This document discusses PlanDas Cache Cloud, a caching solution. It begins by covering concepts like availability, performance, reliability, and manageability as they relate to caching. It then discusses the differences between distributed and global caching approaches. The document outlines how caching can improve performance for web services and help address bottlenecks. It introduces the PlanDas Cache Cloud architecture, which uses consistent hashing for high availability. The document shows how the solution provides a global cache, multi-tenancy, and high performance. It also covers the web management interface and similarities to Redis APIs. Finally, it shares performance test results on AWS and physical machines that show throughput scaling as nodes are added.

1. PlanDas Cache Cloud
Speaker . Gyuman Cho

2. Index
1. Concept
2. Cache : Distributed vs. Global
3. Cache on Service
4. Plandas Cache Cloud
5. Performance

3. CONCEPT
From Cache to Data Store

4. Concept

5. Concept
• Availability : Scale-out is better
• Performance : Higher is Best
• Reliability (Fault Tolerance) : 100% or Partition
• Scalability : Scale Up/Out
• Manageability : Web Management
• Cost : Shared Resource

6. CACHE ON ARCHITECTURE
Distributed vs. Global

7. Web Service Architecture
From “Scalable Web Architecture And Distributed Systems”
Cache : Distributed vs. Global

8. Web Service Architecture
From “Scalable Web Architecture And Distributed Systems”
Cache : Distributed vs. Global

9. Disk vs. SSD vs. Memory
From “Scalable Web Architecture And Distributed Systems”
From The Pathologies of Big Data : reference site
Cache : Distributed vs. Global

10. Web Service Architecture
From “Scalable Web Architecture And Distributed Systems”
Cache : Distributed vs. Global

11. Cache for performance
local cache Nodes with local cache
From “Scalable Web Architecture And Distributed Systems”
Cache : Distributed vs. Global

12. Cache Distributed vs. Global
Distributed cache Global cache
From “Scalable Web Architecture And Distributed Systems”
Cache : Distributed vs. Global

13. Distributed cache features
• Not be same data that
request nodes have.
• Between request nodes
communicate cached
data. And then data is
same.
• The more the number of
nodes is increased traffic.
Distributed Cache
From “Scalable Web Architecture And Distributed Systems”
Cache : Distributed vs. Global

14. Global cache Features
• All request nodes have
same cached data.
• Between request nodes do
not communicate cached
data.
Global Cache
From “Scalable Web Architecture And Distributed Systems”
Cache : Distributed vs. Global

15. Global Cache
Responsible global cache Simple global cache
From “Scalable Web Architecture And Distributed Systems”
1. For each request, the Request Node will
check the global cache first.
2. If the data was not in the cache, the cache
will pull the data from the origin, and then
add the result to the cache for future
requests by the Global Cache.
1. For each request, the Request Node will
check the global cache first.
2. If the data was not in the cache then the
Request Node will retrieve it from the
origin.
3. When data is retrieved from the origin, it
can be added to the cache by the Request
node.
Cache : Distributed vs. Global

16. SERVICE
With Cache& Data Store

17. Service bottleneck
WAS
WAS
WAS
WAS
Web
Server
DBMS
DBMS
5000 – 7000 TPS
CUD : R = 1 : 9

18. Service bottleneck
WAS
WAS
WAS
WAS
Web
Server
DBMS
DBMS
Global
Cache
CUD : R = 1 : 9 Unlimited TPS

19. Data Store
WAS
WAS
WAS
WAS
Web
Server
Global
Cache
Global
Cache
Global
Cache
Global
Data Store
DBMS

20. Message Queue Bottleneck
Message
Consumer
Message
Producer
DBMS
DBMS
Message
Consumer
Message
Consumer
Message
Consumer
Message
Queue

21. Message Queue Bottleneck
Message
Consumer
Message
Producer
Message
Consumer
Message
Consumer
Message
Consumer
Message
Queue
Data
Store

22. Cross service, Sharing
WAS(A Service)
WAS(A Service)
WAS(A Service)
WAS(A Service)
App Server(A Service)
WAS(A Service)
WAS(A Service)
WAS(A Service)
WAS(A Service)
App Server(B Service)
Global Cache
Global Cache
Global Cache
Global Cache
Global Cache
Global Cache
WAS(A Service)
WAS(A Service)
WAS(A Service)
WAS(A Service)
App Server(C Service)
• If multi-tenant
• support services.
• Cross-service Data Sharing
• Support data store

23. PLANDAS CACHE CLOUD
Architecture, Features

24. Plandas Cache Cloud
1. Plandas Architecture
2. HA: Auto Fail-Over (Client/Server)
3. Web (Admin) Management
4. If you want, support immediately
5. Similar to Redis API

25. Plandas Architecture
Plandas Architecture

26. Main Flow
Plandas Architecture
1. Query DNS
2. Query Cache Node’s addresses
1. Authentication
2. Query values
1. Service key-fix
2. Connect key
3. Cache Nodes
3. Connect the Cache Node
4. Request Commands.

27. Ketama Consistent Hashing
CS1
CS2
CS3
CS3
VN
1
VN
2
VN
3
VN
3
VN
n
VN
1
VN
2
VN
3
VN
n
VN
1
VN
2
VN
3
VN
3
VN
n
VN
1
VN
2
VN
3
VN
n

28. HA . Client Fail-Over
Plandas Architecture
Client
Container
Server
Container
Server
Container
Server
1.1.1.1
1.1.1.2
1.1.1.3
Shard by f(x)
Active Address (threshold : 2)
• 1.1.1.1 : 0
• 1.1.1.2 : 0
• 1.1.1.3 : 0

29. HA . Client Fail-Over
Plandas Architecture
Client
Container
Server
Container
Server
Container
Server
1.1.1.1
1.1.1.2
1.1.1.3
Active Address (threshold : 2)
• 1.1.1.1 : 1  2
• 1.1.1.2 : 0
• 1.1.1.3 : 0
Shard by f(x)
fail

30. HA . Fail-Over
Plandas Architecture
Client
Container
Server
Container
Server
Container
Server
1.1.1.1
1.1.1.2
1.1.1.3
Active Address (threshold : 2)
• 1.1.1.1 : 1
• 1.1.1.2 : 0
• 1.1.1.3 : 0
Shard by f(x)
0
5
2
1
3
4

31. HA . Client/Server Fail-Over
Plandas Architecture
Client
Container
Server
Container
Server
Container
Server
1.1.1.1
1.1.1.2
1.1.1.3
Active Address (threshold : 2)
• 1.1.1.1 : 1  2
• 1.1.1.2 : 0
• 1.1.1.3 : 0
Shard by f(x)
fail
0
5
2
1
3
4

32. HA . Client/Server Fail-Over
Plandas Architecture
Client
Container
Server
Container
Server
Container
Server
1.1.1.1
1.1.1.2
1.1.1.3
Active Address (threshold : 2)
• 1.1.1.1 : 1  2
• 1.1.1.2 : 0
• 1.1.1.3 : 0 Shard by f(x)
fail
5
2
1
4

33. Plandas Cache Cloud
 Global Cache : Simple
 NEXT : from Cache to Data Store
 High Performance : Redis / Memcached
 Multi-tenancy : Availability & Cloud
 Partition Tolerant : Multi-Master
NEXT : + replication + live migration => Fault Tolerant
 Cloud management

34. Web Management

35. Web Management

36. Management
Plandas Architecture

37. Management
Plandas Architecture

38. Management
Plandas Architecture

39. Plandas Cache Cloud
If you want, support immediately

40. Similar to
Redis API
Jedis
Keys
Strings
Hashes
Lists
Sets
SortedSets
Connection
Trigger
Pub/Sub
Server
Plandasj
Keys
Strings
Hashes
Lists
Sets
SortedSets

41. Plandasj API
Category Methods
Keys exists,persist,type,ttl,del, etc.
Strings set,get,setbit,getbit,decrBy,incrBy, etc.
Hashes hset,hget,hmset,hmset,hincrBy,hdel, etc.
Lists rpush,lpush,llen,lrem,lpop,rpop, etc.
Sets sadd,spop,smembers, etc.
SortedSets Zadd, zcard, zcount, zincrby, zrange, zrangeByScore , etc.
But Not Support aggregation methods and the method that has gt 2 keys.
Ex) Sets’s sinter, sinterstore, sunion….

42. PLANDAS PERFORMANCE
On AWS EC2
On Physical Machine

43. Test environment on AWS
• Test tool : ngrinder 3.1
• agent vm spec : 10ea (m1.large)
• Cache Cloud (node 개수가 많을수록 tps 는 증가함)
• vm spec : 3ea
• nodes per vm : 2ea
• total nodes : 6ea

44. Data Size / RTT on AWS

45. Data Size / TPS on AWS

46. Nodes : Scale Out on AWS

47. Test environment on P.M
• Test tool : ngrinder 3.1
• Agent count : 10ea
• Cache Cloud (node 개수가 많을수록 tps 는 증가함)
• H/W spec : 24 core / memory 32G / 3ea
• nodes per vm : 2ea
• total nodes : 6ea
• 1vuser 80tps 성능으로 고정

48. Scenario/Result on P.M

49. Scenario/Result on P.M
Load generator count

50. Big size Test on P.M

51. NEXT
• Support all most languages (Proxy Server) : Completed
• Multi-IDC: Completed
• Spatial / Data Store Cloud
• from Cache to Data Store

52. I skate to where the puck is going to be,
not where it has been.
웨인 그레츠키(NFL)