Cloud SQL to AlloyDB migration: what actually changes, what doesn't, and what we'd test first

A Cloud SQL to AlloyDB migration should preserve application meaning while deliberately changing the database operating model.

PostgreSQL compatibility makes the move possible. It doesn’t make it mechanical. Topology, connectivity, flags, extensions, pooling, failover, maintenance, observability, and performance behavior all need to be re-proven against the workload.

The migration is justified only when the new boundary solves a problem worth that proof.

Freeze the application contract

Write down what must remain stable:

• transaction and query correctness
• API and job behavior
• schema and data contracts
• accepted consistency and freshness
• authorization inside PostgreSQL
• recovery objectives
• expected application error handling

Don’t combine the migration with a metric rewrite, ORM upgrade, broad schema cleanup, new auth model, and pooler replacement unless one of those changes is required to make the move. Every extra variable weakens the evidence and rollback.

A separate follow-up change is cheaper than a cutover whose result nobody can attribute.

Inventory PostgreSQL compatibility

Capture the current engine version, extensions, database flags, collations, roles, grants, publications, large objects, procedural code, scheduled jobs, and client-driver behavior.

Classify each item:

supported unchanged
supported with different configuration
replace before migration
remove because it is unused
requires workload test

Run the application test suite against AlloyDB early. Then inspect actual production query shapes, not only application boot and CRUD. Compatibility defects often live in reporting queries, migrations, administration tools, or rarely used transaction paths.

Keep a factual exception register. “PostgreSQL compatible” is a starting assumption. The register is the migration plan.

Baseline the current system

Measure Cloud SQL before tuning or migrating:

• query latency by normalized statement and service
• transaction duration and lock waits
• CPU, memory, I/O, storage growth, and connection pressure
• read and write throughput
• cache behavior
• replica lag where relevant
• maintenance and failover behavior observed by clients
• cost at representative and peak load

This baseline answers two questions. Did AlloyDB solve the stated problem? Did the migration introduce a regression elsewhere?

Without it, the team can’t reliably attribute post-cutover changes to the migration.

Keep the first connectivity path stable

Choose the target network path, endpoint, connector or proxy, TLS posture, authentication, and DNS deliberately. Where possible, preserve the application’s connection method for the first canary.

If Cloud Run currently uses a connector through private networking, reproduce that shape before deciding whether AlloyDB’s connector, Auth Proxy, direct connection, Private Service Connect, IAM database authentication, or managed pooling should replace it.

Changing the database and connection boundary together may be necessary. Treat it as two migrations with separate tests and rollback conditions even when they share a window.

Test connection pressure, not only SQL

Run production-shaped concurrency through the target. Include cold starts, deployment overlap, worker bursts, client reconnects, and pool exhaustion.

Measure local pool acquisition, client connection count, database backends, transaction latency, and error classification. If AlloyDB managed connection pooling is part of the target, test session compatibility and queue behavior separately from direct connections.

Keep the fleet-wide connection budget. A more capable database doesn’t turn unbounded client demand into a useful contract.

Validate data continuously

During migration or replication, track source and target positions, lag, row counts, key counts, checksums or aggregates, sequence values, and failed objects. Reconcile at business boundaries as well as table boundaries.

A table-level count can match while a critical customer, order, or ledger relationship is wrong. Select representative high-value entities and compare the application-visible result.

Define the write-freeze or final synchronization sequence before cutover. Know how long it takes and what happens to writes that arrive near the boundary.

Rehearse failure from the client side

The platform’s availability design matters only through application behavior.

Trigger or simulate controlled database interruption where supported. Observe connection resets, DNS or endpoint behavior, pool recovery, retry rate, transaction ambiguity, and time to restore useful service. Confirm that retries are bounded and idempotent. A fast database failover followed by a ten-minute application reconnect storm isn’t a successful recovery.

Run maintenance and deployment overlap tests as well. Capture what operators see and which alert identifies the actual failed boundary.

Use a narrow canary

Choose a service that represents normal workload without owning the most dangerous transaction path. Route a bounded traffic share or a read-only workload first. Keep source-of-truth writes on the established path until correctness and operations are understood.

Promotion gates should cover:

• no semantic or transactional regression
• query latency and throughput within agreed bounds
• connection and retry behavior understood
• data reconciliation clean
• failover and maintenance rehearsal acceptable
• operator dashboards and runbooks usable
• rollback still executable

Increase traffic only when the next stage proves something the previous stage couldn’t.

Make rollback a data decision

Before target writes begin, rollback may be a routing change. After AlloyDB accepts authoritative writes, rollback requires a plan for data divergence.

Define the point of no return, reverse synchronization or dual-write policy if used, maximum tolerated outage, and who can make the decision. Don’t describe rollback as “switch DNS back” after the target has newer data. DNS can’t reconcile the divergence.

Keep the old Cloud SQL instance intact long enough to support the declared rollback and audit period, but prevent ambiguous dual authority.

Tune after equivalence

First prove that the target preserves behavior under the same logical workload. Then use AlloyDB-specific capabilities, read pools, indexing changes, pooling, or query tuning.

If the application must be redesigned before AlloyDB shows value, state that honestly. The redesign may still be worthwhile, but the database product didn’t independently solve the problem.

Stop when the evidence changes

Pause or reverse when query correctness differs, unsupported behavior appears, connection semantics become less legible, recovery is worse than the source, cost exceeds the accepted model, or operators can’t explain target failures.

The migration can also reveal that the original pain was application-shaped: bad pooling, long transactions, retry storms, or insufficient indexes. Fixing that on Cloud SQL may be the better outcome.

A successful Cloud SQL to AlloyDB migration leaves the application doing the same job on a database boundary that measurably handles the required scale, availability, or operations better. Otherwise, the work is a broader product change and needs its own justification.