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My SQL on Ceph
The document outlines a presentation covering MySQL on Ceph and its performance in cloud environments, highlighting advantages such as market drivers, operational efficiency, and architecture. Key components discussed include the Ceph architecture, tuning MySQL for optimal performance, and the significance of various access methods and data placement strategies. It also emphasizes the importance of hybrid cloud capabilities and high IOPS for effective database management.
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My SQL on Ceph
- 1. MySQL and Ceph 2:20pm –3:10pm Room 203 MySQL in the Cloud Head-to-Head Performance Lab 1:20pm – 2:10pm Room 203
- 2. WHOIS Brent Compton andKyle Bader Storage Solution Architectures Red Hat Yves Trudeau Principal Architect Percona
- 3. AGENDA MySQL on CephMySQL in the Cloud Head-to-Head Performance Lab • MySQL on Ceph vs. AWS • Head-to-head: Performance • Head-to-head: Price/performance • IOPS performance nodes for Ceph • Why MySQL on Ceph • Ceph Architecture • Tuning: MySQL on Ceph • HW Architectural Considerations
- 4. Why MySQL onCeph
- 5. • Ceph #1block storage for OpenStack clouds • MySQL #4 workload on OpenStack (#1-3 often use databases too!) • 70% Apps use LAMP on OpenStack • Ceph leading open-source SDS • MySQL leading open-source RDBMS WHY MYSQL ON CEPH? MARKET DRIVERS
- 6. • Shared, elasticstorage pool • Dynamic DB placement • Flexible volume resizing • Live instance migration • Backup to object pool • Read replicas via copy-on-write snapshots WHY MYSQL ON CEPH? OPS EFFICIENCY
- 7. WHY MYSQL ONCEPH? PUBLIC CLOUD FIDELITY • Hybrid Cloud requires familiar platforms • Developers want platform consistency • Block storage, like the big kids • Object storage, like the big kids • Your hardware, datacenter, staff
- 8. WHY MYSQL ONCEPH? HYBRID CLOUD REQUIRES HIGH IOPS Ceph Provides • Spinning Block – General Purpose • Object Storage - Capacity • SSD Block – High IOPS
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- 10. ARCHITECTURAL COMPONENTS RGW A webservices gateway for object storage, compatible with S3 and Swift LIBRADOS A library allowing apps to directly access RADOS (C, C++, Java, Python, Ruby, PHP) RADOS A software-based, reliable, autonomous, distributed object store comprised of self-healing, self-managing, intelligent storage nodes and lightweight monitors RBD A reliable, fully- distributed block device with cloud platform integration CEPHFS A distributed file system with POSIX semantics and scale- out metadata APP HOST/VM CLIENT
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- 13. RADOS COMPONENTS OSDs • 10sto 10000s in a cluster • Typically one per disk • Serve stored objects to clients • Intelligently peer for replication & recovery Monitors • Maintain cluster membership and state • Provide consensus for distributed decision-making • Small, odd number • These do not serve stored objects to clients
- 14. WHERE DO OBJECTSLIVE? ??
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- 17. EVEN BETTER: CRUSH CLUSTERPLACEMENTGROUPS (PGs)
- 18. CRUSH IS AQUICK CALCULATION CLUSTER
- 19. DYNAMIC DATA PLACEMENT CRUSH: •Pseudo-random placement algorithm • Fast calculation, no lookup • Repeatable, deterministic • Statistically uniform distribution • Stable mapping • Limited data migration on change • Rule-based configuration • Infrastructure topology aware • Adjustable replication • Weighting
- 20. DATA IS ORGANIZEDINTO POOLS CLUSTERPOOLS (CONTAINING PGs) POOL A POOL B POOL C POOL D
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- 22. ARCHITECTURAL COMPONENTS RGW A webservices gateway for object storage, compatible with S3 and Swift LIBRADOS A library allowing apps to directly access RADOS (C, C++, Java, Python, Ruby, PHP) RADOS A software-based, reliable, autonomous, distributed object store comprised of self-healing, self-managing, intelligent storage nodes and lightweight monitors RBD A reliable, fully- distributed block device with cloud platform integration CEPHFS A distributed file system with POSIX semantics and scale- out metadata APP HOST/VM CLIENT
- 23. ARCHITECTURAL COMPONENTS RGW A webservices gateway for object storage, compatible with S3 and Swift LIBRADOS A library allowing apps to directly access RADOS (C, C++, Java, Python, Ruby, PHP) RADOS A software-based, reliable, autonomous, distributed object store comprised of self-healing, self-managing, intelligent storage nodes and lightweight monitors RBD A reliable, fully- distributed block device with cloud platform integration CEPHFS A distributed file system with POSIX semantics and scale- out metadata APP HOST/VM CLIENT
- 24. ACCESSING A RADOSCLUSTER RADOS CLUSTER socket
- 25. RADOS ACCESS FORAPPLICATIONS LIBRADOS • Direct access to RADOS for applications • C, C++, Python, PHP, Java, Erlang • Direct access to storage nodes • No HTTP overhead
- 26. ARCHITECTURAL COMPONENTS RGW A webservices gateway for object storage, compatible with S3 and Swift LIBRADOS A library allowing apps to directly access RADOS (C, C++, Java, Python, Ruby, PHP) RADOS A software-based, reliable, autonomous, distributed object store comprised of self-healing, self-managing, intelligent storage nodes and lightweight monitors RBD A reliable, fully- distributed block device with cloud platform integration CEPHFS A distributed file system with POSIX semantics and scale- out metadata APP HOST/VM CLIENT
- 27. STORING VIRTUAL DISKS RADOSCLUSTER
- 28. STORING VIRTUAL DISKS RADOSCLUSTER
- 29. STORING VIRTUAL DISKS RADOSCLUSTER
- 30. PERCONA ON KRBD RADOSCLUSTER
- 31. TUNING MYSQL ONCEPH
- 32. TUNING FOR HARMONY OVERVIEW TuningMySQL • Buffer pool > 20% • Flush each Tx or batch? • Parallel double write-buffer flush Tuning Ceph • RHCS 1.3.2, tcmalloc 2.4 • 128M thread cache • Co-resident journals • 2-4 OSDs per SSD
- 33. TUNING FOR HARMONY SAMPLEEFFECT OF MYSQL BUFFER POOL ON TpmC - 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0 1000 2000 3000 4000 5000 6000 7000 8000 tpmC Time (seconds) - 1 data point per minute 64x MySQL Instances on Ceph cluster: each with 25x TPC-C Warehouses 1% Buffer Pool 5% Buffer Pool 25% Buffer Pool 50% Buffer Pool 75% Buffer Pool
- 34. TUNING FOR HARMONY SAMPLEEFFECT OF MYSQL Tx FLUSH ON TpmC - 500,000 1,000,000 1,500,000 2,000,000 2,500,000 0 1000 2000 3000 4000 5000 6000 7000 8000 tpmC Time (seconds) - 1 data point per minute 64x MySQL Instances on Ceph cluster: each with 25x TPC-C Warehouses Batch Tx flush (1 sec) Per Tx flush
- 35. TUNING FOR HARMONY SAMPLEEFFECT OF CEPH TCMALLOC VERSION ON TpmC - 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0 1000 2000 3000 4000 5000 6000 7000 8000 tpmC Time (seconds) - 1 data point per minute 64x MySQL Instances on Ceph cluster: each with 25x TPC-C Warehouses Per Tx flush Per Tx flush (tcmalloc v2.4)
- 36. TUNING FOR HARMONY CREATINGA SEPARATE POOL TO SERVE IOPS WORKLOADS Creating multiple pools in the CRUSH map • Distinct branch in OSD tree • Edit CRUSH map, add SSD rules • Create pool, set crush_ruleset to SSD rule • Add Volume Type to Cinder
- 37. TUNING FOR HARMONY IFYOU MUST USE MAGNETIC MEDIA Reducing seeks on magnetic pools • RBD cache is safe • RAID Controllers with write-back cache • SSD Journals • Software caches
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- 39. ARCHITECTURAL CONSIDERATIONS UNDERSTANDING THEWORKLOAD Traditional Ceph Workload • $/GB • PBs • Unstructured data • MB/sec MySQL Ceph Workload • $/IOP • TBs • Structured data • IOPS
- 40. NEXT UP 2:20pm –3:10pm Room 203 MySQL in the Cloud Head-to-Head Performance Lab
Editor's Notes
- #34 MySQL TPC-C tpmC benchmark on XS instances 2.5GB Dataset (25W) per Instance (64x) MySQL Instances on 48x OSD/HDD Ceph Cluster w 1024GB Total Server RAM MySQL Buffer Pool v. Dataset Ratio Varied (1%-100%)
- #35 MySQL TPC-C tpmC benchmark on XS instances 2.5GB Dataset (25W) per Instance (64x) MySQL Instances on 48x OSD/HDD Ceph Cluster w 1024GB Total Server RAM MySQL Buffer Pool v. Dataset Ratio Varied (1%-100%)
- #36 MySQL TPC-C tpmC benchmark on XS instances 2.5GB Dataset (25W) per Instance (64x) MySQL Instances on 48x OSD/HDD Ceph Cluster w 1024GB Total Server RAM MySQL Buffer Pool v. Dataset Ratio Varied (1%-100%)