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 > > > > > > > > > > > > > >
