR().unique().not_null()),
        pgconnect.Column(name="username", type=pgconnect.DataType.VARCHAR()),
        pgconnect.Column(name="password", type=pgconnect.DataType.TEXT()),
        pgconnect.Column(name="created_at", type=pgconnect.DataType.TIMESTAMP().default("NOW()")),
    ],
    cache=True,  # Enable caching
    cache_key="id",  # Specify cache key for lookups
    cache_ttl=60  # Cache TTL in seconds (optional, default is no expiry)
    cache_maxsize=1000  # Maximum number of items in the cache (optional, default is 1000)
)

# First query - data fetched from the database and stored in cache
user = await users.select("id", "username", email="[e-mail]")
print("Fetched from database:", user)

# Subsequent query with the same parameters - data fetched from cache
cached_user = await users.select("id", "username", email="[e-mail]")
print("Fetched from cache:", cached_user)

Performance Comparison:

• Without Caching: Each query triggers a request to the PostgreSQL server, which involves network overhead and database processing time.
• With Caching: After the first query, subsequent requests with the same parameters are served from the cache, reducing query time from milliseconds to microseconds.

By using caching strategically in your application, you can significantly improve response times and reduce database workload.

Usage

Connecting to the Database

import pgconnect
import asyncio

async def main():
    connection = pgconnect.Connection(
        host="your_host",
        port=5432,
        user="your_user",
        password="your_password",
        database="your_database"
    )

    # Define your table schema
    users = pgconnect.Table(
        name="users",
        connection=connection,
        columns=[
            pgconnect.Column(
                name="id",
                type=pgconnect.DataType.SERIAL().primary_key().not_null()
            ),
            pgconnect.Column(
                name="email",
                type=pgconnect.DataType.VARCHAR().unique().not_null()
            ),
            pgconnect.Column(
                name="username",
                type=pgconnect.DataType.VARCHAR()
            ),
            pgconnect.Column(
                name="password",
                type=pgconnect.DataType.TEXT(),
            ),
            pgconnect.Column(
                name="created_at",
                type=pgconnect.DataType.TIMESTAMP().default("NOW()")
            )
        ],
        cache=True,
        cache_key="id"
    )

    await users.create()
    print(users)

if __name__ == "__main__":
    asyncio.run(main())

Inserting Data

await users.insert(
    email="[e-mail]",
    username="example",
    password="password"
)

Here, this is inserting a new row into the users table with the email as [e-mail], username as example, and password as password.

Selecting Data

user = await users.select("id", "username", email="[e-mail]")
print(user)

Here, this is selecting the id and username columns for the row where the email is [e-mail].

Updating Data

await users.update({"id": 1}, username="new_username")

Here, this is updating the username column to new_username for the row where the id is 1.

Deleting Data

await users.delete(id=1)

Here, this is deleting the row from the users table where the id is 1.

Counting Rows

user_count = await users.count()
print(user_count)

Here, this is counting the total number of rows in the users table.

Checking Existence

user_exists = await users.exists(id=1)
print(user_exists)

Here, this is checking if there is any row in the users table where the id is 1.

Getting Columns

columns = await users.get_columns()
print(columns)

Here, this is retrieving the names and data types of all columns in the users table.

Dropping the Table

await users.drop()

Here, this is dropping the users table from the PostgreSQL database.

Truncating the Table

await users.truncate()

Here, this is truncating the users table, which removes all rows from the table.

Example Usage in a Script

Here is a complete example script demonstrating how to use these methods:

import pgconnect
import asyncio

async def main():
    connection = pgconnect.Connection(
        host="your_host",
        port=5432,
        user="your_user",
        password="your_password",
        database="your_database"
    )

    users = pgconnect.Table(
        name="users",
        connection=connection,
        columns=[
            pgconnect.Column(
                name="id",
                type=pgconnect.DataType.SERIAL().primary_key().not_null()
            ),
            pgconnect.Column(
                name="email",
                type=pgconnect.DataType.VARCHAR().unique().not_null()
            ),
            pgconnect.Column(
                name="username",
                type=pgconnect.DataType.VARCHAR()
            ),
            pgconnect.Column(
                name="password",
                type=pgconnect.DataType.TEXT(),
            ),
            pgconnect.Column(
                name="created_at",
                type=pgconnect.DataType.TIMESTAMP().default("NOW()")
            )
        ],
        cache=True,
        cache_key="id",
    )

    await users.create()
    print("Table created")

    # Insert data
    await users.insert(
        email="[e-mail]",
        username="example",
        password="password"
    )
    print("Data inserted")

    # Select data
    user = await users.select("id", "username", email="[e-mail]")
    print("Selected user:", user)

    # Update data
    await users.update({"id": 1}, username="new_username")
    print("Data updated")

    # Delete data
    await users.delete(id=1)
    print("Data deleted")

    # Count rows
    user_count = await users.count()
    print("User count:", user_count)

    # Check existence
    user_exists = await users.exists(id=1)
    print("User exists:", user_exists)

    # Get columns
    columns = await users.get_columns()
    print("Table columns:", columns)

    # Drop table
    await users.drop()
    print("Table dropped")

    # Truncate table
    await users.truncate()
    print("Table truncated")

if __name__ == "__main__":
    asyncio.run(main())

This script demonstrates how to use the Table class to perform various database operations, including creating a table, inserting, updating, deleting, selecting data, counting rows, checking existence, getting columns, dropping the table, and truncating the table.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

AdnanBinPulok - GitHub