~3333 write ops/s ms response

Transcription

1 NoSQL Infrastructure

2 ~3333 write ops/s ms response

3 Woop Japan! David Mytton

4

5 MongoDB at Server Density

6 MongoDB at Server Density 27 nodes

7 MongoDB at Server Density 27 nodes June yrs

8 MongoDB at Server Density 27 nodes June yrs MySQL -> MongoDB

9 MongoDB at Server Density 27 nodes June yrs MySQL -> MongoDB 20TB data per month

10 Why?

11 Replication Why?

12 Why? Replication Official drivers

13 Why? Replication Official drivers Easy deployment

14 Why? Replication Official drivers Easy deployment Fast out of the box

15 It s a little different.

16 Performance Fast network Picture is unrelated! Mmm, ice cream.

17 Performance Fast network EC2 10 Gigabit Ethernet - Cluster Compute - High Memory Cluster - Cluster GPU - High I/O - High Storage

18 Performance Fast network Workload: Read/Write? What is being stored? Result set size

19 Performance Fast network Use Normal Replication (Initial Sync) Replication (Oplog) Backup Network Throughput 0-100Mb/s Burst +100Mb/s 0-100Mb/s Initial Sync + Oplog

20 Performance Fast network Inter-DC LAN

21 Performance Fast network Inter-DC LAN Cross USA Washington, DC - San Jose, CA

22 Performance Fast network Location Within USA Trans-Atlantic Trans-Pacific Europe - Japan Ping RTT Latency 40-80ms 100ms 150ms 300ms

23 Replica sets Failover

24 Failover Replica sets Master/slave

25 Failover Replica sets Master/slave Min 3 nodes

26 Failover Replica sets Master/slave Min 3 nodes Automatic failover

27 Performance Replication lag Location Within USA Trans-Atlantic Trans-Pacific Europe - Japan Ping RTT Latency 40-80ms 100ms 150ms 300ms

28 Replication Lag. Reads: eventual consistency

29 Replication Lag. Reads: eventual consistency. Failover: slave behind

30 Stale data Eventual Consistency

31 Eventual Consistency Stale data Inconsistent data

32 Eventual Consistency Stale data Inconsistent data Changing data

33 Eventual Consistency Use Case Graphs User profile Statistics Alert config Needs consistency? No Yes Depends Yes

34 Replication Lag. Reads: eventual consistency. Failover: slave behind

35 Slave behind Failover: out of date master

36 MongoDB WriteConcern Changed Nov

37 MongoDB WriteConcern Safe by default >>> from pymongo import MongoClient >>> connection = MongoClient(w=int/str) Value Meaning 0 Unsafe 1 Primary 2 Primary + x1 secondary 3 Primary + x2 secondaries

38

39 Tags { _id : "someset", members : [ {_id : 0, host : "A", tags : {"dc": "ny"}}, {_id : 1, host : "B", tags : {"dc": "ny"}}, {_id : 2, host : "C", tags : {"dc": "sf"}}, {_id : 3, host : "D", tags : {"dc": "sf"}}, {_id : 4, host : "E", tags : {"dc": "cloud"}} ] settings : { getlasterrormodes : { veryimportant : {"dc" : 3}, sortofimportant : {"dc" : 2} } } } > db.foo.insert({x:1}) > db.runcommand({getlasterror : 1, w : "veryimportant"})

40 Tags (A or B) + (C or D) + E { _id : "someset", members : [ {_id : 0, host : "A", tags : {"dc": "ny"}}, {_id : 1, host : "B", tags : {"dc": "ny"}}, {_id : 2, host : "C", tags : {"dc": "sf"}}, {_id : 3, host : "D", tags : {"dc": "sf"}}, {_id : 4, host : "E", tags : {"dc": "cloud"}} ] settings : { getlasterrormodes : { veryimportant : {"dc" : 3}, sortofimportant : {"dc" : 2} } } } > db.foo.insert({x:1}) > db.runcommand({getlasterror : 1, w : "veryimportant"})

41 Tags (A + C) or (D + E)... { _id : "someset", members : [ {_id : 0, host : "A", tags : {"dc": "ny"}}, {_id : 1, host : "B", tags : {"dc": "ny"}}, {_id : 2, host : "C", tags : {"dc": "sf"}}, {_id : 3, host : "D", tags : {"dc": "sf"}}, {_id : 4, host : "E", tags : {"dc": "cloud"}} ] settings : { getlasterrormodes : { veryimportant : {"dc" : 3}, sortofimportant : {"dc" : 2} } } } > db.foo.insert({x:1}) > db.runcommand({getlasterror : 1, w : "sortofimportant"})

42 Performance Fast network More RAM Picture is unrelated! Mmm, ice cream.

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46 More RAM = expensive Performance x2 4GB RAM 12 month Prices

47 RAM Cost SSDs Speed Spinning disk

48 Softlayer disk pricing Performance

49 EC2 disk/ram pricing Performance $43/m $295/m $2520/m $2232/m

50 SSD vs Spinning Performance

51 SSD vs Spinning Performance

52 SSD vs Spinning Performance

53 Field names Tips: rand()

54 Tips: rand() Field names Covered indexes

55 Tips: rand() Field names Covered indexes Collections / databases

56 What is the use case? Backups

57 Backups What is the use case? Fixing user errors? Point in time restore? Disaster recovery?

58 Backups Disaster recovery Offsite

59 Backups Disaster recovery Offsite Age

60 Backups Disaster recovery Offsite Age Restore time

61 Backups Frequency Consistency Verification

62 Monitoring System Disk i/o Disk use

63 Monitoring System Disk i/o Disk use Swap

64 Monitoring Replication Optime State

65 mongostat

66 Monitoring tools Run yourself Ganglia

67 Monitoring tools

68

69 David Woop Japan!