The document provides guidance on deploying MongoDB in production environments. It discusses sizing hardware requirements for memory, CPU, and disk I/O. It also covers installing and upgrading MongoDB, considerations for cloud platforms like EC2, security, backups, durability, scaling out, and monitoring. The focus is on performance optimization and ensuring data integrity and high availability.

1. Thoughts on Deployment
roger@10Gen.com
@rogerb

2. Congratulations !
Development done ?
Great ! 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
• Filelock + fsync

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. Thank You :-)
@rogerb

32. download at mongodb.org
conferences, appearances, and meetups
http://www.10gen.com/events
Facebook | Twitter | LinkedIn
http://bit.ly/mongoN @mongodb http://linkd.in/joinmongo