databricks-sdk-patterns

# Databricks SDK Patterns ## Overview Production-ready patterns for the Databricks Python SDK (`databricks-sdk`). Covers singleton client initialization, typed error handling, cluster lifecycle management, type-safe job construction, and pagination. Uses real SDK exception classes and API shapes. ## Prerequisites - `databricks-sdk>=0.20.0` installed - Authentication configured (see `databricks-install-auth`) - Python 3.10+ ## Instructions ### Step 1: Singleton Client with Profile Support Each `WorkspaceClient` holds an HTTP session and re-authenticates. Cache instances. ```python from databricks.sdk import WorkspaceClient, AccountClient from functools import lru_cache @lru_cache(maxsize=4) def get_client(profile: str = "DEFAULT") -> WorkspaceClient: """Cached WorkspaceClient — one per profile.""" return WorkspaceClient(profile=profile) @lru_cache(maxsize=1) def get_account_client() -> AccountClient: """Account-level client for multi-workspace operations.""" return AccountClient( host="https://accounts.cloud.databricks.com", account_id="00000000-0000-0000-0000-000000000000", ) # Usage w = get_client() w_prod = get_client("production") ``` ### Step 2: Structured Error Handling The SDK raises typed exceptions from `databricks.sdk.errors`. Distinguish transient (retryable) from permanent failures. ```python from dataclasses import dataclass from typing import TypeVar, Generic, Optional, Callable from databricks.sdk.errors import ( ...