databricks

Jobs Monitoring Guide

This guide provides best practices for monitoring jobs in Databricks on Google Cloud, ensuring efficient troubleshooting, performance optimization, and cost control. Based on the official Databricks documentation: Monitor Databricks Jobs.

Observability Architecture

graph TB
    subgraph "Data Sources"
        JOBS[Jobs & Workflows]
        CLUSTER[Clusters]
        QUERIES[SQL Queries]
        NOTEBOOKS[Notebooks]
    end

    subgraph "Monitoring Tools"
        UI[Jobs UI<br/>Run History]
        SPARK_UI[Spark UI<br/>Execution Details]
        GANGLIA[Ganglia Metrics<br/>Resource Usage]
        LOGS[Cluster Logs<br/>Driver & Executors]
    end

    subgraph "Alerting & Analysis"
        ALERTS[Databricks Alerts<br/>Failure Notifications]
        EMAIL[Email/Webhook<br/>Notifications]
        DASH[Usage Dashboard<br/>Cost Tracking]
        API[REST API<br/>Programmatic Access]
    end

    subgraph "Performance Optimization"
        BOTTLENECK[Bottleneck Detection]
        CACHE[Caching Strategy]
        PARTITION[Data Partitioning]
        TUNING[Cluster Tuning]
    end

    JOBS --> UI
    CLUSTER --> SPARK_UI
    CLUSTER --> GANGLIA
    CLUSTER --> LOGS
    QUERIES --> SPARK_UI
    NOTEBOOKS --> UI

    UI --> ALERTS
    ALERTS --> EMAIL
    UI --> DASH
    API --> DASH

    SPARK_UI --> BOTTLENECK
    GANGLIA --> BOTTLENECK
    BOTTLENECK --> CACHE
    BOTTLENECK --> PARTITION
    BOTTLENECK --> TUNING

    style UI fill:#1E88E5
    style SPARK_UI fill:#1E88E5
    style ALERTS fill:#FF6F00
    style DASH fill:#43A047
    style BOTTLENECK fill:#E53935

Table of Contents

1. View Job Runs
2. Monitor Job Run Details
3. Set Up Alerts
4. Analyze Job Performance
5. Monitor Job Costs
6. Use REST API for Monitoring

View Job Runs

• Access job run history via the Jobs UI in Databricks.
• Navigate to Workflows > Jobs to see scheduled and manual job runs.
• Filter job runs based on status, start time, duration, and cluster used.
• Reference: Jobs UI.

Monitor Job Run Details

• Click on a specific job run to view logs, execution graphs, and detailed metrics.
• Review the task execution timeline to identify bottlenecks.
• Use cluster logs and Spark UI to debug failures.
• Reference: Spark UI.

Set Up Alerts

• Enable failure notifications via email or webhooks in job settings.
• Use Databricks Alerts to trigger actions when jobs meet specific conditions.
• Configure alerts in the Jobs UI under the Notifications tab.
• Reference: Databricks Alerts.

Job Monitoring Workflow

sequenceDiagram
    participant Admin
    participant JOB as Job/Workflow
    participant CLUSTER as Cluster
    participant SPARK as Spark UI
    participant ALERT as Alert System
    participant MONITOR as Monitoring Dashboard

    Admin->>JOB: Schedule/Trigger Job
    JOB->>CLUSTER: Start Cluster

    activate CLUSTER
    CLUSTER->>SPARK: Initialize Spark Context

    loop Task Execution
        JOB->>CLUSTER: Execute Task
        CLUSTER->>SPARK: Log Metrics
        SPARK->>MONITOR: Update Dashboard

        alt Task Success
            CLUSTER-->>JOB: Task Complete ✓
        else Task Failure
            CLUSTER-->>JOB: Task Failed ✗
            JOB->>ALERT: Trigger Alert
            ALERT->>Admin: Email/Webhook Notification
        end
    end

    JOB->>CLUSTER: All Tasks Complete
    deactivate CLUSTER

    Admin->>SPARK: Review Execution Graph
    SPARK-->>Admin: Timeline, Stages, DAG

    Admin->>MONITOR: Check Resource Usage
    MONITOR-->>Admin: CPU, Memory, Cost Metrics

    Note over Admin,MONITOR: Identify optimization opportunities

Analyze Job Performance

• Use Ganglia Metrics and Spark UI to analyze resource usage.
• Optimize job execution by tuning cluster settings, caching, and parallelism.
• Identify long-running tasks and optimize data partitioning.
• Reference: Performance Tuning.

Monitor Job Costs

• Use the Usage Dashboard to track compute and storage costs.
• Identify high-cost jobs and optimize resource allocation.
• Enable job-level tagging to track costs by project or team.
• Reference: Databricks Usage Dashboard.

Performance Troubleshooting Decision Tree

graph TB
    START[Job Performance Issue]

    START --> CHECK1{Job<br/>Completing?}
    CHECK1 -->|No - Fails| FAIL_CHECK{Error Type?}
    CHECK1 -->|Yes - Slow| PERF_CHECK{Performance<br/>Bottleneck?}

    FAIL_CHECK -->|OOM Error| MEM_ISSUE[Memory Issue<br/>- Increase driver/executor memory<br/>- Reduce partition size<br/>- Enable spill to disk]
    FAIL_CHECK -->|Timeout| TIME_ISSUE[Timeout Issue<br/>- Increase cluster size<br/>- Optimize query<br/>- Check data skew]
    FAIL_CHECK -->|Network| NET_ISSUE[Network Issue<br/>- Check VPC connectivity<br/>- Verify NAT/firewall<br/>- Check data source access]

    PERF_CHECK -->|Shuffle Heavy| SHUFFLE[Shuffle Optimization<br/>- Increase shuffle partitions<br/>- Use broadcast joins<br/>- Repartition data]
    PERF_CHECK -->|I/O Bound| IO[I/O Optimization<br/>- Enable Delta cache<br/>- Use columnar formats<br/>- Partition pruning]
    PERF_CHECK -->|CPU Bound| CPU[CPU Optimization<br/>- Add more workers<br/>- Use better instance types<br/>- Parallelize operations]

    MEM_ISSUE --> SPARK_UI[Review Spark UI<br/>- Stage details<br/>- Executor metrics<br/>- Storage tab]
    TIME_ISSUE --> SPARK_UI
    NET_ISSUE --> LOGS[Review Logs<br/>- Driver logs<br/>- Executor logs<br/>- Network traces]

    SHUFFLE --> GANGLIA[Monitor Ganglia<br/>- Network I/O<br/>- Disk usage<br/>- CPU utilization]
    IO --> GANGLIA
    CPU --> GANGLIA

    SPARK_UI --> FIX[Apply Fix]
    LOGS --> FIX
    GANGLIA --> FIX
    FIX --> TEST[Test & Validate]
    TEST --> END[Performance Improved ✓]

    style START fill:#FF6F00
    style MEM_ISSUE fill:#E53935
    style TIME_ISSUE fill:#E53935
    style NET_ISSUE fill:#E53935
    style SPARK_UI fill:#1E88E5
    style GANGLIA fill:#1E88E5
    style END fill:#43A047

Use REST API for Monitoring

• Retrieve job run details programmatically using the Databricks Jobs API.
• Automate monitoring by integrating with external dashboards or alerting systems.
• API Example:

  curl -X GET https://<databricks-instance>/api/2.1/jobs/runs/list \
       -H "Authorization: Bearer <your-token>"
  

• Reference: Databricks Jobs API.

API-Driven Monitoring Integration

graph TB
    subgraph "Databricks"
        DB_API[Databricks REST API]
        JOBS_API[Jobs API<br/>/api/2.1/jobs/runs/list]
        CLUSTER_API[Clusters API<br/>/api/2.1/clusters/list]
        METRICS_API[Metrics API<br/>system.billing.*]
    end

    subgraph "External Monitoring Tools"
        PROM[Prometheus<br/>Metrics Collection]
        GRAFANA[Grafana<br/>Visualization]
        DATADOG[Datadog<br/>APM]
        SPLUNK[Splunk<br/>Log Analysis]
    end

    subgraph "Custom Integration"
        SCRIPT[Python/Shell Scripts<br/>Scheduled Polling]
        LAMBDA[Cloud Functions<br/>Event-driven]
        AIRFLOW[Apache Airflow<br/>Workflow Integration]
    end

    subgraph "Alerting Platforms"
        PAGER[PagerDuty<br/>Incident Management]
        SLACK[Slack<br/>Team Notifications]
        EMAIL[Email<br/>Alert Delivery]
    end

    DB_API --> JOBS_API
    DB_API --> CLUSTER_API
    DB_API --> METRICS_API

    JOBS_API --> SCRIPT
    CLUSTER_API --> SCRIPT
    METRICS_API --> PROM

    SCRIPT --> LAMBDA
    SCRIPT --> AIRFLOW

    PROM --> GRAFANA
    JOBS_API --> DATADOG
    CLUSTER_API --> SPLUNK

    GRAFANA --> PAGER
    DATADOG --> SLACK
    SPLUNK --> EMAIL

    style DB_API fill:#1E88E5
    style JOBS_API fill:#1E88E5
    style GRAFANA fill:#43A047
    style PROM fill:#43A047
    style PAGER fill:#FF6F00
    style SLACK fill:#FF6F00

Comprehensive Monitoring Strategy

graph LR
    subgraph "Level 1: Real-time Monitoring"
        RT1[Jobs UI Dashboard<br/>Active job tracking]
        RT2[Spark UI<br/>Live execution view]
        RT3[Ganglia<br/>Resource metrics]
    end

    subgraph "Level 2: Historical Analysis"
        H1[System Tables<br/>SQL queries]
        H2[Usage Dashboard<br/>Trends & patterns]
        H3[Audit Logs<br/>Security events]
    end

    subgraph "Level 3: Proactive Alerts"
        A1[Job Failure Alerts<br/>Immediate notification]
        A2[Budget Alerts<br/>Cost thresholds]
        A3[Performance Alerts<br/>SLA violations]
    end

    subgraph "Level 4: Optimization"
        O1[Performance Tuning<br/>Based on insights]
        O2[Cost Optimization<br/>Resource right-sizing]
        O3[Automation<br/>Self-healing]
    end

    RT1 --> H1
    RT2 --> H1
    RT3 --> H1

    H1 --> A1
    H2 --> A2
    H3 --> A3

    A1 --> O1
    A2 --> O2
    A3 --> O3

    style RT1 fill:#1E88E5
    style RT2 fill:#1E88E5
    style RT3 fill:#1E88E5
    style A1 fill:#FF6F00
    style A2 fill:#FF6F00
    style A3 fill:#FF6F00
    style O1 fill:#43A047
    style O2 fill:#43A047
    style O3 fill:#43A047

For more details, refer to the official Databricks Jobs Monitoring Documentation.

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Following these best practices ensures reliable job execution, proactive troubleshooting, and optimized performance in Databricks on Google Cloud.