Steve Millidge, Director of C2B2 Consulting Ltd, presenting at CONFESS_2013 Conference in Vienna on the 4th of April 2013 In-memory Data Grids (IMDGs) and NoSQL data stores are very alike conceptually. Many utilise a Key/Value store programming model and architecturally they are engineered with the same principals distributed horizontal scalability; high availability; performance and reliability. So the questions is when to use one over the other? This session will look at many of the common use cases seen in large scale web systems today and explore whether the problem to solve is a Big Data problem or a Fast Data problem. We'll look at capabilities of the two technologies and decide whether the best technology for each use case is NoSQL or an IMDG.

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Big Data or Fast Data?
Steve Millidge
Director
C2B2
@c2b2consulting
www.c2b2.co.uk

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About Me
• Founder of C2B2
– Leading Independent Middleware Experts
– Non-functional Experts
• Vendor Neutral
– Red Hat (JBoss), Oracle (Fusion), VMWare
(vFabric), Open Source (Apache)
• 20 Years Middleware Expertise
• 15 years Field Consultancy

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Agenda
• Principles of RASP
• NoSQL and IMDG Characteristics
• Use Cases
–Big Data or Fast Data?

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“Reliability, Availability, Scalability
and Performance are prerequisites
for functionality!”
They are Priority 1 Requirements

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Reliability
• No Data Loss or Corruption
• Prevents Exploits
• Fraud
• Reputation Risk
• Legal Challenge
Customers need confidence
in their transactions

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Availability
• System is available
for customers to use
• No availability results
in no transactions
• Transactions = £££
• Receive your P45 if
you can’t sort it!

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Multipliers in Availability
System
1
System
2
System
3
99% Availability 99% Availability 99% Availability
Overall Availability = 0.99*0.99*0.99 = 97%

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HA Techniques
Redundancy Decoupling
System
System
99% Availability
99% Availability
Pair = 1 – (0.01*0.01) = 99.99%
Overall = 0.9999 x 0.9999 x 0.9999 = 99%
System
1
99% Availability
System
2
System
3
99% Availability
99% Availability
Overall = 99%

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Choose Your Poison
• Consistency
– The client perceives that a set of operations has
occurred all at once.
• Availability
– Every operation must terminate in an intended
response.
• Partition Tolerance
– Operations will complete, even if individual
components are unavailable.
http://citeseer.ist.psu.edu/544596.html

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Performance
How fast does a single transaction take to
execute!
• Faster Performance = Happier Customers
• Faster Performance = More Transactions

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Barriers to Performance
• Raw Algorithmic Performance
• Resource Limitations
– Not enough cpu, disk, memory
• Resource Contention
– Locks
• IO Latency
– Network, Disk

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Latency
Time delay in requesting an operation and it
being initiated
• Key factor in large scale distributed
applications
• Typically not taken into account during
development

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Latency Factors
• Network Distance
• Network Reliability
• Data Size
• Operation Granularity
• Resource Contention
• JVM GC

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Scalability
Ability to add more hardware in
response to more demand.
Without a reduction in
performance!

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Scaling Out
Horizontal Scaling
• Add Additional
Servers
• Add Load Balancer
• Distribute traffic
across the servers
• Much Cheaper than
Scale Up
• Has HA benefits

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Linear Scalability
(Nirvana)
0
100
200
300
400
500
600
700
800
900
1 2 3 4
Linear Scalability
Typical Scalability
Users
Cluster Nodes

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In-Memory Data Grids and
No SQL Stores

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The Key Value Store
• Basically a big Map!
• Provides Map
Semantics
– put(Key,Object)
– Object get(Key)
• Schema Less
• Decouples Objects
OrderID -> Order
Name -> Person
Postcode -> Address

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Distributed Key Partitioning
Application
Key
Partition
Application
Key
Partition
Application
Key
Partition
Application
Key
Partition
PUTB
B
GETB
B
BC
PUTC
C

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Consistent Hashing
Key Space
Hash Ring
Node 1
Hash
Node 2
Hash
Node 3
Hash
Node 4
Hash
Key
Hash

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Key Partitioning
• Keys Assigned
Partition via Hash
• Nodes Own a
Partition
• Number of Partitions
Fixed
• Number of Nodes
isn’t
Partition
Partition
Partition
Partition
Partition
Partition
Partition

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HA Key Partitioning
Application
Key
Partition
Application
Key
Partition
Application
Key
Partition
Application
Key
Partition
PUTB
B
BB
NODE
CRASH
!!!
B

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RASP Benefits
• Linear Scalability
– 2 hops for Read (Worst Case), 1 for client aware consistent hash
– 2 hops for Write (Worst Case), 1 for client aware consistent hash
– More Nodes = More Data
• High Availability
– Configurable Duplicates
• Reliability
– Eventual Consistency and Quorum
• Location Independent Access
– Grid knows where data is

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Performance Issues?
Key Partitioning
Application
Key
Partition
Application
Key
Partition
Application
Key
Partition
Application
Key
partition
Process

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In Place Processing
Application
Cache
Application
Cache
Application
Cache
Application
Cache
ProcessProcessProcessProcess

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IMDG and NoSQL
Data Grid
• Stored in Memory
• Optimised for Network IO
NoSQL Store
• Stored on Disk
• Optimised for Disc IO
Application
Keys
Application
Keys
Application
Keys
Application
Keys
Application
Keys
Application
Keys
Data Data Data

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Which to use When?

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3Vs
Reproduced From:
DIYA SOUBRA
http://www.datasciencecentral.c
om/forum/topics/the-3vs-that-
define-big-data
Key Factors in technology
Selection
• Volume
• Velocity

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Volume
(Big Data)
• Petabytes and Terrabytes
– Suitable for No SQL
– Uses cheap disc
• 100s Gigabytes and below
– Ideal for IMDB

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Velocity
(Fast Data)
• Real and Near Real Time
– In-memory access speeds
– Ideal for IMDB
• Batch and Periodic
– Process large data volumes
– Ideal for NoSQL stores

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Variety
(Fast Data)
• Both Support Unstructured Data
• Data Size is Determinant
• Large Blobs
– Video etc.
– Ideal for NoSQL

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Single Key Retrieval Scenario
• Content Management
– Get Photo
• Personalisation
– Get User Info
• Order Retrieval
– Get Order ID
• IMDB typically Faster
– Depends on Volume
• NoSQL response read
through is typical
Application
Keys
Application
Keys
Application
Keys
G
E
T

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Key Processing Small Result
• Financial Risk Calculation
– Can I book this Order
• Bet Placement
– Can I place this Bet
• Hotel Availability Search
– What hotels are available
• Web Translation
– Gather Translated Text
• IMDB the Best Choice
Application
Keys
Application
Keys
Application
Keys

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Write Lots, Read a Little
• Social Media Dumping
– You Tube
• Log Analysis
– Capture Logs
• SMS Billing
– Capture Events
• Cyber Security
– Capture Packets
• Ideal for NoSQL
Application
Keys
Application
Keys
Application
Keys

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Analytical Intelligence
• Consumer Trend Analysis
– Who orders what when
• Route Optimisation
– Delivery Trucks
• Social Graph Search
– Patterns in social media
graphs
• Fraud Detection
– Transaction analysis
• NoSQL the Best Choice
Application
Keys
Application
Keys
Application
Keys

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Real Time “Tipping”
• Product
Recommendations
– Related products
• Alerting
– Truck has Stopped!
• Financial Position Calc
– Check regs during booking
• Nudge Purchasing
– 5 people are looking at this
hotel
• IMDB the Best Choice
Application
Keys
Application
Keys
Application
Keys

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Summary
Fast Data (IMDB)
• 10s Mb – 100s GB
• Few Mb Element Size
• Response Times
– 10ms – few seconds
• Typically in-line with user
request
• Also capable of Push via
Events
Big Data (NoSQL)
• 100sGb – Petabyte
• Size limited by disc
• Response times
– Few s – long batch
• Typically analytical
• Used inline for small
scale direct key retrieval

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