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From Stephen Powis <spo...@salesforce.com>
Subject Re: Hash partition of key with skew
Date Tue, 03 May 2016 18:01:30 GMT
Having difficulty following the use cases behind this thread so this could
be entirely noise... but I'll toss this out there in case its relevant:
https://issues.apache.org/jira/browse/KAFKA-3333

We had an issue where a producer was setting the same key on every message
but distributing the publishes equally across all partitions.  We never
really noticed it until we tried to replicate the cluster to another
datacenter and all the messages ended up on a single partition...oops.

I created the above ticket and tossed together a round robin partitioner to
solve the issue.

On Tue, May 3, 2016 at 1:22 PM, Tauzell, Dave <Dave.Tauzell@surescripts.com>
wrote:

> Srikanth,
>
> I think the most efficient use of the partitions would be to spread all
> messages evenly across all partitions by *not* using a key. Then, all of
> your consumers in the same consumer group would receive about equal numbers
> of messages.   What will you do with the messages as you pull them off of
> Kafka?
>
> -Dave
>
>
> -----Original Message-----
> From: Srikanth [mailto:srikanth.ht@gmail.com]
> Sent: Tuesday, May 03, 2016 12:12 PM
> To: users@kafka.apache.org
> Subject: Re: Hash partition of key with skew
>
> Jens,
> Thanks for the link. That is something to consider. Of course it has
> downsides too.
>
> Wesley,
> That is some good info on hashing. We've explored a couple of these
> options.
> I see that you are hesitant to put these in production. Even we want to
> evaluate or options first.
>
> Dave,
> Our need to do this is similar to Wesley. And consumers of topic can be
> efficient if they get records from one(or a very few) keys.
> Why do you think it is not applicable to Kafka? Are you suggesting that
> there are other ways to handle it when using Kafka?
>
> Srikanth
>
> On Tue, May 3, 2016 at 11:58 AM, Tauzell, Dave <
> Dave.Tauzell@surescripts.com
> > wrote:
>
> > Yeah, that makes sense for the target system (Cassandra for example),
> > but I don't see that you would need that for Kafka.  Good info on
> > hashing, though, that I am going to take a look at when I get time.
> >
> > -Dave
> >
> > Dave Tauzell | Senior Software Engineer | Surescripts
> > O: 651.855.3042 | www.surescripts.com |   Dave.Tauzell@surescripts.com
> > Connect with us: Twitter I LinkedIn I Facebook I YouTube
> >
> >
> > -----Original Message-----
> > From: Wesley Chow [mailto:wes@chartbeat.com]
> > Sent: Tuesday, May 03, 2016 10:51 AM
> > To: users@kafka.apache.org
> > Subject: Re: Hash partition of key with skew
> >
> > I’m not the OP, but in our case, we sometimes want data locality. For
> > example, suppose that we have 100 consumers that are building up a
> > cache of customer -> data mapping. If customer data is spread randomly
> > across all partitions then a query for that customer’s data would have
> > to hit all 100 consumers. If customer data exhibits some locality,
> > then queries for that data only hit a subset of consumers.
> >
> > Wes
> >
> >
> > > On May 3, 2016, at 11:18 AM, Tauzell, Dave
> > > <Dave.Tauzell@surescripts.com>
> > wrote:
> > >
> > > Do you need the messages to be ordered in some way?   Why pass a key if
> > you don't want all the messages to go to one partition?
> > >
> > > -Dave
> > >
> > > Dave Tauzell | Senior Software Engineer | Surescripts
> > > O: 651.855.3042 | www.surescripts.com <http://www.surescripts.com/>
> > > |
> > Dave.Tauzell@surescripts.com <mailto:Dave.Tauzell@surescripts.com>
> > > Connect with us: Twitter I LinkedIn I Facebook I YouTube
> > >
> > >
> > > -----Original Message-----
> > > From: Wesley Chow [mailto:wes@chartbeat.com
> > > <mailto:wes@chartbeat.com>]
> > > Sent: Tuesday, May 03, 2016 9:51 AM
> > > To: users@kafka.apache.org <mailto:users@kafka.apache.org>
> > > Subject: Re: Hash partition of key with skew
> > >
> > > I’ve come up with a couple solutions since we too have a power law
> > distribution. However, we have not put anything into practice.
> > >
> > > Fixed Slicing
> > >
> > > One simple thing to do is to take each key and slice it into some
> > > fixed
> > number of partitions. So your function might be:
> > >
> > > (hash(key) % num) + (hash(key) % 10)
> > >
> > > In order to distribute it across 10 partitions. Or:
> > >
> > > hash(key + ‘0’) % num
> > > hash(key + ‘1’) % num
> > > …
> > > hash(key + ‘9’) % num
> > >
> > >
> > > Hyperspace Hashing
> > >
> > > If your data is multi-dimensional, then you might find hyperspace
> > hashing useful. I’ll give a simple example, but it’s easy to generalize.
> > Suppose that you have two dimensions you’d like to partition on:
> > customer id (C) and city location (L). You’d like to be able to
> > subscribe to all data for some subset of customers, and you’d also
> > like to be able to subscribe to all data for some subset of locations.
> > Suppose that this data goes into a topic with 256 partitions.
> > >
> > > For any piece of data, you’d construct the partition it goes to like
> so:
> > >
> > > ((hash(C) % 16) << 4) + ((hash(L) % 16)
> > >
> > > What that is basically saying is take C and map them to 16 different
> > spots, and set it as the high 4 bits of an 8 bit int. Then take the
> > location, map it to 16 different spots, and set it as the lower 4 bits
> > of the int. The resulting number is the partition that piece of data
> goes to.
> > >
> > > Now if you want one particular C, you subscribe to the 16 partitions
> > that contain that C. If you want some particular L, you subscribe to
> > the 16 partitions that contain that L.
> > >
> > > You can extend this scheme to an arbitrary number of dimensions
> > > subject
> > to the number of partitions in the topic, and you can vary the number
> > of bits that any particular dimension takes. This scheme suffers from
> > a combinatorial explosion of partitions if you really want to query on
> > lots of different dimensions, but you can see the Hyperdex paper for
> > clues on how to deal with this.
> > >
> > >
> > > Unbalanced Hashing
> > >
> > > It’s easy to generate ok but not great hash functions. One is DJB
> > > hash,
> > which relies on two empirically determined constants:
> > >
> > > http://stackoverflow.com/questions/10696223/reason-for-5381-number-i
> > > n-
> > > djb-hash-function
> > > <http://stackoverflow.com/questions/10696223/reason-for-5381-number-
> > > in
> > > -djb-hash-function><http://stackoverflow.com/questions/10696223/reas
> > > -djb-hash-function>on
> > > -for-5381-number-in-djb-hash-function
> > > <http://stackoverflow.com/questions/10696223/reason-for-5381-number-
> > > in
> > > -djb-hash-function>>
> > >
> > > (5381 and 33 in the above example)
> > >
> > > If you can do offline analysis, and your distribution doesn’t change
> > over time, then you can basically exhaustively search for two values
> > that produce a hash function that better distributes the load.
> > >
> > >
> > > Greedy Knapsack
> > >
> > > But if you’re ok doing offline analysis and generating your own hash
> > function, then you can create one that’s simply a hard coded list of
> > mappings for the heaviest keys, and then defaults to a regular hash
> > for the rest. The easiest way to programmatically do this is to use a
> > greedy
> > algorithm:
> > >
> > >  for each heavy key, k:
> > >    assign k to the partition with the least assigned weight
> > >
> > >
> > > The advantage to fixed slicing and hyperspace hashing is that you
> > > don’t
> > have to know your distribution a priori, and it generally scales well
> > as you increase the number of keys. The disadvantage is that one key’s
> > data is split across multiple partitions.
> > >
> > > The advantage to unbalanced hashing and greedy knapsack is that you
> > > can
> > get close to an optimal partitioning scheme and all of one key resides
> > in one partition. The downside is that you need to do partition
> > mapping management as your distribution changes over time.
> > >
> > > Hopefully that gives you some ideas!
> > >
> > > Wes
> > >
> > >
> > >
> > >> On May 3, 2016, at 9:09 AM, Jens Rantil <jens.rantil@tink.se> wrote:
> > >>
> > >> Hi,
> > >>
> > >> Not sure if this helps, but the way Loggly seem to do it is to have
> > >> a separate topic for "noisy neighbors". See [1].
> > >>
> > >> [1]
> > >> https://www.loggly.com/blog/loggly-loves-apache-kafka-use-unbreakab
> > >> le
> > >> -
> > >> messaging-better-log-management/
> > >>
> > >> Cheers,
> > >> Jens
> > >>
> > >> On Wed, Apr 27, 2016 at 9:11 PM Srikanth <srikanth.ht@gmail.com>
> wrote:
> > >>
> > >>> Hello,
> > >>>
> > >>> Is there a recommendation for handling producer side partitioning
> > >>> based on a key with skew?
> > >>> We want to partition on something like clientId. Problem is, this
> > >>> key has an uniform distribution.
> > >>> Its equally likely to see a key with 3k occurrence/day vs 100k/day
> > >>> vs 65million/day.
> > >>> Cardinality of key is around 1500 and there are approx 1 billion
> > >>> records per day.
> > >>> Partitioning by hashcode(key)%numOfPartition will create a few
> > >>> "hot partitions" and cause a few brokers(and consumer threads) to
> > >>> be
> > overloaded.
> > >>> May be these partitions with heavy load are evenly distributed
> > >>> among brokers, may be they are not.
> > >>>
> > >>> I read KIP-22
> > >>> <
> > >>> https://cwiki.apache.org/confluence/display/KAFKA/KIP-22+-+Expose+
> > >>> a+
> > >>> P
> > >>> artitioner+interface+in+the+new+producer
> > >>>>
> > >>> that
> > >>> explains how one could write a custom partitioner.
> > >>> I'd like to know how it was used to solve such data skew.
> > >>> We can compute some statistics on key distribution offline and use
> > >>> it in the partitioner.
> > >>> Is that a good idea? Or is it way too much logic for a partitioner?
> > >>> Anything else to consider?
> > >>> Any thoughts or reference will be helpful.
> > >>>
> > >>> Thanks,
> > >>> Srikanth
> > >>>
> > >> --
> > >>
> > >> Jens Rantil
> > >> Backend Developer @ Tink
> > >>
> > >> Tink AB, Wallingatan 5, 111 60 Stockholm, Sweden For urgent matters
> > >> you can reach me at +46-708-84 18 32.
> > >
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