lfcdemolib 0.0.11

Lakeflow Connect Demo Library

lfcdemolib

Lakeflow Connect Demo Library

A comprehensive Python library for building and managing Databricks Lakeflow Connect (LFC) demonstrations with support for multiple cloud providers and database types.

Features

• Simplified Demo Initialization: One-line setup for Databricks notebooks with DemoInstance
• Multi-Database Support: SQL Server, MySQL, PostgreSQL, Oracle
• Cloud Provider Support: Azure, Oracle Cloud Infrastructure (OCI)
• Change Data Capture (CDC): Built-in CDC/CT (Change Tracking) implementations
• Schema Evolution: Automatic schema evolution and migration handling
• Connection Management: Secure credential storage and retrieval
• DML Operations: Simplified data manipulation with automatic scheduling
• REST API Integration: Databricks workspace API wrapper
• Test Framework: Comprehensive testing utilities for database operations

Installation

pip install lfcdemolib

All database drivers are included as core dependencies:

• pymysql (MySQL)
• psycopg2-binary (PostgreSQL)
• pymssql (SQL Server)
• oracledb (Oracle)

Optional Dependencies

For development tools:

# Development tools (pytest, black, flake8, mypy, isort)
pip install "lfcdemolib[dev]"

# Documentation tools (sphinx)
pip install "lfcdemolib[docs]"

Quick Start

Databricks Notebook

import lfcdemolib

# Configuration
config_dict = {
    "source_connection_name": "lfcddemo-azure-mysql-both",
    "cdc_qbc": "cdc",
    "database": {
        "cloud": "azure",
        "type": "mysql"
    }
}

# One-line initialization
d = lfcdemolib.DemoInstance(config_dict, dbutils, spark)

# Create pipeline
d.create_pipeline(pipeline_spec)

# Execute DML operations
d.dml.execute_delete_update_insert()

# Get recent data
df = d.dml.get_recent_data()
display(df)

Tuple Unpacking (Advanced)

# Get all components
d, config, dbxs, dmls, dbx_key, dml_key, scheduler = lfcdemolib.DemoInstance(
    config_dict, 
    dbutils, 
    spark
)

# Use individual components
config.source_connection_name
dmls[dml_key].execute_delete_update_insert()
scheduler.get_jobs()

Core Components

DemoInstance

Simplified facade for demo initialization with automatic caching and scheduler management.

d = lfcdemolib.DemoInstance(config_dict, dbutils, spark)

Features:

• Singleton scheduler management
• Automatic instance caching
• Simplified one-line initialization
• Delegates to DbxRest for Databricks operations

LfcScheduler

Background task scheduler using APScheduler.

scheduler = lfcdemolib.LfcScheduler()
scheduler.add_job(my_function, 'interval', seconds=60)

DbxRest

Databricks REST API client with connection and secret management.

dbx = lfcdemolib.DbxRest(dbutils=dbutils, config=config, lfc_scheduler=scheduler)
dbx.create_pipeline(spec)

SimpleDML

Simplified DML operations with automatic scheduling.

dml = lfcdemolib.SimpleDML(secrets_json, config=config, lfc_scheduler=scheduler)
dml.execute_delete_update_insert()
df = dml.get_recent_data()

Pydantic Models

Type-safe configuration and credential management.

from lfcdemolib import LfcNotebookConfig, LfcCredential

# Validate configuration
config = LfcNotebookConfig(config_dict)

# Validate credentials
credential = LfcCredential(secrets_json)

Database Support

Supported Databases

• SQL Server: CDC and Change Tracking (CT) support
• MySQL: Full replication support
• PostgreSQL: Logical replication support
• Oracle: 19c and later

Supported Cloud Providers

• Azure: SQL Database, Azure Database for MySQL/PostgreSQL
• OCI: Oracle Cloud Infrastructure databases

Configuration

LfcNotebookConfig

config_dict = {
    "source_connection_name": "lfcddemo-azure-mysql-both",  # Required
    "cdc_qbc": "cdc",                                      # Required: "cdc" or "qbc"
    "target_catalog": "main",                               # Optional: defaults to "main"
    "source_schema": None,                                  # Optional: auto-detect
    "database": {                                           # Required if connection_name is blank
        "cloud": "azure",                                   # "azure" or "oci"
        "type": "mysql"                                     # "mysql", "postgresql", "sqlserver", "oracle"
    }
}

LfcCredential (V2 Format)

credential = {
    "host_fqdn": "myserver.database.windows.net",
    "port": 3306,
    "catalog": "mydb",
    "schema": "dbo",
    "username": "user",
    "password": "pass",
    "db_type": "mysql",
    "cloud": {
        "provider": "azure",
        "region": "eastus"
    },
    "dba": {
        "username": "admin",
        "password": "adminpass"
    }
}

Advanced Features

Automatic Scheduling

# DML operations run automatically
d = lfcdemolib.DemoInstance(config_dict, dbutils, spark)
# Auto-scheduled DML operations every 10 seconds

Custom Scheduler Jobs

def my_task():
    print("Running custom task")

d.scheduler.add_job(my_task, 'interval', seconds=30, id='my_task')

Connection Management

from lfcdemolib import LfcConn

# Manage Databricks connections
lfc_conn = LfcConn(workspace_client=workspace_client)
connection = lfc_conn.get_connection(connection_name)

Secret Management

from lfcdemolib import LfcSecrets

# Manage Databricks secrets
lfc_secrets = LfcSecrets(workspace_client=workspace_client)
secret = lfc_secrets.get_secret(scope='lfcddemo', key='mysql_password')

Local Credential Storage

from lfcdemolib import SimpleLocalCred

# Save credentials locally
cred_manager = SimpleLocalCred()
cred_manager.save_credentials(db_details, db_type='mysql', cloud='azure')

# Load credentials
credential = cred_manager.get_credential(
    host='myserver.database.windows.net',
    db_type='mysql'
)

Testing

SimpleTest

Comprehensive database test suite.

from lfcdemolib import SimpleTest

tester = SimpleTest(workspace_client, config)
results = tester.run_comprehensive_tests()

Command-Line Tools

Deploy Credentials

cd lfc/db/bin
python deploy_credentials_to_workspaces.py \
    --credential-file ~/.lfcddemo/credentials.json \
    --target-workspace prod

Convert Secrets

python convert_secret_to_credential.py \
    --scope-name lfcddemo \
    --secret-name mysql-connection \
    --source azure

Examples

Multi-Database Demo

import lfcdemolib

# MySQL
mysql_d = lfcdemolib.DemoInstance(mysql_config, dbutils, spark)
mysql_d.create_pipeline(mysql_spec)

# PostgreSQL
pg_d = lfcdemolib.DemoInstance(pg_config, dbutils, spark)
pg_d.create_pipeline(pg_spec)

# SQL Server
sqlserver_d = lfcdemolib.DemoInstance(sqlserver_config, dbutils, spark)
sqlserver_d.create_pipeline(sqlserver_spec)

# All share the same scheduler
print(mysql_d.scheduler is pg_d.scheduler)  # True

Monitoring

# Check active jobs
for job in d.scheduler.get_jobs():
    print(f"{job.id}: {job.next_run_time}")

# Check cleanup queue
for item in d.cleanup_queue.queue:
    print(item)

Requirements

• Python >= 3.8
• Databricks Runtime 13.0+
• SQLAlchemy >= 1.4.0
• Pydantic >= 1.8.0 (v1 compatibility)
• APScheduler >= 3.9.0

License

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

Contributing

This is a Databricks Labs project. Contributions are welcome! Please ensure:

• Code follows PEP 8 style guidelines
• All tests pass
• Documentation is updated
• Pydantic v1 compatibility is maintained

Support

For issues, questions, or contributions, please contact the Databricks Labs team.

Changelog

Version 0.0.6 (Current)

• Fixed AttributeError: 'LfcNotebookConfig' object has no attribute 'get' in SimpleConn.py for Pydantic v2
• Added _get_config_value() helper method for safe config access from both Pydantic models and dicts
• Corrected README.md changelog (was incorrectly showing "Version 1.0.0", now shows accurate release history)
• Improved compatibility with both Pydantic v1 and v2 models

Version 0.0.5

• Fixed AttributeError with Pydantic v2 LfcNotebookConfig in SimpleConn.py
• Added _get_config_value() helper method for safe config access
• Improved compatibility with both Pydantic v1 and v2 models

Version 0.0.4

• Added Pydantic v1/v2 compatibility layer (_pydantic_compat.py)
• Now works with both Pydantic v1.10+ and v2.x
• Resolves dependency conflicts with langchain, databricks-agents, etc.
• Updated LfcCredentialModel and LfcNotebookConfig to use compatibility layer

Version 0.0.3

• Fixed VERSION file not included in MANIFEST.in (build error fix)
• Added VERSION to package manifest for proper sdist builds
• Fixed cleanup queue display format in notebooks

Version 0.0.2

• Fixed pydantic version requirement for Databricks compatibility
• Added typing_extensions compatibility
• All database drivers included as core dependencies
• Updated description to "Lakeflow Connect Demo Library"

Version 0.0.1

• Initial release
• DemoInstance facade for simplified initialization
• Support for MySQL, PostgreSQL, SQL Server, Oracle
• Azure and OCI cloud provider support
• Pydantic v1-based validation
• APScheduler integration
• Comprehensive test framework

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