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From Keith Turner <ke...@deenlo.com>
Subject Re: micro compaction
Date Tue, 09 Jun 2015 21:03:44 GMT
On Tue, Jun 9, 2015 at 4:06 PM, roman.drapeko@baesystems.com <
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.
>


One different is the row has to be sent over RPM 19 times vs once.  So the
size of the row will impact this.

Are you using native maps?  The structure of the native map is Map<row,
Map<col val>>.  For a mutation with 19 cols, the row is looked up once to
find the column map.   For non-native map the structure is Map<Key,
Value>.  Conceptually for this you keep looking up the row (or do multuple
compare of the row for each column in the mutation).


>
>
>
>
> *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>
> 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 <
> 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]
> *Sent:* 09 June 2015 19:08
>
>
> *To:* 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>
> 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 <
> 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]
> Sent: 09 June 2015 16:54
> To: 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 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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>
>  Please consider the environment before printing this email. This message
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