MongoDB and Couchbase

Transcription

1 Benchmarking MongoDB and Couchbase No-SQL Databases Alex Voss Chris Choi University of St Andrews

2 TOP 2 Questions Should a social scientist buy MORE or UPGRADE computers? Which DATABASE(s)?

3 Document Oriented Databases

4 The central concept of a document-oriented database is the notion of a Document

5 Both and Uses JSON to store these Documents

6 Example Documents: { } FirstName:"Bob", Address:"5 Oak St.", Hobby:"sailing" { } FirstName:"Jonathan", Address:"15 Wanamassa Point Road", Children:[ {Name:"Michael",Age:10}, {Name:"Jennifer", Age:8}, {Name:"Samantha", Age:5}, {Name:"Elena", Age:2} ] there are no empty 'fields', and it does not require explicitly stating if other pieces of information are left out

7 The Database Tests

8 Database Test Aggregate/Count: Most User Mentioned Most Hashed Tags Most Shared URLs

9 Database Test Aggregate/Count: Typical questions Most User Mentioned Most Hashed Tags Most Shared URLs

10

11 Has two query approaches: Map Reduce & Aggregation Framework

12 Map Reduce For Aggregation Uses JavaScript

13 Aggregation Framework Similar to Map Reduce But Uses C++ (Faster!)

14 Single Node SSD vs HDD Map Reduce Aggregation Framework Most User Mentioned Most Hashed-Tags Most Shared URLs Most User Mentioned Most Hashed-Tags HDD SSD 0 HDD SSD

15 Single Node Aggregation Framework is roughly 2 times faster than Map Reduce

16 When we have more than 1 node ~ Scaling Generally...

17 Scaling Vertically BIGGER and FATTER machine! Scaling Horizontally MORE machines!

18 Replica-Set Master-Slave Replication Replicates data from master to slave Only one node is in charge (the Master), and data only travels in one direction (to the Slave)

19 Replica-Set Single Node vs Replica Set: Aggregation Framework Single Node Replica Set Time (minutes) User Mentioned Hashed-Tags Shared URLs

20 Replica-Set Single Node vs Replica Set: Map Reduce Single Node Replica Set 60 Time (minutes) User Mentioned Hashed-Tags Shared URLs

21 Replicating Data did not improve query performance (MapReduce and Aggregation Framework)... so we tried Sharding

22 Sharding One BIG stack Two or more smaller stacks

23 Master-Slave Replication uses a democratic hierarchical scaling approach

24 But Deployment is an issue with MongoDB... If you want to shard...

25 documentation suggests minimum 11 nodes, to build a fail-safe sharded cluster 2 Shards 6 nodes (3 each) 2 mongos (load balancer) 2 nodes 3 mongod (config servers) 3 nodes Total: 11 nodes

26 The investment to go from single node --> sharded cluster is very HIGH!

27 By omitting Fail-Safe features For our tests, we kind of cheated and used: Note: X is variable 2 X Shards 6 2 nodes 2 1 mongos (load balancer) node 3 1 mongod (config servers) node Total: 11 3 nodes NOT RECOMMENDED IN PRODUCTION

28 Which looks something like this: 4 Shard configuration

29 Or this: 8 Shard configuration

30 Another problem is with querying in a sharded environment...

31 We couldn't perform queries using the Aggregation Framework in the shards, because it was limited by: - Output at every stage of the aggregation can only contain 16MB - >10% RAM usage MongoDB would fail if the limits were reached

32 Sharded Cluster Map Reduce: On 3 modest machines 4 Cores 8GB RAM Most hash tags (mins) Most user mentions (mins) Most shared urls (mins) Time (minutes) Number of Shards Optimal

33 Sharded Cluster Map Reduce: On 3 modest machines 4 Cores 8GB RAM Single Node Three Nodes Time (minutes) Most Hashed-Tags Most User Mentioned Most Shared URLs Time (minutes) Most hash tags (mins) Most user mentions (mins) Most shared urls (mins) HDD SSD Number of Shards

34 Sharded Cluster Map Reduce: On 3 modest machines 4 Cores 8GB RAM Single Node Three Nodes Time (minutes) Time (minutes) Most Hashed-Tags Most Hashed-Tags User Mentioned Most User Shared Mentioned URLs Most Shared URLs Time (minutes) Most hash tags (mins) Most user mentions (mins) Most shared urls (mins) HDD SSD SSD Number of Shards

35 Performance of Single Node (SSD) is very comparable to 3 nodes (HDD) of different number of shards! (Single Node with SSD is 2-3 minutes slower than 3 nodes... [40 GB corpus]) Perhaps its worth purchasing a good SSD over investing more nodes to improve aggregate performance...

36 What happens when we scale vertically with ONE BIG BOX? we get --->

37 Sharded Cluster Map Reduce: On a Big Box 32 Cores 128GB RAM Most hash tags (mins) Most user mentions (mins) Most shared urls (mins) Optimal 8.00 Time (minutes) Number of Shards

38

39 uses a communistic scaling approach Master-Master Replication

40 Single Node Most user mentioned Most hashed tags Most shared urls Time (seconds) HDD Hard Drive Type SSD

41 Single Node VS Map Reduce Time (seconds) 60 Most user mentioned 50 Most hashed tags Most shared urls Most User Mentioned Most Hashed-Tags Most Shared URLs HDD Hard Drive Type SSD 0 HDD SSD

42 Single Node VS Aggregation Framework Time (seconds) 60 Most user mentioned 50 Most hashed tags Most shared urls Most User Mentioned Most Hashed-Tags HDD Hard Drive Type SSD 0 HDD SSD

43 On a SINGLE NODE, while CouchBase is FASTER than MongoDB's MapReduce MongoDB's Aggregation Framework is FASTER than CouchBase

44 That said adding more Couchbase nodes was disappointing...

45 In short...

46 1 Node VS 2 Nodes VS 3 Nodes One Node Two Nodes Three Nodes Most user mentioned Most hashed tags Most shared urls

47 Adding more nodes did not improve query performance...

48 Other users have found similar issues, Couchbase Support says: One known issue with the current developer preview of Couchbase is that it slows down on smaller clusters. The views are optimal on very large clusters. [1] But they did not specify what is very large [1] Couchbase performance - real tests - it's horrible - or am I doing somethin wrong?

49 That was November

50 Which one?

51 The obvious answer is From a performance standpoint.

52 Simple flat file aggregation with Java took approx 60 mins On the same machine, MongoDB took approx 30 mins (MapReduce) 10 mins (Aggregation Framework)

53 Deployment Options Hard drive Map Reduce Node HDD MR 1950 Total Execution Time Costs Total Execution Time (mins) Node SSD MR Nodes HDD MR MR Approximate Total Cost (GBP) Node #2 Fast SSD AF Big Box HDD MR 0 Aggregate Framework

54 But! Has problems with: - Full Text Search - Social Network Graphs - Aggregation

55 's Full Text Search is slow 1 simple query on a single node, 44GB corpus took approx 10 mins!

56 Solution: A Fast Full Text Search Engine The same search took only a fraction of a second

57 To create a Social Network Graph with Can be Complex, since you query multiple times to obtain relationships

58 Solution: A Popular Graph Database Where you can obtain a network graph with VERY FEW queries

59 Example: START john=node:node_auto_index(name = 'John') MATCH john-[:friend]->()-[:friend]->fof RETURN john, fof The above query fetches all nodes who are friends of friends of Johns

60 With Aggregation Is perhaps not the best, we have yet to play with

61 Or even An SQL language to build MapReduce queries With

62 Moral of the story Use the right tools for the right job

63 Future work We aim to experiment with: With possibility in integrating all with