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Wrote about the new system views in PG19. I think this might be the longest I’ve ever spent on a blog 😅 I expanded on the pg_stat_lock (cumulative, cluster-wide lock statistics with one row per lock type). Covered a lot on pg_stat_recovery & pg_stat_autovacuum_scores. All with demo queries! 🐘 🚀

Planet PostgreSQL@planet.postgresql.org · 3w ago

Gülçin Yıldırım Jelínek: New system views in PostgreSQL 19 https://postgr.es/p/9tF #postgresql

Fun fact: instant branching is not a function of data size. 10 TB database is also branched / clones in around 1 second. Here is the power of that using "xata scratch", instant disposable Postgres branch (a scratch pad) that's automatically deleted the moment your session ends.

Xata - Postgres at Scale@xata.io · 2mo ago

2TB+ Postgres. Branch it in seconds, query it, delete it. xata scratch (new Xata CLI cmd) forks a disposable branch off your real DB, drops you into psql or runs SQL inline, tears it down on exit. Demo 👇

Tailscale is the most underappreciated tool in my stack. My RPi NAS in Bangkok is accessible from anywhere in the world. My homelab LM Studio endpoint is callable from my Mac. Zero port forwarding, zero VPN config, zero maintenance. Just works.

I’ve written a blog post on why and how we built our own distributed storage engine. We’re calling it Xatastor and enables us to scale to a huge number of Postgres instances and branches. It’s based on ZFS and NVMe-oF as key technologies. xata.io/blog/xatasto...

Xatastor: ZFS + NVMe-oF for millions of Postgres instances by Tudor Golubenco

Read the technical details of our new distributed storage system, which is the key to scaling to a huge number of Postgres instances.

xata.io

> tailscale is the secure way to connect all your computers! > i've used tailscale to connect my molty to all my computers and internal services and gave it all my keys oh no, not like that

Taking database snapshots and moving large volumes of data over the network is something our customers do regularly. While batching is the de facto way to make this efficient choosing the right batch size is non-trivial considering network variability, latency & system load. Read how we solved it👇🏽

Xata - Postgres at Scale@xata.io · 8mo ago

Batching is often used to process large volumes of data but a batch size that works in one network setup can perform poorly in another. We applied automatic batch size tuning to Postgres snapshots in pgstream to adapt across different network environments. Check the post 👇 xata.io/blog/postgre...

@divyendusingh.com is doing a great job making agents do all sorts of stuff using databases. In our case with a few simple instructions, they are able to do branching operations, run queries, validate bug fixes and more. The blog posts are paired with demo videos, have a look 👀 👇🏽

Xata - Postgres at Scale@xata.io · 8mo ago

AI agents get useful faster with guardrails, not plugins. Repo playbook: gh issue → xata branch create + xata branch wait-ready → xata branch url (not $DATABASE_URL) → psql repro/verify → fix. Video + write-up:

Batching is often used to process large volumes of data but a batch size that works in one network setup can perform poorly in another. We applied automatic batch size tuning to Postgres snapshots in pgstream to adapt across different network environments. Check the post 👇 xata.io/blog/postgre...

Optimizing data throughput for Postgres snapshots with batch size auto-tuning by Esther Minano Sanz

Why static batch size configuration breaks down in real world networks and how automatic batch size tuning improves snapshot throughput.

xata.io

Can an AI Agent follow the same workflow that human developers can? We explored giving AI agent access to the database branch, a compute sandbox to execute code and instructions to follow a developer workflow (in plain English) and the experiment was a success.

So excited for this one, looking forward to sharing our experience of building a LLM powered on-call database agent. As someone who is writing 90+% of code with agents in the last 2 months, looking forward to share the parallels and experience between coding and monitoring agents.

Xata - Postgres at Scale@xata.io · 12mo ago

Can an AI agent handle your Postgres on-call? @divyendusingh.com from @xata.io is speaking at the Postgres Berlin Meetup tomorrow showing how we’re using LLMs to automate DB diagnostics, fixes, and even PRs. Come see “Xata Agent” in action. 📍 RSVP: www.meetup.com/postgresql-m...

Just published a deep dive on how every Vercel preview gets a full production database copy. But that's not even the best bit. Want to know how to safely update your database schema add a column, rename a field, even do complex behind‑the‑scenes transformations while production keeps humming?

Zero Downtime Schema Changes with Vercel and Xata by Divyendu Singh

Discover how Xata’s pgroll‑powered platform plus Vercel preview deployments enable zero‑downtime Postgres schema changes with instant branches.

xata.io

GitHub released Spark yesterday, their extremely well crafted prompt-to-app platform for creating and iterating on React apps with user auth and persistent storage I like it a lot! I reverse engineered it with Spark itself, the details are fascinating simonwillison.net/2025/Jul/24/...

Using GitHub Spark to reverse engineer GitHub Spark

GitHub Spark was released in public preview yesterday. It’s GitHub’s implementation of the prompt-to-app pattern also seen in products like Claude Artifacts, Lovable, Vercel v0, Val Town Townie and Fl...

simonwillison.net

📣 We have a brand new Postgres platform with: Instant Copy-on-Write branching Built-in data anonymization Separation of storage and compute 100% vanilla Postgres It’s for staging/dev environments as well as for production workloads. Blog post: xata.io/blog/xata-po... And more details in 🧵

Xata: Postgres with data branching and PII anonymization | xata

Relaunching Xata as "Postgres at scale". A Postgres platform with Copy-on-Write branching, data masking, and separation of storage from compute.

xata.io

Xata is one of the few female-founded companies in the Postgres world, and 40% of our team are women. Because inclusion is built, not wished for. Proud to host another "Women In Postgres" breakfast at #PGConfDE in Berlin. Great breakfast, even greater conversations! 💜

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