Databricks Lakebase Provisioned

Lakebase Provisioned

Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads.

When to Use

Use this skill when:

• Building applications that need a PostgreSQL database for transactional workloads
• Adding persistent state to Databricks Apps
• Implementing reverse ETL from Delta Lake to an operational database
• Storing chat/agent memory for LangChain applications

Overview

Lakebase Provisioned is Databricks' managed PostgreSQL database service for OLTP (Online Transaction Processing) workloads. It provides a fully managed PostgreSQL-compatible database that integrates with Unity Catalog and supports OAuth token-based authentication.

Available Regions (AWS): us-east-1, us-east-2, us-west-2, eu-central-1, eu-west-1, ap-south-1, ap-southeast-1, ap-southeast-2

Quick Start

Create and connect to a Lakebase Provisioned instance:

from databricks.sdk import WorkspaceClient
import uuid

# Initialize client
w = WorkspaceClient()

# Create a database instance
instance = w.database.create_database_instance(
    name="my-lakebase-instance",
    capacity="CU_1",  # CU_1, CU_2, CU_4, CU_8
    stopped=False
)
print(f"Instance created: {instance.name}")
print(f"DNS endpoint: {instance.read_write_dns}")

Common Patterns

Generate OAuth Token

from databricks.sdk import WorkspaceClient
import uuid

w = WorkspaceClient()

# Generate OAuth token for database connection
cred = w.database.generate_database_credential(
    request_id=str(uuid.uuid4()),
    instance_names=["my-lakebase-instance"]
)
token = cred.token  # Use this as password in connection string

Connect from Notebook

import psycopg
from databricks.sdk import WorkspaceClient
import uuid

# Get instance details
w = WorkspaceClient()
instance = w.database.get_database_instance(name="my-lakebase-instance")

# Generate token
cred = w.database.generate_database_credential(
    request_id=str(uuid.uuid4()),
    instance_names=["my-lakebase-instance"]
)

# Connect using psycopg3
conn_string = f"host={instance.read_write_dns} dbname=postgres user={w.current_user.me().user_name} password={cred.token} sslmode=require"
with psycopg.connect(conn_string) as conn:
    with conn.cursor() as cur:
        cur.execute("SELECT version()")
        print(cur.fetchone())

SQLAlchemy with Token Refresh (Production)

For long-running applications, tokens must be refreshed (expire after 1 hour):

import asyncio
import os
import uuid
from sqlalchemy import event
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker
from databricks.sdk import WorkspaceClient

# Token refresh state
_current_token = None
_token_refresh_task = None
TOKEN_REFRESH_INTERVAL = 50 * 60  # 50 minutes (before 1-hour expiry)

def _generate_token(instance_name: str) -> str:
    """Generate fresh OAuth token."""
    w = WorkspaceClient()
    cred = w.database.generate_database_credential(
        request_id=str(uuid.uuid4()),
        instance_names=[instance_name]
    )
    return cred.token

async def _token_refresh_loop(instance_name: str):
    """Background task to refresh token every 50 minutes."""
    global _current_token
    while True:
        await asyncio.sleep(TOKEN_REFRESH_INTERVAL)
        _current_token = await asyncio.to_thread(_generate_token, instance_name)

def init_database(instance_name: str, database_name: str, username: str) -> AsyncEngine:
    """Initialize database with OAuth token injection."""
    global _current_token
    
    w = WorkspaceClient()
    instance = w.database.get_database_instance(name=instance_name)
    
    # Generate initial token
    _current_token = _generate_token(instance_name)
    
    # Build URL (password injected via do_connect)
    url = f"postgresql+psycopg://{username}@{instance.read_write_dns}:5432/{database_name}"
    
    engine = create_async_engine(
        url,
        pool_size=5,
        max_overflow=10,
        pool_recycle=3600,
        connect_args={"sslmode": "require"}
    )
    
    # Inject token on each connection
    @event.listens_for(engine.sync_engine, "do_connect")
    def provide_token(dialect, conn_rec, cargs, cparams):
        cparams["password"] = _current_token
    
    return engine

Databricks Apps Integration

For Databricks Apps, use environment variables for configuration:

# Environment variables set by Databricks Apps:
# - LAKEBASE_INSTANCE_NAME: Instance name
# - LAKEBASE_DATABASE_NAME: Database name
# - LAKEBASE_USERNAME: Username (optional, defaults to service principal)

import os

def is_lakebase_configured() -> bool:
    """Check if Lakebase is configured for this app."""
    return bool(
        os.environ.get("LAKEBASE_PG_URL") or
        (os.environ.get("LAKEBASE_INSTANCE_NAME") and 
         os.environ.get("LAKEBASE_DATABASE_NAME"))
    )

Add Lakebase as an app resource via CLI:

databricks apps add-resource $APP_NAME \
    --resource-type database \
    --resource-name lakebase \
    --database-instance my-lakebase-instance

Register with Unity Catalog

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# Register database in Unity Catalog
w.database.register_database_instance(
    name="my-lakebase-instance",
    catalog="my_catalog",
    schema="my_schema"
)

MLflow Model Resources

Declare Lakebase as a model resource for automatic credential provisioning:

from mlflow.models.resources import DatabricksLakebase

resources = [
    DatabricksLakebase(database_instance_name="my-lakebase-instance"),
]

# When logging model
mlflow.langchain.log_model(
    model,
    artifact_path="model",
    resources=resources,
    pip_requirements=["databricks-langchain[memory]"]
)

MCP Tools

The following MCP tools are available for managing Lakebase infrastructure. Use type="provisioned" for Lakebase Provisioned.

Database Management

Reverse ETL (Catalog + Synced Tables)

Reference Files

• connection-patterns.md - Detailed connection patterns for different use cases
• reverse-etl.md - Syncing data from Delta Lake to Lakebase

CLI Quick Reference

# Create instance
databricks database create-database-instance \
    --name my-lakebase-instance \
    --capacity CU_1

# Get instance details
databricks database get-database-instance --name my-lakebase-instance

# Generate credentials
databricks database generate-database-credential \
    --request-id $(uuidgen) \
    --json '{"instance_names": ["my-lakebase-instance"]}'

# List instances
databricks database list-database-instances

# Stop instance (saves cost)
databricks database stop-database-instance --name my-lakebase-instance

# Start instance
databricks database start-database-instance --name my-lakebase-instance

Common Issues

SDK Version Requirements

• Databricks SDK for Python: >= 0.61.0 (0.81.0+ recommended for full API support)
• psycopg: 3.x (supports hostaddr parameter for DNS workaround)
• SQLAlchemy: 2.x with postgresql+psycopg driver

%pip install -U "databricks-sdk>=0.81.0" "psycopg[binary]>=3.0" sqlalchemy

Notes

• Capacity values use compute unit sizing: CU_1, CU_2, CU_4, CU_8.
• Lakebase Autoscaling is a newer offering with automatic scaling but limited regional availability. This skill focuses on Lakebase Provisioned which is more widely available.
• For memory/state in LangChain agents, use databricks-langchain[memory] which includes Lakebase support.
• Tokens are short-lived (1 hour) - production apps MUST implement token refresh.

Related Skills

• databricks-app-apx - full-stack apps that can use Lakebase for persistence
• databricks-app-python - Python apps with Lakebase backend
• databricks-python-sdk - SDK used for instance management and token generation
• databricks-asset-bundles - deploying apps with Lakebase resources
• databricks-jobs - scheduling reverse ETL sync jobs