Richard Kreuter's presentation at MongoSV on December 3, 2010

2. So you’re ready to deploy !

3. some points to consider

4. Agenda
• A word on performance
• Sizing Your Hardware
• memory / cpu / disk io
• Software
• os / filesystem
• Installing MongoDB / Upgrades
• EC2 Notes
• Security
• Backup
• Durability
• Upgrading
• Monitoring
• Scaling out

5. A Word on Performance
• Ensure your queries are being executed correctly
• Enable profiling
• db.setProfilingLevel(n)
• n=1: slow operations, n=2: all operations
• Viewing profile information
• db.system.profile.find({info: /test.foo/})
•http://www.mongodb.org/display/DOCS/Database+Profiler
• Query execution plan:
•db.xx.find({..}).explain()
•http://www.mongodb.org/display/DOCS/Optimization
• Make sure your Queries are properly indexed.

6. Sizing Hardware: Memory
• Working set should be as much in memory as possible, but
• your whole data set doesn’t have to
•Memory Mapped files
• Maps Files on Filesystem to Virtual Memory
• Not Physical RAM
• Page Faults - not in memory - from disk - expensive
• Indices
• Part of the regular DB files
• Consider Warm Starting your Database

7. Sizing Hardware: CPU
• MongoDB uses multiple cores
• For working-set queries, CPU usage is minimal
• Generally, faster CPU are better
• Aggregation, Full Tablescans
•Makes heavy use of CPU / Disk
•Instead of counting / computing:
• cache / precompute
• Map Reduce
• Currently Single threaded
•Can be run in parallel across shards.
• This restriction may be eliminated, investigating options

8. Sizing Hardware: I/O
• Disk I/O determines performance of non-working set queries
• More Disks = Better
• Improved throughput, Reduced Seek times
• Raid 0 - Striping: improved write performance
• Raid 1 - Mirroring: survive single disk failure
• Raid 10 - both
• Consider Flash ?
• Expensive, getting cheaper
• Significantly reduced seek time, increased IO throughput
• Network
• It’s easy to saturate your network
• (Average doc size * number of document writes, reads) / sec

9. MongoStat
• Tool that comes with MongoDB
• Shows
• counters for I/O, time spent in write lock, ...

10. IOStat
iostat ‐x 2
iostat ‐w 1
disk0 disk1 disk2 cpu load average
KB/t tps MB/s KB/t tps MB/s KB/t tps MB/s us sy id 1m 5m 15m
12.83 3 0.04 2.01 0 0.00 12.26 2 0.02 11 5 83 0.35 0.26 0.25
11.12 75 0.81 0.00 0 0.00 0.00 0 0.00 60 24 16 0.68 0.34 0.28
4.00 3 0.01 0.00 0 0.00 0.00 0 0.00 60 23 17 0.68 0.34 0.28
avg-cpu: %user %nice %system %iowait %steal %idle
0.00 0.00 7.96 29.85 0.50 61.69
Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s avgrq-sz avgqu-sz await svctm %util
sda1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
sda2 0.50 4761.19 6.47 837.31 75.62 43681.59 51.86 38.38 42.33 0.46 38.41
Monitor disk transfers :
> 200 ‐ 300 Mb/s on XL EC2, but your mileage may vary
CPU usage
> 30 % during normal operations

11. OS
• For production: Use a 64bit OS
• 32bit has 2G limit
• Clients can be 32 bit
• MongoDB supports (little endian only):
• Linux, FreeBSD, OS X
• Windows
• Solaris (joyent)

12. Filesystem
• All data, namespace files stored in data directory
• Possible to create links
• Better to aggregrate IO across disks
•File Allocation

13. Filesystem
• Logfiles:
• --logpath <file>
• Rotate:
• db.runCommand(“logRotate”)
• kill -SIGUSR1 <mongod pid>
•Does not work for ./mongod > <file>
• MongoDB is filesystem-neutral:
• ext3, ext4 and XFS are most used
• ext4 / XFS preferred (posix_allocate())
• improved performance for file allocation
• Support for NTFS for windows

14. MongoDB Version Policy
• Production: run even numbers
• 1.4.x, 1.6.x, 1.8.x
•Development
•1.5.x, 1.7.x
• Critical bugs are back ported to even versions

15. Installing MongoDB
• Installing from Source
• Requires Scons, C++ compiler, Boost libraries, SpiderMonkey,
PCRE
• Installing from Binaries (easiest)
• curl -O http://downloads.mongodb.org/_os_/_version_
• Upgrading database
• Install new version of MongoDB
• Stop previous version
• Start new version
•In case of database file changes,
•mongodump / mongorestore

16. EC2 Notes
• Default storage instance is EXT3
• For best performance, reformat to EXT4 / XFS
• Use recent version of EXT4
• Use Striping (using MDADM or LVM) aggregates I/O
•This is a good thing
• EC2 can experience spikes in latency
• 400-600mS
•This is a bad thing

17. More EC2 Notes
• EBS snapshots can be used for backups
• EBS can disappear
• S3 can be used for longer term backups
• Use Amazon availability zones
• High Availability
• Disaster Recovery

18. Security
• Mongo supports basic security
• We encourage to run mongoDB in a safe environment
• Authenticates a User on a per Database basis
• Start database with --auth
• Admin user stored in the admin database
use admin
db.addUser("administrator", "password")
db.auth(“administrator”, “password”)
• Regular users stored in other databases
use personnel
db.addUser("joe", "password")
db.addUser(“fred”, “password”, true)

19. Backup
• Typically backups are driven from a slave
• Eliminates impact to client / application traffic to master

20. Backup
•Two main Strategies
• mongodump / mongorestore
• Filesystem backup / snapshot
• fsync+lock

21. mongodump
• binary, compact object dump
• each consistent object is written
• not necessarily consistent from start to finish
• unless you lock database:
• db.runCommand({fsync:1,lock:1})
• mongorestore to restore database
• database does not have to be up to restore

22. Filesystem Backup
• MUST
• fsync - flushes buffers to disk
• lock - blocks writes
db.runCommand({fsync:1,lock:1})
• Use file-system / LVM / storage snapshot
• unlock
db.$cmd.sys.unlock.findOne();

23. Database Maintenance
• When doing a lot of updates or deletes
• occasional database compaction might be needed
• indices and datafiles
• db.repair()
• With replica sets
• Rolling: start up node with --repair param

24. Durability
What failures do you need to recover from?
• Loss of a single database node?
• Loss of a group of nodes?

25. Durability - Master only
• Write acknowledged
when in memory on
master only

26. Durability - Master + Slaves
• W=2
• Write acknowledged
when in memory on
master + slave
• Will survive failure of a
single node

27. Durability - Master + Slaves +
fsync
• W=n
• Write acknowledged
when in memory on
master + slaves
• Pick a “majority” of
nodes
• fsync in batches (since
it blocking)

28. Slave delay
• Protection against app
faults
• Protection against
administration mistakes
• Slave runs X amount of
time behind

29. Scale out
read
shard1 shard2 shard3
mongos /
rep_a1 rep_a2 rep_a3 config server
mongos /
rep_b1 rep_b2 rep_b3 config server
mongos /
rep_c2 rep_c2 rep_c3 config server
write

30. Monitoring
• We like Munin ..
• ... but other frameworks
work as well
• Primary function:
• Measure stats over time
• Tells you what is going on with
your system

31. So go and deploy!
• Next up: Scaling MongoDB
• Also, 10gen is hiring. Talk to us!