I have not had a chance to review the new metrics code and its
features carefully (apart from your write-up), but here are my general
thoughts:
Implementing a metrics package correctly is difficult; more so for
people like me, because I'm not a statistician. However, if this new
package: {(i) functions correctly (and we need to define and prove
correctness), (ii) is easy to use, (iii) serves all our current and
anticipated monitoring needs, (iv) is not overly complex that it
becomes a burden to maintain and we are better of with an available
library;} then I think it makes sense to embed it and use it within
the Kafka code. The main wins are: (i) predictability (no changing
APIs and intimate knowledge of the code) and (ii) control with respect
to both functionality (e.g., there are hard-coded decay constants in
metrics-core 2.x) and correctness (i.e., if we find a bug in the
metrics package we have to submit a pull request and wait for it to
become mainstream). I'm not sure it would help very much to pull it
into a separate repo because that could potentially annul these
benefits.
Joel
On Wed, Feb 12, 2014 at 02:50:43PM -0800, Jay Kreps wrote:
> Sriram,
>
> Makes sense. I am cool moving this stuff into its own repo if people think
> that is better. I'm not sure it would get much contribution but when I
> started messing with this I did have a lot of grand ideas of making adding
> metrics to a sensor dynamic so you could add more stuff in real-time(via
> jmx, say) and/or externalize all your metrics and config to a separate file
> like log4j with only the points of instrumentation hard-coded.
>
> -Jay
>
>
> On Wed, Feb 12, 2014 at 2:07 PM, Sriram Subramanian <
> srsubraman...@linkedin.com> wrote:
>
> > I am actually neutral to this change. I found the replies were more
> > towards the implementation and features so far. I would like the community
> > to think about the questions below before making a decision. My opinion on
> > this is that it has potential to be its own project and it would attract
> > developers who are specifically interested in contributing to metrics. I
> > am skeptical that the Kafka contributors would focus on improving this
> > library (apart from bug fixes) instead of developing/contributing to other
> > core pieces. It would be useful to continue and keep it decoupled from
> > rest of Kafka (if it resides in the Kafka code base.) so that we can move
> > it out anytime to its own project.
> >
> >
> > On 2/12/14 1:21 PM, "Jay Kreps" <jay.kr...@gmail.com> wrote:
> >
> > >Hey Sriram,
> > >
> > >Not sure if these are actually meant as questions or more veiled comments.
> > >In an case I tried to give my 2 cents inline.
> > >
> > >On Tue, Feb 11, 2014 at 11:12 PM, Sriram Subramanian <
> > >srsubraman...@linkedin.com> wrote:
> > >
> > >> I think answering the questions below would help to make a better
> > >> decision. I am all for writing better code and having superior
> > >> functionalities but it is worth thinking about stuff outside just code
> > >>in
> > >> this case -
> > >>
> > >> 1. Does metric form a core piece of kafka? Does it help kafka greatly in
> > >> providing better core functionalities? I would always like a project to
> > >>do
> > >> one thing really well. Metrics is a non trivial amount of code.
> > >>
> > >
> > >Metrics are obviously important, and obviously improving our metrics
> > >system
> > >would be good. That said this may or may not be better, and even if it is
> > >better that betterness might not outweigh other considerations. That is
> > >what we are discussing.
> > >
> > >
> > >> 2. Does it make sense to be part of Kafka or its own project? If this
> > >> metrics library has the potential to be better than metrics-core, I
> > >>would
> > >> be interested in other projects take advantage of it.
> > >>
> > >
> > >It could be either.
> > >
> > >3. Can Kafka maintain this library as new members join and old members
> > >> leave? Would this be a piece of code that no one (in Kafka) in the
> > >>future
> > >> spends time improving if the original author left?
> > >>
> > >
> > >I am not going anywhere in the near term, but if I did, yes, this would be
> > >like any other code we have. As with yammer metrics or any other code at
> > >that point we would either use it as is or someone would improve it.
> > >
> > >
> > >> 4. Does it affect the schedule of producer rewrite? This needs its own
> > >> stabilization and modification to existing metric dashboards if the
> > >>format
> > >> is changed. Many times such cost are not factored in and a project loses
> > >> time before realizing the extra time required to make a library as this
> > >> operational.
> > >>
> > >
> > >Probably not. The metrics are going to change regardless of whether we use
> > >the same library or not. If we think this is better I don't mind putting
> > >in
> > >a little extra effort to get there.
> > >
> > >Irrespective I think this is probably not the right thing to optimize for.
> > >
> > >
> > >> I am sure we can do better when we write code to a specific use case (in
> > >> this case, kafka) rather than building a generic library that suits all
> > >> (metrics-core) but I would like us to have answers to the questions
> > >>above
> > >> and be prepared before we proceed to support this with the producer
> > >> rewrite.
> > >
> > >
> > >Naturally we are all considering exactly these things, that is exactly the
> > >reason I started the thread.
> > >
> > >-Jay
> > >
> > >
> > >> On 2/11/14 6:28 PM, "Jun Rao" <jun...@gmail.com> wrote:
> > >>
> > >> >Thanks for the detailed write-up. It's well thought through. A few
> > >> >comments:
> > >> >
> > >> >1. I have a couple of concerns on the percentiles. The first issue is
> > >>that
> > >> >It requires the user to know the value range. Since the range for
> > >>things
> > >> >like message size (in millions) is quite different from those like
> > >>request
> > >> >time (less than 100), it's going to be hard to pick a good global
> > >>default
> > >> >range. Different apps could be dealing with different message size. So
> > >> >they
> > >> >probably will have to customize the range. Another issue is that it can
> > >> >only report values at the bucket boundaries. So, if you have 1000
> > >>buckets
> > >> >and a value range of 1 million, you will only see 1000 possible values
> > >>as
> > >> >the quantile, which is probably too sparse. The implementation of
> > >> >histogram
> > >> >in metrics-core keeps a fix size of samples, which avoids both issues.
> > >> >
> > >> >2. We need to document the 3-part metrics names better since it's not
> > >> >obvious what the convention is. Also, currently the name of the sensor
> > >>and
> > >> >the metrics defined in it are independent. Would it make sense to have
> > >>the
> > >> >sensor name be a prefix of the metric name?
> > >> >
> > >> >Overall, this approach seems to be cleaner than metrics-core by
> > >>decoupling
> > >> >measuring and reporting. The main benefit of metrics-core seems to be
> > >>the
> > >> >existing reporters. Since not that many people voted for metrics-core,
> > >>I
> > >> >am
> > >> >ok with going with the new implementation. My only recommendation is to
> > >> >address the concern on percentiles.
> > >> >
> > >> >Thanks,
> > >> >
> > >> >Jun
> > >> >
> > >> >
> > >> >
> > >> >On Thu, Feb 6, 2014 at 12:51 PM, Jay Kreps <jay.kr...@gmail.com>
> > wrote:
> > >> >
> > >> >> Hey guys,
> > >> >>
> > >> >> I wanted to kick off a quick discussion of metrics with respect to
> > >>the
> > >> >>new
> > >> >> producer and consumer (and potentially the server).
> > >> >>
> > >> >> At a high level I think there are three approaches we could take:
> > >> >> 1. Plain vanilla JMX
> > >> >> 2. Use Coda Hale (AKA Yammer) Metrics
> > >> >> 3. Do our own metrics (with JMX as one output)
> > >> >>
> > >> >> 1. Has the advantage that JMX is the most commonly used java thing
> > >>and
> > >> >> plugs in reasonably to most metrics systems. JMX is included in the
> > >>JDK
> > >> >>so
> > >> >> it doesn't impose any additional dependencies on clients. It has the
> > >> >> disadvantage that plain vanilla JMX is a pain to use. We would need a
> > >> >>bunch
> > >> >> of helper code for maintaining counters to make this reasonable.
> > >> >>
> > >> >> 2. Coda Hale metrics is pretty good and broadly used. It supports JMX
> > >> >> output as well as direct output to many other types of systems. The
> > >> >>primary
> > >> >> downside we have had with Coda Hale has to do with the clients and
> > >> >>library
> > >> >> incompatibilities. We are currently on an older more popular version.
> > >> >>The
> > >> >> newer version is a rewrite of the APIs and is incompatible.
> > >>Originally
> > >> >> these were totally incompatible and people had to choose one or the
> > >> >>other.
> > >> >> I think that has been improved so now the new version is a totally
> > >> >> different package. But even in this case you end up with both
> > >>versions
> > >> >>if
> > >> >> you use Kafka and we are on a different version than you which is
> > >>going
> > >> >>to
> > >> >> be pretty inconvenient.
> > >> >>
> > >> >> 3. Doing our own has the downside of potentially reinventing the
> > >>wheel,
> > >> >>and
> > >> >> potentially needing to work out any bugs in our code. The upsides
> > >>would
> > >> >> depend on the how good the reinvention was. As it happens I did a
> > >>quick
> > >> >> (~900 loc) version of a metrics library that is under
> > >> >>kafka.common.metrics.
> > >> >> I think it has some advantages over the Yammer metrics package for
> > >>our
> > >> >> usage beyond just not causing incompatibilities. I will describe this
> > >> >>code
> > >> >> so we can discuss the pros and cons. Although I favor this approach I
> > >> >>have
> > >> >> no emotional attachment and wouldn't be too sad if I ended up
> > >>deleting
> > >> >>it.
> > >> >> Here are javadocs for this code, though I haven't written much
> > >> >> documentation yet since I might end up deleting it:
> > >> >>
> > >> >> Here is a quick overview of this library.
> > >> >>
> > >> >> There are three main public interfaces:
> > >> >> Metrics - This is a repository of metrics being tracked.
> > >> >> Metric - A single, named numerical value being measured (i.e. a
> > >> >>counter).
> > >> >> Sensor - This is a thing that records values and updates zero or
> > >>more
> > >> >> metrics
> > >> >>
> > >> >> So let's say we want to track three values about message sizes;
> > >> >> specifically say we want to record the average, the maximum, the
> > >>total
> > >> >>rate
> > >> >> of bytes being sent, and a count of messages. Then we would do
> > >>something
> > >> >> like this:
> > >> >>
> > >> >> // setup code
> > >> >> Metrics metrics = new Metrics(); // this is a global "singleton"
> > >> >> Sensor sensor = metrics.sensor("kafka.producer.message.sizes");
> > >> >> sensor.add("kafka.producer.message-size.avg", new Avg());
> > >> >> sensor.add("kafka.producer.message-size.max", new Max());
> > >> >> sensor.add("kafka.producer.bytes-sent-per-sec", new Rate());
> > >> >> sensor.add("kafka.producer.message-count", new Count());
> > >> >>
> > >> >> // now when we get a message we do this
> > >> >> sensor.record(messageSize);
> > >> >>
> > >> >> The above code creates the global metrics repository, creates a
> > >>single
> > >> >> Sensor, and defines 5 named metrics that are updated by that Sensor.
> > >> >>
> > >> >> Like Yammer Metrics (YM) I allow you to plug in "reporters",
> > >>including a
> > >> >> JMX reporter. Unlike the Coda Hale JMX reporter the reporter I have
> > >>keys
> > >> >> off the metric names not the Sensor names, which I think is an
> > >> >> improvement--I just use the convention that the last portion of the
> > >> >>name is
> > >> >> the attribute name, the second to last is the mbean name, and the
> > >>rest
> > >> >>is
> > >> >> the package. So in the above example there is a producer mbean that
> > >>has
> > >> >>a
> > >> >> avg and max attribute and a producer mbean that has a
> > >>bytes-sent-per-sec
> > >> >> and message-count attribute. This is nice because you can logically
> > >> >>group
> > >> >> the values reported irrespective of where in the program they are
> > >> >> computed--that is an mbean can logically group attributes computed
> > >>off
> > >> >> different sensors. This means you can report values by logical
> > >> >>subsystem.
> > >> >>
> > >> >> I also allow the concept of hierarchical Sensors which I think is a
> > >>good
> > >> >> convenience. I have noticed a common pattern in systems where you
> > >>need
> > >> >>to
> > >> >> roll up the same values along different dimensions. An simple
> > >>example is
> > >> >> metrics about qps, data rate, etc on the broker. These we want to
> > >> >>capture
> > >> >> in aggregate, but also broken down by topic-id. You can do this
> > >>purely
> > >> >>by
> > >> >> defining the sensor hierarchy:
> > >> >> Sensor allSizes = metrics.sensor("kafka.producer.sizes");
> > >> >> Sensor topicSizes = metrics.sensor("kafka.producer." + topic +
> > >> >>".sizes",
> > >> >> allSizes);
> > >> >> Now each actual update will go to the appropriate topicSizes sensor
> > >> >>(based
> > >> >> on the topic name), but allSizes metrics will get updated too. I also
> > >> >> support multiple parents for each sensor as well as multiple layers
> > >>of
> > >> >> hiearchy, so you can define a more elaborate DAG of sensors. An
> > >>example
> > >> >>of
> > >> >> how this would be useful is if you wanted to record your metrics
> > >>broken
> > >> >> down by topic AND client id as well as the global aggregate.
> > >> >>
> > >> >> Each metric can take a configurable Quota value which allows us to
> > >>limit
> > >> >> the maximum value of that sensor. This is intended for use on the
> > >> >>server as
> > >> >> part of our Quota implementation. The way this works is that you
> > >>record
> > >> >> metrics as usual:
> > >> >> mySensor.record(42.0)
> > >> >> However if this event occurance causes one of the metrics to exceed
> > >>its
> > >> >> maximum allowable value (the quota) this call will throw a
> > >> >> QuotaViolationException. The cool thing about this is that it means
> > >>we
> > >> >>can
> > >> >> define quotas on anything we capture metrics for, which I think is
> > >> >>pretty
> > >> >> cool.
> > >> >>
> > >> >> Another question is how to handle windowing of the values? Metrics
> > >>want
> > >> >>to
> > >> >> record the "current" value, but the definition of current is
> > >>inherently
> > >> >> nebulous. A few of the obvious gotchas are that if you define
> > >>"current"
> > >> >>to
> > >> >> be a number of events you can end up measuring an arbitrarily long
> > >> >>window
> > >> >> of time if the event rate is low (e.g. you think you are getting 50
> > >> >> messages/sec because that was the rate yesterday when all events
> > >> >>topped).
> > >> >>
> > >> >> Here is how I approach this. All the metrics use the same windowing
> > >> >> approach. We define a single window by a length of time or number of
> > >> >>values
> > >> >> (you can use either or both--if both the window ends when *either*
> > >>the
> > >> >>time
> > >> >> bound or event bound is hit). The typical problem with hard window
> > >> >> boundaries is that at the beginning of the window you have no data
> > >>and
> > >> >>the
> > >> >> first few samples are too small to be a valid sample. (Consider if
> > >>you
> > >> >>were
> > >> >> keeping an avg and the first value in the window happens to be very
> > >>very
> > >> >> high, if you check the avg at this exact time you will conclude the
> > >>avg
> > >> >>is
> > >> >> very high but on a sample size of one). One simple fix would be to
> > >> >>always
> > >> >> report the last complete window, however this is not appropriate here
> > >> >> because (1) we want to drive quotas off it so it needs to be current,
> > >> >>and
> > >> >> (2) since this is for monitoring you kind of care more about the
> > >>current
> > >> >> state. The ideal solution here would be to define a backwards looking
> > >> >> sliding window from the present, but many statistics are actually
> > >>very
> > >> >>hard
> > >> >> to compute in this model without retaining all the values which
> > >>would be
> > >> >> hopelessly inefficient. My solution to this is to keep a configurable
> > >> >> number of windows (default is two) and combine them for the estimate.
> > >> >>So in
> > >> >> a two sample case depending on when you ask you have between one and
> > >>two
> > >> >> complete samples worth of data to base the answer off of. Provided
> > >>the
> > >> >> sample window is large enough to get a valid result this satisfies
> > >>both
> > >> >>of
> > >> >> my criteria of incorporating the most recent data and having
> > >>reasonable
> > >> >> variance at all times.
> > >> >>
> > >> >> Another approach is to use an exponential weighting scheme to combine
> > >> >>all
> > >> >> history but emphasize the recent past. I have not done this as it
> > >>has a
> > >> >>lot
> > >> >> of issues for practical operational metrics. I'd be happy to
> > >>elaborate
> > >> >>on
> > >> >> this if anyone cares...
> > >> >>
> > >> >> The window size for metrics has a global default which can be
> > >> >>overridden at
> > >> >> either the sensor or individual metric level.
> > >> >>
> > >> >> In addition to these time series values the user can directly expose
> > >> >>some
> > >> >> method of their choosing JMX-style by implementing the Measurable
> > >> >>interface
> > >> >> and registering that value. E.g.
> > >> >> metrics.addMetric("my.metric", new Measurable() {
> > >> >> public double measure(MetricConfg config, long now) {
> > >> >> return this.calculateValueToExpose();
> > >> >> }
> > >> >> });
> > >> >> This is useful for exposing things like the accumulator free memory.
> > >> >>
> > >> >> The set of metrics is extensible, new metrics can be added by just
> > >> >> implementing the appropriate interfaces and registering with a
> > >>sensor. I
> > >> >> implement the following metrics:
> > >> >> total - the sum of all values from the given sensor
> > >> >> count - a windowed count of values from the sensor
> > >> >> avg - the sample average within the windows
> > >> >> max - the max over the windows
> > >> >> min - the min over the windows
> > >> >> rate - the rate in the windows (e.g. the total or count divided by
> > >>the
> > >> >> ellapsed time)
> > >> >> percentiles - a collection of percentiles computed over the window
> > >> >>
> > >> >> My approach to percentiles is a little different from the yammer
> > >>metrics
> > >> >> package. My complaint about the yammer metrics approach is that it
> > >>uses
> > >> >> rather expensive sampling and uses kind of a lot of memory to get a
> > >> >> reasonable sample. This is problematic for per-topic measurements.
> > >> >>
> > >> >> Instead I use a fixed range for the histogram (e.g. 0.0 to 30000.0)
> > >> >>which
> > >> >> directly allows you to specify the desired memory use. Any value
> > >>below
> > >> >>the
> > >> >> minimum is recorded as -Infinity and any value above the maximum as
> > >> >> +Infinity. I think this is okay as all metrics have an expected range
> > >> >> except for latency which can be arbitrarily large, but for very high
> > >> >> latency there is no need to model it exactly (e.g. 30 seconds +
> > >>really
> > >> >>is
> > >> >> effectively infinite). Within the range values are recorded in
> > >>buckets
> > >> >> which can be either fixed width or increasing width. The increasing
> > >> >>width
> > >> >> is analogous to the idea of significant figures, that is if your
> > >>value
> > >> >>is
> > >> >> in the range 0-10 you might want to be accurate to within 1ms, but if
> > >> >>it is
> > >> >> 20000 there is no need to be so accurate. I implemented a linear
> > >>bucket
> > >> >> size where the Nth bucket has width proportional to N. An exponential
> > >> >> bucket size would also be sensible and could likely be derived
> > >>directly
> > >> >> from the floating point representation of a the value.
> > >> >>
> > >> >> I'd like to get some feedback on this metrics code and make a
> > >>decision
> > >> >>on
> > >> >> whether we want to use it before I actually go ahead and add all the
> > >> >> instrumentation in the code (otherwise I'll have to redo it if we
> > >>switch
> > >> >> approaches). So the next topic of discussion will be which actual
> > >> >>metrics
> > >> >> to add.
> > >> >>
> > >> >> -Jay
> > >> >>
> > >>
> > >>
> >
> >
