DBLab Engine 4.2 is out! Main thing: you can now pg_upgrade a clone with just one click or in CLI call: ==> dblab clone upgrade my-clone => automation stops the clone, "pg_upgrade --link" runs in place, then clone comes back on a new Postgres major version. As usual, prod and every other clone untouched. Didn't like the result? ==> dblab clone reset No more excuses to avoid full-fledged testing before production Postgres gets upgraded. Also in 4.2: - paste a source URL, DBLab figures out provider, image, databases, settings - branches and snapshots expire on policy (off by default) - secrets from env vars (not plain-text in configs anymore) - clone creation ~2x faster

traditional reminder that simple ALTER TABLE that felt instant on your laptop can put your production Postgres down it needs to be cooked properly in production – use lock_timeout and retries postgres.ai/blog/20210923-ze…

PgQue got a website with docs – pgque.dev

Working on this is a lot of fun. Here I shared how it all started, what original PgQ already has and what didn't, and what v0.2 brings (RC1 stamped yesterday)

What a nice blog post by Christophe Pettus PgQue: Two Snapshots and a Diff thebuild.com/blog/2026/05/03…

Precise (not sampled) wait even analysis is something I didn't expect to have for Postgres - but here we go Being able to zoom into concrete millisecond (!) and see the exact chain of wait events opens new possibilities for RCA, benchmarks, and performance studies links 👇

PgQue v0.1.0 is out. PgQ -- the Postgres queue system built at Skype 20 years ago for 1B-user-scale workloads -- repackaged for the managed-Postgres era. One SQL file. No C extension. No external daemon. pg_cron to tick. Why bother reviving a 2007 architecture? Every major Postgres queue in production today uses some flavor of SKIP LOCKED + UPDATE/DELETE. It works under light load. When you have more data and higher load, it degrades predictably. Then you get posts like these: - Brandur at Heroku, 2015: 60k job backlog in one hour from a single open transaction - PlanetScale, 2026: death spiral at 800 jobs/sec - River issue #59, awa issue #169 and so on, Oban's partitioning work, PGMQ's autovacuum tuning guide and duct-taping with pg_partman The core issue is how Postgres MVCC is implemented and how we deal with it. Dead tuples in the hot path, xmin horizon pinned, vacuum falling behind, query performance quickly degrades. This happens every time you run pg_dump, execute an analytical query, or have a lagging/unused logical replication slot. PgQ solved this in 2007 with snapshot-based batching and TRUNCATE rotation -- zero dead tuples in the event path, by design. But PgQ needed a C extension and an external daemon. Which means it doesn't run on RDS, Aurora, Cloud SQL, AlloyDB, Supabase, or Neon -- i.e., where most Postgres lives now. PgQue closes that gap. 💎 Pure SQL + PL/pgSQL (PgQ engine) 👩‍💻 \i sql/pgque.sql -- you're done 🕑 pg_cron replaces pgqd (optional, recommended) 💻 Python, Go, TypeScript client examples shipped 💙 Apache 2.0 Trade-off: end-to-end event delivery latency is up to a second, it depends on ticking frequency. If you need sub-3ms job dispatch, use River, Oban, or graphile-worker (and avoid anything that blocks xmin horizon). If you need high-throughput event streaming with fan-out inside Postgres -- Kafka-shaped, without Kafka and dealing with transactional outbox implementation -- this is the right shape of tool. Kudos to Marko Kreen and Skype engineers who implemented this decades ago, for the original PgQ, and to Alexander Kukushkin whose recent "Rediscovering PgQ" talk brought this quiet corner of the Postgres ecosystem back into view. Stars, issues, PRs, and honest criticism all welcome. Link 👇