How the data explosion of recent years has spawned many new technologies, and role of in-memory techology currently and in light of advances in flash memory.

1. Middle Tier Scalability
Current challenges and future
directions
DeWayne Filppi
@dfilppi
slideshare.net/dfilppi

2. What are we here to discuss?
Making Sense of the Exploding Data
World
The role of middleware to address
scalability challenges
The role of middleware to address
integration challenges

3. Making Sense of The Exploding Data World

4. GB
TB
PB
DataVolume
Yr Mo Day Hr Min Sec MS µS
Data Mining
Machine
Learning
Data Velocity
Data
Warehouse High Throughput OLTP
Operational Intelligence
Exploratory Analytics
OLTP
Business Intelligence
Streaming
Capacity and Performance Drives
New Data Management Technologies

5. Let’s Look at
Tradeoffs of
Some Selected
Solutions

6. SQL Queries
• Query: SQL
• Semantics:
• CRUD
• Aggregation
• Projection
• Partial update
• Performance: 100’s/Sec
• Consistency: Transactional
• Scaling: Mostly Scale-UP
• Availability: Disk Based

7. NoSQL
• Query: Proprietary but rich
• Semantics:
• CRUD
• Limited Aggregation
(Map/Reduce)
• No Projection*
• No Partial update*
• Performance: 1000s/Sec
• Consistency: Eventual*
• Scaling: Mostly Scale-Out
• Availability: Based on
replication

8. IMDG
• Query: Propriety but rich
• Semantics:
• CRUD
• Aggregation API +
Map/Reduce
• Projection (GigaSpaces)
• Partial Update
(GigaSpaces)
• Performance: 100k/sec
• Consistency: Transactional
• Scaling: Mostly Scale-Out
• Availability: Replication

9. Key/Value
• Query: Key, Value
• Semantics:
• Mostly Read
• No Aggregation
• No Projection
• No Partial update
• Performance: 1M’s/sec
• Consistency: Atomic*
• Scaling: Mostly Scale-Out
• Availability: Limited (varies
quite substantially between
implementations)

10. Stream Processing (Storm)
• Semantics
– Event driven data processing
• Used for continuous
updates
– No need for a costly “SELECT
FOR UPDATE”
• Performance: 10’sM/sec
updates
Spouts
Bolt

11. Common Assumption
Disk is the bottleneck
2010
Performance^10
2000 2020
HDD Latency (Seek & Rotate) = Little Improvement
100X
10,000X
Source: GigaOM Research

12. Capacity and Performance Drives
New Data Management Technologies
(Source: IDC, 2013)
Big Data (Hadoop)
NoSQL
In Memory,
Stream
Processing
RDBMS

13. There’s No One Size Fits All

14. A Typical App Looks Like This..
Front End Analytics
RT
Batch
STORM
The Data Flow
Complexity

15. What if Disk Was no Longer the
Bottleneck?
FLASH Closes the
CPU to Storage Gap

16. Our Application Cloud Look Like This..
Front End
High Speed
Data Store
(Using Flash/NVM)
Key/Value
SQL
Document
Graph
Transactional
Map/Reduce
Disk Becomes
the new Tape
StreamBase
Common Data Store serving
Multiple Semantics/API

17. We're not there yet ..
But..

18. We can use High Speed Data Bus for
Integrating All of our Data Sources
Front End Analytics
RT
Batch
STORM
High Speed
Data Bus
(Built-In
Caching)
RT
Transactional
Data Access
Direct Access
RT Streaming
Hadoop Synch
MySQL Synch
Mongo Synch

19. High Speed Data Bus (Zoom In)

20. Data Grid Ideal Integration Nexus
• Transactional
• HA – Self Healing
• Horizontally scalable
• FIFO (and partial FIFO) support
• Queryable
• Ultra high performance read/write

21. Designed for Transactional and
Analytics Scenarios..
Homeland Security
Real Time Search
Social
eCommerce
User Tracking &
Engagement
Financial Services

22. Typical NoSQL Integration

23. Storm Integration
http://ec2-54-89-152-83.compute-1.amazonaws.com:8090/web/

24. Many API’s – Same Data
Key/Value SQL Document Graph TransactionalMap/Reduce

25. Let’s take a closer look..

26. Nested Queries & Projections

27. Aggregations.

28. Fast Update …

29. Fifo/messaging support
@SpaceClass(fifoSupport=FifoSupport.OPERATION)
public class Person {...}
@EventDriven @Polling
public class SimpleListener {
@SpaceDataEvent
public Data eventListener(Data event)
{ //process Data here
}

30. Transactions support

31. So what?
• Data access not tied to store
implementation.
• Middle tier grows as source of truth.
• Simplifies data access as it grows
• Can support strong consistency as
needed.
• Provides HA platform for integration.

32. - 1KB object size and uniform distribution
- 2 sockets 2.8GHz CPU with total 24 cores, CentOS
5.8, 2 FusionIO SLC PCIe cards RAID
- YCSB measurements performed by SanDisk
62
121
17
56
0
20
40
60
80
100
120
140
160
No Read / 100% Write 100 % Read / No Write
FDF-GigaSpaces on SSDs Stock GigaSpaces in DRAM
Assumptions: 1TB Flash = $2K; 1TB RAM = $20K
The Performance of RAM at a Cost/Capacity Closer to Disk
ZetaScale-GigaSpaces on SSDs
Stock GigaSpaces in DRAM
ZetaScale-GigaSpaces
Provides 2x – 3.6x Better TPS/$ 1:50 More Capacity
ZetaScale™ – XAP MemoryXtend
20
1000
0
200
400
600
800
1000
1200
Capacity
XAP XAP Extend
1:50
242k Read/Sec

33. Take Aways
• Explosion of data has created an
explosion of targeted technologies
• Many architected on “disk is slow”
• Flash changing the equation.
• In-memory tech best suited to take
advantage of flash
• Continued blurring of in-memory
middleware and data storage.

34. Real World Example #1: Fraud
Detection

35. Real World Example #2: Banking

36. Real World Example #3: Clinical
Surveillance

37. Nati Shalom
Check out the slides on http://www.slideshare.net/dfilppi