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Ditch the Disk:Designing great
in-memory architectures
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Your speakers
Gagan MehraChief Evangelist
Terracotta
Orion LetiziCo-founderTerracotta
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What we’ll cover in this webcast
• Why enterprises are ditching their disks
• The top challenges in designinggreat in-memory architectures
• Emerging best practices
• Case study: AdJuggler
• How to start ditching your disks
• Q & A
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4© 2013 Terracotta Inc. | Internal Use Only
Why enterprises are ditching the disks
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The Internet has revealed weaknesses in the standard disk-based architecture
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Ad-hoc data management built into applications results in inconsistent speed, scale, and reliability
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The in-memory data management revolutionFrom disk to RAM
Memory
90% of Data in Database
Database
90% of Data in Memory
SlowExpensiveDifficult to scale
Ultra fastCost-efficientTB-scale servers
modernize
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In-Memory
Explosion in volume of
business data
Big Data
Why in-memory now?Low-cost RAM meets Big Data
Unlock the value in your dataMaximize inexpensive memory
Steep drop in price of RAM
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Who’s ditching the disk?
FINANCIAL SERVICES
GOVERNMENT TELECOMMUNICATIONS
MEDIA ENTERTAINMENT
ECOMMERCEFRAUD
DETECTION
TRANSPORTATION HEALTHCARE TRAVEL TECHNOLOGY
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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-sellingFaster 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 SLAsSmarter business decisions with faster access to data
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A clear correlation with Big Data success
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 in-memory data management.
According to Aberdeen Group*:
*In-memory Computing: Lifting the Burden of Big Data (Jan 2012)
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12© 2013 Terracotta Inc. | Internal Use Only
The top 6 challenges in designing great in-memory architectures
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PERFORMANCEAchieving predictable low latency to Big Data1
Obstacles• Network latency (for distributed in-memory data sets)• Marshalling and unmarshalling of data structures• Garbage collection pauses (Java)
Time
LatencyNOT PREDICTABLE!
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SCALEMinimal server footprint with large data sets2
Obstacles• Limits on in-memory storage per node• Data replication overhead• Other management overhead
GREAT NOT SO GREAT
1 TB IN-MEMORY DATA
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RELIABILITYFault tolerance and high availability3
Obstacles• RAM is volatile• Replicating data across nodes can become complex and
expensive• Failover must be immediate and seamless
DISTRIBUTED IN-MEMORY DATA
X
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RESILIENCYFast Restartability4
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)
X
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CONSISTENCYSynching data across distributed data sets5
Obstacles• Network latency• Consistency flexibility (eventual, strong, transactional)• WAN replication (across regional data centers)
X=1 X=2 X=?
DISTRIBUTED IN-MEMORY DATA
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CONTROLMonitoring and Management6
Obstacles• Few standardized tools• Many in-memory data management tools ship without
management and monitoring dashboards
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Emerging best practices
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Emerging best practices around in-memory data management challenges
Performance: Off-heap storageStoring data off the Java heap lets you keep massive amounts of data in-process by increasing predictability and decreasing latency.
Scale: Tiers, not gridsClassic 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.
321
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Emerging best practices around in-memory data management challenges (cont.)
Resiliency: Fast restartable storesThe best in-memory architectures optimize persistent transaction storage for very fast reload. Loading a terabyte should take minutes, not days.
Consistency: Configurable guaranteesAllow your data management team to set consistency guarantees for each data set: eventual, strong, transactional.
Control: In-memory dashboardsBuild or buy a dashboard for advanced in-memory views and controls showing latency, utilization, and capacity over time.
654
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Case study: AdJuggler
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- AdJuggler VP of Technology Ben Lindquist
“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.”
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AdJuggler in-memory architectureI wanted to throw out the database and, with it, the disks. — AdJuggler VP of Technology Ben Lindquist
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25© 2013 Terracotta Inc. | Internal Use Only
How to start ditching your disks
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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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Ditch the Disk: Q & A
QuestionsPlease type yours in the “Questions” panel or in the chat window.
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Want to learn more about designing in-memory architectures?
1. Download “Ditch the Disk” white paperVisit: www.terracotta.org (Resources > White Papers)
2. Contact Gagan to discuss your in-memory architecture challengesEmail: [email protected]
3. Follow us on TwitterFollow: @big_memory
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