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Sharding Overview
- 1. #MongoDBdays Sharding Edouard Servan-Schreiber, Ph.D. Director of Solution Architecture, 10gen
- 2. Visual representation of vertical scaling 1970 - 2000: Vertical Scalability (scale up)
- 3. Visual representation of horizontal scaling Google, ~2000: Horizontal Scalability (scale out)
- 4. Why shard?
- 5. Working Set Exceeds Physical Memory
- 7. 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
- 8. Distribute data in chunks across nodes • Initially 1 chunk • Default max chunk size: 64mb • MongoDB automatically splits & migrates chunks when max reached
- 9. MongoDB manages data access • Queries routed to specific shards • MongoDB balances cluster • MongoDB migrates data to new nodes
- 10. Architecture
- 11. Data stored in shard • Shard is a node of the cluster • Shard can be a single mongod or a replica set
- 12. 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)
- 13. MongoS manages the data routing • Mongos – Acts as a router / balancer – No local data (persists to config database) – Can have 1 or many
- 15. Mechanics
- 16. Partitioning • Remember it's based on ranges
- 17. Chunk is a section of the entire range
- 18. 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
- 19. 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
- 20. 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” })
- 21. 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
- 22. Committing Migration • When complete, destination shard updates config server - Provides new locations of the chunks
- 23. 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
- 24. Routing Requests
- 25. Cluster Request Routing • Targeted Queries • Scatter Gather Queries • Scatter Gather Queries with Sort
- 26. Cluster Request Routing: Targeted Query
- 28. Request routed to appropriate shard
- 30. Mongos returns results to client
- 31. Cluster Request Routing: Non-Targeted Query
- 33. Request sent to all shards
- 34. Shards return results to mongos
- 35. Mongos returns results to client
- 36. Cluster Request Routing: Non-Targeted Query with Sort
- 37. Non-Targeted request with sort received
- 38. Request sent to all shards
- 39. Query and sort performed locally
- 40. Shards return results to mongos
- 41. Mongos merges sorted results
- 42. Mongos returns results to client
- 43. Shard Key
- 44. 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
- 45. Shard Key Considerations • Cardinality • Write distribution • Query isolation • Data Distribution
- 46. Hashed Sharding
- 47. Hashed Shard Keys • New in MongoDB 2.4 • Uses Hashed indexes • The mongos will route all equality queries to a specific shard or set of shards; however, the mongos must route range queries to all shards. • When using a hashed shard key on an empty collection, MongoDB automatically pre-splits the range of 64-bit hash values into chunks
- 48. Use Cases for Hashed Shard Keys • Write heavy applications • Need good write distribution • Tolerant of slower scatter gather queries • Rarely perform range queries (Everything is directed to one document)
- 49. #ConferenceHashTag Thank You Speaker Name Job Title, 10gen
Notas del editor
- 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
- Indexes should be contained in working set.
- Add arrows for or mention that there is communication between shards (migrations)
- 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