This presentation describes the customer spotlight for Red Hat Storage Day Minneapolis on 4/12/16: Target.

1. Ceph Deployment at Target:
Customer Spotlight

2. Agenda
2
Welcome to Surly!
• Introduction
• Ceph @ Target
• Initial FAIL
• Problems Faced
• Solutions
• Current Implementation
• Future Direction
• Questions

3. Agenda
3
Introduction
Will Boege
Lead Technical Architect
Enterprise Private Cloud Engineering

4. Agenda
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First Ceph Environment at Target went live in October of 2014
• “Firefly” Release
Ceph was backing Target’s first ‘official’ Openstack release
• Icehouse Based
• Ceph is used for:
• RBD for Openstack Instances and Volumes
• RADOSGW for Object (instead of Swift)
• RBD backing Celiometer MongoDB volumes
Replaced traditional array-based approach that was implemented in our
prototype Havana environment.
• Traditional storage model was problematic to integrate
• General desire at Target to move towards open solutions
• Ceph’s tight integration with Openstack a huge selling point

5. Agenda
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Initial Ceph Deployment:
• Dev + 2 Prod Regions
• 3 x Monitor Nodes – Cisco B200
• 12 x OSD Nodes – Cisco C240 LFF
• 12 4TB SATA Disks
• 10 OSD per server
• Journal partition co-located on each OSD disk
• 120 OSD Total = ~ 400 TB
• 2x 10GBE per host
• 1 public_network
• 1 cluster_network
• Basic LSI ‘MegaRaid’ controller – SAS 2008M-8i
• No supercap or cache capability onboard
• 10xRAID0
Initial Rollout

6. Post rollout it became evident that there were performance issues within
the environment.
• KitchenCI users would complain of slow Chef converge times
• Yum transactions / app deployments would take abnormal amounts of time to
complete.
• Instance boot times, especially cloud-init images would take excessively long time to
boot, sometimes timing out.
• General user griping about ‘slowness’
• Unacceptable levels of latency even while cluster was relatively unworked
• High levels of CPU IOWait%
• Poor IOPS / Latency - FIO benchmarks running INSIDE Openstack Instances
$ fio --rw=write --ioengine=libaio --runtime=100 --direct=1 --bs=4k --size=10G --iodepth=32 --name=/tmp/testfile.bin
test: (groupid=0, jobs=1): err= 0: pid=1914
read : io=1542.5MB, bw=452383 B/s, iops=110 , runt=3575104msec
write: io=527036KB, bw=150956 B/s, iops=36 , runt=3575104msec
Initial Rollout

7. Compounding the performance issues we began to see mysterious
reliability issues.
• OSDs would randomly fall offline
• Cluster would enter a HEALTH_ERR state about once a week with ‘unfound objects’
and/or Inconsistent page groups that required manual intervention to fix.
• These problems were usually coupled with a large drop in our already suspect
performance levels
• Cluster would enter a recovery state often bringing client performance to a standstill
Initial Rollout

8. Customer opinion of our Openstack deployment due to
Ceph…..
…which leads to perception of the team....

9. Maybe Ceph isn’t the right solution?

10. What could we have done differently??
• Hardware
• Monitoring
• Tuning
• User Feedback

11. Ceph is not magic. It does the best
with the hardware you give it!
Much ill-advised advice floating around that if you just throw enough crappy disks at Ceph
you will achieve enterprise grade performance. Garbage in – Garbage out. Don’t be greedy
and build for capacity, if your objective is to create more a performant block storage
solution.
Fewer Better Disks > More ‘cheap’ Disks
….depending on your use case.
Hardware

12. • Root cause of HEALTH_ERRs was “unnamed vendor’s” SATA drives in our
solution ‘soft-failing’ – slowly gaining media errors without reporting themselves
as failed. Don’t rely on SMART. Interrogate your disks with a array-level tool, like
MegaRAID to identify drives for proactive replacement.
$ opt/MegaRAID/MegaCli/MegaCli64 -PDList -aALL | grep Media
• In installations with co-located journal partitions, a RAID solution with
cache+BBU for writeback operation would have been a huge performance gain.
Paying more attention to the suitability of hardware our vendor of choice provided
would have saved a lot of headaches
Hardware

13. Monitor Your Implementation From
The Outset!
Ceph provides a wealth of data about the cluster state! Unfortunately, only the most
rudimentary data is exposed by the ‘regular’ documented commands.
Quick Example - Demo Time!
Calamari?? Maybe a decent start … but we quickly outgrew it.
Challenge your monitoring team. If your solution isnt
working for you – go SaaS. Chatops FTW. Develop
those ‘Special Sets Of Skills’!
In monitoring –
ease of collection >depth of feature set
Monitors

14. require 'rubygems'
require 'dogapi'
api_key = “XXXXXXXXXXXXXXXXXXXXXXXX"
dosd = `ceph osd tree | grep down | wc -l`
host = `hostname`
if host.include?("ttb")
envname = "dev"
elsif host.include?("ttc")
envname = "prod-ttc"
else
envname = "prod-tte”
end
dog = Dogapi::Client.new(api_key)
dog.emit_point("ceph.osd_down", dosd, :tags => ["env:#{envname}","app:ceph"])
Monitors

15. #!/bin/bash
# Generate Write Results
write_raw=$(fio --randrepeat=1 --ioengine=libaio --direct=1 --name=./test.write --filename=test
--bs=4k --iodepth=4 –-size=1G --readwrite=randwrite --minimal)
# Generate Read Results
read_raw=$(fio --randrepeat=1 --ioengine=libaio --direct=1 --name=./test.read --filename=test
--bs=4k --iodepth=4 –size=1G --readwrite=randread --minimal)
writeresult_lat=$(echo $write_raw | awk -F; '{print $81}')
writeresult_iops=$(echo $write_raw | awk -F; '{print $49}')
readresult_lat=$(echo $read_raw | awk -F; '{print $40}')
readresult_iops=$(echo $read_raw | awk -F; '{print $8}')
ruby ./submit_lat_metrics.rb $writeresult_iops $readresult_iops $writeresult_lat $readresult_lat)
Monitors

16. Tune to your workload!
• This is unique to your specific workloads
But… in general.....
Neuter the default recovery priority
[osd]
osd_max_backfills = 1
osd_recovery_priority = 1
osd_client_op_priority = 63
osd_recovery_max_active = 1
osd_recovery_max_single_start = 1
Limit the impact of deep scrubing
osd_scrub_max_interval = 1209600
osd_scrub_min_interval = 604800
osd_scrub_sleep = .05
osd_snap_trim_sleep = .05
osd_scrub_chunk_max = 5
osd_scrub_chunk_min = 1
osd_deep_scrub_stride = 1048576
osd_deep_scrub_interval = 2592000
Tuning

17. Get Closer to Your Users!
Don’t Wall Them Off With Process!
• Chatops!
• Ditch tools like Lync / Sametime.
• ’1 to 1’ Enterprise Chat Apps are dead men walking.
• Consider Slack / Hipchat
• Foster an Enterprise community around your tech with available
tools
• REST API integrations allow far more robust notifications of
issues in a ‘stream of consciousness’ fashion.
Quick Feedback

18. Quick Feedback

19. Agenda
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Improved Hardware
• OSD Nodes – Cisco C240M4 SFF
• 20 10k Seagate SAS 1.1TB
• 6 480g Intel S3500 SSD
• We have tried the ’high-durability’ Toshiba SSD
they seem to work pretty well.
• Journal partition on SSD with 5:1 OSD/Journal ratio
• 90 OSD Total = ~ 100 TB
• Improved LSI ‘MegaRaid’ controller – SAS-9271-8i
• Supercap
• Writeback capability
• 18xRAID0
• Writethru on journals, writeback on spinning OSDs.
• Based on “Hammer” Ceph Release
After understanding that slower, high capacity disk wouldn't meet our
needs for an Openstack general purpose block storage solution – we
rebuilt.
Current State

20. • Obtaining metrics from our design change was nearly immediate due to
having effective monitors in place
– Latency improvements have been extreme
– IOWait% within Openstack instances have been greatly reduced
– Raw IOPS throughput has sykrocketed
– Throughput testing with RADOS bench and FIO shows aprox. 10 fold increase
– User feedback has been extremely positive, general Openstack experience at
Target is much improved. Feedback enhanced by group chat tools.
– Performance within Openstack instances has increase about 10x
Results
test: (groupid=0, jobs=1): err= 0: pid=1914
read : io=1542.5MB, bw=452383 B/s,
iops=110 , runt=3575104msec
write: io=527036KB, bw=150956 B/s,
iops=36 , runt=3575104msec
test: (groupid=0, jobs=1): err= 0: pid=2131
read : io=2046.6MB, bw=11649KB/s,
iops=2912 , runt=179853msec
write: io=2049.1MB, bw=11671KB/s,
iops=2917 , runt=179853msec
Current State

22. • Forcing the physical world to bend to our will. Getting datacenter techs to understand the
importance of rack placements in modern ‘scale-out’ IT
– To date our server placement is ‘what's the next open slot?’
– Create a ‘rack’ unit of cloud expansion
– More effectively utilize CRUSH for data placement and
availability
• Normalizing our Private Cloud storage performance offerings
– ENABLE IOPS LIMITS IN NOVA! QEMU supports this natively. Avoid the all you can
eat IO buffet.
nova-manage flavor set_key --name m1.small --key quota:disk_read_iops_sec --value 300
nova-manage flavor set_key --name m1.small --key quota:disk_write_iops_sec --value 300
– Leverage Cinder as the storage ‘menu’ beyond the default offering.
• Experiment with NVME for journal disks – greater journal density.
• Currently testing all SSD pool performance
– All SSD in Ceph has been maturing rapidly – Jewel sounds very promising.
– We have needs for a ‘ultra’ Cinder tier for workloads that require high IOPS / low
latency for use cases such as Apache Cassandra
– Considering Solidfire for this use case
Next Steps
Next Steps

23. • Repurposing legacy SATA hardware into a dedicated object pool
– High capacity, low performance drives should work well in an object use case
– Jewel has per-tenant namespaces for RADOSGW (!)
• Automate deployment with Chef to bring parity with our Openstack automation. ceph-deploy
still seems to be working for us.
– Use TDD to enforce base server configurations
• Broadening Ceph beyond cloud niche use case. Especially with improved object offering.
Next Steps
Next Steps

24. • Before embarking on creating a Ceph environment, have a good idea of what
your objectives are for the environment.
– Capacity?
– Performance?
• If you make wrong decisions it can lead to a negative user perception of Ceph,
and the technologies that depend on it, like Openstack
• Once you understand your objective, understand that your hardware selection is
crucial to your success
• Unless you are architecting for raw capacity, use SSDs for your journal volumes
without exception
– If you must co-locate journals, use a RAID adapter with BBU+Writeback cache
• A hybrid approach may be feasible with SATA ‘capacity’ disks with SSD or
NVME journals. I’ve yet to try this, I’d be interested in seeing some benchmark
data on a setup like this
• Research, experiment, break stuff, consult with Red Hat / Inktank
• Monitor, monitor, monitor and provide a very short feedback loop for your users
to engage you with their concerns
Conclusion
Conclusion

25. Thanks For Your Time!
Questions?
&