I think we discussed the scala/java stuff more fully previously.
Essentially the client is embedded everywhere. Scala is very incompatible
with itself so this makes it very hard to use for people using anything
else in scala. Also Scala stack traces are very confusing. Basically we
thought plain java code would be a lot easier for people to use. Even if
Scala is more fun to write, that isn't really what we are optimizing for.
-Jay
On Thu, Feb 13, 2014 at 8:09 AM, S Ahmed <sahmed1...@gmail.com> wrote:
> Jay, pretty impressive how you just write a 'quick version' like that :)
> Not to get off-topic but why didn't you write this in scala?
>
>
>
> On Wed, Feb 12, 2014 at 6:54 PM, Joel Koshy <jjkosh...@gmail.com> wrote:
>
> > 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
> > > > >> >>
> > > > >>
> > > > >>
> > > >
> > > >
> >
> >
>
