NoSQL Matters : MongoDB Sharding

NoSQL Matters 2012 : MongoDB Sharding

Transcript

1. Sharding Thursday, 31 May 12
2. MongoDB Scaling - Single Node write read single Thursday, 31 May 12
3. Replication http://www.flickr.com/photos/10335017@N07/4570943043 Thursday, 31 May 12
4. Replication • mongoDB replication like MySQL replication • Asynchronous master/slave • Replica sets • A cluster of N servers • All writes to primary • Reads can be to primary (default) or a secondary • Any (one) node can be primary • Consensus election of primary • Automatic failover • Automatic recovery Thursday, 31 May 12
5. How mongoDB Replication works Member 1 Member 2 Member 3 • Set is made up of 2 or more nodes Thursday, 31 May 12
6. How mongoDB Replication works • Election establishes the PRIMARY • Data replication from PRIMARY to SECONDARY Member 1 Member 2 Primary Member 3 Thursday, 31 May 12
7. How mongoDB Replication works • PRIMARY may fail • Automatic election of new PRIMARY if majority exists Member 1 Member 2 DOWN Member 3 negotiate new master Thursday, 31 May 12
8. How mongoDB Replication works • New PRIMARY elected • Replica Set re-established Member 1 Member 2 DOWN Member 3 Primary Thursday, 31 May 12
9. How mongoDB Replication works • Automatic recovery Member 1 Member 3 Primary Member 2 Recovering Thursday, 31 May 12
10. How mongoDB Replication works • Replica Set re-established Member 1 Member 3 Primary Member 2 Thursday, 31 May 12
11. > cfg = { _id : "myset", members : [ { _id : 0, host : "germany1.acme.com" }, { _id : 1, host : "germany2.acme.com" }, { _id : 2, host : "germany3.acme.com" } ] } > use admin > db.runCommand( { replSetInitiate : cfg } ) Creating a Replica Set Thursday, 31 May 12
12. Write scaling - add Shards write read shard1 primary secondary secondary Thursday, 31 May 12
13. Sharding http://www.flickr.com/photos/60218876@N08/6888405266 Thursday, 31 May 12
14. Write scaling - add Shards write read shard1 primary secondary secondary shard2 primary secondary secondary shard3 primary secondary secondary Thursday, 31 May 12
15. Big Data at a Glance Large Dataset Primary Key as “username” a b c d e f g h s t u v w x y z ... Thursday, 31 May 12
16. Big Data at a Glance • Systems like Google File System (which inspired Hadoop’s HDFS) and MongoDB’s Sharding handle the scale problem by chunking Large Dataset Primary Key as “username” a b c d e f g h s t u v w x y z ... Thursday, 31 May 12
17. Big Data at a Glance • Systems like Google File System (which inspired Hadoop’s HDFS) and MongoDB’s Sharding handle the scale problem by chunking • Break up pieces of data into smaller chunks, spread across many data nodes Large Dataset Primary Key as “username” a b c d e f g h s t u v w x y z ... Thursday, 31 May 12
18. Big Data at a Glance • Systems like Google File System (which inspired Hadoop’s HDFS) and MongoDB’s Sharding handle the scale problem by chunking • Break up pieces of data into smaller chunks, spread across many data nodes • Each data node contains many chunks Large Dataset Primary Key as “username” a b c d e f g h s t u v w x y z ... Thursday, 31 May 12
19. Big Data at a Glance • Systems like Google File System (which inspired Hadoop’s HDFS) and MongoDB’s Sharding handle the scale problem by chunking • Break up pieces of data into smaller chunks, spread across many data nodes • Each data node contains many chunks • If a chunk gets too large or a node overloaded, data can be rebalanced Large Dataset Primary Key as “username” a b c d e f g h s t u v w x y z ... Thursday, 31 May 12
20. Big Data at a Glance Large Dataset Primary Key as “username” a b c d e f g h s t u v w x y z Thursday, 31 May 12
21. Big Data at a Glance Large Dataset Primary Key as “username” a b c d e f g h s t u v w x y z MongoDB Sharding ( as well as HDFS ) breaks data into chunks (~64 mb) Thursday, 31 May 12
22. Large Dataset Primary Key as “username” Scaling Data Node 1 25% of chunks Data Node 2 25% of chunks Data Node 3 25% of chunks Data Node 4 25% of chunks a b c d e f g h s t u v w x y z Representing data as chunks allows many levels of scale across n data nodes Thursday, 31 May 12
23. Large Dataset Primary Key as “username” Scaling Data Node 1 25% of chunks Data Node 2 25% of chunks Data Node 3 25% of chunks Data Node 4 25% of chunks a b c d e f g h s t u v w x y z Representing data as chunks allows many levels of scale across n data nodes Thursday, 31 May 12
24. Scaling Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z The set of chunks can be evenly distributed across n data nodes Thursday, 31 May 12
25. Scaling Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z The set of chunks can be evenly distributed across n data nodes Thursday, 31 May 12
26. Add Nodes: Chunk Rebalancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z The goal is equilibrium - an equal distribution. As nodes are added (or even removed) chunks can be redistributed for balance. Thursday, 31 May 12
27. Add Nodes: Chunk Rebalancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z The goal is equilibrium - an equal distribution. As nodes are added (or even removed) chunks can be redistributed for balance. Thursday, 31 May 12
28. Writes Routed to Appropriate Chunk Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z Thursday, 31 May 12
29. Writes Routed to Appropriate Chunk Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z Thursday, 31 May 12
30. Writes Routed to Appropriate Chunk Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z Write to key“ziggy” z Writes are efficiently routed to the appropriate node & chunk Thursday, 31 May 12
31. Writes Routed to Appropriate Chunk Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z Write to key“ziggy” z Writes are efficiently routed to the appropriate node & chunk Thursday, 31 May 12
32. Chunk Splitting & Balancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z Write to key“ziggy” z If a chunk gets too large (default in MongoDB - 64mb per chunk), It is split into two new chunks Thursday, 31 May 12
33. Chunk Splitting & Balancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z z If a chunk gets too large (default in MongoDB - 64mb per chunk), It is split into two new chunks Thursday, 31 May 12
34. Chunk Splitting & Balancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z z If a chunk gets too large (default in MongoDB - 64mb per chunk), It is split into two new chunks Thursday, 31 May 12
35. Chunk Splitting & Balancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z z2 If a chunk gets too large (default in MongoDB - 64mb per chunk), It is split into two new chunks z1 Thursday, 31 May 12
36. Chunk Splitting & Balancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z z2 If a chunk gets too large (default in MongoDB - 64mb per chunk), It is split into two new chunks z1 Thursday, 31 May 12
37. Chunk Splitting & Balancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z z2 z1 Each new part of the Z chunk (left & right) now contains half of the keys Thursday, 31 May 12
38. Chunk Splitting & Balancing Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z z2 z1 As chunks continue to grow and split, they can be rebalanced to keep an equal share of data on each server. Thursday, 31 May 12
39. Reads with Key Routed Efficiently Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y z1 Read Key “xavier” Reading a single value by Primary Key Read routed efficiently to specific chunk containing key z2 Thursday, 31 May 12
40. Reads with Key Routed Efficiently Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y Read Key “xavier” Reading a single value by Primary Key Read routed efficiently to specific chunk containing key z1 z2 Thursday, 31 May 12
41. Reads with Key Routed Efficiently Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y Read Key “xavier” Reading a single value by Primary Key Read routed efficiently to specific chunk containing key z1 z2 Thursday, 31 May 12
42. Reads with Key Routed Efficiently Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y Read Keys “T”->”X” Reading multiple values by Primary Key Reads routed efficiently to specific chunks in range t u v w x z1 z2 Thursday, 31 May 12
43. Reads with Key Routed Efficiently Data Node 1 Data Node 2 Data Node 3 Data Node 4 Data Node 5 a b c d e f g h s t u v w x y Read Keys “T”->”X” Reading multiple values by Primary Key Reads routed efficiently to specific chunks in range t u v w x z1 z2 Thursday, 31 May 12
44. MongoDB Sharding • Automatic partitioning and management • Range based • Convert to sharded system with no downtime • Fully consistent Thursday, 31 May 12
45. How MongoDB Sharding works > db.posts.save( {age:40} ) -∞ +∞ -∞ 40 41 +∞ •Data in inserted •Ranges are split into more “chunks” Thursday, 31 May 12
46. How MongoDB Sharding works > db.posts.save( {age:40} ) > db.posts.save( {age:50} ) > db.posts.save( {age:60} ) -∞ +∞ -∞ 40 41 +∞ 41 50 51 +∞ 61 +∞ 51 60 Thursday, 31 May 12
47. SHARDING ARCHITECTURE Thursday, 31 May 12
48. Architecture Thursday, 31 May 12
49. mongos • Shard Router • Acts just like a MongoD • 1 or as many as you want • Can run on App Servers • Caches meta-data from config servers Thursday, 31 May 12
50. Config Server • 3 of them • Changes use 2 phase commit • If any are down, meta data goes read only • System is online as long as 1/3 is up Thursday, 31 May 12
51. HOW IT WORKS Thursday, 31 May 12
52. Keys { name: “Jared”, email: “jsr@abc.com”, } { name: “Scott”, email: “scott@gmail.com”, } { name: “Dan”, email: “dan@yahoo.com”, } > db.runCommand( { shardcollection: “test.users”, key: { email: 1 }} ) Thursday, 31 May 12
53. Chunks -∞ +∞ Thursday, 31 May 12
54. Chunks -∞ +∞ dan@yahoo.com jsr@abc.com scott@gmail.com Thursday, 31 May 12
55. Chunks -∞ +∞ dan@yahoo.com jsr@abc.com scott@gmail.com Split! Thursday, 31 May 12
56. Chunks -∞ +∞ dan@yahoo.com jsr@abc.com scott@gmail.com Split! This is a chunk This is a chunk Thursday, 31 May 12
57. Chunks -∞ +∞ dan@yahoo.com jsr@abc.com scott@gmail.com Thursday, 31 May 12
58. Chunks -∞ +∞ dan@yahoo.com jsr@abc.com scott@gmail.com Thursday, 31 May 12
59. Chunks -∞ +∞ dan@yahoo.com jsr@abc.com scott@gmail.com Split! Thursday, 31 May 12
60. Chunks Min Key Max Key Shard -∞ adam@yahoo.com 1 adam@yahoo.com jared@abc.com 1 jared@abc.com scott@gmail.com 1 scott@gmail.com +∞ 1 • Stored in the config servers • Cached in MongoS • Used to route requests and keep cluster balanced Thursday, 31 May 12
61. Balancing Shard 1 Shard 2 Shard 3 Shard 4 5 9 1 6 10 2 7 11 3 8 12 4 17 21 13 18 22 14 19 23 15 20 24 16 29 33 25 30 34 26 31 35 27 32 36 28 41 45 37 42 46 38 43 47 39 44 48 40 mongos balancer config config config Chunks! Thursday, 31 May 12
62. Balancing mongos balancer config config config Shard 1 Shard 2 Shard 3 Shard 4 5 9 1 6 10 2 7 11 3 8 12 4 21 22 23 24 33 34 35 36 45 46 47 48 Imbalance Imbalance Thursday, 31 May 12
63. Balancing mongos balancer Move chunk 1 to Shard 2 config config config Shard 1 Shard 2 Shard 3 Shard 4 5 9 1 6 10 2 7 11 3 8 12 4 21 22 23 24 33 34 35 36 45 46 47 48 Thursday, 31 May 12
64. Balancing mongos balancer config config config Shard 1 Shard 2 Shard 3 Shard 4 5 9 6 10 2 7 11 3 8 12 4 21 22 23 24 33 34 35 36 45 46 47 48 1 Thursday, 31 May 12
65. Balancing mongos balancer config config config Shard 1 Shard 2 Shard 3 Shard 4 5 9 6 10 2 7 11 3 8 12 4 21 22 23 24 33 34 35 36 45 46 47 48 1 Thursday, 31 May 12
66. Balancing mongos balancer Chunk 1 now lives on Shard 2 config config config Shard 1 Shard 2 Shard 3 Shard 4 5 9 1 6 10 2 7 11 3 8 12 4 21 22 23 24 33 34 35 36 45 46 47 48 Thursday, 31 May 12
67. SWITCHING Thursday, 31 May 12
68. Switching Request mongos Shard 1 Shard 2 Shard 3 1 2 3 4 1. Query arrives at MongoS 2. MongoS routes query to a single shard 3. Shard returns results of query 4. Results returned to client Thursday, 31 May 12
69. Scatter Gather mongos Shard 1 Shard 2 Shard 3 1 4 1. Query arrives at MongoS 2. MongoS broadcasts query to all shards 3. Each shard returns results for query 4. Results combined and returned to client 2 2 3 3 2 3 Thursday, 31 May 12
70. Writes Inserts Requires shard key db.users.insert({ name: “Jared”, email: “jsr@abc.com”}) Removes Routed db.users.delete({ email: “jsr@abc.com”}) Removes Scattered db.users.delete({name: “Jared”}) Updates Routed db.users.update( {email: “jsr@abc.com”}, {$set: { state: “CA”}}) Updates Scattered db.users.update( {state: “FZ”}, {$set:{ state: “CA”}} ) Thursday, 31 May 12
71. Queries By Shard Key Routed db.users.find( {email: “jsr@abc.com”}) Sorted by shard key Routed in order db.users.find().sort({email:-1}) Find by non shard key Scatter Gather db.users.find({state:”CA”}) Sorted by non shard key Distributed merge sort db.users.find().sort({state:1}) Thursday, 31 May 12
72. conferences, appearances http://www.10gen.com/events download at mongodb.org We’re Hiring ! Chris Harris Email : charris@10gen.com Twitter : cj_harris5 Thursday, 31 May 12