The most critical large-scale applications today, regardless of industry, involve a demand for real-time data transfer and visualization of potentially large volumes of data. With this demand comes numerous challenges and limiting factors, especially if these applications are deployed in virtual or cloud environments. Attend this session and learn how to overcome the top four challenges to real-time application performance: database performance, network data transfer bandwidth limitations, processor performance and lack of real-time predictability. Solutions discussed will include design of the proper data model for the application data, along with design patterns that facilitate optimal and minimal data transfer across networks.

1. Overcoming the Top Four Challenges to
Real‐Time Performance in Large‐Scale,
Data‐Centric Applications
Tom Lubinski
Founder and CEO
F d d CEO
SL Corporation
17 November 2011

2.  Here to talk about Scalability and Performance
y
 Problem Space:
Collection, Analysis, and Visualization in Real-Time
of large volumes of monitoring data from large
large-
scale, complex, distributed applications
Keywords: Real-Time, Large Volumes of Data

3. Challenges
 Challenge #1:
Database Performance
D t b P f
Common to see queries taking minutes
How can you get real-time that way ?

4. Challenges
 Challenge #2:
Network Data-Transfer Bandwidth
N t kD t T f B d idth
Bigger pipes, but there s more data to send
pipes there’s
How do you get the greatest throughput ?

5. Challenges
 Challenge #3:
Processor P f
P Performance
More cores just means more processes !
How do you optimize your utilization ?

6. Challenges
 Challenge #4:
Lack f R l Ti
L k of Real-Time Predictability
P di t bilit
Virtualization is the new time-share !
time share
How can you trust your data ?
“time-sharing”, “network computer”, “cloud”, do
things ever really change ?

7.  Solution – Clues ?
 F t of Life:
Facts f Lif
Database – can’t live with it can’t live without it
can t it, can t
Network – it’s a funnel, no way around it
, y
Processor – must limit what you ask it to do
Virtualization - it’s erratic, have to compensate

8. Solutions
 Solution #1:
Proper Data Model
P D t M d l
Data structures designed for real-time
real time
In-memory structures to buffer database

9. Can your application be …
… like a high‐performance racecar ?

10. What is most important part of racecar ?
(besides the engine)
… the Transmission …

11. For Real‐Time performance, it’s the Cache …
Not a simple High‐performance
High performance
“current value” Real-time Multi-dimensional
cache Data Cache

12. Real‐Time Cache – optimized for performance !
Current / History Tables:
C / Hi T bl
In Out
…
Indexed Insertion ‐ Indexed extraction ‐
asynchronous real‐time data optimized transfer to clients

13. Real‐Time Cache – Data Processing / Aggregation
Detail
Views
Raw
Data Reduced
Summary
 Views
Reduction,
Resolution, Aging Aggregation

14. Real‐Time Cache – Database read/write through
( p
(optimized for timestamped multi‐dimensional data)
f p )
Real‐Time data
Seamless timeline
automatically
automatically
navigation with automatic
written to DB
database query

15. This sounds a bit like Oracle Coherence …
Buffer database
Read/write through
Listeners
Indexed queries
What’s different ?
ff

16. Different tools for different problems !
Real-Time Multi-dimensional d t
R l Ti M lti di i l data:
Current / History Tables:
Multiple rows (time range) of
li l (i ) f
selected columns returned in one
query
Coherence cache distributes objects
(rows) = optimized horizontally
Real‐Time multi‐dimensional cache
manages columns and optimizes
… y
vertically

17. Benefits: Indexed Real-Time Caching
Slow SQL queries minimized
Sl i i i i d
Users shielded from database details
Minimize CPU load using effective indexing

18. Solutions
 Solution #2
Server-Side A
S Sid Aggregation
ti
(am I being too obvious with this one ?)
Know the use cases
Joins and GroupBy done on server
p y
SQL does this, but do you need it ?

19. Problems with SQL Database Queries
Slow
Sl
Slowwer with concurrent queries
If you need it fast, it goes even slowwwwwwer !
fast
SQL = Not p
Q portable
(Timestamps, especially)

20. Know your problem space !
Real-Time Monitoring:
R l Ti M it i
Join and GroupBy heavily used
We wrote our own!
Performed in real-time on server-side data
Optimized for real-time requirements

21.  Example: Server-Side Aggregation/Caching
Servlet Data
GroupBy
App
Raw
Data
App Data Totals By App
Join on
To
App
Clients
Cli t
GroupBy
Server
Server Data Totals By Server
Join on
Server

22.  Each cache can maintain its own history
Servlet Data
Cached
Data Totals By App
And To
Aggregates Clients
Cli t
Totals By Server
…
…
…

23.  Result: trend chart of Totals by History has all
data available immediately
 Using SQL would require:
Query 3 tables
2 GroupBys, 2 Joins, + Join on Timestamp (not portable)

24. Benefits: Server-Side Aggregation
Client requests and gets exactly what i needed
Cli d l h is d d
Client processing = zero
Server processing = done ahead of time
Current/History for aggregates readily available (No SQL)
Response time = fast

25. Solutions
 Solution #3
Use Appropriate D i
U A i t Design Patterns
P tt
Server Side vs Client-Side
Server-Side vs. Client Side Processing
Efficient Data Transfer Patterns

26.  Pattern #1:
Data Compaction
D C i
(obvious, initial approach for any data transfers)
Server Packets only partially filled …
Client
encode decode
… replaced with full packets
… even simple, non‐proprietary
algorithms can make big difference
algorithms can make big difference

27.  Pattern #2:
Data Current / Changed
D C Ch d
(large data tables with sparse real-time updates)
Entire table sent every update …
Server
Client
encode decode
… instead, send only changed rows
… little more complex, requires indexing

28.  Pattern #3:
Data History / C
D Hi Current
(trend chart invoke with real-time updates)
Entire history table sent every update …
Server
Client
manage merge
…
… instead, send history once, then current updates
… similar to current / changed pattern, but specific to history

29.  Pattern #4:
Data Current / Subset
D C S b
(optimizing transfer of data subsets to multiple clients)
Client
Changed subset sent to every client …
Server
listen
indexed
Client
register
indexed
listen
indexed
… instead, send subset only to registered client
… requires registration logic coupled with cache

30. Benefits: Design Patterns for Data Transfer
Same problem over and over again solved similar way
S bl d i l d i il
Reduce load on network
Optimize response time – no unnecessary data

31. Conclusions
 Conclusion #1:
Know your data !
K d t
Data Model designed for real-time
real time
In-memory structures to buffer database
Server-side aggregations
gg g

32. Conclusions
 Conclusion #2
Respect Design P tt
R tD i Patterns !
Server Side vs Client-Side
Server-Side vs. Client Side Processing
Efficient Data Transfer Patterns
Don’t over-generalize – solve the problem
g p

33. About SL Corporation
 Software Product company since 1983
 Headquarters in Marin County CA
County,
 Worldwide presence in Americas, APAC, EMEA
 Over 100,000 licenses sold
 Core expertise in application performance
monitoring – special focus on middleware

34. Select SL RTView Customers
Financial Services
Financial Services E‐Commerce/Retail
E Commerce/Retail Telecommunications
Energy Other

35. RTView: The Solution Offering
Application Oracle
Middleware
Performance Coherence
Monitoring
Monitoring Monitoring
• Collect all necessary •Understand the • Determine how
application-centric and behavior of Coherence applications are
middleware-centric interacting with
p
performance data •Debug and validate
g middleware systems
y
functionality after
• Configure data configuration changes • Assess whether
aggregation and applications are running
persistence, filters, •Integrate OCM with efficiently and reliably
analytics,
analytics alerts and existing monitoring tools
displays to deliver • Ensure the maximum
information tailored for •Enable quick notification benefit from an ESB
app support teams of problems investment

36. RTView – Sample Applications
OOCL World WideShipment Tracking Hospitality Card application at Online Gaming Systems
Harrah’s casino gaming tables
Tax Season at Intuit PJM Real‐time Energy Pricing Banking application in Korea

37. RTView – Multi-Tier Visibility
Unified Real‐time display of data
from all Application tiers
Update for ORCL
In‐depth Monitoring of
Middleware Components
Middleware Components

38. RTView – Large Data Volumes
 Typical large
implementation,
distributed over several
regions with many
custom applications
 Heatmap View showing
current state of entire
system – size represents
number of servers for
b f f
application
 Color represents how
close metric is to SLA –
large red boxes are worst
– drilldown to detail

39. RTView - Drill-Down to Detail Metrics
 Drilldown to detail level
metrics showing i t
t i h i internal l
metrics from each application
 Sophisticated history and
alert view allows fine-tuning
of thresholds for each metric

40. RTView – Complex Visualizations
Observe “internal load balancing” of Data Grid

41. Questions?
See www.sl.com
for more info about SL and RTView