Any chance of an update on the KIP? We are interested in seeing this move
forward.
Thanks,
Habib
Sr SDE, AWS
On Wed, Dec 18, 2019, at 3:27 PM, Habib Nahas wrote:
> 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/>
> >
>
