hi Mike, As per Mike Miller's suggestion I started using org.apache.accumulo.examples.simple.helloworld.ReadData from Accumulo with debugging turned off and a BatchScanner with 10 threads. I redid all the measurements and although this was 20% faster than using the shell there was no difference once I started playing with the hardware configurations.
Guy. On Wed, Aug 29, 2018 at 10:06 PM Michael Wall <mjw...@gmail.com> wrote: > 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.1...@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 <mwa...@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 <els...@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. >>>> > >>>> > >>>> >>>
