databricks-observability（数据块可观察性）

Databricks Observability

Overview

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.

Prerequisites

• Databricks Premium or Enterprise with Unity Catalog enabled
• Access to system.billing, system.compute, and system.access catalogs
• SQL warehouse or cluster for running monitoring queries

Instructions

Step 1: Monitor Job Health via System Tables

-- 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;

Step 2: Track Cluster Utilization and Costs

-- 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;

Step 3: Monitor SQL Warehouse Performance

-- 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;

Step 4: Set Up Alerts with Databricks SQL Alerts

-- 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

Step 5: Export Metrics to External Systems

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})

Error Handling

Examples

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.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

Resources

• Official CI/CD documentation
• Community best practices and patterns
• Related skills in this plugin pack