It's going to be interesting when the Chinese government issues Google a warrant to get data from the US.

Utter crap. Codenomicon are very friendly to FLOSS and FLOSS developers. They're also great guys. They have been providing free test services to the Samba project for many years now, and have helped us fix many many bugs.

In case you hadn't noticed, the code they're reporting on here is closed source proprietary code...

Software on Internet-connected devices is a bit different from your examples though. No matter how insecure cars are, it would be really hard for me to steal a million cars in one night, let alone without being caught. Yet, it's common to see millions of computers/phones being hacked in a very short period of time. And the risk to the person responsible is much lower.

hi mr thinly-sliced, thank you this is awesome advice, really really appreciated.

because we have an extremely limited amount of time as an additional requirement, and we can always rewrite critical portions or later the entire application in c once we have delivered a working system that means that the client can get some money in and can therefore stay in business.

also i worked with david and we benchmarked python-lmdb after adding in support for looped sequential "append" mode and got a staggering performance metric of 900,000 100-byte key/value pairs, and a sequential read performance of 2.5 MILLION records. the equivalent c benchmark is only around double those numbers. we don't *need* the dramatic performance increase that c would bring if right now, at this exact phase of the project, we are targetting something that is 1/10th to 1/5th the performance of c.

so if we want to provide the client with a product *at all*, we go with python.

but one thing that i haven't pointed out is that i am an experienced linux python and c programmer, having been the lead developer of samba tng back from 1997 to 2000. i simpy transferred all of the tricks that i know involving while-loops around non-blocking sockets and so on over to python. ... and none of them helped. if you get 0.5% of the required performance in python, it's so far off the mark that you know something is drastically wrong. converting the exact same program to c is not going to help.

we don't have anything like that [strict timing guarantees] - not for the data itself. the data comes in on a 15 second delay (from the external source that we do not have control over) so a few extra seconds delay is not going to hurt.

so although we need the real-time response to handle the incoming data, we _don't_ need the real-time capability beyond that point.

.... you know, i think this is extremely sensible advice (which i have heard from other sources) so it is good to have that confirmed... my concerns are as follows:

questions:

* how do you then ensure that the process receiving the incoming UDP messages is high enough priority to make sure that the packets are definitely, definitely received?

* what support from the linux kernel is there to ensure that this happens?

* is there a system call which makes sure that data received on a UDP socket *guarantees* that the process receiving it is woken up as an absolute priority over and above all else?

* the message queue destination has to have locking otherwise it will be corrupted. what happens if the message queue that you wish to send the UDP packet to is locked by a *lower* priority process?

* what support in the linux kernel is there to get the lower priority process to have its priority temporarily increased until it lets go of the message queue on which the higher-priority task is critically dependent?

this is exactly the kind of thing that is entirely missing from the linux kernel. temporary automatic re-prioritisation was something that was added to solaris by sun microsystems quite some time ago.

to the best of my knowledge the linux kernel has absolutely no support for these kinds of very important re-prioritisation requirements.

i am running into exactly this problem on my current contract. here is the scenario:

* UDP traffic (an external requirement that cannot be influenced) comes in
* the UDP traffic contains multiple data packets (call them "jobs") each of which requires minimal decoding and processing
* each "job" must be farmed out to *multiple* scripts (for example, 15 is not unreasonable)
* the responses from each job running on each script must be collated then post-processed.

so there is a huge fan-out where jobs (approximately 60 bytes) are coming in at a rate of 1,000 to 2,000 per second; those are being multiplied up by a factor of 15 (to 15,000 to 30,000 per second, each taking very little time in and of themselves), and the responses - all 15 to 30 thousand - must be in-order before being post-processed.

so, the first implementation is in a single process, and we just about achieve the target of 1,000 jobs but only about 10 scripts per job.

anything _above_ that rate and the UDP buffers overflow and there is no way to know if the data has been dropped. the data is *not* repeated, and there is no back-communication channel.

the second implementation uses a parallel dispatcher. i went through half a dozen different implementations.

the first ones used threads, semaphores through python's multiprocessing.Pipe implementation. the performance was beyond dreadful, it was deeply alarming. after a few seconds performance would drop to zero. strace investigations showed that at heavy load the OS call futex was maxed out near 100%.

next came replacement of multiprocessing.Pipe with unix socket pairs and threads with processes, so as to regain proper control over signals, sending of data and so on. early variants of that would run absolutely fine up to some arbitrarry limit then performance would plummet to around 1% or less, sometimes remaining there and sometimes recovering.

next came replacement of select with epoll, and the addition of edge-triggered events. after considerable bug-fixing a reliable implementation was created. testing began, and the CPU load slowly cranked up towards the maximum possible across all 4 cores.

the performance metrics came out *WORSE* than the single-process variant. investigations began and showed a number of things:

1) even though it is 60 bytes per job the pre-processing required to make the decision about which process to send the job were so great that the dispatcher process was becoming severely overloaded

2) each process was spending approximately 5 to 10% of its time doing actual work and NINETY PERCENT of its time waiting in epoll for incoming work.

this is unlike any other "normal" client-server architecture i've ever seen before. it is much more like the mainframe "job processing" that the article describes, and the linux OS simply cannot cope.

i would have used POSIX shared memory Queues but the implementation sucks: it is not possible to identify the shared memory blocks after they have been created so that they may be deleted. i checked the linux kernel source: there is no "directory listing" function supplied and i have no idea how you would even mount the IPC subsystem in order to list what's been created, anyway.

i gave serious consideration to using the python LMDB bindings because they provide an easy API on top of memory-mapped shared memory with copy-on-write semantics. early attempts at that gave dreadful performance: i have not investigated fully why that is: it _should_ work extremely well because of the copy-on-write semantics.

we also gave serious consideration to just taking a file, memory-mapping it and then appending job data to it, then using the mmap'd file for spin-locking to indicate when the job is being processed.

all of these crazy implementations i basically have absolutely no confidence in the linux kernel nor the GNU/Linux POSIX-compliant implementation of the OS on top - i have no confidence that it can handle the load.

so i would be very interested to hear from anyone who has had to design similar architectures, and how they dealt with it.

i think one of two things happened, here. first is that it might have finally sunk in to google that even just *claiming* to have properly verified user identities leaves them open to lawsuits should they fail to have properly carried out the verification checks that other users *believe* they have carried out. every other service people *know* that you don't trust the username: for a service to claim that they have truly verified the identity of the individual behind the username is reprehensibly irresponsible.

second is that they simply weren't getting enough people, so have quotes opened up the doors quotes.

It would certainly be nice, but it's not realistic. For a simple paper, it would likely cost a few thousands, but for anything that requires fancy material, it could easily run in the millions. The only level where fraud prevention makes sense is at the institution (company, lab, university) level.

So you're saying that reviewers should have to reproduce the results (using their own funds) of the authors before accepting the papers or risk being disciplined? Aside from ending up with zero reviewers, I don't see what this could possibly accomplish. Peer review is designed to catch mistakes, not fraud.

Well, there's also the thing that one of the most fundamental assumption you have to make while reviewing is that the author's acting in good faith. It's really hard to review anything otherwise (we're scientists, not a sort of police)

It goes beyond just that. I should have said "multi-dimensional" maybe. In many cases, I want to say "publish this article because the idea is good, despite the implementation being flawed". In other cases, you might want to say "this is technically correct, but boring". In the medical field, it may be useful to publish something pointing out that "maybe chemical X could be harmful and it's worth further investigation" without necessarily buying all of the authors' conclusion.

Personally, I prefer reading flawed papers that come from a genuinely good idea rather than rigorous theoretical papers that are both totally correct and totally useless.

That's the problem here. Should the review system "accept the paper unless it's provably broken" or "reject the paper unless it's provably correct". The former leads to all these issues of false stuff in medical journals and climate research, while the latter leads to good research (like the Shannon example) not being published. This needs to be more than just binary. Personally I prefer to accept if it looks like it could be a good idea, even if some parts may be broken. Then again I don't work on controversial stuff and nobody dies if the algorithm is wrong. I can understand that people in other fields have different opinions, but I guess what we need is non-binary review. Of course, reviewers are also just one part of the equation. My reviews have been overruled by associate editors more often than not.