As part of OTN Tour 2014 believes this presentation which is intented for covers the basic explanation of a solution of IMDG, explains how it works and how it can be used within an architecture and shows some use cases. Enjoy

1. Development of concurrent
services using In-Memory
Data Grids
OTN Tour 2014
by Julio Lorenzo

2. This
Presentation
• Covers the basic
explanation of a
solution of IMDG
• Explains how it works
and how it can be
used within an
architecture
• Shows some use cases
• Si, esta toda en ingles

3. Agenda
• Why?
• What is?
• Brief History
• How Its Works
• Use cases

4. Presentate
Yourself!!
Hi! I’m Julio

5. Why
IMDG?
To have performance
close to big data using
legacies
To solve problems
using traditional
technology are very
expensive

6. Why
IMDG?
Today, more than
ever, there are many
choices when it comes
to storing your data

7. Just a few
years ago
• Storing
– Oracle
– IBM DB2
– IBM Informix
– SAP Sybase
– MySQL
– PosgreSQL
– SQL Server
– Lotus Notes
• Cache
– Memcached
– EhCache
– New Generation
• GridGain
• Tangosol (aka Coherence)
• Terracota

9. So..For
What
Really?
The Need for Speed,
In Real Time…

10. Then,
What is
IMDG?

11. In-
Memory
Data Grid
(IMDG)
Is a data management
software, its data
structure resides
entirely in RAM and is
distributed among
multiple servers
Handle Big Data’s “big-
three V’s”: velocity,
Variability, Volume

12. In Memory
Data
Management
It is a IT solution to
enabled to:

13. Scale-out
Computing
Every node adds their
CPU and RAM to the
cluster, which can be
used by all nodes

14. Resilience
Nodes can fail
randomly without
data loss while
minimizing
performance impact
to running
applications
(non-disruptive
automated detection and
recovery )

15. Programming
Model
A way for developers
to easily program the
cluster of machines as
if it were a single
machine

16. Fast, Big
Data
It enables very large
data sets to be
manipulated in main
memory

17. Dynamic
Scalability
Nodes (computers)
can dynamically join
the other computers
in a grid (cluster)

18. Elastic
Main
Memory
Every node adds their
RAM to the cluster’s
memory pool

19. Computational
Grids
Reliable jobs
execution, scheduling
and load balancing

20. which
products?
• Oracle Coherence
• Hazelcast
• GridGain
• GigaSpaces
• Terracotta
• Red Hat Infinispan
• VMWare Pivotal Gemfire

21. Brief History
Cache
In process caching
of Key->Value data
structure
Distribute
Cache
Partitioned cache
nodes
IMDG
Partitioned system
of record
IMDG.next()

22. How It
Works

23. Kernel
View

24. Conceptual
View

25. Detail
View

26. Clustered Caching Explained
Partitioned, Fault Tolerant, Self-Healing Cache
• Cluster of nodes holding % of primary data locally
• Back-up of primary data is distributed across all other nodes
• Logical view of all data from any node
• All nodes verify health of each other
• In the event a node is unhealthy, other nodes diagnose state
• Unhealthy node isolated from cluster
• Remaining nodes redistribute primary and back-up responsibilities to
healthy nodes
?

27. Caching
Patterns
Cache Aside -
Developer
manages cache
• Check the cache before reading from
data source
• Put data into cache after reading from
data source
• Evict or update cache when updating
data source
Cache
DAO

28. Caching
Patterns
Read
Through/Write
Through
• All data reads/writes occur through
cache
• Cache miss causes load from data source
automatically
• Updates to cache written synchronously
to the data source
DAO Cache

29. Caching
Patterns
Write Behind
• All data writes occur through cache
• Updates to cache written asynchronously
to the data source
DAO Cache

30. Sharding
Unlimited Data
and Processing
Capacity
• Data is load balanced across the data grid.
• Data and processing capacity scales
linearly.
• Ownership responsibilities also
partitioned.
• Access and update latency are constant.
• Best for large sets of frequently updated
data.
In-Memory Data Grid
Data
Applications
Process Process Process Process
Virtual Load Balancing

31. Fault
Tolerance
High
Availability
• Automatic fault tolerance management.
• Backups stored on separate machine.
• Even distribution of backup
responsibilities.
• Configurable number of backup copies.
• Once-and-only-once processing
guarantees.
In-Memory Data Grid
Data
Applications
Process Process Process Process
Virtual Load Balancing
Fault Tolerance Management

32. Replicated
Caching
Rapid Access to
Reference Data
• Entire data set is replicated.
• Data is stored in application ready
format.
• Data access is immediate.
• Updates are replicated across the
data grid.
• Best for small sets of static data
In-Memory Data Grid
Process Process Process Process
Replication
Data
Applications

33. Near
Caching
Rapid Data
Access
• Blend of replicated and partitioned
topologies.
• Recently used data is stored locally.
• Repetitive data access is local and
immediate.
• Automatically populated upon data access.
• Automatic invalidation of updated data.
• Scale tiers independently.
In-Memory Data Grid
Data
Process Process Process Process
Virtual Load Balancing
Application Application Application

34. Parallel
Processing
Querying,
Processing,
Aggregating in
the Data Grid
• Send the processing to where the data lives.
• Processing performed in parallel across the grid.
– Query the Data Grid
– Continuous Query Cache
– Parallel Processing on the Data Grid
– Map/Reduce Aggregation
• Once-and-only-once guarantees.
• Processing scales with the grid.
In-Memory Data Grid
Process Process Process Process
Application
Processing
Unit

35. Event
Notifications
Event Driven
Architectures
• Grid based event notification
– Java Bean Model, key and filter based events
• “Live Objects”
– Objects can respond to own state changes
– State always recoverable
– Build complex Staged Event Driven
Architectures
In-Memory Data Grid
Process Process Process Process
Application Application Application

36. Arq
Patterns

37. Message
Broker /
Process
Sync

38. Web
Session
Clustering

39. Service
Bus Cache

40. Service
IMDG

41. Some
Real Life
Scenarios

42. WebLogic and Coherence Integration
Built in Out of the Box
• Administration, operations and management built into WebLogic
• Declarative scale out session management
• Cache data access with synch/asynch read /write through
• Analytics, events and compute
Coherence
WebLogic
Coherence
WebLogic
Coherence
Coherence Coherence
Coherence
Coherence
WebLogic
Coherence
WebLogic
Coherence Coherence
Coherence
WebLogic
Coherence
WebLogic
Coherence
Coherence Coherence
Coherence
Data Cache Data Cache Query/Event
Query/Event Query/Event
Query/Event
Declarative Session
Management
Persistence Caching with
Read and Write Through
Query, compute and
event

43. • Business Experience
Software
• Real Time processing
• Real time analisys
• Input 2000 events/s
• Stored 4000
records/s

44. Arq

45. Design

46. Elastic Blend

47. Some
Common
Use Cases

48. Fast,
Transactional
Data Access
• Inventory
management
• Financial reference
data
• Real time
transactional data

49. Real Time
Stream
Processing
• Fraud Detection
• Click Stream
Analysis
• Real time analytics
• Continuous
calculation

50. Heavyweight
Offline
Calculations
• Trade
Reconciliation
• Pattern analysis and
detection
• Number crunching

51. Caching
• Database offloading
• Content heavy
websites

52. Wasup?
• IMDG is an IT solution
• It can be seen as a elastic
service
• Deployed Stand-Alone or
Embed
• Provides build in:
– Distributed Data Cache
– Process Comunication
(Queue/Topic)
– Process Sync (Locks)
– Process Executions (Querys,
MPP, Map/Reduce)
• Increase Performance and
Scalability , Reduce
bottleneks

53. Q&A
@jlorenzocima

54. Acknowledgment
• Uri Cohen - In-Memory Data Grids,
Demystified
• Oracle Coherence - Coherence-
cnt1983393.pdf
• Hazelcast - Home Site
• GridGain - Home Site