Set up comprehensive observability for Databricks with metrics, traces, and alerts. Use when implementing monitoring for Databricks jobs, setting up dashboards, or configuring alerting for pipeline health. Trigger with phrases like "databricks monitoring", "databricks metrics", "databricks observability", "monitor databricks", "databricks alerts", "databricks logging".
From databricks-packnpx claudepluginhub nickloveinvesting/nick-love-plugins --plugin databricks-packThis skill is limited to using the following tools:
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Designs, audits, and improves analytics tracking systems using Signal Quality Index for reliable, decision-ready data in marketing, product, and growth.
Enforces A/B test setup with gates for hypothesis locking, metrics definition, sample size calculation, assumptions checks, and execution readiness before implementation.
Monitor Databricks job runs, cluster utilization, query performance, and costs using system tables and the Databricks SDK. Databricks exposes observability data through system tables in the system catalog (audit logs, billing, compute, query history) and real-time Ganglia metrics on clusters.
system.billing, system.compute, and system.access catalogs-- Failed jobs in the last 24 hours with error details
SELECT job_id, run_name, result_state, start_time, end_time,
TIMESTAMPDIFF(MINUTE, start_time, end_time) AS duration_min,
error_message
FROM system.lakeflow.job_run_timeline
WHERE result_state = 'FAILED'
AND start_time > current_timestamp() - INTERVAL 24 HOURS
ORDER BY start_time DESC;
-- DBU consumption by cluster over the last 7 days
SELECT cluster_id, cluster_name, sku_name,
SUM(usage_quantity) AS total_dbus,
SUM(usage_quantity * list_price) AS estimated_cost_usd
FROM system.billing.usage
WHERE usage_date >= current_date() - INTERVAL 7 DAYS
GROUP BY cluster_id, cluster_name, sku_name
ORDER BY estimated_cost_usd DESC
LIMIT 20;
-- Slow queries (>30s) on SQL warehouses
SELECT warehouse_id, statement_id, executed_by,
total_duration_ms / 1000 AS duration_sec, # 1000: 1 second in ms
rows_produced, bytes_scanned_mb
FROM system.query.history
WHERE total_duration_ms > 30000 # 30000: 30 seconds in ms
AND start_time > current_timestamp() - INTERVAL 24 HOURS
ORDER BY total_duration_ms DESC
LIMIT 50;
-- Create alert: notify if any job fails more than 3 times in an hour
-- In Databricks SQL > Alerts > New Alert:
-- Query:
SELECT COUNT(*) AS failure_count
FROM system.lakeflow.job_run_timeline
WHERE result_state = 'FAILED'
AND start_time > current_timestamp() - INTERVAL 1 HOUR;
-- Trigger when: failure_count > 3
-- Notification: Slack webhook or email
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
# Export cluster metrics to Prometheus via pushgateway
for cluster in w.clusters.list():
if cluster.state == 'RUNNING':
events = w.clusters.events(cluster.cluster_id, limit=10)
# Push utilization metrics to your monitoring stack
push_metric('databricks_cluster_state', 1, labels={'cluster': cluster.cluster_name, 'state': cluster.state.value})
| Issue | Cause | Solution |
|---|---|---|
| System tables empty | Unity Catalog not enabled | Enable Unity Catalog for the workspace |
| Query history missing | Serverless warehouse not tracked | Use classic SQL warehouse or check retention |
| Billing data delayed | System table lag (up to 24h) | Use for trend analysis, not real-time alerting |
| Cluster metrics gaps | Cluster was terminated | Check terminated cluster events in audit log |
Basic usage: Apply databricks observability to a standard project setup with default configuration options.
Advanced scenario: Customize databricks observability for production environments with multiple constraints and team-specific requirements.