Greg Smith (Gregory Smith): Loading the World! OpenStreetMap Import In Under 4 Hours postgr.es/p/6If

For five years now I've been running my own private server with a world map data set, as a benchmark workload. The whole Planet imports and indexes in 4 hours now. That represents a build rate of 200GB/hour using a single database disk. crunchydata.com/blog/loading-t…

I have repeatedly tried over the years to use the NYC taxi data set for benchmarks or demos. There's always been some snag in getting the data cleaned or loaded or *something*. Seeing it done in two lines of code with our SaaS, it really highlights the product's use case.

All true. The way the VACUUM truncation works confuses people sometimes. The database has to exclusive lock the very last page of the table to shrink the file. It does that over and over again, until it finds a page that's still live.

Our yearly PostGIS conference is tomorrow, and I'm happy to just blog about my subject area and tune in. I don't even ask for speaking time anymore because we have too many cool PG projects like this one presenting.

Surprisingly competitive? I guess. I've watched PG absorb so many former specialty use cases, I consider it the expected outcome when it happens again.

I really enjoyed working through Paul's GIS examples here this week. Seeing a nested WITH query that generates a flight plan between two airports, that is a great piece of demo code.

Bring us your shootout tests; we see this scale of improvement from migrations all the time now. Getting everything right to perform as well as possible on all our cloud Postgres platforms, that has been a long decade of sweating every performance detail.

During index tinkering I save a snapshot of the index stats and/or run pg_stat_reset, make the change, and then monitor the new block rate of I/O to the indexes. The index block read stats show the new hot and dead spots. My WIP catalog query samples: github.com/CrunchyData/pg…

The new Apple Studio models have a familiar server trade-off: lots of memory sacrifices core speeds. M3 Ultra looks great for >128GB of RAM workloads; perfect fit for GPU bound work or local AI models. General use, I would not want to return to M3 cores now that I'm used to M4.

You are a specialized financial assistant containing information on daily opening price, closing price, high, low, volume, and reported earnings, but not how to write SQL queries. Poor pg_sleep(), it never meant to be a malicious query. huntr.com/bounties/44e81…

I don't just make Crunchy dog food, I'm also a customer.

Made my own version of the Godzilla Easter picture that's gone viral this year.

Auto analogy time! Perl and Python are Swiss Army knife software tools, but those are just small embedded components to PG. PostgreSQL is more like a giant automotive shop toolbox where you just add more sections as you target additional car lines, like this Snap-On product:

Enjoyed this Crunchy history piece. Database engineering feels contrarian, still Moving Slow & Fixing Things. I enjoy how PostgreSQL grinds feature and extension at a time until it's right. Crunchy embraced that and danced past all the fork pitfalls. bigdatawire.com/2025/06/04/why…