Postgres production readiness in 48 hours after a prototype

What production-ready means for Postgres after a prototype

Production-ready Postgres isn’t “perfect database design.” It means your app can handle real users, real traffic, and real mistakes without losing data, falling over, or leaking access.

A prototype optimizes for speed: one database user, default settings, no alerts, and backups that exist mostly as an idea. Production flips the goal. You want predictable behavior under load, clear access rules, and a way back when something breaks.

Most problems show up in three places:

• Data loss when backups are missing, untested, or stored next to the database.
• Outages when traffic rises and every request opens a new connection (a connection storm).
• Security gaps when secrets leak, roles are too powerful, or inputs aren’t handled safely.

A 48-hour production-readiness push is about adding a thin layer of safety, not redesigning everything. In that window, you can usually get backups working, prove a restore, add basic monitoring, add connection pooling, and stop running the app as a superuser. What you usually can’t finish is major schema redesign, rewriting complex queries, or building multi-region failover from scratch.

If you inherited an AI-built prototype (often from tools like Replit or Cursor), use a risk-first order: protect data first, prevent outages second, then tighten permissions.

Hour 0-2: quick inventory and risk triage

Spend the first two hours getting the facts on paper. You can’t make good decisions if you don’t know where Postgres lives, who owns it, and how the app touches it.

Start with a simple inventory:

• Where Postgres is hosted and what version it runs
• How the app connects (direct, proxy, serverless)
• What storage is used and whether snapshots are enabled
• Who has admin access and who can deploy database changes
• Where credentials live (env vars, secrets manager, repo, CI settings)

Then capture what you’d need to reproduce today’s state: a schema dump, installed extensions, and any scheduled jobs or migrations that run automatically. Write down the 5-10 most important actions that hit Postgres (signup/login, checkout, search, admin edits). If you have logs, grab a small sample of slow queries.

Finally, triage based on what hurts now: timeouts during spikes, “too many connections,” one endpoint that’s consistently slow, or auth flows that fail only sometimes.

Backups: pick RPO/RTO and implement the simplest reliable setup

If you do only one thing, make it backups you can actually restore. Start with two numbers:

• RPO (how much data you can lose, like 15 minutes)
• RTO (how fast you must be back up, like 60 minutes)

Those goals drive everything else. If you can tolerate losing a day of data, nightly backups might be enough. If you can’t, you need more frequent backups and likely point-in-time recovery from your Postgres host.

For a practical 48-hour setup, use two layers:

• Snapshots (fast recovery)
• Logical backups (slower, but portable and easier to inspect)

Pick a retention policy you can explain in one sentence, such as “7 daily and 4 weekly.” Store backups outside the database machine or cluster so one failure doesn’t take everything out.

Keep the restore proof small and repeatable. Restoring one small table (or just schema plus a few rows) into a clean database and verifying counts match is enough to catch most backup misconfigurations.

Also decide who gets paged when backups fail. It should be a named person or an on-call rotation, not “the team.”

Restore readiness: prove you can get data back

A backup you’ve never restored is a guess. Restore readiness means you have a routine you can run when you’re tired, stressed, and the app is down.

A fast restore test (30-60 minutes)

Restore into an isolated place: a separate database on the same server, staging, or a temporary container. Don’t touch production while you’re proving the backup is usable.

# Example: restore into a new database
createdb app_restore_test

# If you have a plain SQL dump
psql -d app_restore_test -f backup.sql

# If you have a custom-format dump
pg_restore -d app_restore_test --clean --if-exists backup.dump

After the restore, check the prototype failure points: missing extensions, wrong owners, or app roles that were created only on someone’s laptop.

Validate the basics:

• The app role can connect and do a simple read and write
• Required extensions exist (for example, uuid-ossp, pgcrypto, PostGIS)
• Roles and grants survived the restore (nothing ended up owned by the wrong user)
• A quick smoke test passes (log in, create one record, read it back)

Document it like a runbook

Write down the exact steps you followed: commands, where credentials come from, and what “success” looks like. Time the restore end to end and compare it to your RTO. If restore takes 45 minutes and your RTO is 15, that’s not a tuning problem. It’s a backup and recovery design mismatch.

Monitoring and alerts: the minimum signals that catch real outages

You don’t need a giant dashboard. You need a small set of signals that predict user pain, plus alerts that reach a real person.

Start with a few checks you’ll actually look at:

• Active connections vs max connections
• CPU and memory pressure on the database host
• Free storage and how fast it’s dropping
• Replication lag (if you have replicas)
• Error rates (timeouts, auth failures)

Then add two database checks that catch the sneaky outages: slow queries and lock waits. Prototypes often fail here because one endpoint is doing a table scan, or one background job holds a lock and everything queues behind it.

Keep alert rules simple and actionable. For example: low disk space, connection saturation for several minutes, a big jump in p95 query latency, persistent lock waits, or replication lag above your tolerance.

Be careful with logs. Log slow queries and errors, but don’t log raw request bodies, tokens, passwords, or full SQL that contains user data.

Connection pooling: stop connection storms before they happen

Most prototypes do the simplest thing: open a new connection, run a query, move on. That works until you get a spike (launch, email blast, bot traffic). Postgres has a hard limit on concurrent connections, and each one costs memory. Too many at once and you get a slow crawl, then failures.

Pooling fixes this by making connections reusable and capped.

Where pooling should live

App-layer pooling is fine when you control the code and the runtime stays warm. A managed pooler is easiest if your provider offers it. A dedicated pooler (running next to the database) is often the most predictable when you have multiple app instances.

Safe starter settings

Start small and measure:

• Pool size: 10-30 per app instance (not hundreds)
• Connection timeout: 2-5 seconds
• Idle timeout: 1-5 minutes
• Statement timeout: 10-30 seconds
• Queue timeout: 5-15 seconds

Retries can help with brief network issues, but be conservative. Retry only on clearly transient errors, add a small random delay, and cap attempts (often one retry is enough). Otherwise, you can create a retry storm.

Also confirm you’re not leaking connections: close them, keep transactions short, and don’t hold a transaction open while waiting on external APIs.

Roles and permissions: least privilege without breaking the app

Least privilege is a fast win because it reduces blast radius without changing your schema. Split access by job, not by person.

A simple pattern is three roles:

• Runtime role for the app (day-to-day reads/writes)
• Migrations role for schema changes
• Read-only role for support and analytics

The runtime role shouldn’t be able to create tables, change ownership, or read everything by default. Use the migrations role only in your deploy process, not in the web app.

After you create roles, remove shared admin credentials from app configs. A common prototype mistake is shipping with the same superuser password used in development, copied into multiple services.

Rotate passwords and put them in one source of truth (deployment platform env vars or a secrets manager). Make rotation a repeatable process, not a heroic late-night edit.

Quick hardening checks:

• Postgres isn’t publicly reachable; inbound access is restricted
• TLS is required when connections cross untrusted networks
• The app connects with the runtime role, not admin or migrations

Safety checks: avoid the most common security and data-loss traps

When teams rush to ship, most Postgres incidents come from the same few sources: unsafe queries, leaked credentials, and risky migrations.

Start by naming the main risks you’re actually protecting against: SQL injection, exposed secrets (DB passwords in code or logs), and schema changes that lock or wipe tables.

For query safety, treat SQL string concatenation with user input as a bug. Use parameterized queries everywhere. A quick way to find trouble is searching for raw query helpers, SQL template strings, and code patterns that build SQL with user-provided values.

For migrations, add simple guardrails: take a fresh backup before migrating, review the migration diff, and write down a rollback plan (even if it’s “restore from backup”).

Also choose what you won’t fix in 48 hours, and write it down so it doesn’t vanish. Examples: row-level security, encryption-at-rest for backups, or deeper query and indexing work.

Disaster recovery drill: practice one realistic failure scenario

A disaster recovery drill is a small, planned failure you run on purpose. The goal is to prove your recovery steps work, not to show heroics.

Pick one scenario you can explain in a sentence, such as “a bad migration dropped the users table” or “a script deleted rows without a WHERE clause.” Then practice restoring to a safe point and getting the app working again.

Keep the drill under an hour:

• Announce a drill window and freeze writes (or maintenance mode)
• Simulate failure in a controlled way (ideally on staging or a copy)
• Restore into a separate database and verify key actions
• Decide how you’d recover in production (swap vs copy back)
• Write down how long it took and what surprised you

Even a small incident needs clear ownership: one person to lead decisions, one to run the restore, and one to handle updates.

Common mistakes that waste your 48 hours

The easiest way to miss the window is spending time on work that feels productive but doesn’t reduce risk.

One classic trap is assuming “automated backups” means you’re covered. Backups only matter if you can restore them on demand into a clean environment, and the team knows the steps.

Another trap is using one shared admin database user because it “just works.” It also means any bug or leaked credential has full power.

Connection problems often get “fixed” by raising max_connections. That usually makes things worse under load (more memory use, more context switching, slower queries). Fix connection storms with pooling, not by pushing the server harder.

Alerts can waste time too. Don’t start with 30 noisy alerts that nobody trusts. A small set that catches real pain is enough: backup failures, disk growth, connection saturation, replication lag (if used), and error rate spikes.

What to verify before you call it production-ready

If you only have an hour left, verify the basics end to end. This is less about perfect tuning and more about evidence.

Make sure you can answer “yes” with proof:

• Backups are real and recent. You can name the last successful backup time, retention, and where backups live.
• You can restore under pressure. You’ve done a test restore to a separate database and recorded how long it took.
• Monitoring will catch obvious failures. A real person receives alerts, and you’ve tested at least one.
• Connections are controlled. Pooling and timeouts are in place, and a small burst test doesn’t melt the database.
• Access is scoped. Separate roles exist for runtime and migrations, and secrets aren’t in code or logs.

Example: turning a shaky AI-built prototype into a stable launch setup

A founder ships an AI-built prototype (generated in Cursor and tweaked in Replit). After a launch post, the app starts timing out. Pages hang, logins fail, and Postgres shows hundreds of short-lived connections.

The 48-hour plan stays boring on purpose: stop the bleeding, add visibility, then make data recoverable.

First, fix connection storms with pooling and sane timeouts. Next, add minimal monitoring for connections, slow queries, disk usage, and backup status. Then implement automated backups and do one real restore into a fresh database. Finally, split database roles so the app isn’t running with migrations or admin privileges.

Once the system is stable, performance tuning is a calmer project: review slow queries, add or fix indexes, and sanity-check any “one table does everything” patterns.

Next steps: keep it stable as usage grows

A 48-hour push gets you over the line, but stability comes from small follow-ups that don’t get skipped.

Pick a short backlog with owners and dates: a weekly backup/restore check, a monthly alert review, a scheduled credential rotation, and a quarterly access review. Re-run one disaster recovery drill periodically so recovery doesn’t depend on one person’s memory.

If you inherited an AI-generated codebase and you’re seeing repeat failures (broken auth, exposed secrets, messy migrations, or unscalable patterns), a focused codebase diagnosis can be faster than guessing. FixMyMess (fixmymess.ai) specializes in taking AI-generated prototypes and hardening them for production, starting with a risk-first audit so you know what to fix now and what can wait.