sourcebridgekit 0.2.3

Universal Python connector library for databases, files, cloud storage, and APIs with production-grade features

SourceBridgeKit

Universal Python Connector Library for Databases, Files, Cloud Storage, and APIs

Version: 0.2.0

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What is SourceBridgeKit?

SourceBridgeKit is a standard, reusable Python connector framework that provides:

• One Common API for all data sources (MySQL, Azure Blob, REST APIs, Excel, etc.)
• Configurable Everything - drivers, timeouts, connection pools, retry logic
• Environment Variable Support - secure credential management via ${VAR:default} syntax
• Pandas & Polars Output - fetch data in your preferred DataFrame format
• Batch Operations - memory-efficient reads/writes for large datasets
• Incremental Loading - configurable strategies for change detection
• Production Ready - retry logic, circuit breakers, connection pooling, SSL verification

SourceBridgeKit focuses on source access only - no preprocessing, no transformations, just clean data movement.

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Quick Start

Installation

# Core library only
pip install sourcebridgekit

# With MySQL support
pip install sourcebridgekit[mysql]

# With Azure Blob Storage
pip install sourcebridgekit[azure]

# Everything
pip install sourcebridgekit[all]

Basic Usage

from sourcebridgekit import connect

# Connect with explicit config
with connect('mysql', config={
    'host': 'localhost',
    'database': 'analytics',
    'username': 'app_user',
    'password': '${MYSQL_PASSWORD}',  # From environment
}) as conn:
    result = conn.read('SELECT * FROM orders LIMIT 1000', output='pandas')
    df = result.data

# Or use environment prefix
with connect('mysql', env_prefix='MYSQL_') as conn:
    result = conn.read('SELECT * FROM orders', output='polars')
    df_pl = result.data

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Features

Supported Connectors (V1)

Category	Connectors
Databases	MySQL, PostgreSQL, MSSQL, ClickHouse, MongoDB, Elasticsearch
Files	CSV, JSON/JSONL, Excel, Parquet
Cloud	Azure Blob Storage, Azure Data Lake Gen2
APIs	REST API (with pagination and curl parsing)

Output Formats

• pandas - pandas DataFrame
• polars - Polars DataFrame
• arrow - PyArrow Table
• records - List of dictionaries
• raw - Driver-native format

Core Capabilities

✅ Connection management (connect, disconnect, test)
✅ Data operations (read, write, batch read/write)
✅ Metadata discovery (list databases, tables, describe schema)
✅ Incremental loading (high watermark, timestamp, file modified time)
✅ Checkpoint management (memory, JSON file, SQLite)
✅ Retry logic with exponential backoff
✅ Circuit breaker pattern
✅ Connection pooling
✅ SSL/TLS verification
✅ Secret redaction in logs

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Usage Examples

MySQL Connector

from sourcebridgekit import connect
from sourcebridgekit.connectors.sql import MySQLConfig

config = MySQLConfig(
    host='${MYSQL_HOST:localhost}',
    port=3306,
    database='analytics',
    username='${MYSQL_USER}',
    password='${MYSQL_PASSWORD}',
    driver='pymysql',  # or 'mysql-connector'
    pool={'enabled': True, 'pool_size': 10},
    retry={'enabled': True, 'max_attempts': 3}
)

with connect('mysql', config=config) as conn:
    # Simple read
    result = conn.read('SELECT * FROM orders WHERE status = "active"', output='pandas')
    
    # Batch read for large tables
    for batch in conn.read_batch('SELECT * FROM large_table', batch_size=10000):
        process(batch.data)
    
    # Write data
    conn.write(df, target='staging.new_orders', mode='append')
    
    # Metadata
    print(conn.list_tables(database='analytics'))
    schema = conn.describe_table('orders')

Azure Blob Storage

from sourcebridgekit import connect

config = {
    'account_name': '${AZURE_STORAGE_ACCOUNT}',
    'container_name': 'data',
    'connection_string': '${AZURE_STORAGE_CONNECTION_STRING}',
}

with connect('azure_blob', config=config) as conn:
    # Read file
    result = conn.read('data/sales/2026/sales.csv', output='pandas')
    
    # Write file
    conn.write(df, target='data/output/processed.parquet', format='parquet')
    
    # List files
    files = conn.list_files(prefix='data/sales/', pattern='*.csv')

REST API with Pagination

from sourcebridgekit import connect

config = {
    'base_url': 'https://api.example.com',
    'auth_type': 'bearer',
    'auth_token': '${API_TOKEN}',
    'pagination': {
        'enabled': True,
        'type': 'page',
        'page_size': 100,
        'max_pages': 50
    }
}

with connect('rest_api', config=config) as conn:
    result = conn.read('/v1/users', params={'status': 'active'}, output='pandas')
    df = result.data

Incremental Loading

from sourcebridgekit import connect

incremental_config = {
    'enabled': True,
    'strategy': 'high_watermark',
    'cursor_column': 'updated_at',
    'checkpoint_key': 'tenant_a.orders',
    'lookback_seconds': 300,
    'checkpoint_store': {'type': 'sqlite', 'path': './checkpoints.db'}
}

with connect('mysql', config=mysql_config) as conn:
    result = conn.read_incremental(
        table='orders',
        incremental=incremental_config,
        output='polars'
    )
    
    # Library automatically tracks checkpoint
    print(f"Fetched {result.row_count} new rows")
    print(f"New checkpoint: {result.checkpoint}")

Curl to REST API

from sourcebridgekit.connectors.api import RestConfig

# Parse curl command into structured config
config = RestConfig.from_curl('''
curl -X POST https://api.example.com/orders \
  -H "Authorization: Bearer ${API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{"status":"active"}'
''')

with connect('rest_api', config=config) as conn:
    result = conn.read(output='records')

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Configuration

Environment Variables

All configs support ${VAR_NAME} or ${VAR_NAME:default} syntax:

config = {
    'host': '${DB_HOST:localhost}',  # Fallback to 'localhost'
    'port': '${DB_PORT:5432}',
    'password': '${DB_PASSWORD}',    # Required, no default
}

Secrets Management

Sensitive fields use SecretStr and are redacted from logs:

from pydantic import SecretStr

config = MySQLConfig(
    password=SecretStr('secret123')  # Redacted in logs
)

Connection Pooling

config = MySQLConfig(
    pool={
        'enabled': True,
        'pool_size': 10,
        'max_overflow': 20,
        'pool_timeout': 30,
        'pool_recycle': 3600
    }
)

Retry & Circuit Breaker

config = MySQLConfig(
    retry={
        'enabled': True,
        'max_attempts': 3,
        'backoff_factor': 2.0,
        'timeout_seconds': 30
    },
    circuit_breaker={
        'enabled': True,
        'failure_threshold': 5,
        'recovery_timeout': 60
    }
)

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FetchResult Standard

All read operations return a FetchResult object:

result = conn.read('SELECT * FROM orders', output='pandas')

result.data              # pandas DataFrame
result.output_format     # 'pandas'
result.row_count         # Number of rows
result.columns           # List of column names
result.schema            # Column types
result.execution_time_ms # Query execution time
result.checkpoint        # Incremental checkpoint (if applicable)
result.metadata          # Additional metadata
result.warnings          # Any warnings

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Incremental Strategies

Strategy	Description	Best For
high_watermark	Track max value of cursor column	SQL databases, APIs
incrementing_id	Track max ID value	Append-only tables
timestamp_with_lookback	Timestamp + safety window	Distributed systems
file_modified_time	Track file modification time	Local files, object storage
checksum_or_etag	Detect changes by hash	Files, object storage

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Checkpoint Stores

Store	Use Case
memory	Testing only (state lost on restart)
json_file	Simple local jobs
sqlite	Default persistent checkpoint store

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Security

✅ SSL/TLS verification enabled by default
✅ Secrets redacted from logs and exceptions
✅ No raw shell command execution
✅ Parameterized SQL queries
✅ Configurable timeouts
✅ SecretStr for sensitive fields

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Roadmap

• V1 (Current): Core connectors, batch operations, incremental loading
• V2 (Planned): Async support, Redis/PostgreSQL checkpoint stores, OAuth2, OpenTelemetry
• V3 (Future): CDC (binlog, logical replication), Kafka/RabbitMQ, distributed execution

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Development

# Clone and install in dev mode
git clone https://github.com/yourorg/sourcebridgekit
cd sourcebridgekit
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=sourcebridgekit --cov-report=html

# Format code
black sourcebridgekit/
ruff check sourcebridgekit/

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License

MIT License

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Support

• Documentation: https://sourcebridgekit.readthedocs.io
• Issues: https://github.com/yourorg/sourcebridgekit/issues
• Examples: https://github.com/yourorg/sourcebridgekit/tree/main/examples