Sharding

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Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural......

Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation.

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  • Ops will be most interested in ConfigurationDev will be most interested in Mechanics
  • From mainframes, to RAC Oracle servers.. People solved problems by adding more resources to a single machine.
  • Google was the first to demonstrate that a large scale operation could be had with high performance on commodity hardwareBuild - Document oriented database maps perfectly to object oriented languagesScale - MongoDB presents clear path to scalability that isn't ops intensive - Provides same interface for sharded cluster as single instance
  • In 2005, two ways to achieve datastore scalability:Lots of money to purchase custom hardwareLots of money to develop custom software
  • Indexes should be contained in working set.
  • The solution to the preceding problemsAdd graphic??
  • Add arrows for or mention that there is communication between shards (migrations)
  • MongoDB 2.2 and later only need <host> and <port> for one member of the replica set
  • Really out of place. Can't do it justice in 1 slide and not enough time to do more.
  • Quick review from earlier.
  • Once chunk size is reached, mongos asks mongod to split a chunk + internal function called splitVector()mongod counts number of documents on each side of split + based on avg. document size `db.stats()`Chunk split is a **logical** operation (no data has moved)Max on first chunk should be 14
  • Balancer lock actually held on config server.
  • Moved chunk on shard2 should be gray
  • How do the other mongoses know that their configuration is out of date? When does the chunk version on the shard itself get updated?
  • The mongos does not have to load the whole set into memory since each shard sorts locally. The mongos can just getMore from the shards as needed and incrementally return the results to the client.
  • _id could be unique across shards if used as shard key.we could only guarantee uniqueness of (any) attributes if the keys are used as shard keys with unique attribute equals true
  • Need to rework this section. It adds nothing.
  • Use this slide to talk about clients that have had this problem and worked around it by sharding.
  • Use this slide to talk about clients that have had this problem and worked around it by sharding.

Transcript

  • 1. #MongoChicagoShardingChad TindelSolution Architect, 10gen
  • 2. Agenda• Short history of scaling data• Why shard• MongoDBs approach• Architecture• Configuration• Mechanics• Solutions
  • 3. The story of scalingdata
  • 4. Visual representation of vertical scaling1970 - 2000: Vertical Scalability (scaleup)
  • 5. Visual representation of horizontal scalingGoogle, ~2000: Horizontal Scalability (scaleout)
  • 6. Data Store Scalability in 2005• Custom Hardware – Oracle• Custom Software – Facebook + MySQL
  • 7. Data Store Scalability Today• MongoDB auto-sharding available in 2009• A data store that is – Free – Publicly available – Open source (https://github.com/mongodb/mongo) – Horizontally scalable – Application independent
  • 8. Why shard?
  • 9. Working Set Exceeds PhysicalMemory
  • 10. Read/Write Throughput Exceeds I/O
  • 11. MongoDBs approachto sharding
  • 12. Partition data based on ranges• User defines shard key• Shard key defines range of data• Key space is like points on a line• Range is a segment of that line
  • 13. Distribute data in chunks acrossnodes• Initially 1 chunk• Default max chunk size: 64mb• MongoDB automatically splits & migrates chunks when max reached
  • 14. MongoDB manages data• Queries routed to specific shards• MongoDB balances cluster• MongoDB migrates data to new nodes
  • 15. MongoDB Auto-Sharding• Minimal effort required – Same interface as single mongod• Two steps – Enable Sharding for a database – Shard collection within database
  • 16. Architecture
  • 17. Data stored in shard• Shard is a node of the cluster• Shard can be a single mongod or a replica set
  • 18. Config server stores meta data• Config Server – Stores cluster chunk ranges and locations – Can have only 1 or 3 (production must have 3) – Two phase commit (not a replica set)
  • 19. MongoS manages the data• Mongos – Acts as a router / balancer – No local data (persists to config database) – Can have 1 or many
  • 20. Sharding infrastructure
  • 21. Configuration
  • 22. Example cluster setup• Don’t use this setup in production! - Only one Config server (No Fault Tolerance) - Shard not in a replica set (Low Availability) - Only one Mongos and shard (No Performance Improvement) - Useful for development or demonstrating configuration mechanics
  • 23. Start the config server• “mongod --configsvr”• Starts a config server on the default port (27019)
  • 24. Start the mongos router• “mongos --configdb <hostname>:27019”• For 3 config servers: “mongos --configdb <host1>:<port1>,<host2>:<port2>,<host3>:<port3>”• This is always how to start a new mongos, even if the cluster is already running
  • 25. Start the shard database• “mongod --shardsvr”• Starts a mongod with the default shard port (27018)• Shard is not yet connected to the rest of the cluster• Shard may have already been running in production
  • 26. Add the shard• On mongos: “sh.addShard(„<host>:27018‟)”• Adding a replica set: “sh.addShard(„<rsname>/<seedlist>‟)• In 2.2 and later can use sh.addShard(„<host>:<port>‟)
  • 27. Verify that the shard was added• db.runCommand({ listshards:1 })• { "shards" : [ { "_id”: "shard0000”, "host”: ”<hostname>:27018” } ], "ok" : 1 }
  • 28. Enabling Sharding• Enable sharding on a database – sh.enableSharding(“<dbname>”)• Shard a collection with the given key – sh.shardCollection(“<dbname>.people”,{“country”:1})• Use a compound shard key to prevent duplicates – sh.shardCollection(“<dbname>.cars”,{“year”:1, ”uniqueid”:1})
  • 29. Mechanics
  • 30. Partitioning• Remember its based on ranges
  • 31. Chunk is a section of the entire range
  • 32. Chunk splitting• A chunk is split once it exceeds the maximum size• There is no split point if all documents have the same shard key• Chunk split is a logical operation (no data is moved)• If split creates too large of a discrepancy of chunk count across cluster a balancing round starts
  • 33. Balancing• Balancer is running on mongos• Once the difference in chunks between the most dense shard and the least dense shard is above the migration threshold, a balancing round starts
  • 34. Acquiring the Balancer Lock• The balancer on mongos takes out a “balancer lock”• To see the status of these locks: - use config - db.locks.find({ _id: “balancer” })
  • 35. Moving the chunk• The mongos sends a “moveChunk” command to source shard• The source shard then notifies destination shard• The destination claims the chunk shard-key range• Destination shard starts pulling documents from source shard
  • 36. Committing Migration• When complete, destination shard updates config server - Provides new locations of the chunks
  • 37. Cleanup• Source shard deletes moved data - Must wait for open cursors to either close or time out - NoTimeout cursors may prevent the release of the lock• Mongos releases the balancer lock after old chunks are deleted
  • 38. Routing Requests
  • 39. Cluster Request Routing• Targeted Queries• Scatter Gather Queries• Scatter Gather Queries with Sort
  • 40. Cluster Request Routing: TargetedQuery
  • 41. Routable request received
  • 42. Request routed to appropriate shard
  • 43. Shard returns results
  • 44. Mongos returns results to client
  • 45. Cluster Request Routing: Non-TargetedQuery
  • 46. Non-Targeted Request Received
  • 47. Request sent to all shards
  • 48. Shards return results to mongos
  • 49. Mongos returns results to client
  • 50. Cluster Request Routing: Non-TargetedQuery with Sort
  • 51. Non-Targeted request with sortreceived
  • 52. Request sent to all shards
  • 53. Query and sort performed locally
  • 54. Shards return results to mongos
  • 55. Mongos merges sorted results
  • 56. Mongos returns results to client
  • 57. Shard Key
  • 58. Shard Key• Choose a field common used in queries• Shard key is immutable• Shard key values are immutable• Shard key requires index on fields contained in key• Uniqueness of `_id` field is only guaranteed within individual shard• Shard key limited to 512 bytes in size
  • 59. #MongoChicagoThank YouChad TindelSolution Architect, 10gen