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:[email protected]]
*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 <[email protected]
<mailto:[email protected]>> 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, [email protected]
<mailto:[email protected]> <[email protected]
<mailto:[email protected]>> 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:[email protected] <mailto:[email protected]>]
*Sent:* 09 June 2015 19:08
*To:* [email protected] <mailto:[email protected]>
*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 <[email protected]
<mailto:[email protected]>> 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 [email protected]
<mailto:[email protected]> <[email protected]
<mailto:[email protected]>> 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:[email protected]
<mailto:[email protected]>]
Sent: 09 June 2015 16:54
To: [email protected] <mailto:[email protected]>
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 :)
[email protected]
<mailto:[email protected]> 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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