Scaling MongoDB (Mongo Austin)

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Eliot Horowitz's presentation at Mongo Austin

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  • What is scaling?\nWell - hopefully for everyone here.\n\n
  • ec2 goes up to 64gb, maybe mention 256gb box here??? ($30-40k)\nmaybe can but 256gb box, but i spin up 10 ec2 64gb boxes in 10 minutes\n
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  • Don’t pre-emptively shard - easy to add later\n
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  • Scaling MongoDB (Mongo Austin)

    1. 1. Scaling with MongoDB Eliot Horowitz @eliothorowitz MongoAustin February 15, 2011
    2. 2. Scaling• Storage needs only go up• Operations/sec only go up• Complexity only goes up
    3. 3. Horizontal Scaling• Vertical scaling is limited• Hard to scale vertically in the cloud• Can scale wider than higher
    4. 4. Read Scaling• One master at any time• Programmer determines if read hits master or a slave• Pro: easy to setup, can scale reads very well• Con: reads are inconsistent on a slave• Writes don’t scale
    5. 5. One Master, Many Slaves• Custom Master/Slave setup• Have as many slaves as you want• Can put them local to application servers• Good for 90+% read heavy applications (Wikipedia)
    6. 6. Replica Sets• High Availability Cluster• One master at any time, up to 6 slaves• A slave automatically promoted to master if failure• Drivers support auto routing of reads to slaves if programmer allows• Good for applications that need high write availability but mostly reads (Commenting System)
    7. 7. Sharding• Many masters, even more slaves• Can scale in two dimensions• Add Shards for write and data size scaling• Add slaves for inconsistent read scaling and redundancy
    8. 8. Sharding Basics• Data is split up into chunks• Shard: Replica sets that hold a portion of the data• Config Servers: Store meta data about system• Mongos: Routers, direct direct and merge requests
    9. 9. Architecture Shards mongod mongod mongod ... mongod mongod mongod mongod mongod mongod Config Serversmongod mongos mongos ...mongodmongod client client client client
    10. 10. Common Setup• A common setup is 3 shards with 3 servers per shard: 3 masters, 6 slaves• Can add sharding later to an existing replica set with no down time• Can have sharded and non-sharded collections
    11. 11. Range Based MIN MAX LOCATION A F shard1 F M shard1 M R shard2 R Z shard3• collection is broken into chunks by range• chunks default to 64mb or 100,000 objects
    12. 12. Config Servers• 3 of them• changes are made with 2 phase commit• if any are down, meta data goes read only• system is online as long as 1/3 is up
    13. 13. mongos• Sharding Router• Acts just like a mongod to clients• Can have 1 or as many as you want• Can run on appserver so no extra network traffic• Cache meta data from config servers
    14. 14. Writes• Inserts : require shard key, routed• Removes: routed and/or scattered• Updates: routed or scattered
    15. 15. Queries• By shard key: routed• sorted by shard key: routed in order• by non shard key: scatter gather• sorted by non shard key: distributed merge sort
    16. 16. Splitting• Take a chunk and split it in 2• Splits on the median value• Splits only change meta data, no data change
    17. 17. SplittingT1 MIN MAX LOCATION A Z shard1T2 MIN MAX LOCATION A G shard1 G Z shard1T3 MIN MAX LOCATION A D shard1 D G shard1 G S shard1 S Z shard1
    18. 18. Balancing• Moves chunks from one shard to another• Done online while system is running• Balancing runs in the background
    19. 19. MigratingT3 MIN MAX LOCATION A D shard1 D G shard1 G S shard1 S Z shard1T4 MIN MAX LOCATION A D shard1 D G shard1 G S shard1 S Z shard2T5 MIN MAX LOCATION A D shard1 D G shard1 G S shard2 S Z shard2
    20. 20. Choosing a Shard Key• Shard key determines how data is partitioned• Hard to change• Most important performance decision
    21. 21. Use Case: User Profiles { email : “eliot@10gen.com” , addresses : [ { state : “NY” } ] }• Shard by email• Lookup by email hits 1 node• Index on { “addresses.state” : 1 }
    22. 22. Use Case: Activity Stream { user_id : XXX, event_id : YYY , data : ZZZ }• Shard by user_id• Looking up an activity stream hits 1 node• Writing even is distributed• Index on { “event_id” : 1 } for deletes
    23. 23. Use Case: Photos { photo_id : ???? , data : <binary> } What’s the right key?• auto increment• MD5( data )• now() + MD5(data)• month() + MD5(data)
    24. 24. Use Case: Logging { machine : “app.foo.com” , app : “apache” , when : “2010-12-02:11:33:14” , data : XXX } Possible Shard keys• { machine : 1 }• { when : 1 }• { machine : 1 , app : 1 }• { app : 1 }
    25. 25. Roadmap
    26. 26. Past Releases• First release - February 2009• v1.0 - August 2009• v1.2 - December 2009 - Map/Reduce, lots of small things• v1.4 - March 2010 - Concurrency/Geo• V1.6 - August 2010 - Sharding/Replica Sets
    27. 27. 1.8• Single Server Durability• Covered Indexes• Enhancements to Sharding/Replica Sets
    28. 28. Short List• Better Aggregation• Full Text Search• TTL timeout collections• Concurrency• Compaction
    29. 29. Download MongoDB http://www.mongodb.org and
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