Hey Micke, very cool you're looking to improve C*'s performance, we would absolutely benefit from it.
Have you done any other benchmarks beside the micro one to determine the total effect of these metrics on the system overall? Microbenchmarks are a great way to tune small sections of code but they aren't a great starting point. It would be good if we could put some context around the idea by benchmarking a tuned, single node (so there's less network overhead) running on fast disks with compaction disabled so we can see what kind of impact these metrics are adding. Ideally we'd look at GC promotion and CPU time using something like YourKit to identify the overall effect of the metrics, so we can set our expectations and goals in a reasonable manner. Happy to coordinate with you on this! On Thu, Feb 22, 2018 at 8:08 AM Jeremiah D Jordan <jeremiah.jor...@gmail.com> wrote: > re: nanoTime vs currentTimeMillis there is a good blog post here about the > timing of both and how your choice of Linux clock source can drastically > effect the speed of the calls, and also showing that in general on linux > there is no perf improvement for one over the other. > http://pzemtsov.github.io/2017/07/23/the-slow-currenttimemillis.html > > > On Feb 22, 2018, at 11:01 AM, Blake Eggleston <beggles...@apple.com> > wrote: > > > > Hi Micke, > > > > This is really cool, thanks for taking the time to investigate this. I > believe the metrics around memtable insert time come in handy in > identifying high partition contention in the memtable. I know I've been > involved in a situation over the past year where we got actionable info > from this metric. Reducing resolution to milliseconds is probably a no go > since most things in this path should complete in less than a millisecond. > > > > Revisiting the use of the codahale metrics in the hot path like this > definitely seems like a good idea though. I don't think it's been something > we've talked about a lot, and it definitely looks like we could benefit > from using something more specialized here. I think it's worth doing, > especially since there won't be any major changes to how we do threading in > 4.0. It's probably also worth opening a JIRA and investigating the calls to > nano time. We at least need microsecond resolution here, and there could be > something we haven't thought of? It's worth a look at least. > > > > Thanks, > > > > Blake > > > > On 2/22/18, 6:10 AM, "Michael Burman" <mibur...@redhat.com> wrote: > > > > Hi, > > > > I wanted to get some input from the mailing list before making a JIRA > > and potential fixes. I'll touch the performance more on latter part, > but > > there's one important question regarding the write latency metric > > recording place. Currently we measure the writeLatency (and metric > write > > sampler..) in ColumnFamilyStore.apply() and this is also the metric we > > then replicate to Keyspace metrics etc. > > > > This is an odd place for writeLatency. Not to mention it is in a > > hot-path of Memtable-modifications, but it also does not measure the > > real write latency, since it completely ignores the CommitLog latency > in > > that same process. Is the intention really to measure > > Memtable-modification latency only or the actual write latencies? > > > > Then the real issue.. this single metric is a cause of huge overhead > in > > Memtable processing. There are several metrics / events in the CFS > apply > > method, including metric sampler, storageHook reportWrite, > > colUpdateTimeDeltaHistogram and metric.writeLatency. These are not > free > > at all when it comes to the processing. I made a small JMH benchmark > > here: > https://gist.github.com/burmanm/b5b284bc9f1d410b1d635f6d3dac3ade > > that I'll be referring to. > > > > The most offending of all these metrics is the writeLatency metric. > What > > it does is update the latency in codahale's timer, doing a histogram > > update and then going through all the parent metrics also which update > > the keyspace writeLatency and globalWriteLatency. When measuring the > > performance of Memtable.put with parameter of 1 partition (to reduce > the > > ConcurrentSkipListMap search speed impact - that's separate issue and > > takes a little bit longer to solve although I've started to prototype > > something..) on my machine I see 1.3M/s performance with the metric > and > > when it is disabled the performance climbs to 4M/s. So the overhead > for > > this single metric is ~2/3 of total performance. That's insane. My > perf > > stats indicate that the CPU is starved as it can't get enough data in. > > > > Removing the replication from TableMetrics to the Keyspace & global > > latencies in the write time (and doing this when metrics are requested > > instead) improves the performance to 2.1M/s on my machine. It's an > > improvement, but it's still huge amount. Even when we pressure the > > ConcurrentSkipListMap with 100 000 partitions in one active Memtable, > > the performance drops by about ~40% due to this metric, so it's never > free. > > > > i did not find any discussion replacing the metric processing with > > something faster, so has this been considered before? At least for > these > > performance sensitive ones. The other issue is obviously the use of > > System.nanotime() which by itself is very slow (two System.nanotime() > > calls eat another ~1M/s from the performance) > > > > My personal quick fix would be to move writeLatency to Keyspace.apply, > > change write time aggregates to read time processing (metrics are read > > less often than we write data) and maybe even reduce the nanotime -> > > currentTimeMillis (even given it's relative lack of precision). That > is > > - if these metrics make any sense at all at CFS level? Maybe these > > should be measured from the network processing time (including all the > > deserializations and such) ? Especially if at some point the smarter > > threading / eventlooping changes go forward (in which case they might > > sleep at some "queue" for a while). > > > > - Micke > > > > > > --------------------------------------------------------------------- > > To unsubscribe, e-mail: dev-unsubscr...@cassandra.apache.org > > For additional commands, e-mail: dev-h...@cassandra.apache.org > > > > > > > > > > > > --------------------------------------------------------------------- > > To unsubscribe, e-mail: dev-unsubscr...@cassandra.apache.org > > For additional commands, e-mail: dev-h...@cassandra.apache.org > > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@cassandra.apache.org > For additional commands, e-mail: dev-h...@cassandra.apache.org > >
