[gpfsug-discuss] Preliminary conclusion: single client, single thread, small files - native Scale vs NFS
Tomer Perry
TOMP at il.ibm.com
Wed Oct 17 14:26:52 BST 2018
Just to clarify ( from man exports):
" async This option allows the NFS server to violate the NFS protocol
and reply to requests before any changes made by that request have been
committed to stable storage (e.g.
disc drive).
Using this option usually improves performance, but at the
cost that an unclean server restart (i.e. a crash) can cause data to be
lost or corrupted."
With the Ganesha implementation in Spectrum Scale, it was decided not to
allow this violation - so this async export options wasn't exposed.
I believe that for those customers that agree to take the risk, using
async mount option ( from the client) will achieve similar behavior.
Regards,
Tomer Perry
Scalable I/O Development (Spectrum Scale)
email: tomp at il.ibm.com
1 Azrieli Center, Tel Aviv 67021, Israel
Global Tel: +1 720 3422758
Israel Tel: +972 3 9188625
Mobile: +972 52 2554625
From: "Olaf Weiser" <olaf.weiser at de.ibm.com>
To: gpfsug main discussion list <gpfsug-discuss at spectrumscale.org>
Date: 17/10/2018 16:16
Subject: Re: [gpfsug-discuss] Preliminary conclusion: single
client, single thread, small files - native Scale vs NFS
Sent by: gpfsug-discuss-bounces at spectrumscale.org
Jallo Jan,
you can expect to get slightly improved numbers from the lower response
times of the HAWC ... but the loss of performance comes from the fact,
that
GPFS or (async kNFS) writes with multiple parallel threads - in opposite
to e.g. tar via GaneshaNFS comes with single threads fsync on each file..
you'll never outperform e.g. 128 (maybe slower), but, parallel threads
(running write-behind) <---> with one single but fast threads, ....
so as Alex suggest.. if possible.. take gpfs client of kNFS for those
types of workloads..
From: Jan-Frode Myklebust <janfrode at tanso.net>
To: gpfsug main discussion list <gpfsug-discuss at spectrumscale.org>
Date: 10/17/2018 02:24 PM
Subject: Re: [gpfsug-discuss] Preliminary conclusion: single
client, single thread, small files - native Scale vs NFS
Sent by: gpfsug-discuss-bounces at spectrumscale.org
Do you know if the slow throughput is caused by the network/nfs-protocol
layer, or does it help to use faster storage (ssd)? If on storage, have
you considered if HAWC can help?
I?m thinking about adding an SSD pool as a first tier to hold the active
dataset for a similar setup, but that?s mainly to solve the small file
read workload (i.e. random I/O ).
-jf
ons. 17. okt. 2018 kl. 07:47 skrev Alexander Saupp <
Alexander.Saupp at de.ibm.com>:
Dear Mailing List readers,
I've come to a preliminary conclusion that explains the behavior in an
appropriate manner, so I'm trying to summarize my current thinking with
this audience.
Problem statement:
Big performance derivation between native GPFS (fast) and loopback NFS
mount on the same node (way slower) for single client, single thread,
small files workload.
Current explanation:
tar seems to use close() on files, not fclose(). That is an application
choice and common behavior. The ideas is to allow OS write caching to
speed up process run time.
When running locally on ext3 / xfs / GPFS / .. that allows async destaging
of data down to disk, somewhat compromising data for better performance.
As we're talking about write caching on the same node that the application
runs on - a crash is missfortune but in the same failure domain.
E.g. if you run a compile job that includes extraction of a tar and the
node crashes you'll have to restart the entire job, anyhow.
The NFSv2 spec defined that NFS io's are to be 'sync', probably because
the compile job on the nfs client would survive if the NFS Server crashes,
so the failure domain would be different
NFSv3 in rfc1813 below acknowledged the performance impact and introduced
the 'async' flag for NFS, which would handle IO's similar to local IOs,
allowing to destage in the background.
Keep in mind - applications, independent if running locally or via NFS can
always decided to use the fclose() option, which will ensure that data is
destaged to persistent storage right away.
But its an applications choice if that's really mandatory or whether
performance has higher priority.
The linux 'sync' (man sync) tool allows to sync 'dirty' memory cache down
to disk - very filesystem independent.
-> single client, single thread, small files workload on GPFS can be
destaged async, allowing to hide latency and parallelizing disk IOs.
-> NFS client IO's are sync, so the second IO can only be started after
the first one hit non volatile memory -> much higher latency
The Spectrum Scale NFS implementation (based on ganesha) does not support
the async mount option, which is a bit of a pitty. There might also be
implementation differences compared to kernel-nfs, I did not investigate
into that direction.
However, the principles of the difference are explained for my by the
above behavior.
One workaround that I saw working well for multiple customers was to
replace the NFS client by a Spectrum Scale nsd client.
That has two advantages, but is certainly not suitable in all cases:
- Improved speed by efficent NSD protocol and NSD client side write
caching
- Write Caching in the same failure domain as the application (on NSD
client) which seems to be more reasonable compared to NFS Server side
write caching.
References:
NFS sync vs async
https://tools.ietf.org/html/rfc1813
The write throughput bottleneck caused by the synchronous definition of
write in the NFS version 2 protocol has been addressed by adding support
so that the NFS server can do unsafe writes.
Unsafe writes are writes which have not been committed to stable storage
before the operation returns. This specification defines a method for
committing these unsafe writes to stable storage in a reliable way.
sync() vs fsync()
https://www.ibm.com/support/knowledgecenter/en/ssw_aix_72/com.ibm.aix.performance/using_sync_fsync_calls.htm
- An application program makes an fsync() call for a specified file. This
causes all of the pages that contain modified data for that file to be
written to disk. The writing is complete when the fsync() call returns to
the program.
- An application program makes a sync() call. This causes all of the file
pages in memory that contain modified data to be scheduled for writing to
disk. The writing is not necessarily complete when the sync() call returns
to the program.
- A user can enter the sync command, which in turn issues a sync() call.
Again, some of the writes may not be complete when the user is prompted
for input (or the next command in a shell script is processed).
close() vs fclose()
A successful close does not guarantee that the data has been successfully
saved to disk, as the kernel defers writes. It is not common for a file
system to flush the buffers when the stream is closed. If you need to be
sure that the data is
physically stored use fsync(2). (It will depend on the disk hardware at
this point.)
Mit freundlichen Grüßen / Kind regards
Alexander Saupp
IBM Systems, Storage Platform, EMEA Storage Competence Center
Phone:
+49 7034-643-1512
IBM Deutschland GmbH
Mobile:
+49-172 7251072
Am Weiher 24
Email:
alexander.saupp at de.ibm.com
65451 Kelsterbach
Germany
IBM Deutschland GmbH / Vorsitzender des Aufsichtsrats: Martin Jetter
Geschäftsführung: Matthias Hartmann (Vorsitzender), Norbert Janzen, Stefan
Lutz, Nicole Reimer, Dr. Klaus Seifert, Wolfgang Wendt
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HRB 14562 / WEEE-Reg.-Nr. DE 99369940
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