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From guy sharon <guy.sharon.1...@gmail.com>
Subject Re: benchmarking
Date Tue, 28 Aug 2018 18:50:11 GMT
hi Sean,

Thanks for the advice. I tried bringing up a 5 tserver cluster on AWS with
Muchos (https://github.com/apache/fluo-muchos). My first attempt was using
servers with 2 vCPU, 8GB RAM (m5d.large on AWS). The Hadoop datanodes were
colocated with the tservers and the Accumulo master was on the same server
as the Hadoop namenode. I populated a table with 6M entries using a
modified version of
org.apache.accumulo.examples.simple.helloworld.InsertWithBatchWriter from
Accumulo (the only thing I modified was the number of entries as it usually
inserts 50k). I then did a count with "bin/accumulo shell -u root -p secret
-e "scan -t hellotable -np" | wc -l". That took 15 seconds. I then upgraded
to m5d.xlarge instances (4vCPU, 16GB RAM) and got the exact same result, so
it seems upgrading the servers doesn't help.

Is this expected or am I doing something terribly wrong?

BR,
Guy.



On Tue, Aug 28, 2018 at 10:38 AM Sean Busbey <busbey@apache.org> wrote:

> Hi Guy,
>
> Apache Accumulo is designed for horizontally scaling out for large scale
> workloads that need to do random reads and writes. There's a non-trivial
> amount of overhead that comes with a system aimed at doing that on
> thousands of nodes.
>
> If your use case works for a single laptop with such a small number of
> entries and exhaustive scans, then Accumulo is probably not the correct
> tool for the job.
>
> For example, on my laptop (i7 2 cores, 8GiB memory) with that dataset size
> you can just rely on a file format like Apache Avro:
>
> busbey$ time java -jar avro-tools-1.7.7.jar random --codec snappy --count
> 6300000 --schema '{ "type": "record", "name": "entry", "fields": [ {
> "name": "field0", "type": "string" } ] }' ~/Downloads/6.3m_entries.avro
> Aug 28, 2018 12:31:13 AM org.apache.hadoop.util.NativeCodeLoader <clinit>
> WARNING: Unable to load native-hadoop library for your platform... using
> builtin-java classes where applicable
> test.seed=1535441473243
>
> real    0m5.451s
> user    0m5.922s
> sys     0m0.656s
> busbey$ ls -lah ~/Downloads/6.3m_entries.avro
> -rwxrwxrwx  1 busbey  staff   186M Aug 28 00:31
> /Users/busbey/Downloads/6.3m_entries.avro
> busbey$ time java -jar avro-tools-1.7.7.jar tojson
> ~/Downloads/6.3m_entries.avro | wc -l
>  6300000
>
> real    0m4.239s
> user    0m6.026s
> sys     0m0.721s
>
> I'd recommend that you start at >= 5 nodes if you want to look at rough
> per-node throughput capabilities.
>
>
> On 2018/08/28 06:59:38, guy sharon <guy.sharon.1977@gmail.com> wrote:
> > hi Mike,
> >
> > Thanks for the links.
> >
> > My current setup is a 4 node cluster (tserver, master, gc, monitor)
> running
> > on Alpine Docker containers on a laptop with an i7 processor (8 cores)
> with
> > 16GB of RAM. As an example I'm running a count of all entries for a table
> > with 6.3M entries with "accumulo shell -u root -p secret  -e "scan -t
> > benchmark_table -np" | wc -l" and it takes 43 seconds. Not sure if this
> is
> > reasonable or not. Seems a little slow to me. What do you think?
> >
> > BR,
> > Guy.
> >
> >
> >
> >
> > On Mon, Aug 27, 2018 at 4:43 PM Michael Wall <mjwall@apache.org> wrote:
> >
> > > Hi Guy,
> > >
> > > Here are a couple links I found.  Can you tell us more about your setup
> > > and what you are seeing?
> > >
> > > https://accumulo.apache.org/papers/accumulo-benchmarking-2.1.pdf
> > > https://www.youtube.com/watch?v=Ae9THpmpFpM
> > >
> > > Mike
> > >
> > >
> > > On Sat, Aug 25, 2018 at 5:09 PM guy sharon <guy.sharon.1977@gmail.com>
> > > wrote:
> > >
> > >> hi,
> > >>
> > >> I've just started working with Accumulo and I think I'm experiencing
> slow
> > >> reads/writes. I'm aware of the recommended configuration. Does anyone
> know
> > >> of any standard benchmarks and benchmarking tools I can use to tell
> if the
> > >> performance I'm getting is reasonable?
> > >>
> > >>
> > >>
> >
>

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