Lars Marowsky-Bree, SUSE

1. Modeling, estimating, and
predicting Ceph
Lars Marowsky-Brée
Distinguished Engineer, Architect Storage & HA
lmb@suse.com

2. What is Ceph?

3. 3
From 10,000 Meters
[1] http://www.openstack.org/blog/2013/11/openstack-user-survey-statistics-november-2013/
• Open Source Distributed Storage solution
• Most popular choice of distributed storage for
OpenStack
[1]
• Lots of goodies
‒ Distributed Object Storage
‒ Redundancy
‒ Efficient Scale-Out
‒ Can be built on commodity hardware
‒ Lower operational cost

4. 4
From 1,000 Meters
Object Storage
(Like Amazon S3)
Block Device File System
Unified Data Handling for 3 Purposes
●RESTful Interface
●S3 and SWIFT APIs
●Block devices
●Up to 16 EiB
●Thin Provisioning
●Snapshots
●POSIX Compliant
●Separate Data and
Metadata
●For use e.g. with
Hadoop
Autonomous, Redundant Storage Cluster

5. 5
For a Moment, Zooming to Atom Level
FS
Disk
OSD Object Storage Daemon
File System (btrfs, xfs)
Physical Disk
● OSDs serve storage objects to clients
● Peer to perform replication and recovery

6. 6
Put Several of These in One Node
FS
Disk
OSD
FS
Disk
OSD
FS
Disk
OSD
FS
Disk
OSD
FS
Disk
OSD
FS
Disk
OSD

7. 7
Mix In a Few Monitor Nodes
M • Monitors are the brain cells of the cluster
‒ Cluster Membership
‒ Consensus for Distributed Decision Making
• Do not serve stored objects to clients

8. 8
Voilà, a Small RADOS Cluster
M M
M

9. 9
Several Ingredients
• Basic Idea
‒ Coarse grained partitioning of storage supports policy based
mapping (don't put all copies of my data in one rack)
‒ Topology map and Rules allow clients to “compute” the exact
location of any storage object
• Three conceptual components
‒ Pools
‒ Placement groups
‒ CRUSH: deterministic decentralized placement algorithm

10. 10
Pools
• A pool is a logical container for storage objects
• A pool has a set of parameters
‒ a name
‒ a numerical ID (internal to RADOS)
‒ number of replicas OR erasure encoding settings
‒ number of placement groups
‒ placement rule set
‒ owner
• Pools support certain operations
‒ create/remove/read/write entire objects
‒ snapshot of the entire pool

11. 11
Placement Groups
• Placement groups help balance data across OSDs
• Consider a pool named “swimmingpool”
‒ with a pool ID of 38 and 8192 placement groups (PGs)
• Consider object “rubberduck” in “swimmingpool”
‒ hash(“rubberduck”) % 8192 = 0xb0b
‒ The resulting PG is 38.b0b
• One PG typically exists on several OSDs
‒ for replication
• One OSD typically serves many PGs

12. 12
CRUSH
• CRUSH uses a map of all OSDs in your
cluster
‒ includes physical topology, like row, rack, host
‒ includes rules describing which OSDs to
consider for what type of pool/PG
• This map is maintained by the monitor
nodes
‒ Monitor nodes use standard cluster algorithms
for consensus building, etc

13. 13
swimmingpool/rubberduck

14. 14
CRUSH in Action: Writing
M M
M
M
38.b0b
swimmingpool/rubberduck
Writes go to one
OSD, which then
propagates the
changes to other
replicas

15. Unreasonable expectations

16. 16
Unreasonable questions customers
want answers to
• How to build a storage system that meets my
requirements?
• If I build a system like this, what will its characteristics
be?
• If I change XY in my existing system, how will its
characteristics change?

17. 17
How to design a Ceph cluster today
• Understand your workload (?)
• Make a best guess, based on the desirable properties and
factors
• Build a 1-10% pilot / proof of concept
‒ Preferably using loaner hardware from a vendor to avoid early
commitment. (The outlook of selling a few PB of storage and
compute nodes makes vendors very cooperative.)
• Refine and tune this until desired performance is achieved
• Scale up
‒ Ceph retains most characteristics at scale or even improves (until it
doesn’t), so re-tune

18. Software Defined Storage

19. 19
Legacy Storage Arrays
• Limits:
‒ Tightly controlled
environment
‒ Limited scalability
‒ Few options
‒ Only certain approved drives
‒ Constrained number of disk
slots
‒ Few memory variations
‒ Only very few networking
choices
‒ Typically fixed controller and
CPU
• Benefits:
‒ Reasonably easy to
understand
‒ Long-term experience and
“gut instincts”
‒ Somewhat deterministic
behavior and pricing

20. 20
Software Defined Storage (SDS)
• Limits:
‒ ?
• Benefits:
‒ Infinite scalability
‒ Infinite adaptability
‒ Infinite choices
‒ Infinite flexibility
‒ ... right.

21. 21
Properties of a SDS System
• Throughput
• Latency
• IOPS
• Single client or aggregate
• Availability
• Reliability
• Safety
• Cost
• Adaptability
• Flexibility
• Capacity
• Density

22. 22
Architecting a SDS system
• These goals often conflict:
‒ Availability versus Density
‒ IOPS versus Density
‒ Latency versus Throughput
‒ Everything versus Cost
• Many hardware options
• Software topology offers many configuration choices
• There is no one size fits all

23. A multitude of choices

24. 24
Network
• Choose the fastest network you can afford
• Switches should be low latency with fully meshed
backplane
• Separate public and cluster network
• Cluster network should typically be twice the public
bandwidth
‒ Incoming writes are replicated over the cluster network
‒ Re-balancing and re-mirroring utilize the cluster network

25. 25
Networking (Public and Internal)
• Ethernet (1, 10, 40 GbE)
‒ Can easily be bonded for availability
‒ Use jumbo frames if you can
‒ Various bonding modes to try
• Infiniband
‒ High bandwidth, low latency
‒ Typically more expensive
‒ RDMA support
‒ Replacing Ethernet since the year of the Linux desktop

26. 26
Storage Node
• CPU
‒ Number and speed of cores
‒ One or more sockets?
• Memory
• Storage controller
‒ Bandwidth, performance, cache size
• SSDs for OSD journal
‒ SSD to HDD ratio
• HDDs
‒ Count, capacity, performance

27. 27
Adding More Nodes
• Capacity increases
• Total throughput
increases
• IOPS increase
• Redundancy increases
• Latency unchanged
• Eventually: network
topology limitations
• Temporary impact during
re-balancing

28. 28
Adding More Disks to a Node
• Capacity increases
• Redundancy increases
• Throughput might
increase
• IOPS might increase
• Internal node bandwidth
is consumed
• Higher CPU and memory
load
• Cache contention
• Latency unchanged

29. 29
OSD File System
• btrfs
‒ Typically better write
throughput performance
‒ Higher CPU utilization
‒ Feature rich
‒ Compression, checksums, copy
on write
‒ The choice for the future!
• XFS
‒ Good all around choice
‒ Very mature for data
partitions
‒ Typically lower CPU
utilization
‒ The choice for today!

30. 30
Impact of Caches
• Cache on the client side
‒ Typically, biggest impact on performance
‒ Does not help with write performance
• Server OS cache
‒ Low impact: reads have already been cached on the client
‒ Still, helps with readahead
• Caching controller, battery backed:
‒ Significant benefit for writes

31. 31
Impact of SSD Journals
• SSD journals accelerate bursts and random write IO
• For sustained writes that overflow the journal,
performance degrades to HDD levels
• SSDs help very little with read performance
• SSDs are very costly
‒ ... and consume storage slots -> lower density
• A large battery-backed cache on the storage controller
is highly recommended if not using SSD journals

32. 32
Hard Disk Parameters
• Capacity matters
‒ Often, highest density is not
most cost effective
‒ On-disk cache matters less
• Reliability advantage of
Enterprise drives typically
marginal compared to
cost
‒ Buy more drives instead
‒ Consider validation matrices
for small/medium NAS
servers as a guide
• RPM:
‒ Increase IOPS & throughput
‒ Increases power
consumption
‒ 15k drives quite expensive
still
• SMR/Shingled drives
‒ Density at the cost of write
speed

33. 33
Other parameters
• IO Schedulers
‒ Deadline, CFQ, noop
• Let’s not even talk about
the various mkfs options
• Encryption

34. 34
Impact of Redundancy Choices
• Replication:
‒ n number of exact, full-size
copies
‒ Potentially increased read
performance due to striping
‒ More copies lower
throughput, increase latency
‒ Increased cluster network
utilization for writes
‒ Rebuilds can leverage
multiple sources
‒ Significant capacity impact
• Erasure coding:
‒ Data split into k parts plus m
redundancy codes
‒ Better space efficiency
‒ Higher CPU overhead
‒ Significant CPU and cluster
network impact, especially
during rebuild
‒ Cannot directly be used with
block devices (see next
slide)

35. 35
Cache Tiering
• Multi-tier storage architecture:
‒ Pool acts as a transparent write-back overlay for another
‒ e.g., SSD 3-way replication over HDDs with erasure coding
‒ Can flush either on relative or absolute dirty levels, or age
‒ Additional configuration complexity and requires workload-
specific tuning
‒ Also available: read-only mode (no write acceleration)
‒ Some downsides (no snapshots), memory consumption for
HitSet
• A good way to combine the advantages of replication
and erasure coding

36. 36
Number of placement groups
● Number of hash buckets
per pool
● Data is chunked &
distributed across nodes
● Typically approx. 100 per
OSD/TB
• Too many:
‒ More peering
‒ More resources used
• Too few:
‒ Large amounts of data per
group
‒ More hotspots, less striping
‒ Slower recovery from failure
‒ Slower re-balancing

37. From components to system

38. 38
More than the sum of its parts
• If this seems straightforward enough, it is because it
isn’t.
• The interactions are not trivial or humanly predictable.
• http://ceph.com/community/ceph-performance-part-2-
write-throughput-without-ssd-journals/

39. 39
Hiding in a corner, weeping:

40. When anecdotes are not enough

41. 41
The data drought of big data
• Isolated benchmarks
• No standardized work loads
• Huge variety of reporting formats (not parsable)
• Published benchmarks usually skewed to highlight
superior performance of product X
• Not enough data points to make sense of the “why”
• Not enough data about the environment
• Rarely from production systems

42. 42
Existing efforts
• ceph-brag
‒ Last update: ~1 year ago
• cephperf
‒ Talk proposal for Vancover OpenStack Summit, presented at
Ceph Infernalis Developer Summit
‒ Mostly focused on object storage

43. 43
Measuring Ceph performance
(you were in the previous session by Adolfo, right?)
• rados bench
‒ Measures backend performance of the RADOS store
• rados load-gen
‒ Generate configurable load on the cluster
• ceph tell osd.XX
• fio rbd backend
‒ Swiss army knife of IO benchmarking on Linux
‒ Can also compare in-kernel rbd with user-space librados
• rest-bench
‒ Measures S3/radosgw performance

44. 44
Standard benchmark suite
• Gather all relevant and anonymized data in JSON
format
• Evaluate all components individually, as well as
combined
• Core and optional tests
‒ Must be fast enough to run on a (quiescent) production cluster
‒ Also means: non-destructive tests in core only
• Share data to build up a corpus on big data
performance

45. 45
Block benchmarks – how?
• fio
‒ JSON output, bandwidth, latency
• Standard profiles
‒ Sequential and random IO
‒ 4k, 16k, 64k, 256k block sizes
‒ Read versus write
‒ Mixed profiles
‒ Don’t write zeros!

46. 46
Block benchmarks – where?
• Individual OSDs
• All OSDs in a node
‒ On top of OSD fs, and/or raw disk?
‒ Cache baseline/destructive tests?
• Journal disks
‒ Read first, write back, for non-destructive tests
• RBD performance: rbd.ko, user-space
• One client, multiple clients
• Identify similar machines and run a random selection

47. 47
Network benchmarks
• Latency (various packet sizes): ping
• Bandwidth: iperf3
• Relevant links:
‒ OSD – OSD, OSD – Monitor
‒ OSD – Rados Gateway, OSD - iSCSI
‒ OSD – Client, Monitor – Client, Client – RADOS Gateway

48. 48
Other benchmark data
• CPU loads:
‒ Encryption
‒ Compression
• Memory access speeds
• Benchmark during re-balancing an OSD?
• CephFS (FUSE/cephfs.ko)

49. 49
Environment description
• Hardware setup for all nodes
‒ Not always trivial to discover
• kernel, glibc, ceph versions
• Network layout metadata

50. 50
Different Ceph configurations?
• Different node counts,
• replication sizes,
• erasure coding options,
• PG sizes,
• differently filled pools, ...
• Not necessarily non-destructive, but potentially highly
valuable.

51. 51
Performance monitoring during runs
• Performance Co-Pilot?
• Gather network, CPU, IO, ... metrics
• While not fine grained enough to debug everything,
helpful to spot anomalies and trends
‒ e.g., how did network utilization of the whole run change over
time? Is CPU maxed out on a MON for long?
• Also, pretty pictures (such as those missing in this
presentation)
• LTTng instrumentation?

52. 52
Summary of goals
• Holistic exploration of Ceph cluster performance
• Answer those pesky customer questions
• Identify regressions and brag about improvements
• Use machine learning to spot correlations that human
brains can’t grasp
‒ Recurrent neural networks?
• Provide students with a data corpus to answer
questions we didn’t even think of yet

53. Thank you.
53
Questions and Answers?

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