One pattern we've been seeing internally is that once teams
standardize API interactions through a single interface
(or agent layer), debugging becomes both easier and harder.
Easier because there's a central abstraction,
harder because failures become more opaque.
In production incidents we often end up tracing through
multiple abstraction layers before finding the real root cause.
Curious if you've built anything into the CLI to help with
observability or tracing when something fails.
One thing I'm curious about here is the operational impact.
In production systems we often see Python services scaling horizontally
because of the GIL limitations. If true parallelism becomes common,
it might actually reduce the number of containers/services needed
for some workloads.
But that also changes failure patterns — concurrency bugs,
race conditions, and deadlocks might become more common in
systems that were previously "protected" by the GIL.
It will be interesting to see whether observability and
incident tooling evolves alongside this shift.
This is surely why Facebook was interested in funding this work. It is common to have N workers or containers of Python because you are generally restricted to one CPU core per Python process (you can get a bit higher if you use libs that unlock the GIL for significant work). So the only scaling option is horizontal because vertical scaling is very limited. The main downside of this was memory usage. You would have to load all of your code and libraries N types and in-process caches would become less effective. So by being able to vertically scale a Python process much further you can run less and save a lot of memory.
Generally speaking the optimal horizontal scaling is as little as you have to. You may want a bit of horizontal scaling for redundancy and geo distribution, but past that vertically scaling to fewer larger process tend to be more efficient, easier to load balance and a handful of other benefits.
> The main downside of this was memory usage. You would have to load all of your code and libraries N types and in-process caches would become less effective.
You can load modules and then fork child processes. Children will share memory with each other (if they need to modify any shared memory, they get copy-on-write pages allocated by the kernel) and you'll save quite a lot on memory.
Yes, this can help a lot, but it definitely isn't perfect. Especially since CPython uses reference counting it is likely that many pages get modified relatively quickly as they are accessed. Many other GC strategies are also pretty hostile to CoW memory (for example mark bits, moving, ...) Additionally this doesn't help for lazy loaded data and caches in code and libraries.
But python can fork itself and run multiple processes into one single container. Why would there be a need to run several containers to run several processes?
There's even the multiprocessing module in the stdlib to achieve this.
Threads are cheap, you can do N work simultaneously with N threads in one process, without serialization, IPC or process creation overhead.
With multiprocessing, processes are expensive and work hogs each process. You must serialize data twice for IPC, that's expensive and time consuming.
You shouldn't have to break out multiple processes, for example, to do some simple pure-Python math in parallel. It doesn't make sense to use multiple processes for something like that because the actual work you want to do will be overwhelmed by the IPC overhead.
There are also limitations, only some data can be sent to and from multiple processes. Not all of your objects can be serialized for IPC.
It makes sense to me that a program currently written using multiple processes would now be re-written to use multiple truly parallel threads. But it seems very odd to suggest (as your grandparent comment does) that a program currently run in multiple containers would likely be migrated to run on multiple threads.
In other words, I imagine anyone who cares about the overhead from serialization, IPC, or process creation would already be avoiding (as much as possible) using containers to scale in the first place.
Yeah, I somehow glossed over the whole container thing.
The container thing might be horizontal scaling thing where 1 container runs on 1 instance with 1 vCPU, running multiple processes on instances means you need beefier slices of compute to take advantage of the parallelism, and you can't cleanly scale up and then down using only the resources you need.
If you have a queue distributing work, that model makes sense with single-threaded interpreters where consumers instances are spun up and down as needed, versus pushing work to a thread pool, or multiple instances with their own thread pools, that aren't inhibited by the GIL. The latter could be more efficient depending on the work.
Forking and multi threading do not coexist. Even if one of your transitive dependencies decides to launch a thread that’s 99% idle, it becomes unsafe to fork.
Im curious as to the down votes on this. It's absolutely true, and when I was maintaining a job runner daemon that ran hundreds of thousands of who knows what Python tasks/jobs a day on some shared infra with arbitrary code for a certain megacorp from 2016-2020 or so, this was one of insidious and ugly failure modes to go debug and handle. The docs really make it sound like you can mix threading and multiprocessing but you can never really completely ensure that threading and then bare fork will ever be safe, period. It's really irritating that the docs would have you believe that this is OK or safe, but is in keeping with the Python philosophy of trying to hide the edge of the blade you're using until it's too late and you've cut the shit out of yourself.
In general only the thread calling fork() gets forked, so unless you call exec() soon after, there are a lot of complications with signals, shared memory.
What are the complications? A single thread with its own process sandbox with everything from the parent is exactly what I'd expect coming from C land. Are the complications you refer to specific to the python VM or more general?
Even treating the process as read only after forking is potentially fraught. What if a background thread is mutating some data structure? When it forks the data structure might be internally inconsistent because the work to finish the mutation might not be completed. Imagine there are locks held by various threads when it dies, trying to lock those in the child might deadlock or even worse. There's tons of these types of gotchas.
Okay so just all the usual threading gotchas. Nothing specific to Python.
Conceptually fork "just" noncooperatively preempts and kills all other threads. Use accordingly. Yes it's a giant footgun but then so is all low level "unmanaged" concurrency.
If you have multiple threads, you almost certainly have mutexes. If your fork happens when a non-main thread holds a mutex, your main thread will never again be able to hold that mutex.
An imperfect solution is to require every mutex created to be accompanied by some pthread_atfork, but libraries don’t do that unless forking is specifically requested. In other words, if you don’t control the library you can’t fork.
If you have enough discipline to make sure you only create threads after all the forking is done, then sure. But having such discipline is harder than just forbidding fork or forbidding threads in your program. It turns a careful analysis of timing and causality into just banning a few functions.
And what do you do with that information? Refuse to fork after you detect more than one thread running? I haven’t seen any code that gracefully handles the unable-to-fork scenario. When people write fork-based code, especially in Python, they always expect forking to succeed.
But not the reverse, if its a bare fork and not strictly using basically mutex and shared resource free code (which is hard), and there's little or no warning lights to indicate that this is a terrible idea that fails in really unpredictable and hard to debug ways.
For big things the current way works fine. Having a separate container/deployment for celery, the web server, etc is nice so you can deploy and scale separately. Mostly it works fine, but there are of course some drawbacks. Like prometheus scraping of things then not able to run a web server in parallel etc is clunky to work around.
And for smaller projects it's such an annoyance. Having a simple project running, and having to muck around to get cron jobs, background/async tasks etc. to work in a nice way is one of the reasons I never reach for python in these instances. I hope removing the GIL makes it better, but also afraid it will expose a whole can of worms where lots of apps, tools and frameworks aren't written with this possibility in mind.
As much as I dislike Java the language, this is somewhere where the difference between CPython and JVM languages (and probably BEAM too) is hugely stark. Want to know if garbage collection or memory allocation is a problem in your long running Python program? I hope you're ready to be disappointed and need to roll a lot of stuff yourself. On the JVM the tooling for all kinds of observability is immensely better. I'm not hopeful that the gap is really going to close.
A lot of that has already been solved for by scaling workers to cores along with techniques like greenlets/eventlets that support concurrency without true multithreading to take better advantage of CPU capacity.
But you are still more or less limited to one CPU core per Python process. Yes, you can use that core more effectively, but you still can't scale up very effectively.
Yes, multiple worker processes is what I meant. Few web apps have a meaningful use for parallelism within a single process. So long as you’re keeping all cores busy with independent processes at high concurrency, multithreading adds relatively little.
One thing we've been seeing with production AI agents is that the real risk
isn't just filesystem access, but the chain of actions agents can take once
they have tool access.
Even a simple log-reading capability can escalate if the agent starts
triggering automated workflows or calling internal APIs.
We've been experimenting with incident-aware agents that detect abnormal
behavior and automatically generate incident reports with suggested fixes.
Curious if you're thinking about integrating behavioral monitoring
or anomaly detection on top of the sandbox layer.
’ve spent way too many nights at 3 AM trying to piece together what happened during a P1 incident. The hardest part isn't usually fixing the bug—it's writing the post-mortem report for the leadership 2 hours later while you're exhausted.
Generic LLMs usually hallucinate or lose technical context when you dump 10k lines of logs into them. So I built ProdRescue AI.
It’s specifically designed to:
Sanitize PII automatically from logs.
Correlate multi-service logs and Slack threads into a single timeline.
Map every claim in the report back to a specific log line [evidence-backed].
Generate '5 Whys' and action items based on SRE best practices.
One pattern we've been seeing internally is that once teams standardize API interactions through a single interface (or agent layer), debugging becomes both easier and harder.
Easier because there's a central abstraction, harder because failures become more opaque.
In production incidents we often end up tracing through multiple abstraction layers before finding the real root cause.
Curious if you've built anything into the CLI to help with observability or tracing when something fails.