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From Michael Wall <mjw...@gmail.com>
Subject Re: Accumulo performance on various hardware configurations
Date Wed, 29 Aug 2018 19:06:01 GMT
Guy,

Can you go into specifics about how you are measuring this?  Are you still
using "bin/accumulo shell -u root -p secret -e "scan -t hellotable -np" |
wc -l" as you mentioned earlier in the thread?  As Mike Miller suggested,
serializing that back to the display and then counting 6M entries is going
to take some time.  Try using a Batch Scanner directly.

Mike

On Wed, Aug 29, 2018 at 2:56 PM guy sharon <guy.sharon.1977@gmail.com>
wrote:

> Yes, I tried the high performance configuration which translates to 4G
> heap size, but that didn't affect performance. Neither did setting
> table.scan.max.memory to 4096k (default is 512k). Even if I accept that the
> read performance here is reasonable I don't understand why none of the
> hardware configuration changes (except going to 48 cores, which made things
> worse) made any difference.
>
> On Wed, Aug 29, 2018 at 8:33 PM Mike Walch <mwalch@apache.org> wrote:
>
>> Muchos does not automatically change its Accumulo configuration to take
>> advantage of better hardware. However, it does have a performance profile
>> setting in its configuration (see link below) where you can select a
>> profile (or create your own) based on your the hardware you are using.
>>
>>
>> https://github.com/apache/fluo-muchos/blob/master/conf/muchos.props.example#L94
>>
>> On Wed, Aug 29, 2018 at 11:35 AM Josh Elser <elserj@apache.org> wrote:
>>
>>> Does Muchos actually change the Accumulo configuration when you are
>>> changing the underlying hardware?
>>>
>>> On 8/29/18 8:04 AM, guy sharon wrote:
>>> > hi,
>>> >
>>> > Continuing my performance benchmarks, I'm still trying to figure out
>>> if
>>> > the results I'm getting are reasonable and why throwing more hardware
>>> at
>>> > the problem doesn't help. What I'm doing is a full table scan on a
>>> table
>>> > with 6M entries. This is Accumulo 1.7.4 with Zookeeper 3.4.12 and
>>> Hadoop
>>> > 2.8.4. The table is populated by
>>> > org.apache.accumulo.examples.simple.helloworld.InsertWithBatchWriter
>>> > modified to write 6M entries instead of 50k. Reads are performed by
>>> > "bin/accumulo org.apache.accumulo.examples.simple.helloworld.ReadData
>>> -i
>>> > muchos -z localhost:2181 -u root -t hellotable -p secret". Here are
>>> the
>>> > results I got:
>>> >
>>> > 1. 5 tserver cluster as configured by Muchos
>>> > (https://github.com/apache/fluo-muchos), running on m5d.large AWS
>>> > machines (2vCPU, 8GB RAM) running CentOS 7. Master is on a separate
>>> > server. Scan took 12 seconds.
>>> > 2. As above except with m5d.xlarge (4vCPU, 16GB RAM). Same results.
>>> > 3. Splitting the table to 4 tablets causes the runtime to increase to
>>> 16
>>> > seconds.
>>> > 4. 7 tserver cluster running m5d.xlarge servers. 12 seconds.
>>> > 5. Single node cluster on m5d.12xlarge (48 cores, 192GB RAM), running
>>> > Amazon Linux. Configuration as provided by Uno
>>> > (https://github.com/apache/fluo-uno). Total time was 26 seconds.
>>> >
>>> > Offhand I would say this is very slow. I'm guessing I'm making some
>>> sort
>>> > of newbie (possibly configuration) mistake but I can't figure out what
>>> > it is. Can anyone point me to something that might help me find out
>>> what
>>> > it is?
>>> >
>>> > thanks,
>>> > Guy.
>>> >
>>> >
>>>
>>

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