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From John Vines <vi...@apache.org>
Subject Re: micro compaction
Date Wed, 10 Jun 2015 16:36:53 GMT
Don't forget that the client may not have the same iterators in memory as
the server JVM so that would have to be worked around.

On Wed, Jun 10, 2015 at 12:28 PM Josh Elser <josh.elser@gmail.com> wrote:

> I think re-using Iterators in the client-write path makes sense
> architecturally and is a logical progression for the reasons pointed out
> by Roman and Russ.
>
> The big concern that Keith pointed out, it's hard to directly apply
> iterators on the client-write side because we're not dealing in sorted
> key-values at this point. I think there could be ways to work around this.
>
> I'd say if we have people who are interested in pursuing this, let's
> start a new discussion on dev@ where we can start laying some groundwork
> for the scope and implementation of what this solution would look like.
>
> roman.drapeko@baesystems.com wrote:
> > My view is that introduction of ingest-time iterators would be quite a
> > useful feature. Anyway. J
> >
> > Also, could anyone exactly explain why composite mutation perform pretty
> > much in the same way as a set of individual mutations?
> >
> > One large composite mutation with 19 qualifiers inside is just 10-30%
> > faster than 19 individual mutations.
> >
> > *From:*Russ Weeks [mailto:rweeks@newbrightidea.com]
> > *Sent:* 09 June 2015 20:54
> > *To:* accumulo-user
> > *Subject:* Re: micro compaction
> >
> > For consistency and ease of implementation. Say I've written a stack of
> > combiners that do statistical aggregation, sampling etc. on my table.
> > Rather than port that logic to a Storm topology or to the DStream API
> > I'd just like to turn that stack on in my BatchWriter.
> >
> > On Tue, Jun 9, 2015 at 12:47 PM David Medinets <david.medinets@gmail.com
> > <mailto:david.medinets@gmail.com>> wrote:
> >
> >     Consider using Storm, Pig, Spark, or your own framework to handle
> >     the in-memory aggregation before giving the data to the BatchWriter.
> >     Why would any part of Accumulo code be responsible for this kind of
> >     application-specific data handling?
> >
> >     On Tue, Jun 9, 2015 at 3:17 PM, roman.drapeko@baesystems.com
> >     <mailto:roman.drapeko@baesystems.com> <roman.drapeko@baesystems.com
> >     <mailto:roman.drapeko@baesystems.com>> wrote:
> >
> >     Just to clarify the origin of my question.
> >
> >     I had to do some performance tests to compare different storage
> >     types of “raw” data against each other.
> >
> >     Hopefully, picture below is visible in the mailing list. If not, I
> >     will put it somewhere else.
> >
> >     6 million “original” records, 1.3GB data, 233 bytes per record
> >
> >     Each original record is 40 fields delimited by tab, on average 19 –
> >     not null
> >
> >     Batchwriter, single java program
> >
> >     First three bars represent single “heavy” mutation to insert the
> >     whole tabular line / serialized object.
> >
> >     4,5,6,7 bars – composite mutation (all qualifiers for the same rowid
> >     in one mutation)
> >
> >     8, 9, 10, 11 – individual mutations (all qualifiers for the same
> >     rowid in separate mutations) - ~19 mutations per original record
> >
> >     On average, single “heavy” mutations are 7-10 times faster than
> >     anything else, composite are 10%-35% faster than individual.
> >
> >     I am not an expert how Accumulo is implemented internally, however
> >     it looks like composite mutation is treated more or less in the same
> >     way as a set of individual mutations. Probably, largest overhead is
> >     added by WAL.
> >
> >     Data utilization before and after manual compaction of test table
> >     and all system tables:
> >
> >     It’s not clear why “accumulo du” shows twice less data used
> >     comparing to “hdfs du”.
> >
> >     All these tests made us think that we can improve performance by
> >     doing some calculations in-memory (and our use-case fits very well)
> >     and reducing number of mutations. Now I am trying to understand
> >     whether there is a relatively easy way to do this with Accumulo or
> >     whether it’s time to look closer into something like Spark.
> >
> >     Thanks
> >
> >     Roman
> >
> >     *From:*Adam Fuchs [mailto:afuchs@apache.org <mailto:
> afuchs@apache.org>]
> >     *Sent:* 09 June 2015 19:08
> >
> >
> >     *To:* user@accumulo.apache.org <mailto:user@accumulo.apache.org>
> >     *Subject:* Re: micro compaction
> >
> >     I think this might be the same concept as in-mapper combining, but
> >     applied to data being sent to a BatchWriter rather than an
> >     OutputCollector. See [1], section 3.1.1. A similar performance
> >     analysis and probably a lot of the same code should apply here.
> >
> >     Cheers,
> >
> >     Adam
> >
> >     [1]
> >
> http://lintool.github.io/MapReduceAlgorithms/MapReduce-book-final.pdf
> >
> >     On Tue, Jun 9, 2015 at 1:02 PM, Russ Weeks <rweeks@newbrightidea.com
> >     <mailto:rweeks@newbrightidea.com>> wrote:
> >
> >     Having a combiner stack (more generally an iterator stack) run on
> >     the client-side seems to be the second most popular request on this
> >     list. The most popular being, "How do I write to Accumulo from
> >     inside an iterator?"
> >
> >     Such a thing would be very useful for me, too. I have some cycles to
> >     help out, if somebody can give me an idea of where to get started
> >     and where the potential land-mines are.
> >
> >     -Russ
> >
> >     On Tue, Jun 9, 2015 at 9:08 AM roman.drapeko@baesystems.com
> >     <mailto:roman.drapeko@baesystems.com> <roman.drapeko@baesystems.com
> >     <mailto:roman.drapeko@baesystems.com>> wrote:
> >
> >         Aggregated output is tiny, so if I do same calculations in
> >         memory (instead of sending mutations to Accumulo) , I can reduce
> >         overall number of mutations by 1000x or so
> >
> >
> >
> >         -----Original Message-----
> >         From: Josh Elser [mailto:josh.elser@gmail.com
> >         <mailto:josh.elser@gmail.com>]
> >         Sent: 09 June 2015 16:54
> >         To: user@accumulo.apache.org <mailto:user@accumulo.apache.org>
> >         Subject: Re: micro compaction
> >
> >         Well, you win the prize for new terminology. I haven't ever
> >         heard the term "micro compaction" before.
> >
> >         Can you clarify though, you say hundreds of millions of
> >         mutations that result in megabytes of data. Is that an increase
> >         or decrease in size.
> >         Comparing apples to oranges :)
> >
> >         roman.drapeko@baesystems.com
> >         <mailto:roman.drapeko@baesystems.com> wrote:
> >          > Hi guys,
> >          >
> >          > While doing pre-analytics we generate hundreds of millions of
> >          > mutations that result in 1-100 megabytes of useful data after
> >         major
> >          > compaction. We ingest into Accumulo using MR from Mapper job.
> We
> >          > identified that performance really degrades while increasing
> >         a number of mutations.
> >          >
> >          > The obvious improvement is to do some calculations in-memory
> >         before
> >          > sending mutations to Accumulo.
> >          >
> >          > Of course, at the same time we are looking for a solution to
> >         minimize
> >          > development effort.
> >          >
> >          > I guess I am asking about micro compaction/ingest-time
> >         iterators on
> >          > the client side (before data is sent to Accumulo).
> >          >
> >          > To my understanding, Accumulo does not support them, is it
> >         correct?
> >          > And if so, are there any plans to support this functionality
> >         in the future?
> >          >
> >          > Thanks
> >          >
> >          > Roman
> >          >
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