MangledDlt 0.1.2

Local Databricks Development Bridge - Intercept Spark operations for local Unity Catalog access

MangledDLT - Local Databricks Development Bridge

MangledDLT enables developers to write and test Databricks code locally by intercepting Spark operations and fetching data from remote Unity Catalog environments. Write your PySpark code once and run it anywhere - locally or on Databricks - without changes.

Features

• Transparent Spark Interception: Automatically intercepts spark.read.table() and spark.readStream.table() calls
• Unity Catalog Integration: Fetches data directly from remote Unity Catalog tables
• Smart Caching: LRU cache with TTL for improved development performance
• Multiple Auth Methods: Supports PAT, OAuth, and Service Principal authentication
• Zero Code Changes: Same code works locally and on Databricks
• Connection Pooling: Efficient connection management for better performance
• Error Recovery: Automatic retry with exponential backoff

Installation

pip install MangledDlt

Or with all dependencies:

pip install MangledDlt[all]

Quick Start

from pyspark.sql import SparkSession
from mangledlt import MangledDLT

# Create Spark session as usual
spark = SparkSession.builder \
    .appName("LocalDev") \
    .getOrCreate()

# Enable MangledDLT
mdlt = MangledDLT()
mdlt.enable()

# Now you can read from Unity Catalog!
df = spark.read.table("main.default.customers")
df.show()

# When done, disable interception
mdlt.disable()

Configuration

Using Environment Variables

export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_TOKEN="dapi..."
export DATABRICKS_WAREHOUSE_ID="your-warehouse-id"

Using Databricks CLI Config

# Configure Databricks CLI
databricks configure --token

# MangledDLT will automatically use your configuration

Using Custom Config

from mangledlt import MangledDLT

config = {
    "host": "https://workspace.cloud.databricks.com",
    "token": "your-token",
    "warehouse_id": "warehouse-id",
    "cache_enabled": True,
    "cache_ttl": 600  # 10 minutes
}

mdlt = MangledDLT(config=config)
mdlt.enable()

Development vs Production

from pyspark.sql import SparkSession
from mangledlt import MangledDLT

spark = SparkSession.builder.appName("MyApp").getOrCreate()

# Auto-detect environment
if not spark.conf.get("spark.databricks.service.clusterId"):
    # Running locally - enable MangledDLT
    mdlt = MangledDLT()
    mdlt.enable()
    print("Running locally with MangledDLT")
else:
    print("Running on Databricks")

# Your code works the same in both environments
customers = spark.read.table("catalog.schema.customers")
orders = spark.read.table("catalog.schema.orders")
result = customers.join(orders, "customer_id")
result.show()

Caching

MangledDLT includes intelligent caching to speed up iterative development:

mdlt = MangledDLT(config={
    "cache_enabled": True,
    "cache_ttl": 1800,  # 30 minutes
    "cache_max_size": 100  # Max 100 cached queries
})
mdlt.enable()

# First read - fetches from Unity Catalog
df1 = spark.read.table("catalog.schema.large_table")  # Takes 5 seconds

# Subsequent reads - served from cache
df2 = spark.read.table("catalog.schema.large_table")  # Takes <100ms

# Check cache statistics
stats = mdlt.get_cache_stats()
print(f"Cache hits: {stats['hits']}")
print(f"Hit rate: {stats['hit_rate']}%")

# Clear cache when needed
mdlt.clear_cache()

Error Handling

from mangledlt import MangledDLT
from mangledlt.exceptions import AuthError, TableNotFoundError

try:
    mdlt = MangledDLT()
    mdlt.enable()

    df = spark.read.table("catalog.schema.table")
    df.show()

except AuthError as e:
    print(f"Authentication failed: {e}")
    print("Please check your Databricks credentials")

except TableNotFoundError as e:
    print(f"Table not found: {e}")
    print("Please verify the table exists and you have access")

Multiple Workspaces

from mangledlt import MangledDLT
from mangledlt.config import Config

# Connect to development workspace
dev_config = Config.from_file(profile="DEV")
dev_mdlt = MangledDLT(config=dev_config)
dev_mdlt.enable()

# Read from dev
dev_data = spark.read.table("dev_catalog.schema.table")

# Switch to production
dev_mdlt.disable()
prod_config = Config.from_file(profile="PROD")
prod_mdlt = MangledDLT(config=prod_config)
prod_mdlt.enable()

# Read from production
prod_data = spark.read.table("prod_catalog.schema.table")

API Reference

MangledDLT

Main class for enabling local Databricks development.

• __init__(config=None): Initialize with optional configuration
• enable(): Enable Spark operation interception
• disable(): Disable interception
• get_status(): Get connection status
• clear_cache(): Clear query cache
• get_cache_stats(): Get cache statistics

Config

Configuration management class.

• from_file(path, profile): Load from Databricks CLI config
• from_env(): Load from environment variables
• validate(): Validate configuration

Exceptions

• ConfigError: Configuration issues
• AuthError: Authentication failures
• ConnectionError: Connection problems
• TableNotFoundError: Table doesn't exist
• PermissionError: Insufficient permissions
• InvalidReferenceError: Invalid table reference format

Requirements

• Python 3.9+
• PySpark 3.4+ (user must install separately)
• databricks-sql-connector 2.9+

Development

# Clone the repository
git clone https://github.com/mangledlt/mangledlt.git
cd mangledlt

# Install in development mode
pip install -e .[dev]

# Run tests
pytest tests/

License

MIT License - see LICENSE file for details.

Support

• Issues: https://github.com/mangledlt/mangledlt/issues
• Discussions: https://github.com/mangledlt/mangledlt/discussions
• Documentation: https://github.com/mangledlt/mangledlt/docs