From time to time, I receive questions on how many cores rsyslog can run on a highly parallel system. Rsyslog is massivley multi-threaded, but that does not necessarily mean that each configuration, and even each use case, can actually benefit from it.
The most important thing to gain a speedup from parallelism is the ability to break down the workload (this is called “partitioning”) and distribute it to a set of threads, which than can work in parallel on each part.
For the partitioning to work well, the workload, and configuration, must be “partionable”. Let me give a counter-example. If you have a single sender and a single action (yes, this sometimes is the case!), there can not be much parallelism. Such a config looks like this (using imtcp as an example here):
This can not gain much, because we have on thread for the TCP receiver, one thread for the filtering and one for the output. With the queue engine, we can increase the number of threads that will work on filters in parallel, but these have almost nothing to do in any case. We can not, however, walk in parallel into the output action, because a) the output plugin interface guarantees that only one thread hits a plugin at one time and b) it wouldn’t make much sense here in any case: what would it help if we had hit the output twice and then need top synchronize the file access? No much…
So the bottom line is that a configuration like the one above is highly sequential in nature and consequently there is almost no gain by running some of the tasks concurrently. So, out of the box, rsyslog gains speedup from parallel processing in more complex cases, with more complex rule and many of them.
We are working the provide excellent speedup even for sequential configurations. But this is a long and complex road. For example, in v5 we have now de-coupled message parsing from the receiver thread, resulting in somewhat improved speedup for sequential configs like the one above. Also, we have added batching support in v5, which reduces some overhead involved with multiple threads (and thus reduces the gain we could potentially have). And in late v4 builds we introduced the ability to do double-buffered block i/o for output files, which can considerably reduce i/o overhead for high end systems and also runs in pipeline mode, sequzing a bit more parallelism out of the sequential job.
So with the newer engines, we have been able to apply a basic processing pipeline that looks like
input -> parse & filter -> generate file data -> write
which can be done in parallel. Of course, the file write is action-specific, but I guess you get the idea. What you need to do, however, is configure all that. And even then, you can not expect a 4-time speedup on a quad core system. I’d say you can be happy if the speedup is around 2, depending on a lot of factors.
To get to higher speedups, the job must be made more parallel. One idea is to spread the input, e.g. run it on four ports, then create four rulesets with ruleset queues for each of the inputs. Ideally, to solve the file bottleneck, these should write into four different files. While I did not have the opportunity to test this out in an actual deployment, that should gain a much larger speedup. Because now we have four of this pipelines running in parall, on partitioned data where there is no need to synchronize between them.
Well, almost… The bad news is that the current code base (5.5.0 as of this writing) does unfortunately not yet provide the ability to run the input on more than one thread. So if you have 1000 tcp connections, all of these need to be processed by a single thread (even though they may use different ports, that doesn’t matter…). It is not as bad as it sounds, because the input now is *very* quick (remember the parsing is done concurrently in a different thread [pool!]). But still it causes some loss of parallel processing where not strictly needed. My thinking is that we should either do a “one thread per connection” server (not any longer such a big problem on 64bit machines) or (better but even more effort) do a thread pool for pulling data from the connections. Unfortunately, I do not have time to tackle that beast, but maybe someone is interested in sponsoring that work (that would be *really* useful)?
As you can see, full speedup by using multiple cores is perfectly doable, but going the max requires a lot of careful thinking. And, of course, I have to admit that the best features are present in the newest releases (somewhat naturally…). Obviously, there is some stability risk involved with them, but on the other hand I had some very good success reports from some high-end sites, at least on of them has v5 already deployed in large-scale production.
I could only touch the issue here, but I hope the information is useful. For further reading, I recommend both the doc on queues, as well as my explanation on how messages are processed in rsyslog. These documents are somewhat older and do not cover all details of pipeline processing (which simply did not exist at that time), but I think they will be very useful to read. And, yes, updating them is another thing on my too-long todo list…