pdhborges

I would upvote this comment more if I could.

I already refrained from introducing event sourcing to tackle wierd dependecies multiple time just by justaposing the amount of discipline that the team has that lead to the current state vs the discipline that is required to keep the event source solution going.

I don't agree with this cache take. Adding operations to the cache is easy. Taking the django-redis project as an example there are only two levels until you reach redis-py: The cache abstraction and the client abstraction.

The problem with channels is that if you need to touch the ORM you will have to use a sync_to_async call which will block the event loop.

Why don't you post the original broken Python code.

Might as well just implement virtual threads: https://discuss.python.org/t/add-virtual-threads-to-python/9...

What did you try to debug this?

Scale articles are too focused on architecture. What about business problems that come with scale. At a certain scale rare events are common many cases cease to be fixable by some random process that involves humans you have to handle a lot more business scenarios with your code.

AWS can make major version upgrades automatically now with less downtime. I think they do the logical replication dance internally.

In my experience PMs often work at a very high level. How things shold work are defined in a incosistent way when we take into account all the user flows, subtelness and restrictions of other systems. So programmers end up doing a significant chunk of the work by refining the specs so that the thing actually makes sense.

I will believe this theory if someone shows me that the ratio of scientists to engineers of leading teams of the leading companies deploying AI products is bigger than 1.

The author keeps aluding to the problem of loss of type inference without giving any example. I have no idea if the problem is relevant or not.

>> ARM has supported such capability via the standard CoreSight Program Trace Macrocell (PTM)[3]/Embedded Trace Macrocell (ETM)[4] since at least 2000.

Where are the performace tools that wrap those capabilities? IPT has Magic Trace what is the equivalent tool for ARM?

If the AI is so smart why are we feeding so many dumb humans?

Are you refering to DRF model serializers? For medium to big applications I think they are worthless.

It's not FUD. For all the trouble you claim to have with Postgres I experienced 0 of it in the last 4 years. The only thing extra for a simple setup is a couple of lines in your docker compose files which is completely amortised because you already have a multi process architecture with Python anyway (proxy + webserver + web server works). The upfront cost is so small that for me the expected total cost will rarely make sense even if you assume that your application has 1% of chance of scaling beyond what you can do with sqlite.

Where is that improvement coming from? Hardware is already here to compute gemm as fast as possible.

What do you save by going with sqlite vs how much pain are you going to endure in the future?

This is a cool example but I think it is missing the perspective of what the interface can abstract. For example if I program a data structure to provide an Iterator I get to use these itertool functions for free no matter how complex the data structure is underneath.

The trouble I have with Monads is that what get for free doesn't seem very exciting. Feels like I'm stuck in the world of a particular monad like State or Promises and then to do anything remotly usefull you have to bring ll of this monad tranformer machinery to switch worlds again.

If all monad instances work differently what is the value of the Monad interface? What kind of usefull generic code can one write against the Monad interface.

Related: https://buttondown.com/j2kun/archive/weak-and-strong-algebra...

Java supports it? Are you conflating method overload (which is statically determined) with multimethods?

Small Python backend deployment 30 machines 1 DB.

I'm looking at our Datadog stats right now. It is 64% cpu 36% IO.

Doesn't this depend on the number of servers, crash rates and recovery times? I wouldn't feel confident running 3 servers running at 80% capacity in ultra low latency scenarios. A single crash would overwhelm the other 2 servers in no time.

This is using docker-compose. The old deprecated python standalone tool. Doctor compose v2 is a plug-in written in go for the docker cli.

Edit: looks like Docker release docker-compose v2 as a go standalone tool (https://docs.docker.com/compose/install/standalone/). I stand corrected.

MVC has depencies on LinAlg. Ex Jacobians.

Very nice discussion. A lot of times I see people assuming that Web Apps are IO bound anyway to justify technical decisions. Nevertheless here am I looking at our stats: 60% is python, 40% is IO and Postgres is getting faster than Python at a faster pace.

I get socks for Christmas and I like it.

Well my experience has always been the opposite. New query patterns are always appearing. The difference between an OLTP and an OLAP query is not as clear cut as one might imagine that justifies huge changes to an existing system.

I don't read all the lines of code but I open and scan a ton of files from the code base to get a feel of which concepts abstractions and tricks are used.

  In Python you don't even need a lib, dict is thread safe even in nogil.

Is it? https://google.github.io/styleguide/pyguide.html#218-threadi...