sonnixgres 0.2.0

A production-ready Python library for PostgreSQL database interactions with connection pooling, query caching, and rich console output.

sonnixgres

A production-ready Python library for PostgreSQL database interactions with connection pooling, query caching, and rich console output.

Features

• Connection Pooling: SQLAlchemy and psycopg2 thread-safe connection pooling
• Query Caching: Thread-safe result caching with configurable TTL
• Streaming Queries: Memory-efficient processing of large datasets
• Pagination Support: Built-in LIMIT/OFFSET for query results
• Batch Operations: Optimized data insertion with configurable batch sizes
• Type Inference: Automatic SQL type mapping from pandas DataFrames
• Transaction Management: Context managers for safe transaction handling
• Error Handling: Comprehensive exception hierarchy with retry logic
• Input Validation: SQL injection protection and parameter sanitization
• Logging: Structured JSON logging with performance monitoring

Installation

pip install sonnixgres

Quick Start

import pandas as pd
from sonnixgres import (
    get_connection,
    query_database,
    create_table,
    populate_table
)

# Create a connection using environment variables
with get_connection() as conn:
    # Query with caching
    df = query_database(
        conn,
        "SELECT * FROM users WHERE age > %s",
        params=(25,),
        use_cache=True,
        cache_ttl=300
    )
    print(df)

# Create and populate a table
data = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35]
})

with get_connection() as conn:
    create_table(conn, 'users')
    populate_table(conn, 'users', data)

Configuration

Set the following environment variables:

DB_HOST=localhost
DB_DATABASE=your_database
DB_USER=your_username
DB_PASSWORD=your_password
DB_PORT=5432
DB_SCHEMA=public
DB_TABLES=table1,table2

# Optional logging configuration
LOG_LEVEL=INFO
LOG_FORMAT=json
LOG_FILE=/path/to/logfile.log

Or create a .env file:

DB_HOST=localhost
DB_DATABASE=mydb
DB_USER=admin
DB_PASSWORD=secret
DB_PORT=5432

API Reference

Connection Management

create_connection()

Creates a new database connection.

conn = create_connection()

get_connection()

Gets a connection from the pool (context manager).

with get_connection() as conn:
    # Use connection
    pass

Query Operations

query_database(connection, query, params=None, limit=None, offset=None, use_cache=False, cache_ttl=300)

Execute a query and return results as a DataFrame.

df = query_database(
    conn,
    "SELECT * FROM users WHERE age > %s",
    params=(25,),
    limit=100,
    offset=0,
    use_cache=True,
    cache_ttl=600
)

query_database_streaming(connection, query, params=None, chunk_size=1000)

Stream large query results in chunks.

for chunk in query_database_streaming(conn, "SELECT * FROM large_table", chunk_size=1000):
    process(chunk)

Table Operations

create_table(connection, table_name)

Create a new table.

create_table(conn, 'users')

populate_table(connection, table_name, dataframe)

Populate a table with DataFrame data.

populate_table(conn, 'users', df)

update_records(connection, update_query, params=None)

Execute an UPDATE query.

update_records(conn, "UPDATE users SET age = %s WHERE id = %s", params=(26, 1))

create_view(connection, view_name, view_query)

Create a database view.

create_view(conn, 'active_users', "SELECT * FROM users WHERE active = true")

Utility Functions

save_results_to_csv(dataframe, filename, **kwargs)

Save DataFrame to CSV.

save_results_to_csv(df, 'results.csv')

display_results_as_table(dataframe, title='Results', max_rows=None)

Display results in console with rich formatting.

display_results_as_table(df, title='User Data', max_rows=50)

Classes

PostgresCredentials

Load credentials from environment variables.

from sonnixgres import PostgresCredentials

creds = PostgresCredentials()
print(creds.host, creds.database)

MetadataCache

Cache database metadata for performance.

from sonnixgres import MetadataCache

cache = MetadataCache(engine)
cache.refresh_metadata_cache()
columns = cache.retrieve_columns_info()

Error Handling

sonnixgres provides a comprehensive exception hierarchy:

from sonnixgres import (
    SonnixgresError,
    ConnectionError,
    QueryError,
    DataError,
    TableError,
    ValidationError
)

try:
    with get_connection() as conn:
        df = query_database(conn, "SELECT * FROM users")
except ConnectionError as e:
    print(f"Connection failed: {e}")
except QueryError as e:
    print(f"Query failed: {e}")
except ValidationError as e:
    print(f"Invalid input: {e}")

Testing

Run the test suite:

# Install test dependencies
pip install -r requirements.txt

# Run tests
pytest

# Run with coverage
pytest --cov=sonnixgres --cov-report=html

Development

# Clone repository
git clone https://github.com/SuperSonnix71/sonnixgres.git
cd sonnixgres

# Install in development mode
pip install -e .

# Run tests
pytest

# Build distribution
python -m build

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass
6. Submit a pull request

License

BSD-3-Clause License. See LICENSE file for details.

Author

Sonny Mir ([e-mail])

Links

• GitHub: https://github.com/SuperSonnix71/sonnixgres
• Issues: https://github.com/SuperSonnix71/sonnixgres/issues