Monitor Cloud Run Jobs

Dash0 collects metrics for Cloud Run Jobs, providing visibility into batch workload execution, task completion rates, and resource utilization across all jobs in your connected GCP projects.

Cloud Run Jobs metrics help you monitor the health of scheduled and on-demand batch workloads, identify failing tasks, and optimize resource allocation for cost-effective processing.

Viewing Cloud Run Jobs Metrics

Navigate to Infrastructure > Google Cloud > Cloud Run Jobs to see all Cloud Run jobs across your connected projects. The view displays:

• Job name and region for each Cloud Run job
• Execution status showing recent runs and success rates
• Task metrics including attempts, successes, and failures
• Resource consumption including billable instance time

Click any job to drill into detailed metrics and execution history.

Available Metrics

Dash0 collects the following Cloud Run Jobs metrics from Google Cloud Monitoring. Metrics are stored using their GCP Cloud Monitoring metric type names and can be queried using otel_metric_nameCopy in PromQL.

Task Execution

• run.googleapis.com/job/completed_task_attempt_countCopy — Total number of completed task attempts (includes both succeeded and failed). Filter by resultCopy label to distinguish outcomes:
  • result="succeeded"Copy — Successfully completed tasks
  • result="failed"Copy — Failed task attempts

Cost and Resource Usage

• run.googleapis.com/job/billable_instance_timeCopy — Billable instance time (used for cost calculation)

Common Use Cases

Cloud Run Jobs metrics help you monitor batch workload reliability and cost efficiency. The following examples show how to track job health and optimize resource usage.

Monitoring Job Success Rates

Track the ratio of successful to failed task attempts to identify problematic jobs:

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# Calculate success rate for each job
sum by (job_name) ({otel_metric_name="run.googleapis.com/job/completed_task_attempt_count", result="succeeded"})
/
sum by (job_name) ({otel_metric_name="run.googleapis.com/job/completed_task_attempt_count"})

If success rates drop below acceptable thresholds, investigate:

• Application logic errors in task processing
• Resource constraints causing tasks to timeout or crash
• External dependencies that may be unavailable

Tracking Job Completion Trends

Monitor run.googleapis.com/job/completed_task_attempt_countCopy over time to understand job execution patterns:

• Scheduled jobs: Verify that cron-triggered jobs execute on schedule
• On-demand jobs: Identify usage patterns and peak execution times
• Retry behavior: Detect if tasks are being retried more frequently than expected

Cost Analysis

Use otel_metric_name="run.googleapis.com/job/billable_instance_time"Copy to understand the cost impact of batch workloads:

Compare billable time across jobs to identify cost-intensive workloads and candidates for optimization.

Detecting Failed Tasks

Alert on increases in failed task attempts to catch issues early:

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# Alert on failed tasks in the last hour
increase({otel_metric_name="run.googleapis.com/job/completed_task_attempt_count", result="failed"}[1h]) > 5

Failed tasks may indicate:

• Application bugs or unhandled exceptions
• Resource exhaustion (CPU, memory, or disk)
• Timeout issues for long-running tasks
• Problems with external services or APIs

Correlating with Application Telemetry

Cloud Run Jobs metrics are most valuable when combined with application-level telemetry:

• Logs from job executions (via Google Cloud Logging or direct OTLP export)
• Traces showing task execution flow and external dependencies
• Custom metrics emitted by job code using OpenTelemetry SDKs

When investigating a failed job, pivot from infrastructure metrics to logs and traces to understand the root cause.

Querying Cloud Run Jobs Metrics

Use PromQL in the Query Builder to analyze job performance:

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# Total completed tasks by job
sum by (job_name) (gcp_cloud_run_job_completed_task_attempt_count)

# Failure rate across all jobs
sum(rate(gcp_cloud_run_job_failed_task_attempt_count[5m]))
/
sum(rate(gcp_cloud_run_job_completed_task_attempt_count[5m]))

# Cost per job over the last 7 days
sum by (job_name) (increase(gcp_cloud_run_job_billable_instance_time_rate[7d]))

All Cloud Run Jobs metrics include the following labels:

• job_nameCopy — The Cloud Run job name
• locationCopy — The GCP region (e.g., us-central1Copy)
• project_idCopy — The GCP project ID

Alerting on Cloud Run Jobs

Create check rules to detect job execution issues:

• High failure rate: Alert when the ratio of failed to completed tasks exceeds thresholds
• Missing executions: Detect when scheduled jobs don't execute on time
• Cost spikes: Notify when billable instance time increases unexpectedly
• Task retries: Alert on excessive retry attempts indicating systemic issues

See Alerting for details on creating check rules with infrastructure metrics.

Differences Between Jobs and Services

Cloud Run Jobs differ from Cloud Run Services in several ways:

• Execution model: Jobs run to completion and terminate, while services handle ongoing HTTP requests
• Scaling: Jobs scale based on task parallelism, while services scale based on request concurrency
• Metrics: Jobs focus on task completion and failure rates, while services emphasize request latency and throughput
• Cost structure: Jobs are billed for total execution time, while services are billed per request and idle time

Use Cloud Run Jobs for batch processing, data pipelines, scheduled tasks, and any workload that runs on a defined schedule or trigger. Use Cloud Run Services for HTTP APIs, webhooks, and request-driven applications.

Further Reading

• Monitor Cloud Run Services — Monitor Cloud Run service metrics
• GCP Resources — Resource naming and equality rules
• Set Up Google Cloud Integration — Configure the GCP integration
• Google Cloud Monitoring Metrics — Full list of available Cloud Run metrics