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Versioning reference data in Git with Liquibase OSS changelogs enables consistent, auditable, and automated deployments across environments. Using loadUpdateData with versioned CSV files provides fast, reliable rollbacks and reduces production risk. Modern applications do not just depend on schema changes. They also depend on data that powers the application itself.

"Connection pool exhausted" is one of PostgreSQL's most misleading production errors. The database is often healthy, while your application waits behind full connection pools. In Kubernetes, every pod and replica maintains its own pool, amplifying issues like connection leaks and slow queries across the cluster.

This quarter we’ve been busy investing a lot of effort into improving how users understand and control what they pay for VictoriaMetrics Cloud, and how to move around the console while doing it, with major UX improvements across different sections and functionalities. The big piece of work is transparent credits reporting, giving prepaid customers full visibility into balance, consumption, and expiration.

I wanted a Postgres replica that falls behind its primary. Sounds easy. It wasn't. Every time I cut the replica off from the primary, its lag jumped for a moment and then dropped right back to zero. It just would not stay behind. Chasing down why turned into a fun little tour of how a Postgres standby keeps itself alive, and what CloudNativePG quietly sets up behind the scenes.

With a robust background in cloud technology and a passion for promoting its merits and potential, I am thrilled to spearhead conversations and actions that help shape the future of this industry. Kellyn has authored numerous technical books, white papers and solution repositories in GitHub on database, AI and engineering topics.

As AI agents accelerate software development and spin up applications at scale, visibility into what's happening behind the scenes, including query performance and database health, has never been more important. Gaining that level of insight requires observability that can keep pace.

Autovacuum is one of those Postgres background jobs that quietly keeps your database healthy. It cleans up the dead row versions that every UPDATE and DELETE leaves behind, and it keeps the database away from a hard transaction-ID limit that would take it offline. Most of the time you don't think about it, because it just works.

This post is a joint effort by the OpenLIT and VictoriaMetrics teams. OpenLIT brings the OTel-native GPU collector for NVIDIA, AMD, and Intel hardware, while VictoriaMetrics provides the storage and query layer for the resulting metrics. We wrote it together to show how the two projects fit into a single, self-hosted observability pipeline, and to share the queries and rules that worked well for us along the way.

When platform providers talk about autoscaling, they usually mean one thing: application autoscaling, adding or removing web instances based on traffic. The pattern is well understood. Define a metric, set a threshold, and let the platform act on it. Most platforms still leave database capacity as a separate, mostly manual problem. Teams either pre-provision for the worst hour of the worst day, upgrade plans on a calendar, or wire together their own scaling logic on Kubernetes or RDS.