The principles and foundations of in memory data grids

1. Uri Cohen
Head of Product @ GigaSpaces
@uri1803
github.com/uric
In-Memory Data Grids,
Demystified

2. Agenda
• Why IMDG?
• Brief History
• How It Works
– Data model & placement
– HA and fault tolerance
– Consistency
– Internals

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

4. ® Copyright 2011 Gigaspaces Ltd. All Rights
Reserved
4
But There
Many
Solutions

5. Just A Few Years Back
® Copyright 2011 Gigaspaces Ltd. All Rights
Reserved
5

6. So Why Indeed??

7. The Need for
Speed, In
Real Time…

8. Some Facts

9. Memory will
always be faster
than disk
(usually by
orders of
magnitude)

10. Recent Survey

11. 67%
The ratio of IT
managers that think
that real time
analysis is the
biggest challenge
for
big data
implementations

12. 40%
• Plan to use in
memory
technologies for
big data projects.
• Only 32%
mentioned
Hadoop

13. Stream Processing

14. Hell, Even Gartner Thinks So
“In memory computing (IMC) … provides
transformational opportunities. The execution of
certain-types of hours-long batch processes can be
squeezed into minutes or even seconds …
Millions of events can be scanned in a matter of a few
tens of millisecond to detect correlations and patterns
pointing at emerging opportunities and threats "as
things happen.”

15. And
nowadays
HW and SW
just makes it
a whole lot
cheaper

16. Some
Common
Use Cases

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

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

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

20. Caching
• Database
offloading
• Content heavy
websites

21. The
Evolution of
Data Grids

22. First There Were Local Caches
Cache
In process caching
of Key->Value data
structure
Distribute Cache
Partitioned cache
nodes
IMDG
Partitioned system
of record
IMDG.next()
Good for repetitive-data reads
Limited in capacity
Doesn’t handle write-heavy scenarios
Reads are only part latency path

23. Then Came Distributed Caches
Cache
In process caching
of Key->Value data
structure
Distribute Cache
Partitioned cache
nodes
IMDG
Partitioned system
of record
Increased Capacity
Still no support for write-heavy scenarios
Limited to ID-based reads
Reads are only part latency path
IMDG.next()

24. In Memory Data Grids
Cache
In process caching
of Key->Value data
structure
Increased capacity
Write scalability
Can serve as system of record with querying & transaction
semantics
Still limited in capacity
Latency can come from other parts of your app
Distribute Cache
Partitioned cache
nodes
IMDG
Partitioned system
of record
IMDG.next()

25. How It
Works

26. Data Models

27. Data Placement – Fixed Hashing
27
hash(key) % #nodes

28. Fixed Hashing - HA
28
hash(key) % #nodes

29. Fixed Hashing – Scaling
29
Source: http://www.griddynamics.com/distributed-algorithms-in-nosql-databases/

30. Data Placement – Consistent Hashing
30
Source: http://www.griddynamics.com/distributed-algorithms-in-nosql-databases/

31. Data Placement – Consistent Hashing
31
Source: http://www.griddynamics.com/distributed-algorithms-in-nosql-databases/

32. Data Placement – Consistent Hashing
32
Source: http://www.griddynamics.com/distributed-algorithms-in-nosql-databases/

33. Data Placement – Consistent Hashing
33
Source: http://www.griddynamics.com/distributed-algorithms-in-nosql-databases/

34. Data Placement – Consistent Hashing
34
Source: http://www.griddynamics.com/distributed-algorithms-in-nosql-databases/

35. Data
Consistency
Since we’re dealing
with distributed
data, consistency
cannot be taken for
granted
• Read after write
• Read after read
• Write-write consistency

36. Solution 1:
Single
Master

37. Solution 2:
Read/Write
Quorums

38. Some More Concerns
• Transactions
• Querying
• Failure detection
• Leader election
• Persistency
• Interoperability

39. IMDG.next()
Using IMDG for
messaging, BL

40. IMDG.next()
SSD FTW!

41. Thank You!
docs.gigaspaces.com