Postgres Queues Actually Scale
The conventional wisdom around Postgres-backed queues is that they don't scale. To handle a large workload, you can't use Postgres, but instead need a dedicated queueing system like RabbitMQ + Celery or Redis + BullMQ. There's a reason people say this: queues really are a demanding workload for Postgres.
- ▪The conventional wisdom around Postgres-backed queues is that they don't scale.
- ▪To handle a large workload, you can't use Postgres, but instead need a dedicated queueing system like RabbitMQ + Celery or Redis + BullMQ.
- ▪There's a reason people say this: queues really are a demanding workload for Postgres.
Hacker News (Front Page) files mainly under programming. We currently carry 842 of its stories. Top-voted stories on Hacker News.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Dbos |
| Canonical URL | https://www.dbos.dev/blog/making-postgres-queues-scale |
| Publication time | Thu, 30 Jul 2026 18:39:32 +0000 |
| Retrieval time | 2026-07-30T19:02:33.329Z |
| Last seen | 2026-07-30T19:02:33.329Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | mnN_O1ehpKoP · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
The conventional wisdom around Postgres-backed queues is that they don't scale. To handle a large workload, you can't use Postgres, but instead need a dedicated queueing system like RabbitMQ + Celery or Redis + BullMQ. There's a reason people say this: queues really are a demanding workload for Postgres. At scale, thousands of workers are polling your queues table at the same time, creating contention and churning indexes. But with the right optimizations, Postgres can handle it.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Dbos.
Discussion
0 commentsMore from Dbos
