ants_a

That's what happens in pgjdbc by default. In binary transfer format int64 microseconds since '2000-01-01 00:00Z' gets sent over the wire regardless off connection timezone setting.

Meh, a T-SQL batch is just an anonymous transient stored procedure.

I guess we agree on them being an API that gets deployed on the database, but I disagree that it needs to be crude. It's exactly as crude as you make it. If you don't have authentication, testing, versioning, observability and source control for your database you are doing it wrong.

This is how the built in planner works already. It generates all possible plans and picks the one with the lowest cost. But calculating the cost is based on statistics and models, and these are wrong. Usually useful, but always wrong.

Rebuilding statistics will not help if the cause of the bad plan is something that the cost based optimizer is not even trying to model.

Stored procedures in this context are just a clumsy workaround to control the planner so your comment about API layer is irrelevant. But if you like, you can write stored procedures in a ton of different languages. And if you do not have source control for your database artifacts, you are doing it wrong. A common reason for stored procedures is to not have a bunch network roundtrips in the middle of your transaction logic while you are holding onto locks / have an open conflict window.

This is not a big database usecase. It just needs one to not do silly things like opening a new database session for every query when it's well documented that this is expensive.

pgbouncer added support for prepared statements a couple years back.

Over here we use a PKI cert for that. A smartcard providing the root of that trust is provided by the government after verifying your identity using the typical stuff used for identity documents (any biometric data on file, birth certificate, etc.). That still doesn't mean that it's impossible to steal an identity, or acquire a made up one, but it does make it a whole lot harder.

What you are describing here does not match how postgres works. A read on the replica does not generate temporary tables, nor can anything on the replica create locks on the primary. The only two things a replica can do is hold back transcation log removal and vacuum cleanup horizon. I think you may have misdiagnosed your problem.

There is no backpressure from replication and streaming replication is asynchronous by default. Replicas can ask the primary to hold back garbage collection (off by default), which will eventually cause a slow down, but not blocking. Lagging replicas can also ask the primary to hold onto WAL needed to catch up (again, off by default), which will eventually cause disk to fill up, which I guess is blocking if you squint hard enough. Both will take considerable amount of time and are easily averted by monitoring and kicking out unhealthy replicas.

These are limitations in the current PostgreSQL implementation. It's quite possible to have consistent commits and snapshots across sharded databases. Hopefully some day in PostgreSQL too.

Postgres setups are typically based on physical replication, which is not an option on MySQL. My testing shows the limit to be about 177k tps with each transaction consisting of 3 updates and 1 insert.

For updating a single resource where the order of updates matters the best throughput one can hope for is the inverse of locking duration. Typical postgres using applications follow the pattern where a transaction involves multiple round trips between the application and the database to make decisions in the code running on the application server.

But this pattern is not required by PostgreSQL, it's possible to run arbitrarily complex transactions all on server side using more complex query patterns and/or stored procedures. In this case the locking time will be mainly determined by time-to-durability. Which, depending on infrastructure specifics, might be one or two orders of magnitude faster. Or in case of fast networks and slow disks, it might not have a huge effect.

One can also use batching in PostgreSQL to update the resource multiple times for each durability cycle. This will require some extra care from application writer to avoid getting totally bogged down by deadlocks/serializability conflicts.

What will absolutely kill you on PostgreSQL is high contention and repeatable read and higher isolation levels. PostgreSQL handles update conflicts with optimistic concurrency control, and high contention totally invalidates all of that optimism. So you need to be clever enough to achieve necessary correctness guarantees with read committed and the funky semantics it has for update visibility. Or use some external locking to get rid of contention in the database. The option for pessimistic locking would be very helpful for these workloads.

What would also help is a different kind of optimism, that would remove durability requirement from lock hold time, which would then result in readers having to wait for durability. Postgres can do tens of thousands of contended updates per second with this model. See the Eventual Durability paper for details.

I'm wondering if it would make sense to integrate the rim, motor and wheel bearing into a single assembly to save weight and cost. That combined with the weight and packaging benefits of not having half shafts and differentials might make it worth it. Plus there can be additional benefits, like the extra maneuverability that ZF Easy Turn and Hyundai's e-Corner have demonstrated.

30kW sustained/60 kW per wheel peak power is easily enough even for large passenger vehicles. Sustained could take 3 ton vehicle up a 10% grade at 120 km/h.

In that snippet are links to Postgres docs and two blog posts, one being the blog post under discussion. None of those contain the information needed to make the presented claims about throughput.

To make those claims it's necessary to know what work is being done while the lock is held. This includes a bunch of various resource cleanup, which should be cheap, and RecordTransactionCommit() which will grab a lock to insert a WAL record, wait for it to get flushed to disk and potentially also for it to get acknowledged by a synchronous replica. So the expected throughput is somewhere between hundreds and tens of thousands of notifies per second. But as far as I can tell this conclusion is only available from PostgreSQL source code and some assumptions about typical storage and network performance.

Triggers are not even particularly slow. They just hide the extra work that is being done and thus sometimes come back to bite programmers by adding a ton of work to statements that look like they should be quick.