-> about 800 live SSTables Well, that would occupy 1.5MB of hyperloglogs each having 2000 bytes. That's peanuts. Instead of going 800 times to the disk every minute.
On Thu, Jan 2, 2025 at 8:18 PM Chris Lohfink <clohfin...@gmail.com> wrote: > > Regarding allocation details. The DB host had the following stats at > that time: 5K/sec local reads, 3K/sec local writes, about 800 live > SSTables, the profile was collected with duration = 5 minutes. I do not > have an allocation rate info for that time period. > > What was the allocation rate on heap I mean. If there's 100mb/s of heap > allocation it doesn't really matter if it's 5%. If there's 5gb/s it does. > Assuming replacement takes 0% isn't always safe to assume either. Could > alternatively provide a different "partition count" metric that just uses > the index summary, it would be cheap/freeish for metric collections when > accuracy doesnt matter, but then for things like `nodetool tablestats` can > still be accurate more expensive path. > > I can be convinced with benchmarks but I've never seen this so much as a > blimp on the radar, but we are still running 4.1 so maybe things have > improved so much it's become relevant. Just want to make sure that whatever > we replace it with is benchmarked to not make things worse. > > Chris > > > > On Thu, Jan 2, 2025 at 10:42 AM Dmitry Konstantinov <netud...@gmail.com> > wrote: > >> Let me clarify my comment regarding allocation. I am not saying that >> switching to another implementation will make it better and we need to do >> it right now :-), any such switch is a subject for pros/cons analysis (and >> memory allocation I think should be one of criteria). What I wanted to say: >> this place has attracted my attention as potentially suspicious from >> another perspective, so it makes sense to analyse it in more detail, not >> more as of now. >> >> Regarding allocation details. The DB host had the following stats at that >> time: 5K/sec local reads, 3K/sec local writes, about 800 live SSTables, the >> profile was collected with duration = 5 minutes. I do not have an >> allocation rate info for that time period. >> >> >> On Thu, 2 Jan 2025 at 16:02, Benedict <bened...@apache.org> wrote: >> >>> I am strongly opposed to updating libraries simply for the sake of it. >>> Something like HLL does not need much ongoing maintenance if it works. >>> We’re simply asking for extra work and bugs by switching, and some risk >>> without understanding the quality control for the new library project’s >>> releases. >>> >>> That said, I was not very impressed with the clear spring library when I >>> looked at it, so I would be open to a stronger argument about data sketches >>> being superior otherwise in a way that matters to us. >>> >>> If we are to replace the library, we should at the very least do proper >>> due diligence by reviewing the new library’s implementation(s) ourselves. >>> We cannot simply assume the new library behaves well for our use cases, or >>> is well maintained. >>> >>> We should also not use the fallback intersection method, as this would >>> represent a regression to compaction on upgrade. We should really convert >>> from one HLL to another. >>> >>> The proposal to reduce allocations appears to be orthogonal to this >>> library, so let’s separate out that discussion? If there’s evidence this >>> library alone improves the memory profile let’s discuss that. >>> >>> >>> On 2 Jan 2025, at 15:26, Chris Lohfink <clohfin...@gmail.com> wrote: >>> >>> >>> I think switching to datasketches is a good idea first off simply >>> because of the lack of maintenance and improvements from clearspring. I am >>> however, am not sold that it will actually improve anything significantly. >>> Caches might help on small cases, but those small cases probably are not >>> actually impacted. In the large cases the caches cost more in complexity, >>> memory, and ultimately wont matter when theres 50k sstables and the cache >>> holds 1k so everythings hitting disk anyway. >>> >>> The 5% is missing some relevant information like what the allocation >>> rate was, how many tables there are etc. On an idle system thats >>> meaningless, if there were 5gb/s allocations of reads/writes happening at >>> the time thats huge. >>> >>> On Thu, Jan 2, 2025 at 8:42 AM Štefan Miklošovič <smikloso...@apache.org> >>> wrote: >>> >>>> Interesting, thanks for this. Well ... 5% here, 5% there ... it >>>> compounds. I think it is worth trying to do something with this. Would be >>>> great if you were part of this effort! >>>> >>>> On Thu, Jan 2, 2025 at 3:38 PM Dmitry Konstantinov <netud...@gmail.com> >>>> wrote: >>>> >>>>> I have seen this place in async profiler memory allocation profile on >>>>> one of production environments some time ago, it was visible but not >>>>> dramatic, about 5% of allocations: >>>>> <image.png> >>>>> >>>>> The amount of overhead also depends on a metric collection frequency >>>>> (in my case it was once per 60 seconds) >>>>> >>>>> Regards, >>>>> Dmitry >>>>> >>>>> On Thu, 2 Jan 2025 at 14:21, Štefan Miklošovič <smikloso...@apache.org> >>>>> wrote: >>>>> >>>>>> Indeed, I plan to measure it and compare, maybe some bench test would >>>>>> be cool to add .. >>>>>> >>>>>> I strongly suspect that the primary reason for the slowness (if it is >>>>>> verified to be true) is us going to the disk every time and reading stats >>>>>> for every SSTable all over again. >>>>>> >>>>>> While datasketches say that it is way faster to update (1), we are >>>>>> living in a realm of nanoseconds here and I don't think that itself would >>>>>> make any meaningful difference when merging one hyperloglog with another >>>>>> as >>>>>> part of partition rows estimation computation. The only place we are >>>>>> updating is SortableTableWriter#endParition which calls >>>>>> metadatacollector.addKey(key.getKey()) which eventually updates the >>>>>> estimator via cardinality#offeredHashed. >>>>>> >>>>>> In other words, I think that going to the disk and reading it >>>>>> repeatedly is disproportionally more IO / time intensive than switching >>>>>> the >>>>>> hyperloglog implementation. >>>>>> >>>>>> However, I consider the replacement of the library still important. I >>>>>> feel uneasy about staying with an abandoned library where there is >>>>>> clearly >>>>>> a well-maintained replacement. >>>>>> >>>>>> What we could do is to cache all cardinality estimators and just >>>>>> merge it all when asked upon metric resolution. That is different from >>>>>> going to disk to deserialize StatsComponent's all over again. >>>>>> >>>>>> Then on SSTable removal, we would remove that from cache too. I think >>>>>> there is some kind of an observer when SSTable is removed ... >>>>>> >>>>>> However, I am not sure I can just hold it all in the memory, it works >>>>>> for laptop testing but if we have thousands of SSTables with non-trivial >>>>>> number of rows things start to get interesting. >>>>>> >>>>>> (1) https://datasketches.apache.org/docs/HLL/Hll_vs_CS_Hllpp.html - >>>>>> section HllSketch vs. HyperLogLogPlus Update Speed Behavior >>>>>> >>>>>> On Thu, Jan 2, 2025 at 2:46 PM Jon Haddad <j...@rustyrazorblade.com> >>>>>> wrote: >>>>>> >>>>>>> Sounds interesting. I took a look at the issue but I'm not seeing >>>>>>> any data to back up "expensive". Can this be quantified a bit more? >>>>>>> >>>>>>> Anytime we have a performance related issue, there should be some >>>>>>> data to back it up, even if it seems obvious. >>>>>>> >>>>>>> Jon >>>>>>> >>>>>>> On Thu, Jan 2, 2025 at 8:20 AM Štefan Miklošovič < >>>>>>> smikloso...@apache.org> wrote: >>>>>>> >>>>>>>> Hello, >>>>>>>> >>>>>>>> I just stumbled upon this library we are using for getting >>>>>>>> estimations of the number of partitions in a SSTable which are used >>>>>>>> e.g. in >>>>>>>> EstimatedPartitionCount metric. (1) >>>>>>>> >>>>>>>> A user reported in (1) that it is an expensive operation. When one >>>>>>>> looks into what it is doing, it calls >>>>>>>> SSTableReader.getApproximateKeyCount() (6) which basically goes to disk >>>>>>>> every single time, it loads all Stats components and it looks into >>>>>>>> CompactionMetadata where the cardinality estimator is located. >>>>>>>> >>>>>>>> We are serializing the hyperloglog to disk as part of a SSTable and >>>>>>>> we deserialize it back in runtime for every SSTable in a CF and we >>>>>>>> merge >>>>>>>> them all to one cardinality again. >>>>>>>> >>>>>>>> I do not think there is a way around this because of the nature of >>>>>>>> how a cardinality estimator works (hyperloglog). We can not "cache >>>>>>>> it", it >>>>>>>> would work only in case we are adding SSTables only - hence we would >>>>>>>> just >>>>>>>> merge again - but if we remove an SSTable as part of the compaction, >>>>>>>> we can >>>>>>>> not "unmerge" it. >>>>>>>> >>>>>>>> That being said, we are currently using this library for >>>>>>>> hyperloglog (1) which was archived in summer 2020 and nothing was >>>>>>>> contributed to that for 6 years. That lib is dead. >>>>>>>> >>>>>>>> There is very nice replacement of that (2) directly from Apache >>>>>>>> (!!!) and they are even giving the detailed and in-depth comparison of >>>>>>>> hyperloglog implementation found in stream-lib we happen to use (3) >>>>>>>> (stream-lib = Clearspring) where they are saying that updating is way >>>>>>>> faster and it is also giving better estimations in general. >>>>>>>> >>>>>>>> I have implemented the usage of both cardinality estimators (4), >>>>>>>> (5). The reason we need to keep the old one around is that we may have >>>>>>>> old >>>>>>>> SSTables around and we need to work with them too. That translates to >>>>>>>> a new >>>>>>>> SSTable version (ob) which uses new implementation and for versions < >>>>>>>> ob, >>>>>>>> it uses the old one. When SSTables are upgraded from oa to ob, the old >>>>>>>> estimator will not be used anymore. >>>>>>>> >>>>>>>> There is also a case of a user not upgrading his oa SSTables, >>>>>>>> turning a node on and creating new SSTables with ob version. When this >>>>>>>> happens and we ask what is the cardinality (e.g via nodetool >>>>>>>> tablestats), I >>>>>>>> am checking if all SSTables are on the same version or not. If they >>>>>>>> are, >>>>>>>> they will use either an old or new estimator. (we can not merge >>>>>>>> estimations >>>>>>>> from two different hyperloglog implementations). If they are not, it >>>>>>>> will >>>>>>>> compute that from index summaries. (The computation for index >>>>>>>> summaries was >>>>>>>> already in place (6) as a fail-over in case the estimation computation >>>>>>>> failed / was not present). >>>>>>>> >>>>>>>> Does this all make sense to drive further to the completion and >>>>>>>> eventually merge this work to trunk? >>>>>>>> >>>>>>>> Worth to add that Apache Datasketches are just two dependencies for >>>>>>>> us, it has zero external dependencies. >>>>>>>> >>>>>>>> (1) https://github.com/addthis/stream-lib >>>>>>>> (2) https://datasketches.apache.org/ >>>>>>>> (3) https://datasketches.apache.org/docs/HLL/Hll_vs_CS_Hllpp.html >>>>>>>> (4) https://issues.apache.org/jira/browse/CASSANDRA-13338 >>>>>>>> (5) https://github.com/apache/cassandra/pull/3767 >>>>>>>> (6) >>>>>>>> https://github.com/apache/cassandra/blob/trunk/src/java/org/apache/cassandra/io/sstable/format/SSTableReader.java#L284-L338 >>>>>>>> >>>>>>>> Regards >>>>>>>> >>>>>>> >>>>> >>>>> -- >>>>> Dmitry Konstantinov >>>>> >>>> >> >> -- >> Dmitry Konstantinov >> >
