PostgreSQL Disaster Recovery#

A DR plan for PostgreSQL has three layers: streaming replication for fast failover, WAL archiving for point-in-time recovery, and a backup tool like pgBackRest for managing retention. Each layer covers a different failure mode – replication for server crashes, WAL archiving for data corruption that replicates, full backups for when everything goes wrong.

Streaming Replication for DR#

Synchronous vs Asynchronous – The Core Tradeoff#

Asynchronous replication is the default. The primary streams WAL to the standby, but does not wait for confirmation before committing. This means the primary is fast, but the standby can be seconds behind. If the primary dies, those uncommitted-on-standby transactions are lost.

Database Cross-Region Replication Patterns#

Cross-region replication exists because regions fail. AWS us-east-1 has had multiple multi-hour outages. If your database runs in a single region, a regional failure takes your application down entirely. Cross-region replication gives you a copy of the data somewhere else so you can recover.

The fundamental problem is physics. Light through fiber between US East and US West takes about 30ms one way. Every replication strategy is a different answer to the question: do you wait for the remote region to confirm it has the data before telling the client the write succeeded?

Backup Verification and Restore Testing#

An untested backup is not a backup. It is a file that might contain your data and might be restorable. Teams discover the difference during an actual incident, when the database backup turns out to be corrupted, the restore takes 6 hours instead of the expected 30 minutes, or the backup process silently stopped running three weeks ago.

Backup verification is the practice of regularly proving that your backups contain valid data and can be restored within your required RTO.

Database High Availability Patterns#

Every database HA decision starts with two numbers: RPO (Recovery Point Objective – how much data you can afford to lose) and RTO (Recovery Time Objective – how long the database can be unavailable). These numbers dictate the pattern, and each pattern carries specific operational tradeoffs.

Core Concepts#

RPO = 0 means zero data loss. Every committed transaction must survive a failure. This requires synchronous replication, which adds latency to every write.

Database Performance Investigation Runbook#

When a database is slow, resist the urge to immediately tune configuration parameters. Follow this sequence: identify what is slow, understand why, then fix the specific bottleneck. Most performance problems are caused by missing indexes or a single bad query, not global configuration issues.

Phase 1 – Identify Slow Queries#

The first step is always finding which queries are consuming the most time.

PostgreSQL: pg_stat_statements#

Enable the extension if not already loaded:

Devcontainer Sandbox Templates#

Devcontainers provide disposable, reproducible development environments that run in a container. You define the tools, extensions, and configuration in a .devcontainer/ directory, and any compatible host – GitHub Codespaces, Gitpod, VS Code with Docker, or the devcontainer CLI – builds and launches the environment from that definition.

For infrastructure validation, devcontainers solve a specific problem: giving every developer and every CI run the exact same set of tools at the exact same versions, without requiring them to install anything on their local machine. A Kubernetes devcontainer includes kind, kubectl, helm, and kustomize at pinned versions. A Terraform devcontainer includes terraform, tflint, checkov, and cloud CLIs. The environment is ready to use the moment it starts.

Planning and Executing Database Migrations#

Database migrations are the highest-risk routine operations most teams perform. A bad migration can cause downtime, data loss, or application errors that cascade across every service that touches the affected tables. This operational sequence walks through the assessment, planning, execution, and rollback of database migrations from simple column additions to full platform changes.

Phase 1 – Assessment#

Step 1: Classify the Migration#

Every migration falls into one of three categories, each with a different risk profile:

PostgreSQL Backup and Recovery#

A backup you have never tested restoring is not a backup. This covers the main backup tools, when to use each, point-in-time recovery, and automation.

Logical Backups: pg_dump and pg_dumpall#

pg_dump exports a single database as SQL or a compressed binary format. It takes a consistent snapshot without blocking writes.

# Custom format (compressed, supports parallel restore)
pg_dump -U postgres -Fc -d myapp -f myapp.dump

# Directory format (parallel dump)
pg_dump -U postgres -Fd -j 4 -d myapp -f myapp_dir/

pg_dumpall exports every database plus cluster-wide objects. In practice, dump roles separately and per-database for flexibility:

PostgreSQL Debugging#

When PostgreSQL breaks, it usually falls into a handful of patterns. This is a reference for diagnosing each one with specific queries and commands.

Connection Refused#

Work through these in order:

1. Is PostgreSQL running?

sudo systemctl status postgresql-16

2. Is it listening on the right address?

ss -tlnp | grep 5432

If it shows 127.0.0.1:5432 but you need remote access, set listen_addresses = '*' in postgresql.conf.

3. Does pg_hba.conf allow the connection? Check logs for no pg_hba.conf entry for host:

PostgreSQL Performance Tuning#

Most PostgreSQL performance problems come from missing indexes, bad query plans, connection overhead, or table bloat. This covers how to diagnose each one.

Reading EXPLAIN ANALYZE#

EXPLAIN shows the query plan. EXPLAIN ANALYZE actually executes the query and shows real timings.

EXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 42 AND status = 'pending';

Index Scan using idx_orders_customer on orders  (cost=0.43..8.45 rows=1 width=120) (actual time=0.023..0.025 rows=3 loops=1)
  Index Cond: (customer_id = 42)
  Filter: (status = 'pending'::text)
  Rows Removed by Filter: 12
Planning Time: 0.152 ms
Execution Time: 0.048 ms

What to look for: Seq Scan on large tables means a missing index. Rows Removed by Filter means the index fetched extra rows that a composite index would eliminate. actual rows far from estimated rows means stale statistics – run ANALYZE tablename;. Nested Loop with high loops count usually wants a hash join; check the inner table’s indexes.