Re: Accumulo performance on various hardware configurations
OK, good news at last! Performance has improved by one order of magnitude. The bad news is that I don't know why. As far as I can tell the only things I changed were table.scan.max.memory and the performance profile on Muchos. Both had no effect when I initially tested them and I doubt that there's a "settling in" period for both of these so I don't know what it is. But at least now it's clear that it's possible to get better performance. I'll try to investigate why it happened. Thanks everyone for helping! Regarding my use case: I'm trying to use Accumulo as a graph database. Traversing a graph, or several graphs at once, means getting a row (vertex) by ID, sending it to the client, deciding if it's relevant and then retrieving the neighboring vertices. So lots of reads by ID and back and forth between client and server. A full table scan is not exactly like that but it was the simplest use case I could think of that looked somewhat similar. On Wed, Aug 29, 2018 at 10:45 PM wrote: > This may suggest an issue with client, either getting the data to the > client or the client itself (although I think there are other performance > related changes you could make). I’m curious what the end goal is here. Is > this a real world use case? If you are using this type of benchmark to > evaluate the speed of Accumulo, then you will likely not get the same > performance when you apply your data and your real use cases. > > > > *From:* guy sharon > *Sent:* Wednesday, August 29, 2018 3:13 PM > *To:* user@accumulo.apache.org > *Subject:* Re: Accumulo performance on various hardware configurations > > > > 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 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 > 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 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 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
RE: Accumulo performance on various hardware configurations
This may suggest an issue with client, either getting the data to the client or the client itself (although I think there are other performance related changes you could make). I’m curious what the end goal is here. Is this a real world use case? If you are using this type of benchmark to evaluate the speed of Accumulo, then you will likely not get the same performance when you apply your data and your real use cases. From: guy sharon Sent: Wednesday, August 29, 2018 3:13 PM To: user@accumulo.apache.org Subject: Re: Accumulo performance on various hardware configurations 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 mailto: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 mailto: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 mailto: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 mailto: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. > >
Re: Accumulo performance on various hardware configurations
Couple of things to look at/try 1 - Is the data spread out amongst all the tablets and tservers when you have multiple tservers? 2 - How much of the data is in memory on the tablet server and how much is on disk. You can try flushing the table before running your scan. 3 - You could also launch compaction before running your scan to minimize the number of rfiles per tablet Mike On Wed, Aug 29, 2018 at 3:12 PM guy sharon wrote: > 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 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 >> 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 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 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. > > > > >
Re: Accumulo performance on various hardware configurations
Guy, In the case where you added servers and splits, did you check the tablet locations to see if they migrated to separate hosts? On Wed, Aug 29, 2018 at 3:12 PM guy sharon wrote: > > 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 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 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 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 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. > > > >
Re: Accumulo performance on various hardware configurations
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 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 > 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 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 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. > > >>>
Re: Accumulo performance on various hardware configurations
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 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 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 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. >>> > >>> > >>> >>
Re: Accumulo performance on various hardware configurations
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 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 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. >> > >> > >> >
Re: Accumulo performance on various hardware configurations
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 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. > > > > >
Re: Accumulo performance on various hardware configurations
50K/sec on any SQL database on a single node would be very good. On Aug 29, 2018, at 12:37 PM, Jonathan Yom-Tov mailto:jon.yom...@metismart.com>> wrote: I'm not 100% sure it's slow. Coming from RDBMS it seems it might be, but I wanted the opinion of others since I'm not experienced with Accumulo. From your reply I assume you think it's reasonable? On Wed, Aug 29, 2018 at 6:33 PM, Jeremy Kepner mailto:kep...@ll.mit.edu>> wrote: Why do you think 500K/sec is slow? On Wed, Aug 29, 2018 at 04:39:32PM +0300, guy sharon wrote: > Well, in one experiment I used a machine with 48 cores and 192GB and the > results actually came out worse. And in another I had 7 tservers on servers > with 4 cores. I think I'm not configuring things correctly because I'd > expect the improved hardware to improve performance and that doesn't seem > to be the case. > > On Wed, Aug 29, 2018 at 4:00 PM Jeremy Kepner > mailto:kep...@ll.mit.edu>> wrote: > > > Your node is fairly underpowered (2 cores and 8 GB RAM) and is less than > > most laptops. That said > > > > 6M / 12sec = 500K/sec > > > > is good for a single node Accumulo instance on this hardware. > > > > Spitting might not help since you only have 2 cores so added parallism > > can't > > be exploited. > > > > Why do you think 500K/sec is slow? > > > > To determine slowness one would have to compare with other database > > technology on the same platform. > > > > > > On Wed, Aug 29, 2018 at 03:04:51PM +0300, 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. > >
Re: Accumulo performance on various hardware configurations
No, it doesn't. What would you recommend changing? Heap space, or something else? On Wed, Aug 29, 2018 at 6:35 PM, Josh Elser 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.s >> imple.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. >> >> >>
Re: Accumulo performance on various hardware configurations
To answer your original question: YCSB is a standard benchmarking tool for databases that provides various types of read/write workloads. https://github.com/brianfrankcooper/YCSB/tree/master/accumulo1.7 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.
Re: Accumulo performance on various hardware configurations
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.
Re: Accumulo performance on various hardware configurations
Why do you think 500K/sec is slow? On Wed, Aug 29, 2018 at 04:39:32PM +0300, guy sharon wrote: > Well, in one experiment I used a machine with 48 cores and 192GB and the > results actually came out worse. And in another I had 7 tservers on servers > with 4 cores. I think I'm not configuring things correctly because I'd > expect the improved hardware to improve performance and that doesn't seem > to be the case. > > On Wed, Aug 29, 2018 at 4:00 PM Jeremy Kepner wrote: > > > Your node is fairly underpowered (2 cores and 8 GB RAM) and is less than > > most laptops. That said > > > > 6M / 12sec = 500K/sec > > > > is good for a single node Accumulo instance on this hardware. > > > > Spitting might not help since you only have 2 cores so added parallism > > can't > > be exploited. > > > > Why do you think 500K/sec is slow? > > > > To determine slowness one would have to compare with other database > > technology on the same platform. > > > > > > On Wed, Aug 29, 2018 at 03:04:51PM +0300, 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. > >
Re: Accumulo performance on various hardware configurations
In my limited experience of cloud services, I/O bandwidth seems to be pretty low. Can you run a benchmark eg bonnie++? On Wed, 29 Aug 2018, 14:39 guy sharon, wrote: > Well, in one experiment I used a machine with 48 cores and 192GB and the > results actually came out worse. And in another I had 7 tservers on servers > with 4 cores. I think I'm not configuring things correctly because I'd > expect the improved hardware to improve performance and that doesn't seem > to be the case. > > On Wed, Aug 29, 2018 at 4:00 PM Jeremy Kepner wrote: > >> Your node is fairly underpowered (2 cores and 8 GB RAM) and is less than >> most laptops. That said >> >> 6M / 12sec = 500K/sec >> >> is good for a single node Accumulo instance on this hardware. >> >> Spitting might not help since you only have 2 cores so added parallism >> can't >> be exploited. >> >> Why do you think 500K/sec is slow? >> >> To determine slowness one would have to compare with other database >> technology on the same platform. >> >> >> On Wed, Aug 29, 2018 at 03:04:51PM +0300, 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. >> >
Re: Accumulo performance on various hardware configurations
Well, in one experiment I used a machine with 48 cores and 192GB and the results actually came out worse. And in another I had 7 tservers on servers with 4 cores. I think I'm not configuring things correctly because I'd expect the improved hardware to improve performance and that doesn't seem to be the case. On Wed, Aug 29, 2018 at 4:00 PM Jeremy Kepner wrote: > Your node is fairly underpowered (2 cores and 8 GB RAM) and is less than > most laptops. That said > > 6M / 12sec = 500K/sec > > is good for a single node Accumulo instance on this hardware. > > Spitting might not help since you only have 2 cores so added parallism > can't > be exploited. > > Why do you think 500K/sec is slow? > > To determine slowness one would have to compare with other database > technology on the same platform. > > > On Wed, Aug 29, 2018 at 03:04:51PM +0300, 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. >
Re: Accumulo performance on various hardware configurations
hi Marc, Just ran the test again with the changes you suggested. Setup: 5 tservers on CentOS 7, 4 CPUs and 16 GB RAM, Accumulo 1.7.4, table with 6M rows. org.apache.accumulo.examples.simple.helloworld.ReadData now uses a BatchScanner with 10 threads. I got: $ time install/accumulo-1.7.4/bin/accumulo org.apache.accumulo.examples.simple.helloworld.ReadData -i muchos -z localhost:2181 -u root -t hellotable -p secret real0m16.979s user0m13.670s sys0m0.599s So this doesn't really improve things. That looks strange to me as I'd expect Accumulo to use the threads to speed things up. Unless the full scan makes it use just one thread with the assumption that the entries are next to each other on the disk making it faster to read them sequentially rather than jump back and forth with threads. What do you think? BR, Guy. On Wed, Aug 29, 2018 at 3:25 PM Marc wrote: > Guy, > To clarify : > > [1] If you have four tablets it's reasonable to suspect that the RPC > time to access those servers may increase a bit if you access them > sequentially versus in parallel. > On Wed, Aug 29, 2018 at 8:16 AM Marc wrote: > > > > Guy, > > The ReadData example appears to use a sequential scanner. Can you > > change that to a batch scanner and see if there is improvement [1]? > > Also, while you are there can you remove the log statement or set your > > log level so that the trace message isn't printed? > > > > In this case we are reading the entirety of that data. If you were to > > perform a query you would likely prefer to do it at the data instead > > of bringing all data back to the client. > > > > What are your expectations since it appears very slow. Do you want > > faster client side access to the data? Certainly improvements could be > > made -- of that I have no doubt -- but the time to bring 6M entries to > > the client is a cost you will incur if you use the ReadData example. > > > > [1] If you have four tablets it's reasonable to suspect that the RPC > > time to access those servers may increase a bit. > > > > On Wed, Aug 29, 2018 at 8:05 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. > > > > > > >
Re: Accumulo performance on various hardware configurations
Your node is fairly underpowered (2 cores and 8 GB RAM) and is less than most laptops. That said 6M / 12sec = 500K/sec is good for a single node Accumulo instance on this hardware. Spitting might not help since you only have 2 cores so added parallism can't be exploited. Why do you think 500K/sec is slow? To determine slowness one would have to compare with other database technology on the same platform. On Wed, Aug 29, 2018 at 03:04:51PM +0300, 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.
Re: Accumulo performance on various hardware configurations
Guy, To clarify : [1] If you have four tablets it's reasonable to suspect that the RPC time to access those servers may increase a bit if you access them sequentially versus in parallel. On Wed, Aug 29, 2018 at 8:16 AM Marc wrote: > > Guy, > The ReadData example appears to use a sequential scanner. Can you > change that to a batch scanner and see if there is improvement [1]? > Also, while you are there can you remove the log statement or set your > log level so that the trace message isn't printed? > > In this case we are reading the entirety of that data. If you were to > perform a query you would likely prefer to do it at the data instead > of bringing all data back to the client. > > What are your expectations since it appears very slow. Do you want > faster client side access to the data? Certainly improvements could be > made -- of that I have no doubt -- but the time to bring 6M entries to > the client is a cost you will incur if you use the ReadData example. > > [1] If you have four tablets it's reasonable to suspect that the RPC > time to access those servers may increase a bit. > > On Wed, Aug 29, 2018 at 8:05 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. > > > >
Re: Accumulo performance on various hardware configurations
Guy, The ReadData example appears to use a sequential scanner. Can you change that to a batch scanner and see if there is improvement [1]? Also, while you are there can you remove the log statement or set your log level so that the trace message isn't printed? In this case we are reading the entirety of that data. If you were to perform a query you would likely prefer to do it at the data instead of bringing all data back to the client. What are your expectations since it appears very slow. Do you want faster client side access to the data? Certainly improvements could be made -- of that I have no doubt -- but the time to bring 6M entries to the client is a cost you will incur if you use the ReadData example. [1] If you have four tablets it's reasonable to suspect that the RPC time to access those servers may increase a bit. On Wed, Aug 29, 2018 at 8:05 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. > >
Accumulo performance on various hardware configurations
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.
