dbxsql 1.0.4

A comprehensive Databricks SQL handler with Pydantic models, OAuth authentication, and connection management

Databricks SQL Handler

A comprehensive Python package for interacting with Databricks SQL warehouses using Pydantic models, OAuth authentication, and robust connection management.

Features

• OAuth Authentication: Secure client credentials flow authentication
• Pydantic Models: Type-safe data models for query results
• Connection Management: Robust connection handling with automatic retry logic
• Query Execution: Execute single or multiple queries with structured results
• Built-in Models: Pre-defined models for common Databricks operations
• Extensible: Easy to extend with custom data models
• CLI Interface: Interactive command-line interface for testing and development

Installation

pip install dbxsql

Quick Start

Basic Usage

from dbxsql import QueryHandler, DatabricksSettings, NexsysRecord

# Configure settings (or use environment variables)
settings = DatabricksSettings(
    client_id="your_client_id",
    client_secret="your_client_secret", 
    server_hostname="your_databricks_hostname",
    http_path="/sql/1.0/warehouses/your_warehouse_id"
)

# Use as context manager
with QueryHandler(settings) as handler:
    # Execute a simple query
    result = handler.execute_query("SELECT * FROM my_table LIMIT 10")
    
    # Execute with Pydantic model parsing
    result = handler.execute_query(
        "SELECT * FROM nexsys_table LIMIT 10", 
        NexsysRecord
    )
    
    # Access structured data
    for record in result.data:
        print(f"ID: {record.id}, Name: {record.name}")

Environment Variables

Create a .env file:

DATABRICKS_CLIENT_ID=your_client_id
DATABRICKS_CLIENT_SECRET=your_client_secret
DATABRICKS_SERVER_HOSTNAME=your_hostname.databricks.com
DATABRICKS_HTTP_PATH=/sql/1.0/warehouses/warehouse_id
DATABRICKS_LOG_LEVEL=INFO

CLI Usage

# Interactive mode
dbxsql --interactive

# Run example queries
dbxsql --examples

# Execute a single query
dbxsql --query "SELECT current_timestamp()"

Custom Models

Create your own Pydantic models:

from pydantic import BaseModel
from datetime import datetime
from typing import Optional

class MyCustomModel(BaseModel):
    id: int
    name: str
    created_at: datetime
    amount: Optional[float] = None

# Register the model
from dbxsql import register_model
register_model("my_custom", MyCustomModel)

# Use it in queries and get a list of MyCustomModels
result = handler.execute_query("SELECT * FROM my_table", "my_custom")

Configuration

All configuration can be done via environment variables with the DATABRICKS_ prefix or programmatically:

Setting	Environment Variable	Default	Description
client_id	DATABRICKS_CLIENT_ID	Required	OAuth client ID
client_secret	DATABRICKS_CLIENT_SECRET	Required	OAuth client secret
server_hostname	DATABRICKS_SERVER_HOSTNAME	Required	Databricks hostname
http_path	DATABRICKS_HTTP_PATH	Required	SQL warehouse HTTP path
log_level	DATABRICKS_LOG_LEVEL	INFO	Logging level
max_retries	DATABRICKS_MAX_RETRIES	3	Query retry attempts
query_timeout	DATABRICKS_QUERY_TIMEOUT	300	Query timeout (seconds)

API Reference

QueryHandler

Main class for executing queries and managing connections.

Methods

• execute_query(query, model_class=None): Execute single query
• execute_multiple_queries(queries, model_classes=None): Execute multiple queries
• execute_query_with_retry(query, model_class=None): Execute with automatic retry
• list_files(path): List files in Databricks path
• show_tables(database=None): Show tables in database
• test_connection(): Test database connectivity

Built-in Models

• NexsysRecord: For NEXSYS system data
• SalesRecord: For sales transaction data
• FileInfo: For file listing results
• TableInfo: For table information
• GenericRecord: For unknown data structures

Error Handling

The package provides specific exceptions:

from dbxsql import (
    AuthenticationError,
    ConnectionError, 
    QueryExecutionError,
    SyntaxError,
    TimeoutError,
    DataParsingError
)

try:
    result = handler.execute_query("SELECT * FROM table")
except AuthenticationError:
    print("Authentication failed")
except QueryExecutionError as e:
    print(f"Query failed: {e}")

Troubleshooting

CERTIFICATE_VERIFY_FAILED

Sometimes, depending on an operational system the next error message could be returned:

dbxsql.exceptions.ConnectionError: Failed to connect to Databricks: Error during request to server: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain (_ssl.c:1007)

This error can be fixed by setting an environment variable SSL_CERT_FILE. This can be done in a terminal:

  export SSL_CERT_FILE=/path/to/databricks.pem

or directly in the code:

import os
import certifi

from dbxsql import QueryHandler
from dbxsql.settings import settings
from dbxsql.main import ApplicationRunner

os.environ["SSL_CERT_FILE"]  = certifi.where()


with QueryHandler(settings) as handler:
    app_runner = ApplicationRunner(handler)
    app_runner.run_example_queries()

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.