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Deployment Strategy
 

Deployment Strategy

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Deployment Strategy Deployment Strategy Presentation Transcript

  • Thoughts on Deployment roger@10Gen.com @rogerb
  • Congratulations ! Development done ? Great ! Ready to Deploy :-)
  • some points to consider
  • 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
  • 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.
  • 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
  • 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
  • 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
  • MongoStat • Tool that comes with MongoDB • Shows • counters for I/O, time spent in write lock, ...
  • 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

  • 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)
  • Filesystem • All data, namespace files stored in data directory • Possible to create links • Better to aggregrate IO across disks •File Allocation
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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)
  • Backup • Typically backups are driven from a slave • Eliminates impact to client / application traffic to master
  • Backup •Two main Strategies • mongodump / mongorestore • Filesystem backup / snapshot • Filelock + fsync
  • 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
  • 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();
  • 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
  • Durability What failures do you need to recover from? • Loss of a single database node? • Loss of a group of nodes?
  • Durability - Master only • Write acknowledged when in memory on master only
  • Durability - Master + Slaves • W=2 • Write acknowledged when in memory on master + slave • Will survive failure of a single node
  • 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)
  • Slave delay • Protection against app faults • Protection against administration mistakes • Slave runs X amount of time behind
  • 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
  • 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
  • Thank You :-) @rogerb
  • download at mongodb.org conferences,
appearances,
and
meetups http://www.10gen.com/events Facebook









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