Hi,
I've changed the code a little bit, so that it uses a thread pool (via the
Future):
val ranges500 = ranges.asScala.grouped(500) // this means 6 BatchScanners
will be created
for (ranges <- ranges500) {
val bscan = instance.createBatchScanner(ARTIFACTS, auths, 2)
bscan.setRanges(ranges.asJava)
Future {
time("mult-scanner") {
bscan.asScala.toList // toList forces the iteration of the iterator
}
}
}
Here are the results:
background log: info: mult-scanner time: 4807.289358 ms
background log: info: mult-scanner time: 4930.996522 ms
background log: info: mult-scanner time: 9510.010808 ms
background log: info: mult-scanner time: 11394.152391 ms
background log: info: mult-scanner time: 13297.247295 ms
background log: info: mult-scanner time: 14032.704837 ms
background log: info: single-scanner time: 15322.624393 ms
Every Future completes independent, but in return every batch scanner iterator
needs more time to complete. :(
This means the batch scanners aren't really processed in parallel on the server
side?
Should I reconfigure something? Maybe the tablet servers haven't/can't allocate
enough threads or memory? (Every of the two nodes has 8 cores and 64GB memory
and a storage with ~300MB/s...)
Regards,
Sven
--
Sven Hodapp, M.Sc.,
Fraunhofer Institute for Algorithms and Scientific Computing SCAI,
Department of Bioinformatics
Schloss Birlinghoven, 53754 Sankt Augustin, Germany
[email protected]
www.scai.fraunhofer.de
----- Ursprüngliche Mail -----
> Von: "Josh Elser" <[email protected]>
> An: "user" <[email protected]>
> Gesendet: Mittwoch, 24. August 2016 18:36:42
> Betreff: Re: Accumulo Seek performance
> Ahh duh. Bad advice from me in the first place :)
>
> Throw 'em in a threadpool locally.
>
> [email protected] wrote:
>> Doesn't this use the 6 batch scanners serially?
>>
>> ------------------------------------------------------------------------
>> *From: *"Sven Hodapp" <[email protected]>
>> *To: *"user" <[email protected]>
>> *Sent: *Wednesday, August 24, 2016 11:56:14 AM
>> *Subject: *Re: Accumulo Seek performance
>>
>> Hi Josh,
>>
>> thanks for your reply!
>>
>> I've tested your suggestion with a implementation like that:
>>
>> val ranges500 = ranges.asScala.grouped(500) // this means 6
>> BatchScanners will be created
>>
>> time("mult-scanner") {
>> for (ranges <- ranges500) {
>> val bscan = instance.createBatchScanner(ARTIFACTS, auths, 1)
>> bscan.setRanges(ranges.asJava)
>> for (entry <- bscan.asScala) yield {
>> entry.getKey()
>> }
>> }
>> }
>>
>> And the result is a bit disappointing:
>>
>> background log: info: mult-scanner time: 18064.969281 ms
>> background log: info: single-scanner time: 6527.482383 ms
>>
>> I'm doing something wrong here?
>>
>>
>> Regards,
>> Sven
>>
>> --
>> Sven Hodapp, M.Sc.,
>> Fraunhofer Institute for Algorithms and Scientific Computing SCAI,
>> Department of Bioinformatics
>> Schloss Birlinghoven, 53754 Sankt Augustin, Germany
>> [email protected]
>> www.scai.fraunhofer.de
>>
>> ----- Ursprüngliche Mail -----
>> > Von: "Josh Elser" <[email protected]>
>> > An: "user" <[email protected]>
>> > Gesendet: Mittwoch, 24. August 2016 16:33:37
>> > Betreff: Re: Accumulo Seek performance
>>
>> > This reminded me of https://issues.apache.org/jira/browse/ACCUMULO-3710
>> >
>> > I don't feel like 3000 ranges is too many, but this isn't quantitative.
>> >
>> > IIRC, the BatchScanner will take each Range you provide, bin each Range
>> > to the TabletServer(s) currently hosting the corresponding data, clip
>> > (truncate) each Range to match the Tablet boundaries, and then does an
>> > RPC to each TabletServer with just the Ranges hosted there.
>> >
>> > Inside the TabletServer, it will then have many Ranges, binned by Tablet
>> > (KeyExtent, to be precise). This will spawn a
>> > org.apache.accumulo.tserver.scan.LookupTask will will start collecting
>> > results to send back to the client.
>> >
>> > The caveat here is that those ranges are processed serially on a
>> > TabletServer. Maybe, you're swamping one TabletServer with lots of
>> > Ranges that it could be processing in parallel.
>> >
>> > Could you experiment with using multiple BatchScanners and something
>> > like Guava's Iterables.concat to make it appear like one Iterator?
>> >
>> > I'm curious if we should put an optimization into the BatchScanner
>> > itself to limit the number of ranges we send in one RPC to a
>> > TabletServer (e.g. one BatchScanner might open multiple
>> > MultiScanSessions to a TabletServer).
>> >
>> > Sven Hodapp wrote:
>> >> Hi there,
>> >>
>> >> currently we're experimenting with a two node Accumulo cluster (two
>> tablet
>> >> servers) setup for document storage.
>> >> This documents are decomposed up to the sentence level.
>> >>
>> >> Now I'm using a BatchScanner to assemble the full document like this:
>> >>
>> >> val bscan = instance.createBatchScanner(ARTIFACTS, auths, 10) //
>> ARTIFACTS table
>> >> currently hosts ~30GB data, ~200M entries on ~45 tablets
>> >> bscan.setRanges(ranges) // there are like 3000 Range.exact's in the
>> ranges-list
>> >> for (entry<- bscan.asScala) yield {
>> >> val key = entry.getKey()
>> >> val value = entry.getValue()
>> >> // etc.
>> >> }
>> >>
>> >> For larger full documents (e.g. 3000 exact ranges), this operation
>> will take
>> >> about 12 seconds.
>> >> But shorter documents are assembled blazing fast...
>> >>
>> >> Is that to much for a BatchScanner / I'm misusing the BatchScaner?
>> >> Is that a normal time for such a (seek) operation?
>> >> Can I do something to get a better seek performance?
>> >>
>> >> Note: I have already enabled bloom filtering on that table.
>> >>
>> >> Thank you for any advice!
>> >>
>> >> Regards,
>> >> Sven
