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.

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.

from dataclasses import dataclass
from typing import TypeVar, Generic, Optional, Callable
from databricks.sdk.errors import (
    NotFound,
    PermissionDenied,
    TooManyRequests,
    TemporarilyUnavailable,
    ResourceConflict,
    InvalidParameterValue,
    ResourceAlreadyExists,
)

T = TypeVar("T")

@dataclass
class Result(Generic[T]):
    value: Optional[T] = None
    error: Optional[str] = None
    retryable: bool = False

    @property
    def ok(self) -> bool:
        return self.error is None

def safe_call(func: Callable, *args, **kwargs) -> Result:
    """Execute a Databricks API call with structured error classification."""
    try:
        return Result(value=func(*args, **kwargs))
    except NotFound as e:
        return Result(error=f"Not found: {e.message}", retryable=False)
    except PermissionDenied as e:
        return Result(error=f"Permission denied: {e.message}", retryable=False)
    except InvalidParameterValue as e:
        return Result(error=f"Invalid parameter: {e.message}", retryable=False)
    except ResourceAlreadyExists as e:
        return Result(error=f"Already exists: {e.message}", retryable=False)
    except ResourceConflict as e:
        return Result(error=f"Conflict: {e.message}", retryable=False)
    except TooManyRequests as e:
        return Result(error=f"Rate limited (retry after {e.retry_after_secs}s)", retryable=True)
    except TemporarilyUnavailable as e:
        return Result(error=f"Unavailable: {e.message}", retryable=True)

# Usage
result = safe_call(w.clusters.get, cluster_id="0123-456789-abcde")
if result.ok:
    print(f"Cluster state: {result.value.state}")
elif result.retryable:
    print(f"Retry later: {result.error}")
else:
    print(f"Permanent failure: {result.error}")

Step 3: Cluster Lifecycle Context Manager

Ensure ephemeral clusters are terminated even on exceptions.

from contextlib import contextmanager
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.compute import State

@contextmanager
def managed_cluster(w: WorkspaceClient, **cluster_config):
    """Create a cluster, yield it, terminate on exit."""
    cluster = w.clusters.create_and_wait(**cluster_config)
    try:
        yield cluster
    finally:
        if cluster.state in (State.RUNNING, State.PENDING, State.RESIZING):
            w.clusters.delete(cluster_id=cluster.cluster_id)
            print(f"Terminated cluster {cluster.cluster_id}")

# Usage — cluster auto-cleaned even if job fails
with managed_cluster(w,
    cluster_name="ephemeral-etl",
    spark_version="14.3.x-scala2.12",
    node_type_id="i3.xlarge",
    num_workers=2,
    autotermination_minutes=30,
) as cluster:
    run = w.jobs.submit(
        run_name="one-off",
        tasks=[SubmitTask(
            task_key="task1",
            existing_cluster_id=cluster.cluster_id,
            notebook_task=NotebookTask(notebook_path="/Repos/team/etl/main"),
        )],
    ).result()

Step 4: Type-Safe Job Builder

Use SDK dataclasses instead of raw dicts for compile-time safety.

from databricks.sdk.service.jobs import (
    CreateJob, JobCluster, Task, NotebookTask,
    CronSchedule, JobEmailNotifications, WebhookNotifications, Webhook,
)
from databricks.sdk.service.compute import ClusterSpec, AutoScale

def build_etl_job(
    name: str,
    notebook_path: str,
    cron: str,
    alert_email: str,
    webhook_id: str | None = None,
) -> CreateJob:
    """Build a fully-typed ETL job definition."""
    return CreateJob(
        name=name,
        job_clusters=[
            JobCluster(
                job_cluster_key="etl_cluster",
                new_cluster=ClusterSpec(
                    spark_version="14.3.x-scala2.12",
                    node_type_id="i3.xlarge",
                    autoscale=AutoScale(min_workers=1, max_workers=4),
                ),
            )
        ],
        tasks=[
            Task(
                task_key="main",
                job_cluster_key="etl_cluster",
                notebook_task=NotebookTask(notebook_path=notebook_path),
            )
        ],
        schedule=CronSchedule(quartz_cron_expression=cron, timezone_id="UTC"),
        email_notifications=JobEmailNotifications(on_failure=[alert_email]),
        webhook_notifications=WebhookNotifications(
            if webhook_id else []
        ),
        max_concurrent_runs=1,
    )

# Create the job
job_def = build_etl_job(
    name="daily-sales-etl",
    notebook_path="/Repos/team/etl/sales_pipeline",
    cron="0 0 6 * * ?",
    alert_email="oncall@company.com",
)
created = w.jobs.create(**job_def.as_dict())
print(f"Job created: {created.job_id}")

Step 5: Paginated Collection with Progress

The SDK auto-paginates via iterators. Wrap for progress tracking and filtering.

from typing import Iterator

def collect_with_progress(iterator: Iterator, label: str, batch_log: int = 100) -> list:
    """Drain a paginated iterator with progress logging."""
    items = []
    for i, item in enumerate(iterator, 1):
        items.append(item)
        if i % batch_log == 0:
            print(f"  {label}: {i} items fetched...")
    print(f"  {label}: {len(items)} total")
    return items

# Usage
all_jobs = collect_with_progress(w.jobs.list(), "Jobs")
all_clusters = collect_with_progress(w.clusters.list(), "Clusters")
running = [c for c in all_clusters if c.state == State.RUNNING]
print(f"Running: {len(running)}/{len(all_clusters)} clusters")

Output

• Singleton WorkspaceClient with profile-based caching
• Result[T] wrapper for typed, structured error handling
• Context manager for ephemeral cluster lifecycle
• Type-safe job builder using SDK dataclasses
• Pagination helper with progress logging

Error Handling

Examples

Health Check Script

w = get_client()
me = w.current_user.me()
print(f"User: {me.user_name}")
print(f"Host: {w.config.host}")
print(f"Auth: {w.config.auth_type}")
print(f"Running clusters: {sum(1 for c in w.clusters.list() if c.state == State.RUNNING)}")
print(f"Jobs defined: {sum(1 for _ in w.jobs.list())}")

Multi-Workspace Inventory

acct = get_account_client()
for ws in acct.workspaces.list():
    ws_client = WorkspaceClient(host=f"https://{ws.deployment_name}.cloud.databricks.com")
    clusters = list(ws_client.clusters.list())
    running = [c for c in clusters if c.state == State.RUNNING]
    print(f"{ws.workspace_name}: {len(running)} running / {len(clusters)} total")

Resources

• Databricks SDK for Python
• SDK Error Classes
• SDK GitHub

Next Steps

Apply patterns in databricks-core-workflow-a for Delta Lake ETL.