Qdrant on Cloud Run — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Qdrant is a high-performance vector database and similarity search engine built for AI workloads — RAG pipelines, recommendation systems, semantic search, and embeddings storage. This lab takes you through the full operational lifecycle of the Qdrant on Cloud Run module on Google Cloud: deploy it, access and verify it, run it day-to-day, observe it, diagnose common problems, and tear it down.

The lab focuses on operating the Cloud Run module and the Google Cloud platform, not on Qdrant product features. For the complete list of provisioned services and every configuration input (organised by group), see the Configuration Guide — this lab deliberately does not duplicate that detail so it stays accurate over time.

Objectives

By the end of this lab you will be able to:

• Deploy the module from the RAD platform and locate the resources it provisions.
• Access and verify the running service.
• Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
• Observe the service with Cloud Logging and Cloud Monitoring.
• Diagnose and resolve the most common deployment and runtime issues.
• Tear the deployment down cleanly.

Prerequisites

• Services_GCP deployed in the target project (provides the VPC, Artifact Registry, and shared service accounts this module depends on).
• A Google Cloud project with billing enabled.
• gcloud CLI authenticated: gcloud auth login and gcloud auth application-default login.
• Project Owner (or equivalent) IAM on the project.
• RAD platform access with permission to deploy modules into the project.

Set these shell variables once; every task below reuses them:

export PROJECT="<your-gcp-project-id>"
export REGION="us-central1"          # the region you deploy into

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Task 1 — Deploy the module [Automated]

1. Click Deploy in the RAD platform top navigation, open Qdrant (Cloud Run) from the Platform Modules list to start configuration, set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Review the estimated cost (if credits are enabled) and click Deploy, which opens the deployment status page with real-time logs.

2. The platform provisions the Cloud Run v2 (Gen2) service, a Cloud Storage bucket mounted at /qdrant/storage via GCS FUSE, builds the container image, and stores an API key in Secret Manager when enable_api_key = true. Qdrant has no SQL database and no initialization job. First deploys typically take 8–15 minutes (image build dominates).

3. When it completes, discover the resources with name-agnostic filters (so the commands keep working regardless of the deployment suffix):

   SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~qdrant" --format="value(metadata.name)" --limit=1)
   SERVICE_URL=$(gcloud run services describe "$SERVICE" \
     --project="$PROJECT" --region="$REGION" --format="value(status.url)")
   echo "Service: $SERVICE"
   echo "URL:     $SERVICE_URL"

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Task 2 — Access & verify [Manual]

1. Confirm the service is healthy. Qdrant exposes two distinct health endpoints — use /readyz to confirm it has finished loading collections, and /livez to confirm the process is alive:

   curl -s "$SERVICE_URL/readyz"    # expect {"result":true,"status":"ok",...}
   curl -s "$SERVICE_URL/livez"     # expect {"result":true,"status":"ok",...}

   The default ingress_settings = "internal" restricts access to the VPC. Run these commands from a VM or Cloud Shell instance in the same VPC, or temporarily switch ingress to allow your source IP.

2. If enable_api_key = true, retrieve the API key from Secret Manager before making authenticated requests:

   API_KEY_SECRET=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~qdrant AND name~api-key" --format="value(name)" --limit=1)
   gcloud secrets versions access latest --secret="$API_KEY_SECRET" --project="$PROJECT"

   Pass the retrieved value as the api-key header on all Qdrant REST calls.

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Task 3 — Operate & keep it running (Day-2) [Manual]

1. Inspect the service and its revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

   gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
   gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

2. Scale by changing the min/max instance inputs in the RAD platform and applying it via Update — the module owns the service spec, so scaling is a configuration change, not a manual gcloud edit (a manual edit would be reverted on the next apply). Keep max_instance_count = 1; Qdrant is a single-writer store and multiple instances sharing the same GCS FUSE mount corrupt collections.

3. Update the application version by changing the version input in the RAD UI and applying it via Update; a new image builds and a new revision rolls out.

4. Manage secrets and backups:

   gcloud secrets list --project="$PROJECT" --filter="name~qdrant"
   gcloud run jobs list --project="$PROJECT" --region="$REGION"   # scheduled backup jobs

5. Inspect the GCS storage bucket where Qdrant persists its WAL, collection data, and HNSW index files:

   BUCKET=$(gcloud storage buckets list --project="$PROJECT" \
     --filter="name~qdrant" --format="value(name)" --limit=1)
   gcloud storage ls "gs://$BUCKET/"

---

Task 4 — Observe: Logging & Monitoring [Manual]

1. Logs — from the CLI or the Logs Explorer:

   gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50

   Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>".

2. Monitoring — open the Cloud Run dashboard for the service and review request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation. The module also provisions an uptime check (against /readyz); confirm it is green under Monitoring → Uptime checks, and review Alerting → Policies.

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Task 5 — Troubleshoot & debug [Manual]

Durable techniques for the failure modes you are most likely to hit. These are platform-level diagnostics and do not change with Qdrant releases.

• Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm env vars and secrets resolved.

  gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
  gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100

• Slow startup / /readyz returns 503: Qdrant loads all collections from GCS FUSE into memory on startup. Large collections can take tens of seconds to load. The startup probe waits for /readyz; allow additional time before declaring the revision unhealthy.
• GCS FUSE mount errors: confirm the Cloud Storage bucket exists, the runtime service account has storage.objectAdmin on the bucket, and the service is using the Gen2 execution environment (required for GCS FUSE).
• API key errors (401/403): confirm enable_api_key = true was set at deploy time, the secret exists, and the api-key header is present on requests.
• Image build failed: review Cloud Build history for the failed build's log.
• 403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

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Task 6 — Tear down [Automated]

On the Deployments page, open the deployment and click the Trash icon (Delete). Delete runs terraform destroy and is irreversible (the deployment record is retained for history). If a deployment is stuck and the RAD platform can no longer manage it (for example after manual changes that conflict with the Terraform state), use Purge instead — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the project). This removes everything the module created — the Cloud Run service, Cloud Storage bucket (and all persisted collections), Secret Manager secrets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run, GCS storage bucket, and optional API key secret
2 — Access & verify	Manual	Health checks pass on /readyz and /livez; API key retrieved if enabled
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups, inspect storage
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, GCS FUSE, API key, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources