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From Jason Gustafson <ja...@confluent.io>
Subject Re: KIP-41: KafkaConsumer Max Records
Date Mon, 04 Jan 2016 19:40:08 GMT
Hey Gwen,

I was thinking along the lines of TCP window scaling in order to
dynamically find a good consumption rate. Basically you'd start off
consuming say 100 records and you'd let it increase until the consumption
took longer than half the session timeout (for example). You /might/ be
able to achieve the same thing using pause/resume, but it would be a lot
trickier since you have to do it at the granularity of partitions. But
yeah, database write performance doesn't always scale in a predictable
enough way to accommodate this, so I'm not sure how useful it would be in
practice. It might also be more difficult to implement since it wouldn't be
as clear when to initiate the next fetch. With a static setting, the
consumer knows exactly how many records will be returned on the next call
to poll() and can send fetches accordingly.

On the other hand, I do feel a little wary of the need to tune the session
timeout and max messages though since these settings might depend on the
environment that the consumer is deployed in. It wouldn't be a big deal if
the impact was relatively minor, but getting them wrong can cause a lot of
rebalance churn which could keep the consumer from making any progress.
It's not a particularly graceful failure.

-Jason

On Mon, Jan 4, 2016 at 10:49 AM, Gwen Shapira <gwen@confluent.io> wrote:

> I can't speak to all use-cases, but for the database one, I think
> pause-resume will be necessary in any case, and therefore dynamic batch
> sizes are not needed.
>
> Databases are really unexpected regarding response times - load and locking
> can affect this. I'm not sure there's a good way to know you are going into
> rebalance hell before it is too late. So if I were writing code that
> updates an RDBMS based on Kafka, I'd pick a reasonable batch size (say 5000
> records), and basically pause, batch-insert all records, commit and resume.
>
> Does that make sense?
>
> On Mon, Jan 4, 2016 at 10:37 AM, Jason Gustafson <jason@confluent.io>
> wrote:
>
> > Gwen and Ismael,
> >
> > I agree the configuration option is probably the way to go, but I was
> > wondering whether there would be cases where it made sense to let the
> > consumer dynamically set max messages to adjust for downstream slowness.
> > For example, if the consumer is writing consumed records to another
> > database, and that database is experiencing heavier than expected load,
> > then the consumer could halve its current max messages in order to adapt
> > without risking rebalance hell. It could then increase max messages as
> the
> > load on the database decreases. It's basically an easier way to handle
> flow
> > control than we provide with pause/resume.
> >
> > -Jason
> >
> > On Mon, Jan 4, 2016 at 9:46 AM, Gwen Shapira <gwen@confluent.io> wrote:
> >
> > > The wiki you pointed to is no longer maintained and fell out of sync
> with
> > > the code and protocol.
> > >
> > > You may want  to refer to:
> > >
> > >
> >
> https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol
> > >
> > > On Mon, Jan 4, 2016 at 4:38 AM, Jens Rantil <jens.rantil@tink.se>
> wrote:
> > >
> > > > Hi guys,
> > > >
> > > > I realized I never thanked yall for your input - thanks!
> > > > Jason: I apologize for assuming your stance on the issue! Feels like
> we
> > > all
> > > > agreed on the solution. +1
> > > >
> > > > Follow-up: Jason made a point about defining prefetch and fairness
> > > > behaviour in the KIP. I am now working on putting that down in
> writing.
> > > To
> > > > do be able to do this I think I need to understand the current
> prefetch
> > > > behaviour in the new consumer API (0.9) a bit better. Some specific
> > > > questions:
> > > >
> > > >    - How does a specific consumer balance incoming messages from
> > multiple
> > > >    partitions? Is the consumer simply issuing Multi-Fetch requests[1]
> > for
> > > > the
> > > >    consumed assigned partitions of the relevant topics? Or is the
> > > consumer
> > > >    fetching from one partition at a time and balancing between them
> > > >    internally? That is, is the responsibility of partition balancing
> > (and
> > > >    fairness) on the broker side or consumer side?
> > > >    - Is the above documented somewhere?
> > > >
> > > > [1]
> > > >
> > > >
> > >
> >
> https://cwiki.apache.org/confluence/display/KAFKA/Writing+a+Driver+for+Kafka
> > > > ,
> > > > see "Multi-Fetch".
> > > >
> > > > Thanks,
> > > > Jens
> > > >
> > > > On Wed, Dec 23, 2015 at 2:44 AM, Ismael Juma <ismael@juma.me.uk>
> > wrote:
> > > >
> > > > > On Wed, Dec 23, 2015 at 1:24 AM, Gwen Shapira <gwen@confluent.io>
> > > wrote:
> > > > >
> > > > > > Given the background, it sounds like you'll generally want each
> > call
> > > to
> > > > > > poll() to return the same number of events (which is the number
> you
> > > > > planned
> > > > > > on having enough memory / time for). It also sounds like tuning
> the
> > > > > number
> > > > > > of events will be closely tied to tuning the session timeout.
> That
> > > is -
> > > > > if
> > > > > > I choose to lower the session timeout for some reason, I will
> have
> > to
> > > > > > modify the number of records returning too.
> > > > > >
> > > > > > If those assumptions are correct, I think a configuration makes
> > more
> > > > > sense.
> > > > > > 1. We are unlikely to want this parameter to be change at the
> > > lifetime
> > > > of
> > > > > > the consumer
> > > > > > 2. The correct value is tied to another configuration parameter,
> so
> > > > they
> > > > > > will be controlled together.
> > > > > >
> > > > >
> > > > > I was thinking the same thing.
> > > > >
> > > > > Ismael
> > > > >
> > > >
> > > >
> > > >
> > > > --
> > > > Jens Rantil
> > > > Backend engineer
> > > > Tink AB
> > > >
> > > > Email: jens.rantil@tink.se
> > > > Phone: +46 708 84 18 32
> > > > Web: www.tink.se
> > > >
> > > > Facebook <https://www.facebook.com/#!/tink.se> Linkedin
> > > > <
> > > >
> > >
> >
> http://www.linkedin.com/company/2735919?trk=vsrp_companies_res_photo&trkInfo=VSRPsearchId%3A1057023381369207406670%2CVSRPtargetId%3A2735919%2CVSRPcmpt%3Aprimary
> > > > >
> > > >  Twitter <https://twitter.com/tink>
> > > >
> > >
> >
>

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