Everyone's buzzing about the incredible performance gains from in-memory data management. But how do you move all of your data into RAM while still ensuring enterprise-grade availability, consistency, and control? Join us as we highlight the benefits of a great in-memory architecture, the challenges of building one, and emerging best practices in the field.

1. Ditch the Disk:
Designing great
in-memory
architectures
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2. Your speakers
Gagan Mehra
Chief Evangelist
Terracotta
Orion Letizi
Co-founder
Terracotta
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3. What we’ll cover in this webcast
•
Why enterprises are ditching their disks
•
The top challenges in designing
great in-memory architectures
•
Emerging best practices
•
Case study: AdJuggler
•
How to start ditching your disks
•
Q&A
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4. Why enterprises are
ditching the disks
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5. The Internet has revealed weaknesses in the
standard disk-based architecture
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6. Ad-hoc data management built into applications
results in inconsistent speed, scale, and reliability
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7. The in-memory data management revolution
From disk to RAM
90% of Data in
Database
Memory



Slow
Expensive
Difficult to scale
90% of Data in
Memory
modernize
Database



Ultra fast
Cost-efficient
TB-scale servers
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8. Why in-memory now?
Low-cost RAM meets Big Data
In-Memory
Maximize inexpensive memory
Steep drop in
price of RAM
Big Data
Unlock the value in your data
Explosion in
volume of
business data
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9. Who’s ditching the disk?
FINANCIAL
SERVICES
GOVERNMENT
TELECOMMUNICATIONS
MEDIA
ENTERTAINMENT
ECOMMERCE
FRAUD
DETECTION
TRANSPORTATION
HEALTHCARE
TRAVEL
TECHNOLOGY
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10. The business case for ditching the disk (ROI)
Additional revenue/profit
$10 million to $2 billion (based on media, financial services, e-commerce)
Ability to handle more customers (speed at scale)
Smarter selling and cross-selling
Faster insights
Database license savings:
Oracle Enterprise edition per processor = $47,500 + 20% license support
Other ROI opportunities:
Reduced monitoring and management overhead (people and tools)
Reduced penalties for failing to meet SLAs
Smarter business decisions with faster access to data
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11. A clear correlation with Big Data success
According to Aberdeen Group*:
44% of best-in-class Big Data
performers are already using
in-memory data management, and
more are planning to deploy it.
0% of Big Data laggards use inmemory data management.
*In-memory Computing: Lifting the Burden of Big Data (Jan 2012)
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12. The top 6 challenges in designing
great in-memory architectures
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13. 1
PERFORMANCE
Achieving predictable low latency to Big Data
Latency
NOT PREDICTABLE!
Time
Obstacles
• Network latency (for distributed in-memory data sets)
• Marshalling and unmarshalling of data structures
• Garbage collection pauses (Java)
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14. 2
SCALE
Minimal server footprint with large data sets
1 TB IN-MEMORY DATA
GREAT
NOT SO GREAT
Obstacles
• Limits on in-memory storage per node
• Data replication overhead
• Other management overhead
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15. 3
RELIABILITY
Fault tolerance and high availability
DISTRIBUTED IN-MEMORY DATA
X
Obstacles
• RAM is volatile
• Replicating data across nodes can become complex and
expensive
• Failover must be immediate and seamless
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16. 4
RESILIENCY
Fast Restartability
X
Obstacles
• Large data sets can require very long reload times
• Traditional databases are not well suited as persistent
storage for in-memory data (slow reloads)
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17. 5
CONSISTENCY
Synching data across distributed data sets
X=1
X=2
X=?
DISTRIBUTED IN-MEMORY DATA
Obstacles
• Network latency
• Consistency flexibility (eventual, strong, transactional)
• WAN replication (across regional data centers)
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18. 6
CONTROL
Monitoring and Management
Obstacles
• Few standardized tools
• Many in-memory data management tools ship without
management and monitoring dashboards
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19. Emerging best practices
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20. Emerging best practices around in-memory
data management challenges
1
2
Scale:
3
Performance:
Off-heap storage
Storing data off the Java heap lets you keep massive amounts of data in-process by
increasing predictability and decreasing latency.
Tiers, not grids
Classic P2P data grids require as many as 5x the number of servers due to
management overhead. (More if off-heap storage unavailable.)
Reliability:
Mirrored stripes
With an active and mirror for each server stripe in your array, you can failover
automatically to increase availability and reliability.
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21. Emerging best practices around in-memory
data management challenges (cont.)
4
Consistency:
5
Control:
6
Resiliency:
Fast restartable stores
The best in-memory architectures optimize persistent transaction storage for very fast
reload. Loading a terabyte should take minutes, not days.
Configurable guarantees
Allow your data management team to set consistency guarantees for each data set:
eventual, strong, transactional.
In-memory dashboards
Build or buy a dashboard for advanced in-memory views and controls showing latency,
utilization, and capacity over time.
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22. Case study: AdJuggler
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© 2013 Terracotta Inc. | Internal Use Only
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23. “At AdJuggler, we’re building a 1 million
transaction-per-second advertising
marketplace. Speed at scale is everything,
and we are past the point where we can do
things in traditional ways.”
- AdJuggler VP of Technology Ben Lindquist
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© 2013 Terracotta Inc.
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24. AdJuggler in-memory architecture
I wanted to throw out
the database and, with
it, the disks.
— AdJuggler VP of
Technology Ben
Lindquist
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25. How to start
ditching your disks
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© 2013 Terracotta Inc. | Internal Use Only
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26. How to start ditching your disks?
• Start with a low-risk, high-return opportunity
with potential for a quick win
• Get early buy-in from senior executives
• Define and track success metrics so you can
expand your “ditch the disk” project
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27. Ditch the Disk: Q & A
Questions
Please type yours in the “Questions” panel or in the chat window.
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28. Want to learn more about designing in-memory
architectures?
1. Download “Ditch the Disk” white paper
Visit: www.terracotta.org (Resources > White Papers)
2. Contact Gagan to discuss your in-memory
architecture challenges
Email: gagan@terracotta.org
3. Follow us on Twitter
Follow: @big_memory
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