> hybrid OLAP+OLTP .... in Postgres this pattern requires careful planning to avoid falling afoul of max_standby_streaming_delay for example
This is a really gnarly problem at scale I've rarely seen anyone else bring up. Either you use max_standby_streaming_delay and queries that conflict with replication cause replication to lag or you use hot_standby_feedback and long running queries on the OLAP replica cause problems on the primary.
Logical Decoding on a replica in also needs hot standby feedback which is a giant PITA for your ETL replica.
Hallucinations are now plausibly wrong which is in some ways harder to deal with. GPT4.1 still generates Rust with imaginary crates and says “your tests passed, we can now move on” to a completely failed test run.
The USA hasn’t managed to completely impose their idea of intellectual property on everyone yet. Some countries you can’t sign away authorship even if you can commercial rights.
Yeah the older Pilot Sport tyres wore out quickly. I had PS4 before and now PS5 and they are wearing at something like half to a third as fast? Very happy. Only slightly less grip in cold weather.
My understanding is the way Aurora DSQL distributes data widely makes bulk writes extremely slow/expensive. So no COPY, INSERT with >3k rows, TRUNCATE etc
I've been looking into PgDog for sharding a 40TB Postgres database atm vs building something ourselves. This could be a good opportunity to collaborate because what we need is something more like Vitess for PostgreSQL. The scatter gather stuff is great but what we really need is config management via something like etcd, shard splitting, best-effort transactions for doing schema changes across all shards etc.
Almost totally unrelated but have you had good success using pg_query.rs to re-write queries? Maybe I misunderstood how pg_query.rs works but re-writing an AST seems like a nightmare with how the AST types don't really support mutability or deep cloning. I ended up using the sqlparser crate which supports mutability via Visitors. I have a side project I'm chipping away at to build online schema change for PG using shadow tables and logical replication ala gh-ost.
Exactly that. It's an arms race between companies that offer a large number of residential IPs as proxies and companies that run unauthenticated web services trying not to die from denial of service.
If you round up great engineering orgs that ship impactful stuff more of them don't use JIRA than do. Linear, Basecamp, Asana, Monday etc.
My experience is by the time an org gets hundreds of priorities and can't effectively delegate to sub orgs they're already fucked and there's no point working there if you want to do anything meaningful.
Does the CPython GIL give you a stronger effective guarantee than the one in CRuby? Because the one in CRuby will absolutely not save you from data races.