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Bit of a news catchup on the pg_clickhouse project.

What’s New

First up, a couple weeks ago the ClickHouse Blog published What’s New in pg_clickhouse, in which I covered various improvements to the extension:

A beginner’s guide to encrypting your database connections

“’SSL is enabled’ and ‘SSL is actually working’ are two very different things.”

PostgreSQL 19 finally lets wal_level adapt dynamically to your actual replication slots, eliminating the always-on WAL cost of logical standby insurance.

Introduction

Over the past year, I’ve worked in a handful of contexts managing large volumes of data stored as JSONB in PostgreSQL. The scenario is common: users appreciate the flexibility of a document-oriented storage model, avoiding the need to predefine schemas or constantly migrate table structures as their data requirements evolve. JSONB documents can be deeply nested with numerous optional fields, and they scale to hundreds of kilobytes per record without issue.

autovacuum_work_mem sets the maximum memory each autovacuum worker may use for tracking dead tuple identifiers (TIDs) during a vacuum. Default is -1, which means “inherit from maintenance_work_mem.” Context is sighup. The parameter exists so that autovacuum’s memory consumption can be tuned indep…

Autovacuum's most powerful tuning lever: the scale factor that determines when dead tuples trigger a vacuum. On large tables, the 20% default waits too long.

VIEWs should be the cleanest abstraction SQL, and therefore Postgres, has on offer. I love the concept. The promise of decoupling logical intent from physical storage is perfect on paper. In practice, few things in the database world trigger such a heated debate or carry as much historical baggage. VIEWs mix big promises with false hopes, and the promises rarely survive contact with production.

PostgreSQL 18 finally fixes the autovacuum formula that left billion-row tables waiting for 200M dead tuples.

Six years after PostgreSQL shipped the table access method API, the alternative storage engine ecosystem is thriving—but messier than early predictions…

Snowflake's pg_lake and Databricks' Lakebase both wrap PostgreSQL for lakehouse workloads, but they're nearly opposite architectures.

Recently at Postgres Conference 2026 in San Jose, I presented a talk called Let's Build a Postgres Extension! Since that entire presentation was primarily focused on writing a C extension while exploring the Postgres source code, I only mentioned pure SQL extensions as an aside.

PostgreSQL 13 added insert-triggered autovacuum to solve a critical problem: append-only tables never vacuumed, breaking index-only scans and delaying tuple…

Hardware breakpoints can trigger eBPF programs when specific memory addresses are accessed, leveraging CPU hardware support for low overhead. By utilizing these hardware breakpoints, we can efficiently monitor PostgreSQL’s internal variable updates, such as transaction ID generation and OID assignment. In this post, we will discuss what hardware breakpoints are, whether they have less overhead than uprobes, and how to answer questions like “How many transactions are being executed per second?” or “Which backend is consuming the most OIDs?” with bpftrace.

When a maintainer rewrites a project with AI and changes its license, they've crossed a line.

Most Postgres management tools ask you to pick a lane. You can manage databases, or you can manage the services around them. You can run in containers, or you can run on bare metal. You get one deployment model, one operational surface, one set of assumptions about how your infrastructure works.The pgEdge Control Plane just added two features that refuse to pick a lane: Supporting Services and systemd Support. Together, they push the Control Plane into territory that, as far as we can tell, nobody else in the Postgres world is covering.

PostgreSQL 19 expands MultiXactOffset to 64 bits, eliminating a real outage failure mode. So when do regular transaction IDs get the same treatment?

A PostgreSQL primary loses power at 2am. Writes resume in under thirty seconds. The on-call engineer reads the alert in the morning, sees that the cluster healed itself, and goes back to coffee. That is the outcome PostgreSQL high availability is supposed to deliver.

That’s the last post of the series about the talks at the upcoming PG DATA 2026 conference, covering the remaining Friday talks.

Part 1

Part 2

Three autovacuum parameters control how often PostgreSQL vacuums, how hard it works, and how long it pauses.