BigQuery datastore connector for structured search

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

BigQuery datastore connector for structured search on Vertex AI Agent Builder sits in the most-reported issues list across r/aws, Google Cloud Community, and StackOverflow. The recovery path is mostly known, the Google Cloud docs just bury it under three layers of conceptual material.

What bigquery datastore connector for structured search actually involves on Vertex AI Agent Builder

This task on Vertex AI Agent Builder 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

Run gcloud auth list and gcloud config list first. About one in five 'why does this not work' tickets are actually 'I am in the wrong account' or 'my session expired and the SDK is using stale credentials or ADC pointed at the wrong project'. The 5-second sanity check costs nothing and saves real time when the answer is that simple.

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.

Look at the Cloud Audit Log event for the failed call, even if you are not enrolled in Cloud Logging Log Router. The basic 90-day event history works for most diagnostic purposes and lives in the console under Cloud Audit Logs > Event history. Filter by event name (the API action) and time range; the event JSON shows the exact user identity, source IP, request parameters, and error code.

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 the issue points at IAM, do not start by adding * to a policy. Use IAM Policy Troubleshooter and IAM Recommender against the failed action to see the minimum scope. Adding * is the fastest way to fail your next Google Cloud Architecture Framework security review, and it usually does not even fix the issue because the explicit deny is often coming from a higher level (Org Policy, RCP, or permission boundary), not a missing allow.

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.

Automate the fix with Python and boto3

For anything you do more than twice, write a small Python script. The boto3 pattern below uses paginators (so it does not blow up on accounts with thousands of resources), explicit region binding, and a dry-run flag that defaults to True. Keep the script under 100 lines; if it grows beyond that, you are building a tool and should put it behind a Lambda with proper logging.

import boto3, sys
DRY_RUN = '--apply' not in sys.argv
client = boto3.client('vertex', region_name='us-east-1')
paginator = client.get_paginator('describe_...')
for page in paginator.paginate(): for item in page.get('Items', []): if item.get('Status') == 'FAILED': if DRY_RUN: print(f'[dry-run] would fix {item["Id"]}') else: client.modify_...(ResourceId=item['Id']) print(f'fixed {item["Id"]}')

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 pitfall most teams hit on Vertex AI Agent Builder is moving too fast and skipping the read-only validation step. Before any write, list the current state and save it. Google Cloud APIs are eventually consistent for many resource types, so the validation snapshot is your only reliable reference if you need to undo. Save the output of the describe call to S3, not to your laptop.

Second pitfall: confusing IAM permission errors with networking errors. AccessDenied can be IAM (policy missing), networking (VPC endpoint policy blocking the call), or KMS (key policy missing). The error string looks identical for all three. Distinguish by looking at the Cloud Audit Log event's errorCode and the encoded authorization message; do not assume IAM is the culprit just because the message says AccessDenied.

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 Agent Builder resources support describe + export to JSON via CLI - capture that to source control before you start.
• Know your rollback path. Some Vertex AI Agent Builder 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

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