Thanks Sean. Look forward to the updated KIP.
Regards,
Habib
On Fri, Dec 13, 2019, at 6:22 AM, Sean Glover wrote:
> Hi,
>
> After my last reply I had a nagging feeling something wasn't right, and I
> remembered that epoch time is UTC. This makes the discussion about
> timezone irrelevant, since we're always using UTC. This makes the need for
> the LatencyTime interface that I proposed in the design irrelevant as well,
> since I can no longer think about how that might be useful. I'll update
> the KIP. I'll also review KIP-32 to understand message timestamps better
> so I can explain the different types of latency results that could be
> reported with this metric.
>
> Regards,
> Sean
>
> On Thu, Dec 12, 2019 at 6:25 PM Sean Glover <sean.glo...@lightbend.com>
> wrote:
>
> > Hi Habib,
> >
> > Thanks for question! If the consumer is in a different timezone than the
> > timezone used to produce messages to a partition then you can use an
> > implementation of LatencyTime to return the current time of that timezone.
> > The current design assumes that messages produced to a partition must all
> > be produced from the same timezone. If timezone metadata were encoded into
> > each message then it would be possible to automatically determine the
> > source timezone and calculate latency, however the current design will not
> > pass individual messages into LatencyTime to retrieve message metadata.
> > Instead, the LatencyTime.getWallClockTime method is only called once per
> > fetch request response per partition and then the metric is recorded once
> > the latency calculation is complete. This follows the same design as the
> > current consumer lag metric which calculates offset lag based on the last
> > message of the fetch request response for a partition. Since the metric is
> > just an aggregate (max/mean) over some time window we only need to
> > occasionally calculate latency, which will have negligible impact on the
> > performance of consumer polling.
> >
> > A simple implementation of LatencyTime that returns wall clock time for
> > the Asia/Singapore timezone for all partitions:
> >
> > import java.time.*;
> >
> > class SingaporeTime implements LatencyTime {
> > ZoneId zoneSingapore = ZoneId.of("Asia/Singapore");
> > Clock clockSingapore = Clock.system(zoneSingapore);
> >
> > @Override
> > public long getWallClockTime(TopicPartition tp) {
> > return clockSingapore.instant.getEpochSecond();
> > }
> >
> > ...
> > }
> >
> > Regards,
> > Sean
> >
> >
> > On Thu, Dec 12, 2019 at 6:18 AM Habib Nahas <ha...@hbnet.io> wrote:
> >
> >> Hi Sean,
> >>
> >> Thanks for the KIP.
> >>
> >> As I understand it users are free to set their own timestamp on
> >> ProducerRecord. What is the recommendation for the proposed metric in a
> >> scenario where the user sets this timestamp in timezone A and consumes the
> >> record in timezone B. Its not clear to me if a custom implementation of
> >> LatencyTime will help here.
> >>
> >> Thanks,
> >> Habib
> >>
> >> On Wed, Dec 11, 2019, at 4:52 PM, Sean Glover wrote:
> >> > Hello again,
> >> >
> >> > There has been some interest in this KIP recently. I'm bumping the
> >> thread
> >> > to encourage feedback on the design.
> >> >
> >> > Regards,
> >> > Sean
> >> >
> >> > On Mon, Jul 15, 2019 at 9:01 AM Sean Glover <sean.glo...@lightbend.com>
> >> > wrote:
> >> >
> >> > > To hopefully spark some discussion I've copied the motivation section
> >> from
> >> > > the KIP:
> >> > >
> >> > > Consumer lag is a useful metric to monitor how many records are
> >> queued to
> >> > > be processed. We can look at individual lag per partition or we may
> >> > > aggregate metrics. For example, we may want to monitor what the
> >> maximum lag
> >> > > of any particular partition in our consumer subscription so we can
> >> identify
> >> > > hot partitions, caused by an insufficient producing partitioning
> >> strategy.
> >> > > We may want to monitor a sum of lag across all partitions so we have a
> >> > > sense as to our total backlog of messages to consume. Lag in offsets
> >> is
> >> > > useful when you have a good understanding of your messages and
> >> processing
> >> > > characteristics, but it doesn’t tell us how far behind *in time* we
> >> are.
> >> > > This is known as wait time in queueing theory, or more informally it’s
> >> > > referred to as latency.
> >> > >
> >> > > The latency of a message can be defined as the difference between when
> >> > > that message was first produced to when the message is received by a
> >> > > consumer. The latency of records in a partition correlates with lag,
> >> but a
> >> > > larger lag doesn’t necessarily mean a larger latency. For example, a
> >> topic
> >> > > consumed by two separate application consumer groups A and B may have
> >> > > similar lag, but different latency per partition. Application A is a
> >> > > consumer which performs CPU intensive business logic on each message
> >> it
> >> > > receives. It’s distributed across many consumer group members to
> >> handle the
> >> > > load quickly enough, but since its processing time is slower, it takes
> >> > > longer to process each message per partition. Meanwhile, Application
> >> B is
> >> > > a consumer which performs a simple ETL operation to land streaming
> >> data in
> >> > > another system, such as HDFS. It may have similar lag to Application
> >> A, but
> >> > > because it has a faster processing time its latency per partition is
> >> > > significantly less.
> >> > >
> >> > > If the Kafka Consumer reported a latency metric it would be easier to
> >> > > build Service Level Agreements (SLAs) based on non-functional
> >> requirements
> >> > > of the streaming system. For example, the system must never have a
> >> latency
> >> > > of greater than 10 minutes. This SLA could be used in monitoring
> >> alerts or
> >> > > as input to automatic scaling solutions.
> >> > >
> >> > > On Thu, Jul 11, 2019 at 12:36 PM Sean Glover <
> >> sean.glo...@lightbend.com>
> >> > > wrote:
> >> > >
> >> > >> Hi kafka-dev,
> >> > >>
> >> > >> I've created KIP-489 as a proposal for adding latency metrics to the
> >> > >> Kafka Consumer in a similar way as record-lag metrics are
> >> implemented.
> >> > >>
> >> > >>
> >> > >>
> >> https://cwiki.apache.org/confluence/display/KAFKA/489%3A+Kafka+Consumer+Record+Latency+Metric
> >> > >>
> >> > >> Regards,
> >> > >> Sean
> >> > >>
> >> > >> --
> >> > >> Principal Engineer, Lightbend, Inc.
> >> > >>
> >> > >> <http://lightbend.com>
> >> > >>
> >> > >> @seg1o <https://twitter.com/seg1o>, in/seanaglover
> >> > >> <https://www.linkedin.com/in/seanaglover/>
> >> > >>
> >> > >
> >> > >
> >> > > --
> >> > > Principal Engineer, Lightbend, Inc.
> >> > >
> >> > > <http://lightbend.com>
> >> > >
> >> > > @seg1o <https://twitter.com/seg1o>, in/seanaglover
> >> > > <https://www.linkedin.com/in/seanaglover/>
> >> > >
> >> >
> >>
> >
> >
>
> --
> Sean Glover
> Principal Engineer, Alpakka, Lightbend, Inc. <https://lightbend.com>
> @seg1o <https://twitter.com/seg1o>, in/seanaglover
> <https://www.linkedin.com/in/seanaglover/>
>
