terracotta ditch the disk webcast

© 2013 Terracotta Inc. | Internal Use Only

Ditch the Disk:Designing great

in-memory architectures

© 2013 Terracotta Inc. | Internal Use Only 2

Your speakers

Gagan MehraChief Evangelist

Terracotta

Orion LetiziCo-founderTerracotta


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


4© 2013 Terracotta Inc. | Internal Use Only

Why enterprises are ditching the disks


The Internet has revealed weaknesses in the standard disk-based architecture


Ad-hoc data management built into applications results in inconsistent speed, scale, and reliability


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


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

© 2013 Terracotta Inc. | Internal Use Only 9© 2013 Terracotta Inc. | Internal Use Only 9

Who’s ditching the disk?

FINANCIAL SERVICES

GOVERNMENT TELECOMMUNICATIONS

MEDIA ENTERTAINMENT

ECOMMERCEFRAUD

DETECTION

TRANSPORTATION HEALTHCARE TRAVEL TECHNOLOGY


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


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)



The top 6 challenges in designing great in-memory architectures


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!


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


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


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


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


CONTROLMonitoring and Management6

Obstacles• Few standardized tools• Many in-memory data management tools ship without

management and monitoring dashboards



Emerging best practices


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.

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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.

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Case study: AdJuggler

© 2013 Terracotta Inc. | Internal Use Only 23© 2013 Terracotta Inc. 23

- 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.”


AdJuggler in-memory architectureI wanted to throw out the database and, with it, the disks. — AdJuggler VP of Technology Ben Lindquist



How to start ditching your disks


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


Ditch the Disk: Q & A

QuestionsPlease type yours in the “Questions” panel or in the chat window.


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

http://www.terracotta.org/

mailto:[email protected]

mailto:[email protected]

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