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video_editing_poc/docs/runbooks.md

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Runbooks

This document captures the operational readiness artifact for plan item Runbooks.

Scope:

  • API and worker incidents for video-clipping-service
  • Database-backed queue behavior
  • Current dashboard and alert coverage
  • Cleanup and retention job behavior

Primary references:

  • Dashboard: dashboards/video-clipping-overview-grafana.json
  • Dashboard notes: docs/dashboards.md
  • Alert rules: docs/alerts.md
  • Autoscaling policy: docs/autoscaling-policies.md
  • Queue timeout tuning: docs/queue-visibility-timeout-tuning.md

Service Model

Current implementation assumptions:

  • Metrics are exposed at /actuator/prometheus.
  • Health endpoints are exposed through Spring Boot Actuator.
  • Queue mode may be memory or db, but operational queue metrics and retry behavior documented here assume video-clipping.queue=db.
  • Database-backed queue messages move through PENDING, PROCESSING, COMPLETED, or DLQ.
  • Worker retries are driven by:
    • video-clipping.database-queue.max-attempts
    • video-clipping.database-queue.retry-backoff-ms
    • video-clipping.database-queue.visibility-timeout-ms
  • Cleanup jobs are driven by:
    • video-clipping.cleanup.enabled
    • video-clipping.cleanup.local-artifact-poll-interval-ms
    • video-clipping.cleanup.retention-poll-interval-ms

Quick Triage

When an alert fires:

  1. Identify whether the impact is API availability, worker throughput, or data cleanup.
  2. Check the Grafana dashboard for:
    • pending queue depth
    • oldest pending age
    • DLQ count
    • API request rate
    • API p95 latency
    • CPU usage
    • heap usage
  3. Check /actuator/health for general service status.
  4. Correlate logs using jobId, assetId, tenantId, and traceId if available.
  5. Determine whether the issue is isolated to one pod or systemic across API and worker replicas.

Runbook: Queue Age High

Signals:

  • VideoClippingQueueOldestPendingHigh
  • VideoClippingQueueOldestPendingCritical
  • Dashboard shows elevated Oldest Pending Age and rising Pending Queue Depth

Likely causes:

  • Worker capacity is too low for current workload.
  • Workers are crash-looping or not polling.
  • Source staging, FFmpeg, or clip upload is slow.
  • Database queue messages are stuck until visibility timeout reclaim.

What the code does:

  • The database queue poller claims PENDING messages and marks them PROCESSING.
  • Expired PROCESSING messages are reclaimed after video-clipping.database-queue.visibility-timeout-ms.
  • Retry scheduling uses video-clipping.database-queue.retry-backoff-ms.

Immediate actions:

  1. Confirm queue mode is db. This runbook does not apply as written to memory mode.
  2. Check worker replica health and restart status.
  3. Check whether CPU is saturated on workers.
  4. Check whether pending depth is growing faster than processing count.
  5. Check recent deploys, node pressure, object storage reachability, and FFmpeg availability.

Mitigation:

  1. Scale worker replicas up within the bounds in docs/autoscaling-policies.md.
  2. If workers are healthy but backlog remains, inspect whether EXACT jobs or slower object storage throughput are dominating runtime.
  3. If many jobs appear stuck in PROCESSING, compare observed runtime to video-clipping.database-queue.visibility-timeout-ms.
  4. If the timeout is too small for production runtime, raise it before increasing concurrency aggressively.

Exit criteria:

  • video_clipping_queue_oldest_pending_age_seconds trends down
  • pending depth stabilizes or shrinks
  • no new DLQ growth caused by the mitigation

Runbook: DLQ Messages Present

Signals:

  • VideoClippingDlqPresent
  • Dashboard DLQ Messages stat is nonzero

What the code does:

  • A failed message is retried until video-clipping.database-queue.max-attempts.
  • On final failure, the queue message is marked DLQ.
  • The clip job is marked FAILED.
  • Job events include DLQ and FAILED.

Likely causes:

  • FFmpeg processing failure
  • invalid or corrupt source media
  • object storage materialization or upload failure
  • persistent code regression affecting all retries

Immediate actions:

  1. Determine whether DLQ growth is isolated or systemic.
  2. Inspect failed job records and recent job events for representative jobId values.
  3. Check whether failures began after a deploy or configuration change.
  4. Verify object storage, database, and worker runtime dependencies.

Mitigation:

  1. If the issue is a bad deployment, roll back or stop worker rollout.
  2. If the issue is data-specific, isolate affected tenants or assets and avoid mass redrive.
  3. If the issue is dependency-related, restore storage, FFmpeg, or database health first.
  4. Redrive only after the root cause is fixed.

Redrive guidance:

  • There is no dedicated automated redrive artifact yet in the repo.
  • Redrive must preserve idempotency and avoid replaying into an unresolved failure condition.
  • Use representative sample jobs first before bulk replay.

Exit criteria:

  • DLQ count stops increasing
  • new jobs succeed normally
  • redriven sample jobs no longer fail for the same reason

Runbook: API 5xx Rate High

Signals:

  • VideoClippingApi5xxRateHigh
  • VideoClippingDownloadUrl5xxRateHigh
  • Rising API p95 latency or reduced request success rate

Likely causes:

  • storage signing or retrieval issues
  • unexpected server exception
  • dependency degradation
  • hot path regression after deploy

Immediate actions:

  1. Check whether all endpoints are failing or only a specific route such as /v1/clips/{clipId}/download-url.
  2. Review recent application logs for exception bursts.
  3. Check /actuator/health.
  4. Compare API symptoms with worker and queue signals to decide whether the issue is upstream, downstream, or local to the API.

Mitigation:

  1. If failures are isolated to download-url, verify storage adapter health and signing configuration.
  2. If failures affect create/upload/job endpoints broadly, verify repository and queue dependency health.
  3. If failures correlate to a new release, roll back the API deployment.
  4. If latency is high before 5xx increases, consider scaling API replicas while root cause analysis continues.

Exit criteria:

  • 5xx ratio returns below alert threshold
  • p95 latency returns to expected baseline
  • no recurring exception burst in logs

Runbook: Instance Down

Signals:

  • VideoClippingInstanceDown
  • missing target in Prometheus
  • Kubernetes readiness or liveness failures

Immediate actions:

  1. Determine whether the issue affects API pods, worker pods, or both.
  2. Check pod phase, restart count, and last termination reason.
  3. Check readiness and liveness probe failures.
  4. Verify whether the issue is caused by node loss, crash loop, bad config, or scrape misconfiguration.

Mitigation:

  1. If the service is crash-looping after deploy, roll back.
  2. If only one target is affected, replace the pod and monitor the rest of the replica set.
  3. If Prometheus scraping changed but the app is healthy, fix scrape config and keep incident scope narrow.

Exit criteria:

  • target is consistently scraped again
  • pod restarts stop increasing
  • related queue or API alerts clear

Runbook: Process CPU High

Signals:

  • VideoClippingProcessCpuHigh
  • dashboard CPU panel shows sustained process_cpu_usage

Likely causes:

  • worker saturation due to EXACT jobs
  • insufficient worker replica count
  • node oversubscription
  • runaway request or processing loop

Immediate actions:

  1. Determine whether high CPU is on API or worker pods.
  2. Compare CPU saturation with queue age and pending depth.
  3. Check whether workload mix changed toward more CPU-intensive jobs.

Mitigation:

  1. If workers are saturated and queue backlog is growing, scale workers first.
  2. If API pods are saturated, scale API pods and inspect hot endpoints.
  3. If a specific deployment introduced the condition, roll back.
  4. If the node is pressured, rebalance or move workloads.

Exit criteria:

  • CPU drops below sustained threshold
  • queue age and latency normalize

Runbook: Cleanup Or Retention Not Keeping Up

Signals:

  • local disk usage continues to grow
  • old generated artifacts remain under tmp/
  • expired clips or source objects are not being removed on schedule

What the code does:

  • LocalArtifactCleanupJob removes expired files under:
    • configured FFmpeg input directory
    • configured FFmpeg output directory
    • tmp/in-memory-storage
    • tmp/stub-output
  • RetentionCleanupJob deletes expired clips and expired source objects, then marks assets deleted.

Immediate actions:

  1. Verify video-clipping.cleanup.enabled=true.
  2. Confirm poll intervals and retention hours are set as expected.
  3. Check whether the service instance that owns scheduled jobs is running.
  4. Verify object storage delete operations are succeeding.

Mitigation:

  1. If cleanup was disabled accidentally, re-enable it and monitor deletion progress.
  2. If local artifacts are too large for current retention windows, temporarily scale down worker throughput or increase local storage while cleanup catches up.
  3. If object deletion is failing, fix storage access first before manually deleting metadata.

Exit criteria:

  • local artifact growth stops
  • expired assets and clips are removed on schedule

Escalation Rules

Escalate immediately if:

  • DLQ count is growing and new jobs continue to fail after rollback
  • queue age stays above critical threshold after scaling workers
  • API 5xx affects upload completion, job creation, or clip download across tenants
  • cleanup lag risks disk exhaustion or unbounded storage growth

Known Gaps

  • No dedicated first-class metric exists yet for clip-job failure rate.
  • No dedicated FFmpeg exit-code metrics exist yet.
  • No dedicated object storage failure counter exists yet.
  • No dedicated runbook automation exists yet for DLQ redrive.

These gaps are consistent with docs/alerts.md and should be closed before treating the operational surface as fully production-complete.