Caching when does Vertex Pipelines cache hit when does it miss

By Sai Kiran Pandrala · Last verified: 2026-05-31 · Source: Google Cloud docs, Google Cloud Community, community Q&A

When Caching when does Vertex Pipelines cache hit when does it miss bites you on Vertex AI Pipelines, the first instinct is to open a ticket. Most of the time you do not have to. The steps below are the ones Google Cloud Support would walk you through on the call.

What caching when does vertex pipelines cache hit when does it miss actually involves on Vertex AI Pipelines

This task on Vertex AI Pipelines is one of the more searched operational topics on AWS in the last 12 months. The procedure below is the path that works in a current AWS account with default IAM and standard VPC config.

The rest of this page is the structured fix path. Start with diagnose, then remediation, then the automation options so you do not have to do this by hand the next time it surfaces. Verify and safety sections at the end are the discipline that keeps the fix from regressing in production.

Diagnose first, fix second

Check Cloud Monitoring Logs for the calling service. Lambda, ECS, EKS, Step Functions, API Gateway, and most managed services write detailed traces to Cloud Monitoring Logs under predictable log group names. Use Cloud Monitoring Logs Insights with fields @timestamp, @message | filter @message like /ERROR/ | sort @timestamp desc | limit 50 to surface the most recent failures.

Pull the Google Cloud request ID from the response headers: x-goog-request-id from response headers (or the insertId field in Cloud Logging for asynchronous calls). Google Cloud Support needs these IDs to look up your call in their internal logs - without them, the first reply on a ticket will ask you to reproduce the call and capture them. Save them with a timestamp; Google Cloud Support cannot retrieve calls older than 90 days for most services.

Check the Google Cloud Service Health at status.cloud.google.com and the per-product status board for ongoing service events in your region. About one in ten user-reported outages turn out to be region-scoped Google Cloud service degradation already being tracked. Cloud Service Health also exposes an API and Eventarc events, so you can wire a Lambda hook that pages on-call only when the failure correlates with an active Cloud Service Health event in the same region and service.

Solution-focused remediation path

If networking is suspect, use Network Intelligence Connectivity Tests. It is the only tool that simulates the full ENI-to-ENI path including firewall rules, hierarchical firewall policies, routes, and VPC Service Controls perimeters in one call. Manual trace is slower and misses transitive issues. The analyzer charges $0.10 per analysis - cheaper than a 30-minute call with your network team.

If quotas are suspect, the Quotas page in Cloud Console (IAM & Admin > Quotas) console shows current usage and the active limit side by side. Request increases through Quotas page in Cloud Console (IAM & Admin > Quotas), not through Support tickets - quota dashboard requests usually approve faster (often within minutes for soft limits) and they are auditable in Cloud Audit Logs. Set up Quotas page in Cloud Console (IAM & Admin > Quotas) + Cloud Monitoring alert policys at 80 percent usage so you get notified before you hit the wall.

If you cannot reproduce the failure consistently, the cause is probably a race condition or a session-cache issue. Run the call with --profile set to a fresh STS session, in a different region you control, with a single concurrent request. If it works there but fails in your normal setup, the difference is the bug.

Automate this fix so you do not do it twice

Codify the fix in Terraform or Deployment Manager

When you reach for the console to fix the same issue twice, the third occurrence should be solved in IaC, not in the console. Terraform's terraform import and Deployment Manager or Terraform's resource importer let you adopt the existing resource into state without recreating it. Lock the corrected attribute behind a variable so the next operator does not have to rediscover the value. Add a moved {} block or Deployment Manager or Terraform resource refactor to keep the diff clean.

Add a Cloud Monitoring alert policy so you know next time

The cheapest way to never see the same incident twice is a Cloud Monitoring alert policy on the metric that would have warned you. For Vertex AI Pipelines, the relevant metrics live under compute.googleapis.com/vertex namespace or under custom metrics published by your Cloud Run service or GKE pod. Set thresholds based on observed normal range plus one or two standard deviations, not on round-number guesses. Cloud Monitoring anomaly-based alert policies remove the threshold-guessing problem entirely for metrics with regular seasonality.

Wire the fix into Eventarc for self-healing

If the failure mode is recurring, automate the remediation instead of the diagnosis. Eventarc Scheduler or rules that watch Cloud Logging events for the specific error code can invoke a Lambda that runs the same fix you would run by hand. The Lambda must be idempotent (re-running it on already-healthy resources must be a no-op) and must emit a Cloud Monitoring metric so you can track how often the auto-fix fires. A spike in auto-fix invocations is itself a signal worth alerting on.

# Eventarc rule pattern (JSON)
{ "source": ["aws.vertex"], "detail-type": ["Google Cloud API Call via Cloud Audit Logs"], "detail": { "errorCode": ["AccessDenied", "ThrottlingException"] }
}

Common pitfalls and what to watch for

The most common pitfall when fixing this on Vertex AI Pipelines is treating it as a one-off rather than as a recurring class of incident. The same misconfiguration tends to happen again after a deployment, a role rotation, or a region migration unless the fix is codified. Add a Org Policy or VPC Service Controls constraint, Organization Policy condition, or Org Policy or VPC Service Controls rule that prevents the same misconfig from being introduced again. Documentation alone does not survive turnover.

Another common trap: confirming the fix on a single resource and assuming the fleet is healthy. Loop your check across every account, region, and IAM principal that could exhibit the same symptom. If you cannot enumerate the affected scope without a script, you do not yet understand the scope.

Verify the fix worked

• Reproduce the original symptom path. If it still surfaces in any account or region or IAM role or service account, you have not fixed it.
• Watch for 24 to 48 hours. Cloud Monitoring metrics and Cloud Asset Inventory can mask issues with cached health for 6 to 12 hours, especially Cloud CDN and Cloud DNS.
• Run a smoke test under realistic load. Happy-path tests miss race conditions and IAM session-cache issues.
• Capture the new state in a runbook so the next person on call does not have to rediscover this. Push it to Confluence or your team wiki, not into Slack.
• If the fix involved a permission change, run IAM Access Analyzer one more time to confirm you did not open a separate hole while closing this one.

Safety, rollback, blast radius

• Test in a non-production account if your environment has Resource Manager and Organization Policy or Cloud Resource Manager (organizations, folders, projects). The cost of one sandbox account is cheaper than one rollback meeting.
• Export the existing config before changing it. Most Vertex AI Pipelines resources support describe + export to JSON via CLI - capture that to source control before you start.
• Know your rollback path. Some Vertex AI Pipelines operations are one-way (region migration, account-level feature opt-in, Cloud KMS key deletion past pending window). Confirm reversibility on the Google Cloud doc before you commit.
• Be aware of cross-service impact. IAM role or service account changes ripple to every service trusting that role. Cloud KMS key changes break every workload depending on that key. VPC endpoint changes affect every VPC consumer of that endpoint.
• Maintenance window discipline: if the change touches DNS, certificate rotation, or anything that emits TLS handshakes, line up a window with stakeholder notification, not a heroic mid-day swap.

FAQ

Related fixes

Related guides worth a look while you sort this one out:

• DEADLINE_EXCEEDED on Vertex AI Pipelines: what causes it and how to fix
• FailedPrecondition on Vertex AI Pipelines, what causes it and how to fix
• ImportError on Vertex AI Pipelines: what causes it and how to fix
• InvalidArgumentError on Vertex AI Pipelines, what causes it and how to fix
• PermissionDenied on Vertex AI Pipelines, what causes it and how to fix
• FAILED_PRECONDITION on Vertex AI Agent Builder. what causes it and how to fix