E-Commerce and In-Memory Computing: How In-Memory computing can accelerate the adoption of omni-channel and real-time retail solutions.

1. E-Commerce and In-Memory Computing
Crossing the Scalability Chasm
Ali Hodroj
Director, Solution Architecture

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Agenda
Introduction to GigaSpaces
• Intro to GigaSpaces
• Today’s e-commerce challenges
Omni-Channel Personalization Holiday
Traffic
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Why In-Memory Computing?
• In-Memory Data Grids
E-Commerce Case Studies
• GigaSpaces Use Cases for E-Commerce
• Q&A

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About GigaSpaces
GigaSpaces provides software middleware for deployment,
management and scaling of mission-critical applications on
cloud environments.
GigaSpaces serves more than 500 large enterprises & ISVs,
over 50 of which are Fortune-listed.
300+
Direct customers
40+ / 500+
Fortune / Organizations
75+
Cloud Customers
25+
ISVs

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Selected Customers

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What can XAP do
for you?
Scaling the Data Tier
Multi-site deployment &
DR across remote sites
Batch processing of
large data sets
Real time processing of
large event stream
Real time querying and analysis
Online transaction
processing
of large datasets
Scaling the Web Tier

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Today’s e-commerce landscape
Is characterized by processing data from numerous sources
Data Explosion Decision Time
Compression
Critical Time to
Analytics
Timeframe

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Latency
Why worry about it?

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• Number of Holiday Season transactions grows exponentially
• Tolerance for system response time reduces significantly
Death by
120,000/sec
Product lookups
Source: Akamai PVS stats for a major US retailer

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Source: IBM Commerce Holiday retail readiness report 2014
An e-commerce site which takes more than 3 seconds to load
will witness a 40% bounce rate

10. Omni-Channel: Seamlessly converging around one brand
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across many channels

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Personalization
The shift from a “smart consumer” to “entitled consumer”
Real-time Contextual

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Today’s modern retail ecosystem

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…and the Omni-Channel perspective is
Consumer Experience Tier
Infrastructure Tier

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But, the infrastructure latency points are…
Holiday
Traffic
Customer
Segmentation
Massive
Data silos
Analytics +
Rules
Synchronous
bottlenecks
And Cost
Real-time
retargeting
5x Traffic
Generators
Database
bottlenecks

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In-Memory
Computing
Approach
“In memory computing (IMC) …
provides transformational
opportunities. The execution of
certain-types of hours-long batch
processes can be squeezed into
minutes or even seconds …
Millions of events can be scanned
in a matter of a few tens of
millisecond to detect correlations
and patterns pointing at emerging
opportunities and threats "as
things happen.”

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Data Grid
Data Grid is a cluster of
machines that work
together to create a
resilient shared data
fabric for low-latency
data access and
extreme transaction
processing

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Why In-Memory Computing?
Facebook keeps 80% of its data in Memory
(Stanford research)
RAM is 100-1000x faster than Disk (Random seek)
Disk: 5 -10ms
RAM: ~0.001msec

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E-commerce today belongs to the real-time world …
Social User Tracking &
Engagement
Homeland Security
Healthcare
eCommerce Financial Services Internet of Things
Real Time Search

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In-Memory Computing Use Cases
Omni-Channel
Holiday Traffic
Analytics +
Personalization
Content + Data
Legacy Integration

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Context
Omni-Channel
Top 500 Retailer / Home Shopping TV - $8.6B in revenue
Challenge
Establish an omni-channel
strategy by establishing a
unified data layer that can
control customer experience,
sourcing, and supply chain
Solution
Medium scale XAP cluster (8
nodes) with built-in disaster
recovery
Results
* Handles up to 250,000 Product reads/second
* Unified user experience across TV, mobile, web, and operations
Omni-Channel

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Terabyte-scale
data grid as a
unified data
and business
logic
infrastructure
tier

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Context
Content + Data
Top 500 Retailer / Home Shopping TV - $8.6B in revenue
Challenge
Globally available and fault
tolerant shopping cart across
data centers
Solution
Geographically distributed in-memory
data grid with
content replication
Content + Data

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• E-commerce
platform agnostic
shopping cart
storage tier
• CDN-style caching
at the application
layer
• Redundant
shopping cart
storage across
multiple data
centers

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Context
Holiday Traffic
Top 20 Retailer / 1,100 Stores - $19B in revenue
Challenge
Handle peak loads without over-provisioning
for maximum traffic
(following 2009 system crash
resulting in loss of millions of $$)
Solution
Implemented inventory
management on top of XAP within
4 months
Results
*Was N. America’s best performing e-commerce website on Black Friday 2010
Holiday Traffic

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• Holiday season
traffic spikes
• Offers / Promotion
campaign traffic
• Elastic scaling

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Context
Personalization
Top 10 Retailer - $36B in revenue
Challenge
Adding social network functionality
to e-commerce platform supporting
over a million users every day –and
up to 10x that on holidays
Solution
XAP powers Sears social networking
search engine with in-memory
storage and dynamic scalability
Results
* Fast effective social feature functionality, with performance to support peak usage
Analytics + Personalization

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Real Time Big Data Analytics
Personalization platform using
social networking data
• Recommendation engines
• Cross-Channel Analytics
• Large-scale Clickstream
analytics
• Retrospective, event
analytics
• Behavior based analytics

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Context
Legacy Systems
Integration
Top 500 Retailer / Home Shopping TV - $8.6B in revenue
Challenge
Synchronous bottlenecks and
MIPS cost pose a showstopper
for omni-channel
Solution
Offload Mainframe data into
a data grid, reducing cost
Legacy Integration

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• 80% cost savings from
mainframe access
reduction
• Integrate legacy data
into modern omni-channel
tier
• Minimize contention
with mainframe access

30. Check us out: www.gigaspaces.com
Email us: info@gigaspaces.com
Call us: 646-421-2830
Follow us: @GigaSpaces, @CloudifySource