Quick-and-Easy Deployment of a Ceph Storage Cluster
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Quick-and-Easy Deployment of a Ceph Storage Cluster
- 1. Quick & Easy Deployment of a Ceph Storage Cluster with SUSE Enterprise Storage Lars Marowsky-Brée Distinguished Engineer, Architect Storage & HA lmb@suse.com
- 2. 2 Agenda • Introduction • Setting the stage • What is Ceph, and how does it work? • Designing Ceph clusters • Deploying Ceph • Q & A
- 4. 4 2014: $1.5B - 2017: $2.8B - 22.5% CAGR (IDC) By 2018, open-source storage will gain 20% of the market share, up from less than 1% in 2013 (Gartner)
- 5. 5 Enterprise Data Storage: Market Segments
- 6. 6 Enterprise Data Capacity Utilization 50-60% of Enterprise Data 20-25% 15-20% 1-3% Tier 0 Ultra High Performance Tier 1 High-value, OLTP, Revenue Generating Tier 2 Backup/Recovery, Reference Data, Bulk Data Tier 3 Object, Archive, Compliance Archive, Long-term Retention Source: Horison Information Strategies - Fred Moore
- 7. 7 LOW FUNCTIONALITY HIGH FUNCTIONALITY Object Storage Archive Storage Data Backup Video Audio Big Data Bulk Storage Data Analytics OLTP CRM ERP Email HPC Compliance Archive Enterprise Data Storage: Use Cases CAPACITY OPTIMIZED PERFORMANCE OPTIMIZED VM-Aware DR Target
- 8. 8 Ceph at SUSE • 20+ years success with Linux & Enterprise storage ‒ Ceph as technology preview in SUSE Cloud 3 ‒ General support with SUSE Cloud 4 ‒ Dedicated storage product since February 2015 • Fast growing team, still hiring! • Focus areas: ‒ Enterprise-readiness ‒ Ease of use ‒ Stabilization and integration
- 9. 9 LOW FUNCTIONALITY HIGH FUNCTIONALITY Object Storage Archive Storage Data Backup Video Audio Big Data Bulk Storage Data Analytics OLTP CRM ERP Email HPC Compliance Archive SUSE Enterprise Storage: Initial Target Market CAPACITY OPTIMIZED PERFORMANCE OPTIMIZED VM-Aware DR Target Initial Target Market
- 10. 10 SUSE Enterprise Storage Pricing Model SES Base Configuration – Priority Subscription ● SES and limited use of SUSE Linux Enterprise Server to provide: ● 4 SES storage OSD nodes (1-2 sockets) ● 3 -5 SES MON nodes (customer selects redundancy level) ● 1 SES management node SES Expansion Node – Priority Subscription
- 11. What is Ceph?
- 12. 12 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 ‒ Extensible ‒ Can be built on commodity hardware ‒ Lower operational cost
- 13. 13 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
- 14. 14 Component Names radosgw Object Storage RBD Block Device Ceph FS File System RADOS librados Direct Application Access to RADOS
- 15. 15 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
- 16. 16 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
- 17. 17 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
- 18. 18 Voilà, a Small RADOS Cluster M M M
- 19. 19 Application Access M M M Application librados Application librados radosgw Socket Socket REST
- 20. 20 Linux Host RADOS Block Device M M M M krbd librados
- 21. 21 RADOS Block Device - VMs M M M M qemu-rbd librados VM KVM
- 22. 22 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 ‒ CRUSH: deterministic decentralized placement algorithm • Three conceptual components ‒ Objects / images ‒ Pools ‒ Placement groups
- 23. 23 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 ‒ number of placement groups ‒ placement rule set ‒ owner • Pools support certain operations ‒ create/remove/read/write entire objects ‒ snapshot of the entire pool
- 24. 24 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 spans several OSDs ‒ for balancing ‒ for replication • One OSD typically serves many PGs
- 25. 25 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
- 27. 27 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
- 28. Self-Healing
- 30. 30 Self-Healing M M M M 38.b0b swimmingpool/rubberduck Monitors allocate other OSDs to PG and update CRUSH map
- 32. 32 Self-Healing M M M M 38.b0b swimmingpool/rubberduck Future writes update the new replica
- 33. 33 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) ‒ Plenty of algorithms to choose from
- 34. 34 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 ‒ Additional configuration complexity and requires workload- specific tuning ‒ Also available: read-only mode (no write acceleration) ‒ Some downsides (not all features supported), memory consumption for HitSet • A good way to combine the advantages of replication and erasure coding
- 35. Software Defined Storage and Performance
- 36. 36 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
- 37. 37 Software Defined Storage (SDS) • Limits: ‒ ? ‒ (Essentially, all limits are merely bugs.) • Benefits: ‒ Infinite scalability ‒ Infinite adaptability ‒ Infinite choices ‒ Infinite flexibility
- 38. 38 Properties of a SDS System • Throughput • Latency • IOPS • Single client or aggregate • Availability • Reliability • Safety • Cost • Adaptability • Flexibility • Capacity • Density
- 39. 39 Architecting a SDS system • These goals often conflict: ‒ Availability versus Density ‒ IOPS versus Density ‒ Latency versus Throughput ‒ “Performance” during rebuild ‒ Everything versus Cost • Many hardware options • Software topology offers many configuration choices • There is no one size fits all
- 40. 40 Designing a Ceph cluster • 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. • Refine and tune this until desired performance is achieved • Scale up from there, re-tuning as you go
- 41. 41 Measuring Ceph performance • 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
- 42. Deploying a Ceph cluster
- 43. 43 Assumptions • You have three nodes: ‒ node1, node2, node3 ‒ These nodes will be both OSDs and MONitor nodes ‒ Each of these has /dev/vdb as a disk to be used for ceph ‒ admin ‒ A node from which you want to run calamari and host the web UI
- 44. 44 Prepare the nodes • Setup NTP • Create a ceph user on each ‒ Setup sudo for the ceph user • Enable sshd and allow all nodes to login to each other ‒ ssh-copy-id • Install SUSE Linux Enterprise Server 12 and the SUSE Enterprise Storage 1.0 products on each • Apply all available online updates
- 45. 45 Deploy ceph! • su - ceph • ceph-deploy new node1 node2 node3 • ceph-deploy mon create-initial • ceph-deploy osd create node1:vdb node2:vdb node3:vdb • Sorry, we’re done ;-)
- 46. 46 Deploy the calamari web frontend • On the admin node: • zypper in calamari-clients • calamari-ctl initialize • Open http://admin.node/ • ceph-deploy calamari –master admin –connect node1 node2 node3
- 49. In closing
- 50. 50 Summary: Why Ceph? • Can be scaled arbitrarily ‒ No central bottleneck “master” servers • Low operational cost ‒ Automation ‒ Commodity hardware • Can move data close to application • Redundancy through data replication ‒ Self-healing ‒ No need for RAID • APIs and cloud integration for self-service ‒ Software defined storage
- 51. Thank you. 51 Questions and Answers?
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