1. Senior Solutions Architect, MongoDB Inc. Massimo Brignoli #MongoDB Introduction to Sharding

2. Notes to the presenter Themes for this presentation: Horizontal scale only viable approach Cover the scenarios when to shard Explain the mechanics Themes: MongoDB makes this easy MongoDB includes this functionality free Best approach to with consistency

3. Agenda Scaling Data MongoDB's Approach Architecture Configuration Mechanics

4. Scaling Data

5. Examining Growth User Growth – 1995: 0.4% of the world’s population – Today: 30% of the world is online (~2.2B) – Emerging Markets & Mobile Data Set Growth – Facebook’s data set is around 100 petabytes – 4 billion photos taken in the last year (4x a decade ago)

6. Read/Write Throughput Exceeds I/O

7. Working Set Exceeds Physical Memory

8. Vertical Scalability (Scale Up)

9. Horizontal Scalability (Scale Out)

10. Data Store Scalability Custom Hardware – Oracle Custom Software – Facebook + MySQL – Google

11. Data Store Scalability Today MongoDB Auto-Sharding A data store that is – Free – Publicly available – Open Source (https://github.com/mongodb/mongo) – Horizontally scalable – Application independent

12. MongoDB's Approach to Sharding

13. Partitioning 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

14. Initially 1 chunk Default max chunk size: 64mb MongoDB automatically splits & migrates chunks when max reached Data Distribution

15. Queries routed to specific shards MongoDB balances cluster MongoDB migrates data to new nodes Routing and Balancing

16. MongoDB Auto-Sharding Minimal effort required – Same interface as single mongod Two steps – Enable Sharding for a database – Shard collection within database

17. Architecture

18. What is a Shard? Shard is a node of the cluster Shard can be a single mongod or a replica set

19. Meta Data Storage Config Server – Stores cluster chunk ranges and locations – Can have only 1 or 3 (production must have 3) – Not a replica set

20. Routing and Managing Data Mongos – Acts as a router / balancer – No local data (persists to config database) – Can have 1 or many

21. Sharding infrastructure

22. Configuration

23. Example Cluster

24. mongod --configsvr Starts a configuration server on the default port (27019) Starting the Configuration Server

25. mongos --configdb :27019 For 3 configuration servers: mongos --configdb :, :, : This is always how to start a new mongos, even if the cluster is already running Start the mongos Router

26. 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 Start the shard database

27. On mongos: – sh.addShard(‘ :27018’) Adding a replica set: – sh.addShard(‘ / ’) Add the Shard

28. db.runCommand({ listshards:1 }) { "shards" : [{"_id”: "shard0000”,"host”: ” :27018” } ], "ok" : 1 } Verify that the shard was added

29. Enabling Sharding Enable sharding on a database sh.enableSharding(“ ”) Shard a collection with the given key sh.shardCollection(“.people”,{“country”:1}) Use a compound shard key to prevent duplicates sh.shardCollection(“.cars”,{“year”:1, ”uniqueid”:1})

30. Tag Aware Sharding Tag aware sharding allows you to control the distribution of your data Tag a range of shard keys – sh.addTagRange(,,, ) Tag a shard – sh.addShardTag(, )

31. Mechanics

32. Partitioning Remember it's based on ranges

33. Chunk is a section of the entire range

34. 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) Chunk splitting

35. 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 Balancing

36. The balancer on mongos takes out a “balancer lock” To see the status of these locks: use config db.locks.find({ _id: “balancer” }) Acquiring the Balancer Lock

37. The mongos sends a moveChunk command to source shard The source shard then notifies destination shard Destination shard starts pulling documents from source shard Moving the chunk

38. When complete, destination shard updates config server – Provides new locations of the chunks Committing Migration

39. 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 The mongos releases the balancer lock after old chunks are deleted Cleanup

40. Routing Requests

41. Cluster Request Routing Targeted Queries Scatter Gather Queries Scatter Gather Queries with Sort

42. Cluster Request Routing: Targeted Query

43. Routable request received

44. Request routed to appropriate shard

45. Shard returns results

46. Mongos returns results to client

47. Cluster Request Routing: Non-Targeted Query

48. Non-Targeted Request Received

49. Request sent to all shards

50. Shards return results to mongos

51. Mongos returns results to client

52. Cluster Request Routing: Non-Targeted Query with Sort

53. Non-Targeted request with sort received

54. Request sent to all shards

55. Query and sort performed locally

56. Shards return results to mongos

57. Mongos merges sorted results

58. Mongos returns results to client

59. Shard Key

60. Shard key is immutable Shard key values are immutable Shard key must be indexed Shard key limited to 512 bytes in size Shard key used to route queries – Choose a field commonly used in queries Only shard key can be unique across shards – `_id` field is only unique within individual shard

61. Shard Key Considerations Cardinality Write Distribution Query Isolation Reliability Index Locality

62. Notes to the presenter The upcoming section goes into detail about shard key considerations by working through a small example. This is a large deck, and if you’re pressed for time you might opt to skip this. However, if you’re delivering the combined sharding talk (meaning there isn’t an advanced sharding talk accompanying this one) it might be worth diving into this breifly for a few minutes.

63. { _id: ObjectId(), user: 123, time: Date(), subject: “...”, recipients: [], body: “...”, attachments: [] } Example: Email Storage Most common scenario, can be applied to 90% cases Each document can be up to 16MB Each user may have GBs of storage Most common query: get user emails sorted by time Indexes on {_id}, {user, time}, {recipients}

64. Example: Email Storage Cardinality Write Scaling Query Isolation Reliability Index Locality

65. Example: Email Storage Cardinality Write Scaling Query Isolation Reliability Index Locality _idDoc levelOne shard Scatter/gat her All users affected Good

66. Example: Email Storage Cardinality Write Scaling Query Isolation Reliability Index Locality _idDoc levelOne shard Scatter/gat her All users affected Good hash(_id)Hash levelAll Shards Scatter/gat her All users affected Poor

67. Example: Email Storage Cardinality Write Scaling Query Isolation Reliability Index Locality _idDoc levelOne shard Scatter/gat her All users affected Good hash(_id)Hash levelAll Shards Scatter/gat her All users affected Poor userMany docsAll ShardsTargeted Some users affected Good

68. Example: Email Storage Cardinality Write Scaling Query Isolation Reliability Index Locality _idDoc levelOne shard Scatter/gat her All users affected Good hash(_id)Hash levelAll Shards Scatter/gat her All users affected Poor userMany docsAll ShardsTargeted Some users affected Good user, timeDoc levelAll ShardsTargeted Some users affected Good

69. Conclusion

70. Read/Write Throughput Exceeds I/O

71. Working Set Exceeds Physical Memory

72. Sharding Enables Scale MongoDB’s Auto-Sharding – Easy to Configure – Consistent Interface – Free and Open Source

73. What’s next? – [ Insert Related Talks ] – [ Insert Upcoming Webinars ] – MongoDB User Group Resources https://education.10gen.com/ http://www.10gen.com/presentations

74. Thank You Software Engineer, MongoDB Speaker Name #hashtag