Databricks Cost Tuning

Databricks Cost Tuning

Overview

Optimize Databricks costs through cluster policies, instance selection, and monitoring.

Prerequisites

• Workspace admin access
• Access to billing data
• Understanding of workload patterns

Instructions

Step 1: Implement Cluster Policies

// policies/​cost_controlled_policy.json
{
  "cluster_type": {
    "type": "fixed",
    "value": "job"
  },
  "autotermination_minutes": {
    "type": "range",
    "minValue": 10,
    "maxValue": 60,
    "defaultValue": 30
  },
  "num_workers": {
    "type": "range",
    "minValue": 1,
    "maxValue": 10,
    "defaultValue": 2
  },
  "node_type_id": {
    "type": "allowlist",
    "values": [
      "Standard_DS3_v2",
      "Standard_DS4_v2",
      "Standard_E4ds_v4"
    ],
    "defaultValue": "Standard_DS3_v2"
  },
  "spark_version": {
    "type": "regex",
    "pattern": "14\\.[0-9]+\\.x-scala2\\.12"
  },
  "azure_attributes.spot_bid_max_price": {
    "type": "fixed",
    "value": -1
  },
  "azure_attributes.first_on_demand": {
    "type": "fixed",
    "value": 1
  },
  "custom_tags.cost_center": {
    "type": "fixed",
    "value": "data-engineering"
  }
}

# Create policy via SDK
from databricks.sdk import WorkspaceClient
import json

def create_cost_policy(w: WorkspaceClient, policy_name: str) -> str:
    """Create cost-controlled cluster policy."""

    policy_definition = {
        "autotermination_minutes": {
            "type": "range",
            "minValue": 10,
            "maxValue": 60,
            "defaultValue": 30,
        },
        "num_workers": {
            "type": "range",
            "minValue": 1,
            "maxValue": 10,
        },
        "node_type_id": {
            "type": "allowlist",
            "values": [
                "Standard_DS3_v2",
                "Standard_DS4_v2",
            ],
        },
        "azure_attributes.spot_bid_max_price": {
            "type": "fixed",
            "value": -1,  # Use spot instances
        },
        "azure_attributes.first_on_demand": {
            "type": "fixed",
            "value": 1,  # At least 1 on-demand for driver
        },
    }

    policy = w.cluster_policies.create(
        name=policy_name,
        definition=json.dumps(policy_definition),
    )

    return policy.policy_id

Step 2: Spot Instance Configuration

# resources/​cost_optimized_cluster.yml
job_clusters:
  - job_cluster_key: cost_optimized
    new_cluster:
      spark_version: "14.3.x-scala2.12"
      node_type_id: "Standard_DS3_v2"
      num_workers: 4

      # Azure Spot Configuration
      azure_attributes:
        first_on_demand: 1         # Driver on-demand
        availability: SPOT_AZURE   # Workers on spot
        spot_bid_max_price: -1     # Pay up to on-demand price

      # AWS Spot Configuration (alternative)
      # aws_attributes:
      #   first_on_demand: 1
      #   availability: SPOT_WITH_FALLBACK
      #   spot_bid_price_percent: 100

      autoscale:
        min_workers: 1
        max_workers: 8

      custom_tags:
        cost_center: analytics
        environment: production

Step 3: Instance Pool for Faster Startup

# Create instance pool for reduced startup time and costs
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.compute import (
    InstancePoolAndStats,
    InstancePoolAzureAttributes,
)

def create_cost_optimized_pool(
    w: WorkspaceClient,
    pool_name: str,
    min_idle: int = 1,
    max_capacity: int = 10,
    idle_timeout: int = 15,
) -> str:
    """
    Create instance pool for cost optimization.

    Benefits:
    - Faster cluster startup (instances pre-warmed)
    - Lower spot instance preemption risk
    - Consistent performance
    """
    pool = w.instance_pools.create(
        instance_pool_name=pool_name,
        node_type_id="Standard_DS3_v2",
        min_idle_instances=min_idle,
        max_capacity=max_capacity,
        idle_instance_autotermination_minutes=idle_timeout,
        azure_attributes=InstancePoolAzureAttributes(
            availability="SPOT_AZURE",
            spot_bid_max_price=-1,
        ),
        preloaded_spark_versions=["14.3.x-scala2.12"],
        custom_tags={
            "pool_type": "cost_optimized",
            "managed_by": "terraform",
        },
    )

    return pool.instance_pool_id

Step 4: Cost Monitoring

-- Analyze cluster costs from system tables
SELECT
    cluster_name,
    cluster_id,
    DATE(usage_date) as date,
    SUM(usage_quantity) as dbu_usage,
    SUM(usage_quantity * list_price) as estimated_cost
FROM system.billing.usage
WHERE usage_date > current_date() - INTERVAL 30 DAYS
  AND usage_type = 'COMPUTE'
GROUP BY cluster_name, cluster_id, DATE(usage_date)
ORDER BY estimated_cost DESC;

-- Top cost drivers by job
SELECT
    job_name,
    COUNT(DISTINCT run_id) as runs,
    SUM(duration) / 3600000 as total_hours,
    AVG(duration) / 60000 as avg_minutes,
    SUM(dbu_usage) as total_dbus
FROM system.lakeflow.job_run_timeline r
JOIN system.billing.usage u ON r.cluster_id = u.cluster_id
WHERE r.start_time > current_timestamp() - INTERVAL 30 DAYS
GROUP BY job_name
ORDER BY total_dbus DESC
LIMIT 20;

-- Identify idle clusters
SELECT
    cluster_name,
    cluster_id,
    state,
    last_activity_time,
    TIMESTAMPDIFF(HOUR, last_activity_time, current_timestamp()) as idle_hours
FROM system.compute.clusters
WHERE state = 'RUNNING'
  AND TIMESTAMPDIFF(MINUTE, last_activity_time, current_timestamp()) > 30
ORDER BY idle_hours DESC;

Step 5: Cost Allocation Tags

# Implement cost allocation with tags
from databricks.sdk import WorkspaceClient

def enforce_cost_tags(
    w: WorkspaceClient,
    required_tags: list[str] = ["cost_center", "team", "environment"],
) -> list[dict]:
    """
    Audit and report clusters missing required cost tags.

    Returns list of non-compliant clusters.
    """
    non_compliant = []

    for cluster in w.clusters.list():
        tags = cluster.custom_tags or {}
        missing_tags = [t for t in required_tags if t not in tags]

        if missing_tags:
            non_compliant.append({
                "cluster_id": cluster.cluster_id,
                "cluster_name": cluster.cluster_name,
                "missing_tags": missing_tags,
                "creator": cluster.creator_user_name,
            })

    return non_compliant

# Cost allocation report
def generate_cost_report(w: WorkspaceClient, spark) -> dict:
    """Generate cost allocation report by tags."""
    query = """
        SELECT
            custom_tags:cost_center as cost_center,
            custom_tags:team as team,
            SUM(usage_quantity * list_price) as total_cost
        FROM system.billing.usage
        WHERE usage_date > current_date() - INTERVAL 30 DAYS
        GROUP BY custom_tags:cost_center, custom_tags:team
        ORDER BY total_cost DESC
    """
    return spark.sql(query).toPandas().to_dict('records')

Step 6: Auto-Termination and Scheduling

# Job scheduling for cost optimization
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.jobs import CronSchedule

def configure_off_hours_schedule(
    w: WorkspaceClient,
    job_id: int,
    cron_expression: str = "0 0 2 * * ?",  # 2 AM daily
    timezone: str = "America/​New_York",
) -> None:
    """Configure job to run during off-peak hours."""
    w.jobs.update(
        job_id=job_id,
        new_settings={
            "schedule": {
                "quartz_cron_expression": cron_expression,
                "timezone_id": timezone,
            }
        }
    )

# Cluster auto-stop policy
def set_aggressive_auto_termination(
    w: WorkspaceClient,
    cluster_id: str,
    minutes: int = 15,
) -> None:
    """Set aggressive auto-termination for development clusters."""
    cluster = w.clusters.get(cluster_id)
    w.clusters.edit(
        cluster_id=cluster_id,
        spark_version=cluster.spark_version,
        node_type_id=cluster.node_type_id,
        autotermination_minutes=minutes,
    )

Output

• Cost-controlled cluster policies
• Spot instance optimization
• Instance pools configured
• Cost monitoring dashboards
• Auto-termination policies

Error Handling

Examples

Cost Savings Calculator

def estimate_spot_savings(
    on_demand_cost: float,
    spot_ratio: float = 0.7,  # 70% workers on spot
    spot_discount: float = 0.6,  # 60% discount
) -> dict:
    """Estimate savings from spot instances."""
    on_demand_portion = on_demand_cost * (1 - spot_ratio)
    spot_portion = on_demand_cost * spot_ratio * (1 - spot_discount)
    new_cost = on_demand_portion + spot_portion
    savings = on_demand_cost - new_cost

    return {
        "original_cost": on_demand_cost,
        "optimized_cost": new_cost,
        "savings": savings,
        "savings_percent": (savings / on_demand_cost) * 100,
    }

Weekly Cost Alert

-- Alert when costs exceed threshold
CREATE ALERT weekly_cost_alert
AS SELECT
    SUM(usage_quantity * list_price) as weekly_cost
FROM system.billing.usage
WHERE usage_date > current_date() - INTERVAL 7 DAYS
HAVING weekly_cost > 10000  -- $10k threshold
SCHEDULE CRON '0 9 * * 1'  -- Monday 9 AM
NOTIFICATIONS (email_addresses = ['[email protected]']);

Resources

• Databricks Pricing
• Cluster Policies
• Instance Pools
• Cost Management

Next Steps

For reference architecture, see databricks-reference-architecture.