Could you please share fio command line used for this test?
Additionally, can you tell me the time needed to extract the kernel source?
Il 2 nov 2017 11:24 PM, "Ramon Selga" <[email protected]
<mailto:[email protected]>> ha scritto:
Hi,
Just for your reference we got some similar values in a customer setup
with three nodes single Xeon and 4x8TB HDD each with a double 10GbE
backbone.
We did a simple benchmark with fio tool on a virtual disk (virtio) of a
1TiB of size, XFS formatted directly no partitions no LVM, inside a VM
(debian stretch, dual core 4GB RAM) deployed in a gluster volume disperse
3 redundancy 1 distributed 2, sharding enabled.
We run a sequential write test 10GB file in 1024k blocks, a random read
test with 4k blocks and a random write test also with 4k blocks several
times with results very similar to the following:
writefile: (g=0): rw=write, bs=1M-1M/1M-1M/1M-1M, ioengine=libaio,
iodepth=200
fio-2.16
Starting 1 process
writefile: (groupid=0, jobs=1): err= 0: pid=11515: Thu Nov 2 16:50:05 2017
write: io=10240MB, bw=473868KB/s, iops=462, runt= 22128msec
slat (usec): min=20, max=98830, avg=1972.11, stdev=6612.81
clat (msec): min=150, max=2979, avg=428.49, stdev=189.96
lat (msec): min=151, max=2979, avg=430.47, stdev=189.90
clat percentiles (msec):
| 1.00th=[ 204], 5.00th=[ 249], 10.00th=[ 273], 20.00th=[ 293],
| 30.00th=[ 306], 40.00th=[ 318], 50.00th=[ 351], 60.00th=[ 502],
| 70.00th=[ 545], 80.00th=[ 578], 90.00th=[ 603], 95.00th=[ 627],
| 99.00th=[ 717], 99.50th=[ 775], 99.90th=[ 2966], 99.95th=[ 2966],
| 99.99th=[ 2966]
lat (msec) : 250=5.09%, 500=54.65%, 750=39.64%, 1000=0.31%, 2000=0.07%
lat (msec) : >=2000=0.24%
cpu : usr=7.81%, sys=1.48%, ctx=1221, majf=0, minf=11
IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.2%, 32=0.3%,
>=64=99.4%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%,
>=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%,
>=64=0.1%
issued : total=r=0/w=10240/d=0, short=r=0/w=0/d=0, drop=r=0/w=0/d=0
latency : target=0, window=0, percentile=100.00%, depth=200
Run status group 0 (all jobs):
WRITE: io=10240MB, aggrb=473868KB/s, minb=473868KB/s, maxb=473868KB/s,
mint=22128msec, maxt=22128msec
Disk stats (read/write):
vdg: ios=0/10243, merge=0/0, ticks=0/2745892, in_queue=2745884, util=99.18
benchmark: (g=0): rw=randread, bs=4K-4K/4K-4K/4K-4K, ioengine=libaio,
iodepth=128
...
fio-2.16
Starting 4 processes
benchmark: (groupid=0, jobs=4): err= 0: pid=11529: Thu Nov 2 16:52:40 2017
read : io=1123.9MB, bw=38347KB/s, iops=9586, runt= 30011msec
slat (usec): min=1, max=228886, avg=415.40, stdev=3975.72
clat (usec): min=482, max=328648, avg=52664.65, stdev=30216.00
lat (msec): min=9, max=527, avg=53.08, stdev=30.38
clat percentiles (msec):
| 1.00th=[ 12], 5.00th=[ 22], 10.00th=[ 23], 20.00th=[ 25],
| 30.00th=[ 33], 40.00th=[ 38], 50.00th=[ 47], 60.00th=[ 55],
| 70.00th=[ 64], 80.00th=[ 76], 90.00th=[ 95], 95.00th=[ 111],
| 99.00th=[ 151], 99.50th=[ 163], 99.90th=[ 192], 99.95th=[ 196],
| 99.99th=[ 210]
lat (usec) : 500=0.01%, 750=0.01%, 1000=0.01%
lat (msec) : 10=0.03%, 20=3.59%, 50=52.41%, 100=36.01%, 250=7.96%
lat (msec) : 500=0.01%
cpu : usr=0.29%, sys=1.10%, ctx=10157, majf=0, minf=549
IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%,
>=64=99.9%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%,
>=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%,
>=64=0.1%
issued : total=r=287705/w=0/d=0, short=r=0/w=0/d=0, drop=r=0/w=0/d=0
latency : target=0, window=0, percentile=100.00%, depth=128
Run status group 0 (all jobs):
READ: io=1123.9MB, aggrb=38346KB/s, minb=38346KB/s, maxb=38346KB/s,
mint=30011msec, maxt=30011msec
Disk stats (read/write):
vdg: ios=286499/2, merge=0/0, ticks=3707064/64, in_queue=3708680,
util=99.83%
benchmark: (g=0): rw=randwrite, bs=4K-4K/4K-4K/4K-4K, ioengine=libaio,
iodepth=128
...
fio-2.16
Starting 4 processes
benchmark: (groupid=0, jobs=4): err= 0: pid=11545: Thu Nov 2 16:55:54 2017
write: io=422464KB, bw=14079KB/s, iops=3519, runt= 30006msec
slat (usec): min=1, max=230620, avg=1130.75, stdev=6744.31
clat (usec): min=643, max=540987, avg=143999.57, stdev=66693.45
lat (msec): min=8, max=541, avg=145.13, stdev=67.01
clat percentiles (msec):
| 1.00th=[ 34], 5.00th=[ 75], 10.00th=[ 87], 20.00th=[ 100],
| 30.00th=[ 109], 40.00th=[ 116], 50.00th=[ 123], 60.00th=[ 135],
| 70.00th=[ 151], 80.00th=[ 182], 90.00th=[ 241], 95.00th=[ 289],
| 99.00th=[ 359], 99.50th=[ 416], 99.90th=[ 465], 99.95th=[ 490],
| 99.99th=[ 529]
lat (usec) : 750=0.01%, 1000=0.01%
lat (msec) : 2=0.01%, 4=0.01%, 10=0.01%, 20=0.05%, 50=1.80%
lat (msec) : 100=18.07%, 250=71.25%, 500=8.80%, 750=0.02%
cpu : usr=0.29%, sys=1.28%, ctx=115493, majf=0, minf=33
IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%,
>=64=99.8%
submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%,
>=64=0.0%
complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%,
>=64=0.1%
issued : total=r=0/w=105616/d=0, short=r=0/w=0/d=0, drop=r=0/w=0/d=0
latency : target=0, window=0, percentile=100.00%, depth=128
Run status group 0 (all jobs):
WRITE: io=422464KB, aggrb=14079KB/s, minb=14079KB/s, maxb=14079KB/s,
mint=30006msec, maxt=30006msec
Disk stats (read/write):
vdg: ios=0/105235, merge=0/0, ticks=0/3727048, in_queue=3734796,
util=99.81%
Basically we got sequential write around 470MBps, random read 4k 9500IOPS
and random write 4k 3500IOPS.
Hope it helps!
El 01/11/17 a les 12:03, Shyam Ranganathan ha escrit:
On 10/31/2017 08:36 PM, Ben Turner wrote:
* Erasure coded volumes with sharding - seen as a good fit for VM disk
storage
I am working on this with a customer, we have been able to do 400-500 MB
/ sec writes! Normally things max out at ~150-250. The trick is to use
multiple files, create the lvm stack and use native LVM striping. We
have found that 4-6 files seems to give the best perf on our setup. I
don't think we are using sharding on the EC vols, just multiple files
and LVM striping. Sharding may be able to avoid the LVM striping, but I
bet dollars to doughnuts you won't see this level of perf:) I am
working on a blog post for RHHI and RHEV + RHS performance where I am
able to in some cases get 2x+ the performance out of VMs / VM storage.
I'd be happy to share my data / findings.
Ben, we would like to hear more, so please do share your thoughts
further. There are a fair number of users in the community who have this
use-case and may have some interesting questions around the proposed method.
Shyam
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