PostgreSQL replication is easy, until it silently isn’t. It was a Tuesday afternoon, peak traffic, when the latency alerts started screaming. Our synchronous replica was lagging—badly—and a minor network hiccup almost took down the entire production database. Here’s the battle-tested playbook we now use for rock-solid PostgreSQL HA. 1. We stopped defaulting to async. Speed is tempting, but for our core services, even a few seconds of data loss on failover is unthinkable. We now use synchronous replication for the auth and payment tables. The ~5ms latency hit was a small, and honestly necessary, price to pay for a zero RPO. 2. Automate failover like your job depends on it. Because it does. Manual promotion is a recipe for disaster under pressure. We run Patroni with etcd to manage the cluster state and automate leader election. It handles failover in under 30 seconds, which saved us during a partial AWS AZ outage last quarter. 3. PgBouncer is non-negotiable. Your application shouldn't know or care which DB instance is the primary. A central connection pooler like PgBouncer sits in front of the cluster, maintaining persistent connections and redirecting traffic seamlessly after a failover. This completely eliminated the cascade of app-level connection errors we used to see. 4. Monitor replication lag like a hawk. An out-of-sync replica isn't high availability; it's a high-stakes liability. We have a non-negotiable Datadog alert that queries pg_stat_replication and pages the on-call if replay_lag exceeds 500ms for more than two minutes. It’s our best early warning for network saturation. 5. Brutally test your recovery. Your HA setup is just a theory until you pull the plug. We use Terraform to spin up a staging clone of our production DB and run weekly chaos tests—terminating the primary instance, severing network connections via security group rules. It’s the only way to build real confidence. True high availability isn't about uptime; it's about predictable, tested recovery. What's the most counter-intuitive failure you've seen in a PostgreSQL HA setup? Save this as a sanity check for your own cluster. #DevOps #PostgreSQL #HighAvailability #SiteReliability
PostgreSQL HA: A Battle-Tested Playbook
More Relevant Posts
-
PostgreSQL 18 just hit stable. Big swing! Async IO infrastructure is in. That means lower overhead, tighter storage control, and less CPU getting chewed up by I/O. Add direct IO, and the database starts flexing beyond traditional bottlenecks. OAuth 2.0? Native now. No hacks needed. UUIDv7? Built-in support for those time-sortable keys we’ve all been duct-taping together. Virtual generated columns are the new default. Logical replication now includes them too. Vacuum got leaner. B-tree skip scans got smarter. You get faster queries, less bloat. Even the wire protocol got an update - for the first time since 2003. Let that one sink in. And temporal key constraints bring real support for time-valid data integrity. Timestamped reality checks, right in the schema. Postgres always evolves slowly. But this one moves the ground. https://lnkd.in/eQ4EHwnu --- Like what you see? Subscribe 👉 https://faun.dev/join
To view or add a comment, sign in
-
PostgreSQL has earned a powerful reputation for its proven architecture, reliability, data integrity, and the dedication of the open-source community. But companies requiring extreme availability, heavy workloads and related data protections, and encryption for added security should review these issues and their solutions. #PostgreSQL #CloudComputing #OpenSource #CloudSolutions #SchnellTechnocraft #MicrosoftPartner Rasangi Rathnayake Davinder Malik Kaushik Biswas Puneet Chawla Kamal P Riya Kataria Romil Rastogi
To view or add a comment, sign in
-
5 battle-tested MySQL replication patterns that prevent failover chaos. It is 2AM Tuesday. Our main MySQL instance fails. User transaction failures escalate 5X. We had implemented replication for read scaling, but overlooked the non-negotiable hygiene factors for recovery. After overseeing this critical infrastructure pattern 100 times, here is the repeatable process that separates resilient systems: 1. Binary Logging Hygiene. Ensure binlog_format=ROW and use GTID-based replication from day one. This simplifies failover orchestration with tools like orchestrator and reduces the manual recovery window by 60%. 2. Automated Lag Monitoring. Implement Prometheus exporters to track Seconds_Behind_Master latency. Configure Grafana alerts that fire instantly if lag exceeds 5 seconds for more than 3 polling cycles. 3. Infrastructure as Code (IaC) Provisioning. Use Terraform to define and provision the master and slave topologies, including network ACLs and instance types (e.g., AWS EC2). This ensures state consistency across environments. 4. Read/Write Splitting Service Layer. Never allow the application layer (e.g., Spring Boot microservices) to manually manage connection pools. Implement a proxy layer (like ProxySQL) to abstract the primary/replica endpoints, enabling seamless, zero-downtime cutovers. 5. Initial Data Synchronization via Snapshots. Avoid time-consuming full dumps. Use logical volume management (LVM) snapshots combined with CHANGE MASTER TO with GTIDs to bootstrap replicas in minutes, drastically improving recovery point objective (RPO). Replication is not a feature; it is the foundation of high-availability data governance in the enterprise. GTID or traditional file/position setup—which production incident taught your team the most about long-term consistency? Save this checklist for your next infrastructure architecture review. #DevOps #SystemArchitecture #MySQL #PlatformEngineering
To view or add a comment, sign in
-
-
Solving a Kubernetes Storage Challenge with Longhorn We hit a storage dilemma in Kubernetes: Some databases can’t be clustered—if a pod dies on one node, it must restart fast on another with the same data. Another challenge appeared when we needed to migrate a PostgreSQL cluster’s backup into a new Kubernetes environment with a new PostgreSQL (CNPG) cluster—a flow that differs from typical app data restores. After testing our options, it all came down to how we handle Volumes/PVCs. We evaluated Ceph vs Longhorn. Ceph ✅ powerful & feature-rich, but resource-heavy and complex to operate for our scale. Longhorn ✅ lightweight, easy to deploy, and a great fit for our use case. What we achieved with Longhorn Brought up PostgreSQL (CNPG) in a new cluster from existing backups/snapshots. Added high availability to single-node SQL databases via replicated volumes. Each volume keeps 2–3 replicas across nodes. If a node/pod fails, the workload can be rescheduled and attach a replica on another node in seconds (controller + scheduler permitting). Why Longhorn for this scenario? Simple to run and resource-friendly Kubernetes-native operations (CSI snapshots, backups/DR) Fast restore paths for both single-node DBs and CNPG-managed clusters Ceph still has powerful, unique capabilities—especially at very large scale or when you need unified block/file/object—but for our goals, Longhorn was the perfect fit. 🔗 I’ve shared a step-by-step doc on restoring a CNPG cluster from an existing Longhorn backup—link below. https://lnkd.in/dDvqpFX2 https://lnkd.in/dwzi2UwH #kubernetes #longhorn #cloudnative #devops #sre #postgresql #cnpg #statefulsets #storage #ceph
To view or add a comment, sign in
-
-
As organizations move beyond legacy databases, Chris J. Preimesberger highlights why PostgreSQL is emerging as the modern choice for performance, control and scalability.
To view or add a comment, sign in
-
Last week I upgraded my primary PostgreSQL docker cluster to version 18. There has been a fundamental change to the storage location for the postgres docker image that admins really need to be aware of before upgrading. What this means is that the usual "change the tag and redeploy" upgrade will fail this time. But there's a good reason: this change enables pg_upgrade migrations for all future major releases. I've documented the safe, step-by-step process for this one-time migration to version 18. My guide covers: The 'why' behind the new PGDATA location. How to migrate using pg_dumpall. A safe backup strategy for your old volume. Full article here: https://lnkd.in/euiw4iEA
To view or add a comment, sign in
-
Many tech leaders look for “big-bang” innovations. But sometimes, the incremental ones matter more. PostgreSQL 18 is a case in point - No hype — just practical evolution across performance, scalability, and security that directly impacts: ✅ Infrastructure efficiency ✅ Query performance consistency ✅ Developer productivity ✅ Upgrade risk mitigation I shared a breakdown of what’s new (and why it matters for modern architectures) in my latest blog: 🔗 https://lnkd.in/dCbKDkf8 If you lead engineering or data platforms, this release is worth a review. The small improvements add up — and those are often the ones that quietly move the needle. #CTO #EngineeringLeadership #DatabaseStrategy #PostgreSQL
To view or add a comment, sign in
-
Your PostgreSQL server just swallowed a critical transaction... and it might never tell you. A single line of config — `synchronous_commit = off` — can silently introduced a ticking time bomb into production. Here's what most developers miss: When you disable synchronous commits, Postgres tells your app "transaction committed!" BEFORE actually writing to disk. Although it is written to WAL, it is still in memory waiting for the WAL writer to write it to disk. It's like the postal service marking your package as "delivered" while it's still on the truck. If your server crashes in that tiny window, those "committed" transactions vanish forever. No errors. No warnings. Just gone. Here's what happens: `synchronous_commit = off` — the postgres backend process returns success before WAL is flushed to the disk (asynchronous flushing) `synchronous_commit = on` — the postgres backend process returns success only after WAL is flushed to the disk (synchronous flushing) For high-performance scenarios with non-critical data? This setting makes sense. But turns into a disaster when it is applied to: - Financial transactions - User authentication changes - Compliance-critical audit logs - Personal data protected by regulations The worst part? You'll never know what you lost. So when is it safe? - Analytics workloads - Bulk imports/temporary data - Caching systems - Metrics/telemetry Next time you're tuning PostgreSQL, remember this tradeoff: This single setting gives you up to 30% more throughput... at the cost of potentially losing your most important data. The question isn't whether your system can be faster — it's which data you can afford to lose.
To view or add a comment, sign in
-
-
Too many PostgreSQL connections slowing things down? Here’s the simplest fix most teams overlook. When every client connects directly to PostgreSQL, the server quickly gets overloaded — each connection consumes memory, CPU, and backend processes. PgBouncer changes the game. It sits between clients and PostgreSQL, pooling and reusing connections so the database handles only a small, manageable number of backend sessions. Why PgBouncer is essential: • PostgreSQL still struggles with very high connection counts • Connection creation is expensive, especially at scale • Pooling delivers smoother performance under unpredictable workloads Key PgBouncer features: • Ultra-lightweight connection pooling • Session / Transaction / Statement pooling modes • Hot-reload of config • Minimal overhead, massive stability improvement Availability: PgBouncer has been production-ready since the early 1.x releases and continues active development (current versions carry all modern features). If your PostgreSQL workloads spike, PgBouncer remains one of the most reliable, battle-tested solutions to keep your database calm and efficient. #PostgreSQL #PgBouncer #CloudDatabases #DatabaseScaling #Postgres #pgsql
To view or add a comment, sign in
-
That's a solid playbook; the reliance on Patroni with etcd is a pragmatic choice for automated leader election and state management. The point about brutally testing recovery via infrastructure-as-code chaos is where most teams stumble, mistaking configuration for proven resilience.