GCP Alloydb

GCP AlloyDB

Overview

AlloyDB is Google Cloud's managed PostgreSQL-compatible database. It separates compute and storage so they scale independently, runs an analytics-grade columnar engine alongside the transactional rows, and ships with AlloyDB AI — vector search, hybrid search, and model-endpoint management for building RAG and semantic-search apps directly against the database.

Instructions

Core Concepts

• Cluster — the top-level resource that owns storage, backups, and configuration shared across instances
• Primary instance — the read/write instance; one per cluster
• Read pool instance — horizontally scalable read replicas backed by the same storage
• Secondary cluster — cross-region replica for DR
• Continuous backup — point-in-time recovery within the recovery window (default 14 days)
• AlloyDB AI — native vector extension, google_ml_integration for inference, hybrid search

Prerequisites

# Enable APIs
gcloud services enable alloydb.googleapis.com \
  servicenetworking.googleapis.com \
  compute.googleapis.com

# Reserve a private IP range for VPC peering (one-time per VPC)
gcloud compute addresses create alloydb-peering \
  --global --purpose=VPC_PEERING --prefix-length=16 \
  --network=default

gcloud services vpc-peerings connect \
  --service=servicenetworking.googleapis.com \
  --ranges=alloydb-peering --network=default

Creating a Cluster and Primary Instance

# Create a cluster (storage + config; no compute yet)
gcloud alloydb clusters create prod-cluster \
  --region=us-central1 \
  --network=default \
  --password="$(openssl rand -base64 24)" \
  --automated-backup-days-of-week=MONDAY,THURSDAY \
  --automated-backup-start-times=02:00 \
  --automated-backup-retention-count=14

# Create the primary instance (the actual compute)
gcloud alloydb instances create prod-primary \
  --cluster=prod-cluster \
  --region=us-central1 \
  --instance-type=PRIMARY \
  --cpu-count=4 \
  --availability-type=REGIONAL \
  --database-flags="cloudsql.iam_authentication=on,alloydb.enable_pgvector=on"

For production, prefer IAM database authentication over password auth — it removes static credentials entirely and integrates with service accounts.

Read Pool for Scaling Reads

# Add a read pool (e.g., for analytics or reporting traffic)
gcloud alloydb instances create reports-pool \
  --cluster=prod-cluster \
  --region=us-central1 \
  --instance-type=READ_POOL \
  --read-pool-node-count=3 \
  --cpu-count=4

Connecting from an Application

# Run the Auth Proxy locally (handles IAM + TLS)
./alloydb-auth-proxy \
  projects/my-project/locations/us-central1/clusters/prod-cluster/instances/prod-primary \
  --port 5432

# Python connection using the AlloyDB connector + IAM auth
from google.cloud.alloydb.connector import Connector, IPTypes
import sqlalchemy

connector = Connector()

def getconn():
    return connector.connect(
        "projects/my-project/locations/us-central1/clusters/prod-cluster/instances/prod-primary",
        "pg8000",
        user="app-sa@my-project.iam",
        db="orders",
        enable_iam_auth=True,
        ip_type=IPTypes.PRIVATE,
    )

engine = sqlalchemy.create_engine("postgresql+pg8000://", creator=getconn)

with engine.connect() as conn:
    result = conn.execute(sqlalchemy.text("SELECT current_database(), version()"))
    print(result.fetchone())

AlloyDB AI — Vector Search

-- Enable extensions (once per database, run as superuser)
CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS google_ml_integration;

-- Schema with embeddings
CREATE TABLE products (
  id BIGSERIAL PRIMARY KEY,
  name TEXT NOT NULL,
  description TEXT,
  embedding vector(768)
);

-- Generate embeddings inline using a registered model endpoint
UPDATE products
SET embedding = embedding('textembedding-gecko@003', description)
WHERE embedding IS NULL;

-- ScaNN index for fast approximate nearest-neighbor search
CREATE INDEX ON products USING scann (embedding cosine)
WITH (num_leaves = 100);

-- Hybrid search: semantic + lexical
SELECT id, name, description,
       1 - (embedding <=> embedding('textembedding-gecko@003', 'wireless headphones with noise cancellation')) AS similarity
FROM products
WHERE description ILIKE '%bluetooth%'
ORDER BY embedding <=> embedding('textembedding-gecko@003', 'wireless headphones with noise cancellation')
LIMIT 10;

Cross-Region DR with Secondary Cluster

# Promote a secondary cluster in a different region
gcloud alloydb clusters create-secondary dr-cluster \
  --region=us-east1 \
  --primary-cluster=projects/my-project/locations/us-central1/clusters/prod-cluster \
  --network=default

# Failover (promote secondary to standalone primary)
gcloud alloydb clusters promote dr-cluster --region=us-east1

Terraform

resource "google_alloydb_cluster" "prod" {
  cluster_id = "prod-cluster"
  location   = "us-central1"
  network_config {
    network = data.google_compute_network.default.id
  }

  initial_user {
    user     = "postgres"
    password = var.alloydb_password
  }

  automated_backup_policy {
    location      = "us-central1"
    backup_window = "1800s"
    enabled       = true
    weekly_schedule {
      days_of_week = ["MONDAY", "THURSDAY"]
      start_times { hours = 2 }
    }
    quantity_based_retention { count = 14 }
  }
}

resource "google_alloydb_instance" "primary" {
  cluster       = google_alloydb_cluster.prod.name
  instance_id   = "prod-primary"
  instance_type = "PRIMARY"

  machine_config { cpu_count = 4 }
  availability_type = "REGIONAL"

  database_flags = {
    "alloydb.enable_pgvector" = "on"
    "cloudsql.iam_authentication" = "on"
  }
}

Examples

Example 1 — Set up a production cluster with read replicas

User asks for a production-ready AlloyDB deployment. Create the cluster with regional availability, configure automated backups with 14-day retention, provision a primary instance with IAM auth enabled, then add a 3-node read pool sized for the analytics workload. Hand the user the Auth Proxy connection string and a Terraform module they can commit.

Example 2 — Add semantic search to an existing PostgreSQL app

User has a products table on AlloyDB and wants vector search. Enable vector and google_ml_integration extensions, add an embedding vector(768) column, backfill embeddings using embedding('textembedding-gecko@003', description), create a ScaNN index, and rewrite the search endpoint to combine cosine distance with existing WHERE filters for hybrid retrieval.

Guidelines

• Use IAM database authentication in production — service accounts authenticate to the database without storing passwords
• Set availability-type=REGIONAL for HA; ZONAL only for dev/test
• Keep the Auth Proxy in your container or sidecar — never expose AlloyDB to the public internet
• Read pools are the right scaling lever before considering bigger primary CPUs
• For vector workloads, ScaNN indexes outperform ivfflat and hnsw at scale; tune num_leaves to ~sqrt(rows)
• Use secondary clusters for DR; failover is manual but typically completes in minutes
• Continuous backup gives PITR within the recovery window — separate from automated backups (which are snapshot-based)
• Monitor slow queries via Query Insights (built into the AlloyDB console)