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Kafka >> mail # user >> Analysis of producer performance

Piotr Kozikowski 2013-04-08, 23:43
Jun Rao 2013-04-09, 04:49
Guy Doulberg 2013-04-09, 06:34
Piotr Kozikowski 2013-04-09, 17:23
Otis Gospodnetic 2013-04-10, 19:05
Piotr Kozikowski 2013-04-10, 20:11
Yiu Wing TSANG 2013-04-11, 02:47
Jun Rao 2013-04-11, 05:18
Piotr Kozikowski 2013-04-12, 00:46
Jun Rao 2013-04-12, 14:54
Piotr Kozikowski 2013-04-12, 23:09
Jun Rao 2013-04-15, 01:06
Philip OToole 2013-04-12, 15:22
S Ahmed 2013-04-12, 15:28
Philip OToole 2013-04-12, 15:59
Philip OToole 2013-04-12, 17:04
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Re: Analysis of producer performance -- and Producer-Kafka reliability

We would not use spooling to local disk on the producer to deal with
problems with the connection to the brokers, but rather to absorb temporary
spikes in traffic that would overwhelm the brokers. This is assuming that
1) those spikes are relatively short, but when they come they require much
higher throughput than normal (otherwise we'd just have a capacity problem
and would need more brokers), and 2) the spikes are long enough for just a
RAM buffer to be dangerous. If the brokers did go down, spooling to disk
would give us more time to react, but that's not the primary reason for
wanting the feature.


On Fri, Apr 12, 2013 at 8:21 AM, Philip O'Toole <[EMAIL PROTECTED]> wrote:

> This is just my opinion of course (who else's could it be? :-)) but I think
> from an engineering point of view, one must spend one's time making the
> Producer-Kafka connection solid, if it is mission-critical.
> Kafka is all about getting messages to disk, and assuming your disks are
> solid (and 0.8 has replication) those messages are safe. To then try to
> build a system to cope with the Kafka brokers being unavailable seems like
> you're setting yourself for infinite regress. And to write code in the
> Producer to spool to disk seems even more pointless. If you're that
> worried, why not run a dedicated Kafka broker on the same node as the
> Producer, and connect over localhost? To turn around and write code to
> spool to disk, because the primary system that *spools to disk* is down
> seems to be missing the point.
> That said, even by going over local-host, I guess the network connection
> could go down. In that case, Producers should buffer in RAM, and start
> sending some major alerts to the Operations team. But this should almost
> *never happen*. If it is happening regularly *something is fundamentally
> wrong with your system design*. Those Producers should also refuse any more
> incoming traffic and await intervention. Even bringing up "netcat -l" and
> letting it suck in the data and write it to disk would work then.
> Alternatives include having Producers connect to a load-balancer with
> multiple Kafka brokers behind it, which helps you deal with any one Kafka
> broker failing. Or just have your Producers connect directly to multiple
> Kafka brokers, and switch over as needed if any one broker goes down.
> I don't know if the standard Kafka producer that ships with Kafka supports
> buffering in RAM in an emergency. We wrote our own that does, with a focus
> on speed and simplicity, but I expect it will very rarely, if ever, buffer
> in RAM.
> Building and using semi-reliable system after semi-reliable system, and
> chaining them all together, hoping to be more tolerant of failure is not
> necessarily a good approach. Instead, identifying that one system that is
> critical, and ensuring that it remains up (redundant installations,
> redundant disks, redundant network connections etc) is a better approach
> Philip
> On Fri, Apr 12, 2013 at 7:54 AM, Jun Rao <[EMAIL PROTECTED]> wrote:
> > Another way to handle this is to provision enough client and broker
> servers
> > so that the peak load can be handled without spooling.
> >
> > Thanks,
> >
> > Jun
> >
> >
> > On Thu, Apr 11, 2013 at 5:45 PM, Piotr Kozikowski <[EMAIL PROTECTED]
> > >wrote:
> >
> > > Jun,
> > >
> > > When talking about "catastrophic consequences" I was actually only
> > > referring to the producer side. in our use case (logging requests from
> > > webapp servers), a spike in traffic would force us to either tolerate a
> > > dramatic increase in the response time, or drop messages, both of which
> > are
> > > really undesirable. Hence the need to absorb spikes with some system on
> > top
> > > of Kafka, unless the spooling feature mentioned by Wing (
> > > https://issues.apache.org/jira/browse/KAFKA-156) is implemented. This
> is
> > > assuming there are a lot more producer machines than broker nodes, so
> > each
> > > producer would absorb a small part of the extra load from the spike.

David Arthur 2013-04-23, 12:22
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB