> My main concern is that we don't do the migration in 0.8.3, we will be left with some metrics in YM format and some others in KM format (as we start sharing client code on the broker). This is probably a worse situation to be in.
+1. I am not sure how our servo adaptor will work if there are two formats for metrics? unless there is an easy way to check the format (YM/KM). On Tue, Mar 31, 2015 at 9:40 AM, Jun Rao <j...@confluent.io> wrote: > (2) The metrics are clearly part of the client API and we are not changing > that (at least for the new client). Arguably, the metrics are also part of > the broker side API. However, since they affect fewer parties (mostly just > the Kafka admins), it may be easier to make those changes. > > My main concern is that we don't do the migration in 0.8.3, we will be left > with some metrics in YM format and some others in KM format (as we start > sharing client code on the broker). This is probably a worse situation to > be in. > > Thanks, > > Jun > > On Tue, Mar 31, 2015 at 9:26 AM, Gwen Shapira <gshap...@cloudera.com> > wrote: > > > (2) I believe we agreed that our metrics are a public API. I believe > > we also agree we don't break API in minor releases. So, it seems > > obvious to me that we can't make breaking changes to metrics in minor > > releases. I'm not convinced "we did it in the past" is a good reason > > to do it again. > > > > Is there a strong reason to do it in a 0.8.3 time-frame? > > > > On Tue, Mar 31, 2015 at 7:59 AM, Jun Rao <j...@confluent.io> wrote: > > > (2) Not sure why we can't do this in 0.8.3. We changed the metrics > names > > in > > > 0.8.2 already. Given that we need to share code btw the client and the > > > core, and we need to keep the metrics consistent on the broker, it > seems > > > that we have no choice but to migrate to KM. If so, it seems that the > > > sooner that we do this, the better. It is important to give people an > > easy > > > path for migration. However, it may not be easy to keep the mbean names > > > exactly the same. For example, YM has hardcoded attributes (e.g. > > > 1-min-rate, 5-min-rate, 15-min-rate, etc for rates) that are not > > available > > > in KM. > > > > > > One benefit out of this migration is that one can get the metrics in > the > > > client and the broker in the same way. > > > > > > Thanks, > > > > > > Jun > > > > > > On Mon, Mar 30, 2015 at 9:26 PM, Gwen Shapira <gshap...@cloudera.com> > > wrote: > > > > > >> (1) It will be interesting to see what others use for monitoring > > >> integration, to see what is already covered with existing JMX > > >> integrations and what needs special support. > > >> > > >> (2) I think the migration story is more important - this is a > > >> non-compatible change, right? So we can't do it in 0.8.3 timeframe, it > > >> has to be in 0.9? And we need to figure out how will users migrate - > > >> do we just tell everyone "please reconfigure all your monitors from > > >> scratch - don't worry, it is worth it?" > > >> I know you keep saying we did it before and our users are used to it, > > >> but I think there are a lot more users now, and some of them have > > >> different compatibility expectations. We probably need to find: > > >> * A least painful way to migrate - can we keep the names of at least > > >> most of the metrics intact? > > >> * Good explanation of what users gain from this painful migration > > >> (i.e. more accurate statistics due to gazillion histograms) > > >> > > >> > > >> > > >> > > >> > > >> > > >> On Mon, Mar 30, 2015 at 6:29 PM, Jun Rao <j...@confluent.io> wrote: > > >> > If we are committed to migrating the broker side metrics to KM for > the > > >> next > > >> > release, we will need to (1) have a story on supporting common > > reporters > > >> > (as listed in KAFKA-1930), and (2) see if the current histogram > > support > > >> is > > >> > good enough for measuring things like request time. > > >> > > > >> > Thanks, > > >> > > > >> > Jun > > >> > > > >> > On Mon, Mar 30, 2015 at 3:03 PM, Aditya Auradkar < > > >> > aaurad...@linkedin.com.invalid> wrote: > > >> > > > >> >> If we do plan to use the network code in client, I think that is a > > good > > >> >> reason in favor of migration. It will be unnecessary to have > metrics > > >> from > > >> >> multiple libraries coexist since our users will have to start > > monitoring > > >> >> these new metrics anyway. > > >> >> > > >> >> I also agree with Jay that in multi-tenant clusters people care > about > > >> >> detailed statistics for their own application over global numbers. > > >> >> > > >> >> Based on the arguments so far, I'm +1 for migrating to KM. > > >> >> > > >> >> Thanks, > > >> >> Aditya > > >> >> > > >> >> ________________________________________ > > >> >> From: Jun Rao [j...@confluent.io] > > >> >> Sent: Sunday, March 29, 2015 9:44 AM > > >> >> To: dev@kafka.apache.org > > >> >> Subject: Re: Metrics package discussion > > >> >> > > >> >> There is another thing to consider. We plan to reuse the client > > >> components > > >> >> on the server side over time. For example, as part of the security > > >> work, we > > >> >> are looking into replacing the server side network code with the > > client > > >> >> network code (KAFKA-1928). However, the client network already has > > >> metrics > > >> >> based on KM. > > >> >> > > >> >> Thanks, > > >> >> > > >> >> Jun > > >> >> > > >> >> On Sat, Mar 28, 2015 at 1:34 PM, Jay Kreps <jay.kr...@gmail.com> > > wrote: > > >> >> > > >> >> > I think Joel's summary is good. > > >> >> > > > >> >> > I'll add a few more points: > > >> >> > > > >> >> > As discussed memory matter a lot if we want to be able to give > > >> >> percentiles > > >> >> > at the client or topic level, in which case we will have > thousands > > of > > >> >> them. > > >> >> > If we just do histograms at the global level then it is not a > > concern. > > >> >> The > > >> >> > argument for doing histograms at the client and topic level is > that > > >> >> > averages are often very misleading, especially for latency > > >> information or > > >> >> > other asymmetric distributions. Most people who care about this > > kind > > >> of > > >> >> > thing would say the same. If you are a user of a multi-tenant > > cluster > > >> >> then > > >> >> > you probably care a lot more about stats for your application or > > your > > >> >> topic > > >> >> > rather than the global, so it could be nice to have histograms > for > > >> >> these. I > > >> >> > don't feel super strongly about this. > > >> >> > > > >> >> > The ExponentiallyDecayingSample is internally > > >> >> > a ConcurrentSkipListMap<Double, Long>. This seems to have an > > overhead > > >> of > > >> >> > about 64 bytes per entry. So a 1000 element sample is 64KB. For > > global > > >> >> > metrics this is fine, but for granular metrics not workable. > > >> >> > > > >> >> > Two other issues I'm not sure about: > > >> >> > > > >> >> > 1. Is there a way to get metric descriptions into the coda hale > JMX > > >> >> output? > > >> >> > One of the really nicest practical things about the new client > > >> metrics is > > >> >> > that if you look at them in jconsole each metric has an > associated > > >> >> > description that explains what it means. I think this is a nice > > >> usability > > >> >> > thing--it is really hard to know what to make of the current > > metrics > > >> >> > without this kind of documentation and keeping separate docs > > >> up-to-date > > >> >> is > > >> >> > really hard and even if you do it most people won't find it. > > >> >> > > > >> >> > 2. I'm not clear if the sample decay in the histogram is actually > > the > > >> >> same > > >> >> > as for the other stats. It seems like it isn't but this would > make > > >> >> > interpretation quite difficult. In other words if I have N > metrics > > >> >> > including some Histograms some Meters, etc are all these > > measurements > > >> all > > >> >> > taken over the same time window? I actually think they are not, > it > > >> looks > > >> >> > like there are different sampling methodologies across. So this > > means > > >> if > > >> >> > you have a dashboard that plots these things side by side the > > >> measurement > > >> >> > at a given point in time is not actually comparable across > multiple > > >> >> stats. > > >> >> > Am I confused about this? > > >> >> > > > >> >> > -Jay > > >> >> > > > >> >> > > > >> >> > On Fri, Mar 27, 2015 at 6:27 PM, Joel Koshy <jjkosh...@gmail.com > > > > >> wrote: > > >> >> > > > >> >> > > For the samples: it will be at least double that estimate I > think > > >> >> > > since the long array contains (eight byte) references to the > > actual > > >> >> > > longs, each of which also have some object overhead. > > >> >> > > > > >> >> > > Re: testing: actually, it looks like YM metrics does allow you > to > > >> >> > > drop in your own clock: > > >> >> > > > > >> >> > > > > >> >> > > > >> >> > > >> > > > https://github.com/dropwizard/metrics/blob/master/metrics-core/src/main/java/com/codahale/metrics/Clock.java > > >> >> > > > > >> >> > > > > >> >> > > > >> >> > > >> > > > https://github.com/dropwizard/metrics/blob/master/metrics-core/src/main/java/com/codahale/metrics/Meter.java#L36 > > >> >> > > > > >> >> > > Not sure if it was mentioned in this (or some recent) thread > but > > a > > >> >> > > major motivation in the kafka-common metrics (KM) was absorbing > > API > > >> >> > > changes and even mbean naming conventions. For e.g., in the > early > > >> >> > > stages of 0.8 we picked up YM metrics 3.x but collided with > > client > > >> >> > > apps at LinkedIn which were still on 2.x. We ended up changing > > our > > >> >> > > code to use 2.x in the end. Having our own metrics package > makes > > us > > >> >> > > less vulnerable to these kinds of changes. The multiple version > > >> >> > > collision problem is obviously less of an issue with the broker > > but > > >> we > > >> >> > > are still exposed to possible metric changes in YM metrics. > > >> >> > > > > >> >> > > I'm wondering if we need to weigh too much toward the memory > > >> overheads > > >> >> > > of histograms in making a decision here simply because I don't > > think > > >> >> > > we have found them to be an extreme necessity for > > >> >> > > per-clientid/per-partition metrics and they are more critical > for > > >> >> > > aggregate (global) metrics. > > >> >> > > > > >> >> > > So it seems the main benefits of switching to KM metrics are: > > >> >> > > - Less exposure to YM metrics changes > > >> >> > > - More control over the actual implementation. E.g., there is > > >> >> > > considerable research on implementing > > approximate-but-good-enough > > >> >> > > histograms/percentiles that we can try out > > >> >> > > - Differences (improvements) from YM metrics such as: > > >> >> > > - hierarchical sensors > > >> >> > > - integrated with quota enforcement > > >> >> > > - mbeans can logically group attributes computed from > different > > >> >> > > sensors. So there is logical grouping (as opposed to a > > separate > > >> >> > > mbean per sensor as is the case in YM metrics). > > >> >> > > > > >> >> > > The main disadvantages: > > >> >> > > - Everyone's graphs and alerts will break and need to be > updated > > >> >> > > - Histogram support needs to be tested more/improved > > >> >> > > > > >> >> > > The first disadvantage is a big one but we aren't exactly > immune > > to > > >> >> > > that if we stick with YM. > > >> >> > > > > >> >> > > BTW with KM metrics we should also provide reporters (graphite, > > >> >> > > ganglia) but we probably need to do this anyway since the new > > >> clients > > >> >> > > are on KM metrics. > > >> >> > > > > >> >> > > Thanks, > > >> >> > > > > >> >> > > Joel > > >> >> > > > > >> >> > > On Fri, Mar 27, 2015 at 06:48:48PM +0000, Aditya Auradkar > wrote: > > >> >> > > > Adding to what Jay said. > > >> >> > > > > > >> >> > > > The library maintains 1k samples by default. The > UniformSample > > >> has a > > >> >> > > long array so about 8k overhead per histogram. The > > >> >> > > ExponentiallyDecayingSample (which is what we use) has a 16 > byte > > >> >> overhead > > >> >> > > per stored sample, so about 16k per histogram. So 10k > histograms > > >> (worst > > >> >> > > case? metrics per partition and client) is about 160MB of > memory > > in > > >> the > > >> >> > > broker. > > >> >> > > > > > >> >> > > > Copying is also a problem. For percentiles on > HistogramMBean, > > the > > >> >> > > implementation does a copy of the entire array. For e.g., if we > > >> called > > >> >> > > get50Percentile() and get75Percentile(), the entire array would > > get > > >> >> > copied > > >> >> > > twice which is pretty bad if we called each metric on every > > MBean. > > >> >> > > > > > >> >> > > > Another point Joel mentioned is that codahale metrics are > > harder > > >> to > > >> >> > > write tests against because we cannot pass in a Clock. > > >> >> > > > > > >> >> > > > IMO, if a library is preventing us from adding all the > metrics > > >> that > > >> >> we > > >> >> > > want to add and we have a viable alternative, we should replace > > it. > > >> It > > >> >> > > might be short term pain but in the long run we will have more > > >> useful > > >> >> > > graphs. > > >> >> > > > What do people think? I can start a vote thread on this once > we > > >> have > > >> >> a > > >> >> > > couple more opinions. > > >> >> > > > > > >> >> > > > Thanks, > > >> >> > > > Aditya > > >> >> > > > ________________________________________ > > >> >> > > > From: Jay Kreps [jay.kr...@gmail.com] > > >> >> > > > Sent: Thursday, March 26, 2015 2:29 PM > > >> >> > > > To: dev@kafka.apache.org > > >> >> > > > Subject: Re: Metrics package discussion > > >> >> > > > > > >> >> > > > Yeah that is a good summary. > > >> >> > > > > > >> >> > > > The reason we don't use histograms heavily in the server is > > >> because > > >> >> of > > >> >> > > the > > >> >> > > > memory issues. We originally did use histograms for > everything, > > >> then > > >> >> we > > >> >> > > ran > > >> >> > > > into all these issues, and ripped them out. Whether they are > > >> really > > >> >> > > useful > > >> >> > > > or not, I don't know. Averages can be pretty misleading so it > > can > > >> be > > >> >> > nice > > >> >> > > > but I don't know that it is critical. > > >> >> > > > > > >> >> > > > -Jay > > >> >> > > > > > >> >> > > > On Thu, Mar 26, 2015 at 1:58 PM, Aditya Auradkar < > > >> >> > > > aaurad...@linkedin.com.invalid> wrote: > > >> >> > > > > > >> >> > > > > From what I can tell, Histograms don't seem to be used > > >> extensively > > >> >> in > > >> >> > > the > > >> >> > > > > Kafka server (only in RequestChannel.scala) and I'm not > sure > > we > > >> >> need > > >> >> > > them > > >> >> > > > > for per-client metrics. Topic metrics use meters currently. > > >> >> > Migrating > > >> >> > > > > graphing, alerting will be quite a significant effort for > all > > >> users > > >> >> > of > > >> >> > > > > Kafka. Do the potential benefits of the new metrics package > > >> >> outweigh > > >> >> > > this > > >> >> > > > > one time migration? In the long run it seems nice to have a > > >> unified > > >> >> > > metrics > > >> >> > > > > package across clients and server. If we were starting out > > from > > >> >> > scratch > > >> >> > > > > without any existing deployments, what decision would we > > take? > > >> >> > > > > > > >> >> > > > > I suppose the relative effort in supporting is a useful > data > > >> point > > >> >> in > > >> >> > > this > > >> >> > > > > discussion. We need to throttle based on the current byte > > rate > > >> >> which > > >> >> > > should > > >> >> > > > > be a "Meter" in codahale terms. The Meter implementation > > uses a > > >> 1, > > >> >> 5 > > >> >> > > and 15 > > >> >> > > > > minute exponential window moving average. The library also > > does > > >> not > > >> >> > > use the > > >> >> > > > > most recent samples of data for Metered metrics. For > > calculating > > >> >> > > rates, the > > >> >> > > > > EWMA class has a scheduled task that runs every 5 seconds > and > > >> >> adjusts > > >> >> > > the > > >> >> > > > > rate using the new data accordingly. In that particular > > case, I > > >> >> think > > >> >> > > the > > >> >> > > > > new library is superior since it is more responsive. If we > > do > > >> >> choose > > >> >> > > to > > >> >> > > > > remain with Yammer on the server, here are a few ideas on > > how to > > >> >> > > support > > >> >> > > > > quotas with relatively less effort. > > >> >> > > > > > > >> >> > > > > - We could have a new type of Meter called "QuotaMeter" > that > > can > > >> >> wrap > > >> >> > > the > > >> >> > > > > existing meter code that follows the same pattern that the > > >> Sensor > > >> >> > does > > >> >> > > in > > >> >> > > > > the new metrics library. This QuotaMeter needs to be > > configured > > >> >> with > > >> >> > a > > >> >> > > > > Quota and it can have a finer grained rate than 1 minute > (10 > > >> >> seconds? > > >> >> > > > > configurable?). Anytime we call "mark()", it update the > > >> underlying > > >> >> > > rates > > >> >> > > > > and throw a QuotaViolationException if required. This class > > can > > >> >> > either > > >> >> > > > > extend Meter or be a separate implementation of the Metric > > >> >> superclass > > >> >> > > that > > >> >> > > > > every metric implements. > > >> >> > > > > > > >> >> > > > > - We can also consider implementing these quotas with the > new > > >> >> metrics > > >> >> > > > > package and have these co-exist with the existing metrics. > > This > > >> >> leads > > >> >> > > to 2 > > >> >> > > > > metric packages being used on the server, but they are both > > >> pulled > > >> >> in > > >> >> > > as > > >> >> > > > > dependencies anyway. Using this for metrics we can quota on > > may > > >> not > > >> >> > be > > >> >> > > a > > >> >> > > > > bad place to start. > > >> >> > > > > > > >> >> > > > > Thanks, > > >> >> > > > > Aditya > > >> >> > > > > ________________________________________ > > >> >> > > > > From: Jay Kreps [jay.kr...@gmail.com] > > >> >> > > > > Sent: Wednesday, March 25, 2015 11:08 PM > > >> >> > > > > To: dev@kafka.apache.org > > >> >> > > > > Subject: Re: Metrics package discussion > > >> >> > > > > > > >> >> > > > > Here was my understanding of the issue last time. > > >> >> > > > > > > >> >> > > > > The yammer metrics use a random sample of requests to > > estimate > > >> the > > >> >> > > > > histogram. This allocates a fairly large array of longs > > (their > > >> >> values > > >> >> > > are > > >> >> > > > > longs rather than floats). A reasonable sample might be 8k > > >> entries > > >> >> > > which > > >> >> > > > > would give about 64KB per histogram. There are bounds on > > >> accuracy, > > >> >> > but > > >> >> > > they > > >> >> > > > > are only probabilistic. I.e. if you try to get 99% < 5 ms > of > > >> >> > > inaccuracy, > > >> >> > > > > you will 1% of the time get more than this. This is okay > but > > if > > >> you > > >> >> > > try to > > >> >> > > > > alert, in which you realize that being wrong 1% of the time > > is a > > >> >> lot > > >> >> > > if you > > >> >> > > > > are computing stats every second continuously on many > metrics > > >> >> (i.e. 1 > > >> >> > > in > > >> >> > > > > 100 estimates will be outside you bound). This array is > > copied > > >> in > > >> >> > full > > >> >> > > > > every time you check the metric which is the other cause of > > the > > >> >> > memory > > >> >> > > > > pressure. > > >> >> > > > > > > >> >> > > > > The better approach to histograms is to calculate buckets > > >> >> boundaries > > >> >> > > and > > >> >> > > > > record arbitrarily many values in those buckets. A simple > > >> bucketing > > >> >> > > > > approach for latency would be 0, 5ms, 10ms, 15ms, etc, and > > you > > >> just > > >> >> > > count > > >> >> > > > > how many fall in each bucket. Your precision is > > >> deterministically > > >> >> > > bounded > > >> >> > > > > by the bucket boundaries, so if you had 5ms buckets you > would > > >> never > > >> >> > > have > > >> >> > > > > more than 5ms loss of precision. By using non-uniform > bucket > > >> sizes > > >> >> > you > > >> >> > > can > > >> >> > > > > make this work even better (e.g. give ~1ms precision for > > >> latencies > > >> >> in > > >> >> > > the > > >> >> > > > > 1ms range, but give only 1 second precision for latencies > in > > >> the 30 > > >> >> > > second > > >> >> > > > > range). That is what is implemented in that metrics > package. > > >> >> > > > > > > >> >> > > > > I think this bucketing approach is popular now. There is a > > whole > > >> >> "HDR > > >> >> > > > > histogram" library that gives lots of different bucketing > > >> methods > > >> >> and > > >> >> > > > > implements dynamic resizing so you don't have to specify an > > >> upper > > >> >> > > bound. > > >> >> > > > > https://github.com/HdrHistogram/HdrHistogram > > >> >> > > > > > > >> >> > > > > Whether this matters depends entirely if you want > histograms > > >> broken > > >> >> > > down at > > >> >> > > > > the client, topic, partition, or broker level or just want > > >> overall > > >> >> > > metrics. > > >> >> > > > > If we just want per sever aggregates for histograms then I > > think > > >> >> the > > >> >> > > memory > > >> >> > > > > usage is not a huge issue. If you want a histogram per > topic > > or > > >> >> > client > > >> >> > > or > > >> >> > > > > partition and have 10k of these then that is where you > start > > >> >> talking > > >> >> > > like > > >> >> > > > > 1GB of memory with the yammer package, which is what we hit > > last > > >> >> > time. > > >> >> > > > > Getting percentiles on the client level is nice, > percentiles > > are > > >> >> > > definitely > > >> >> > > > > better than averages, but I'm not sure it is required. > > >> >> > > > > > > >> >> > > > > -Jay > > >> >> > > > > > > >> >> > > > > On Wed, Mar 25, 2015 at 9:43 PM, Neha Narkhede < > > >> n...@confluent.io> > > >> >> > > wrote: > > >> >> > > > > > > >> >> > > > > > Aditya, > > >> >> > > > > > > > >> >> > > > > > If we are doing a deep dive, one of the things to > > investigate > > >> >> would > > >> >> > > be > > >> >> > > > > > memory/GC performance. IIRC, when I was looking into > > codahale > > >> at > > >> >> > > > > LinkedIn, > > >> >> > > > > > I remember it having quite a few memory management and GC > > >> issues > > >> >> > > while > > >> >> > > > > > using histograms. In comparison, histograms in the new > > metrics > > >> >> > > package > > >> >> > > > > > aren't very well tested. > > >> >> > > > > > > > >> >> > > > > > Thanks, > > >> >> > > > > > Neha > > >> >> > > > > > > > >> >> > > > > > On Wed, Mar 25, 2015 at 8:25 AM, Aditya Auradkar < > > >> >> > > > > > aaurad...@linkedin.com.invalid> wrote: > > >> >> > > > > > > > >> >> > > > > > > Hey everyone, > > >> >> > > > > > > > > >> >> > > > > > > Picking up this discussion after yesterdays KIP > hangout. > > For > > >> >> > > anyone who > > >> >> > > > > > > did not join the meeting, we have 2 different metrics > > >> packages > > >> >> > > being > > >> >> > > > > used > > >> >> > > > > > > by the clients (custom package) and the server > > (codahale). > > >> We > > >> >> are > > >> >> > > > > > > discussing whether to migrate the server to the new > > package. > > >> >> > > > > > > > > >> >> > > > > > > What information do we need in order to make a > decision? > > >> >> > > > > > > > > >> >> > > > > > > Some pros of the new package: > > >> >> > > > > > > - Using the most recent information by combining data > > from > > >> >> > > previous and > > >> >> > > > > > > current samples. I'm not sure how codahale does this so > > I'll > > >> >> > > > > investigate. > > >> >> > > > > > > - We can quota on anything we measure. This is pretty > > cool > > >> IMO. > > >> >> > > I've > > >> >> > > > > > > investigate the feasibility of adding this feature in > > >> codahale. > > >> >> > > > > > > - Hierarchical metrics. For example: we can define a > > sensor > > >> for > > >> >> > > overall > > >> >> > > > > > > bytes-in/bytes-out and also per-client. Updating the > > client > > >> >> > sensor > > >> >> > > will > > >> >> > > > > > > cause the global byte rate sensor to get modified too. > > >> >> > > > > > > > > >> >> > > > > > > What are some of the issues with codahale? One previous > > >> >> > discussion > > >> >> > > > > > > mentions high memory usage but I don't have any > > experience > > >> with > > >> >> > it > > >> >> > > > > > myself. > > >> >> > > > > > > > > >> >> > > > > > > Thanks, > > >> >> > > > > > > Aditya > > >> >> > > > > > > > > >> >> > > > > > > > > >> >> > > > > > > > > >> >> > > > > > > > > >> >> > > > > > > > > >> >> > > > > > > > >> >> > > > > > > > >> >> > > > > > -- > > >> >> > > > > > Thanks, > > >> >> > > > > > Neha > > >> >> > > > > > > > >> >> > > > > > > >> >> > > > > >> >> > > > > >> >> > > > >> >> > > >> > > >
