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".
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name
databricks-observability
description
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".
Monitor Databricks jobs, clusters, SQL warehouses, and costs using system tables in the system catalog. System tables provide queryable observability data: system.lakeflow (job runs), system.billing (costs), system.query (SQL history), system.access (audit logs), and system.compute (cluster metrics). Data updates throughout the day, not real-time.
Prerequisites
Databricks Premium or Enterprise with Unity Catalog enabled
Access to system.billing, system.lakeflow, system.query, and system.access schemas
-- DBU consumption by cluster (last 7 days)SELECT usage_metadata.cluster_id,
COALESCE(usage_metadata.cluster_name, 'unnamed') AS cluster_name,
sku_name,
SUM(usage_quantity) AS total_dbus,
ROUND(SUM(usage_quantity * p.pricing.default), 2) AS cost_usd
FROM system.billing.usage u
LEFTJOIN system.billing.list_prices p ON u.sku_name = p.sku_name
WHERE u.usage_date >=current_date() -INTERVAL7 DAYS
GROUPBY usage_metadata.cluster_id, cluster_name, u.sku_name
ORDERBY cost_usd DESC
LIMIT 20;
Step 3: SQL Warehouse Performance
-- Slow queries (>30s) on SQL warehousesSELECT warehouse_id, statement_id, executed_by,
ROUND(total_duration_ms /1000, 1) AS duration_sec,
rows_produced,
ROUND(bytes_scanned /1048576, 1) AS scanned_mb,
LEFT(statement_text, 200) AS query_preview
FROM system.query.history
WHERE total_duration_ms >30000AND start_time >current_timestamp() -INTERVAL24 HOURS
ORDERBY total_duration_ms DESC
LIMIT 50;
-- Warehouse queue times (right-sizing indicator)SELECT warehouse_id, warehouse_name,
COUNT(*) AS query_count,
ROUND(AVG(total_duration_ms) /1000, 1) AS avg_sec,
ROUND(MAX(queue_duration_ms) /1000, 1) AS max_queue_sec
FROM system.query.history
WHERE start_time >current_timestamp() -INTERVAL7 DAYS
GROUPBY warehouse_id, warehouse_name;
Step 4: Cost-per-Job Analysis
SELECT j.name AS job_name,
COUNT(DISTINCT r.run_id) AS run_count,
ROUND(AVG(TIMESTAMPDIFF(MINUTE, r.start_time, r.end_time)), 1) AS avg_min,
ROUND(SUM(b.usage_quantity), 1) AS total_dbus,
ROUND(SUM(b.usage_quantity * p.pricing.default), 2) AS total_cost_usd
FROM system.lakeflow.job_run_timeline r
JOIN system.lakeflow.jobs j ON r.job_id = j.job_id
LEFTJOIN system.billing.usage b
ON r.run_id = b.usage_metadata.job_run_id
LEFTJOIN system.billing.list_prices p ON b.sku_name = p.sku_name
WHERE r.start_time >current_timestamp() -INTERVAL7 DAYS
GROUPBY j.name
ORDERBY total_cost_usd DESC
LIMIT 15;
Step 5: SQL Alerts for Automated Notifications
-- Create as SQL Alert: trigger when failure_count > 3-- Schedule: every 15 minutes-- Notification destination: Slack/emailSELECTCOUNT(*) AS failure_count
FROM system.lakeflow.job_run_timeline
WHERE result_state ='FAILED'AND start_time >current_timestamp() -INTERVAL1HOUR;
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
# Create SQL alert programmatically
alert = w.alerts.create(
name="Hourly Job Failure Alert",
query_id="<saved-query-id>",
options={"column": "failure_count", "op": ">", "value": "3"},
rearm=900, # re-alert after 15 min if still triggered
)
Step 6: Export Metrics to External Systems
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
# Export cluster state metrics for Prometheus/Datadogfor cluster in w.clusters.list():
if cluster.state.value == "RUNNING":
print(f"databricks_cluster_workers{{name=\"{cluster.cluster_name}\"}} "f"{cluster.num_workers}")
print(f"databricks_cluster_running{{name=\"{cluster.cluster_name}\"}} 1")
# Export job success rate for Grafana
runs = list(w.jobs.list_runs(limit=100, completed_only=True))
success = sum(1for r in runs if r.state.result_state and r.state.result_state.value == "SUCCESS")
print(f"databricks_job_success_rate {success / len(runs):.2f}")
Step 7: Audit Log Monitoring
-- Security: who accessed what in the last 7 daysSELECT event_time, user_identity.email, action_name,
request_params, response.status_code
FROM system.access.audit
WHERE service_name IN ('unityCatalog', 'jobs', 'clusters')
AND event_date >=current_date() -7AND action_name NOTIN ('getStatus', 'list') -- exclude noisy readsORDERBY event_time DESC
LIMIT 100;
Output
Job health dashboard (success rate, duration, failures)
Automated SQL alerts with Slack/email notifications
External metric export for Prometheus/Grafana
Error Handling
Issue
Cause
Solution
System tables empty
Unity Catalog not enabled
Enable in Account Console > Settings
TABLE_OR_VIEW_NOT_FOUND
Schema not accessible
Request admin to grant SELECT ON system.billing
Billing data delayed
System table refresh lag (up to 24h)
Use for trends and alerts, not real-time
Query history missing
Serverless queries not tracked
Use classic SQL warehouse or check retention
Examples
Daily Standup Dashboard
-- Single query for daily pipeline healthSELECT'Last 24h'ASperiod,
COUNT(*) AS total_runs,
COUNT(CASEWHEN result_state ='SUCCESS'THEN1END) AS ok,
COUNT(CASEWHEN result_state ='FAILED'THEN1END) AS failed,
ROUND(AVG(TIMESTAMPDIFF(MINUTE, start_time, end_time)), 1) AS avg_min
FROM system.lakeflow.job_run_timeline
WHERE start_time >current_timestamp() -INTERVAL24 HOURS;
