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Deployment Preparedness

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  • 1. #MongoDBTokyoDeploymentPreparednessAlvin RichardsTechnical Director, 10gen
  • 2. Plan A because there is no PlanB http://bit.ly/QlJULZ
  • 3. Part OneBefore you deploy…
  • 4. Prototype Ops Playbook Test Capacity Planning MonitorReinventing the wheel
  • 5. Essentials• Disable NUMA• Pick appropriate file-system (xfs, ext4)• Pick 64-bit O/S – Recent Linux kernel, Win2k8R2• More RAM – Spend on RAM not Cores• Faster Disks – SSDs vs. SAN – Separate Journal and Data Files
  • 6. Key things to consider• Profiling – Baseline/Blue print: Understand what should happen – Ensure good Index usage• Monitoring – SNMP, munin, zabix, cacti, nagios – MongoDB Monitoring Service (MMS)• Sizing – Understand Capability (RAM, IOPs) – Understand Use Cases + Schema
  • 7. What is your SLA?• High Availability? – 24x7x365 operation? – Limited maintenance window?• Data Protection? – Failure of a Single Node? – Failure of a Data Center?• Disaster Recovery? – Manual or automatic failover? – Data Center, Region, Continent?
  • 8. Build & Test your Playbook• Backups• Restores (backups are not enough)• Upgrades• Replica Set Operations• Sharding Operations
  • 9. Part TwoUnder the cover…
  • 10. How to see metrics• mongostat• MongoDB plug ins for – munin, zabix, cacti, ganglia•Hosted Services – MMS - 10gen – Server Density, Cloudkick• Profiling
  • 11. Operation Counters
  • 12. Metrics in detail: opcounters• Counts: Insert, Update, Delete, Query, Commands• Operation counters are mostly straightforward: more is better• Some operations in a replica set primary are accounted differently in a secondary• getlastError(), system.status etc are also counted
  • 13. Resident Memory counter
  • 14. Metrics in detail: residentmemory• Key metric: to a very high degree, the performance of a mongod is a measure of how much data fits in RAM.• If this quantity is stably lower than available physical memory, the mongod is likely performing well.• Correlated metrics: page faults, B-Tree misses
  • 15. Page Faults counter
  • 16. Collection 1 Virtual Disk Address Space 1 Physical RAM Index 1 100 ns = 10,000,000 ns =
  • 17. Metrics in detail: page faults• This measures reads or writes to pages of data file that arent resident in memory• If this is persistently non-zero, your data doesnt fit in memory.• Correlated metrics: resident memory, B-Tree misses, iostats
  • 18. Working Set> db.blogs.stats(){ Size of data "ns" : "test.blogs", "count" : 1338330, "size" : 46915928, Average "avgObjSize" : 35.05557523181876, document size "storageSize" : 86092032, "numExtents" : 12, Size on disk (and "nindexes" : 2, in memory!) "lastExtentSize" : 20872960, "paddingFactor" : 1, "flags" : 0, "totalIndexSize" : 99860480, Size of all "indexSizes" : { indexes "_id_" : 55877632, "name_1" : 43982848 Size of each }, index "ok" : 1}
  • 19. Lock % counter
  • 20. Metrics in detail: lockpercentage and queues• By itself, lock % can be misleading: a high lock percentage just means that writing is happening.• But when lock % is high and queued readers or writers is non-zero, then the mongod probably at its write capacity.• Correlated metrics: iostats
  • 21. Log fileMon Dec 3 15:05:37 [conn81]getmore scaleout.nodes query: { ts: { $lte: new Date(1354547123142) } }cursorid:8607875337747748011ntoreturn:0keyUpdates:0numYields: 216locks(micros) r:615830nreturned:27055reslen:4194349551ms
  • 22. explain, hint// explain() shows the plan used by the operation> db.c.find(<query>).explain()// hint() forces a query to use a specific index// x_1 is the name of the index from db.c.getIndexes()> db.c.find( {x:1} ).hint("x_1")
  • 23. B-Tree Counter
  • 24. Metrics in detail: B-Tree• Indicates b-tree accesses including page fault service during an index lookup• If misses are persistently non-zero, your indexes dont fit in RAM. (You might need to change or drop indexes, or shard your data.)• Correlated metrics: resident memory, page faults, iostats
  • 25. B-Trees strengths• B-Tree indexes are designed for range queries over a single dimension• Think of a compound index on { A, B } as being an index on the concatenation of the A and B values in documents• MongoDB can use its indexes for sorting as well
  • 26. B-Trees weaknesses• Ranges queries on the first field of a compound index are suboptimal• Range queries over multiple dimensions are suboptimal• In both these cases, a suboptimal index might be better than nothing, but best is to try to see if you cant change the problem
  • 27. Indexing dark corners• Some functionality cant currently always use indexes: – $where JavaScript clauses – $mod, $not, $ne – regex• Negation maybe transformed into a range query – Index can be used• Complicated regular expressions scan a whole index
  • 28. Other tricks
  • 29. Warming the Cache> db.c.find( {unused_key: 1} ).explain()> db.c.find( {unused_key: 1} ) .hint( {random_index:1} ) .explain()# cat /data/db/* > /dev/null// New in 2.2> db.runCommand( { touch: "blogs", data: true, index: true } )
  • 30. Journal on another disk•The journals write load is very different than thedata files – journal = append-only – data files = randomly accessed•Putting the journal on a separate disk or RAID(e.g., with a symlink) will minimize any seek-timerelated journaling overhead
  • 31. --directoryperdb• Allows storage tiering – Different access patterns – Different Disk Types / Speeds• use --directoryperdb• add symlink into database directory
  • 32. Dynamically change log level// Change logging level to get more info> db.adminCommand({ setParameter: 1, logLevel: 1 })> db.adminCommand({ setParameter: 1, logLevel: 0 })
  • 33. Because you now have aPlan B http://bit.ly/QlJULZ