1. Dec, 7 2011
Real NoSQL
Applications in the
Enterprise Today.
Apache
Cassandra
Jonathan Ellis, CTO DataStax
Matt Aslett, 451 Group

2. Welcome and Housekeeping
 We will email the presentation after the
webinar
 Please ask questions using the Q&A panel.
I will ask the panelists at the end of the
presentation.
 You can contact me at
mweir@datastax.com

3. Our presenters
 Matt Aslett - Senior Analyst  Jonathan Ellis – CTO
451 Group DataStax
Matthew covers data Jonathan is CTO and co-founder
management software for The at DataStax. Prior to DataStax,
451 Group's Information Jonathan worked extensively
Management practice, including with Apache Cassandra while
relational and non-relational employed at Racksace. Prior to
databases, data warehousing Rackspace, Jonathan built a
and data caching. Matthew is multi-petabyte, scalable storage
also an expert in open source system based on Reed-Solomon
software and contributes encoding for backup provider
regularly to reports produced Mozy. In addition to his work with
through the 451 Commercial DataStax, Jonathan is project
Adoption of Open Source chair of Apache Cassandra.
(CAOS) Research Service, as well
as to the 451 CAOS Theory blog.

4. The
451
Group
451
Research
is
focused
on
the
business
of
enterprise
IT
innovaAon.
The
company’s
analysts
provide
criAcal
and
Amely
insight
into
the
compeAAve
dynamics
of
innovaAon
in
emerging
technology
segments.
Tier1
Research
is
a
single-­‐source
research
and
advisory
firm
covering
the
mulA-­‐tenant
datacenter,
hosAng,
IT
and
cloud-­‐compuAng
sectors,
blending
the
best
of
industry
and
financial
research.
The
UpAme
InsAtute
is
‘ The
Global
Data
Center
Authority’
and
a
pioneer
in
the
creaAon
and
facilitaAon
of
end-­‐user
knowledge
communiAes
to
improve
reliability
and
uninterrupAble
availability
in
datacenter
faciliAes.
TheInfoPro
is
a
leading
IT
advisory
and
research
firm
that
provides
real-­‐world
perspecAves
on
the
customer
and
market
dynamics
of
the
enterprise
informaAon
technology
landscape,
harnessing
the
collecAve
knowledge
and
insight
of
leading
IT
organizaAons
worldwide.
ChangeWave
Research
is
a
research
firm
that
idenAfies
and
quanAfies
‘change’
in
consumer
spending
behavior,
corporate
purchasing,
and
industry,
company
and
technology
trends.
©
2011
by
The
451
Group.
All
rights
reserved

5. 451
Research
 MaRhew
AsleR
• Senior
analyst,
enterprise
soTware
• With
The
451
Group
since
2007
• www.twiRer.com/masleR
©
2011
by
The
451
Group.
All
rights
reserved

6. Relevant
reports
 NoSQL,
NewSQL
and
Beyond
 Assessing
the
drivers
behind
the
development
and
adopAon
of
NoSQL
and
NewSQL
databases,
as
well
as
data
grid/
caching
technologies
 Released
April
2011
 Role
of
open
source
in
driving
innovaAon
 sales@the451group.com
©
2011
by
The
451
Group.
All
rights
reserved

7. NoSQL,
NewSQL
and
Beyond
NoSQL
 New
breed
of
non-­‐relaAonal
database
products
 RejecAon
of
fixed
table
schema
and
join
operaAons
 Designed
to
meet
scalability
requirements
of
distributed
architectures
 And/or
schema-­‐less
data
management
requirements
©
2011
by
The
451
Group.
All
rights
reserved

8. NoSQL,
NewSQL
and
Beyond
NoSQL
NewSQL
 New
breed
of
non-­‐relaAonal
 New
breed
of
relaAonal
database
products
database
products
 RejecAon
of
fixed
table
schema
 Retain
SQL
and
ACID
and
join
operaAons
 Designed
to
meet
scalability
 Designed
to
meet
scalability
requirements
of
distributed
requirements
of
distributed
architectures
architectures
 Or
improve
performance
so
 And/or
schema-­‐less
data
horizontal
scalability
is
no
management
requirements
longer
a
necessity
©
2011
by
The
451
Group.
All
rights
reserved

9. NoSQL,
NewSQL
and
Beyond
NoSQL
NewSQL
 New
breed
of
non-­‐relaAonal
 New
breed
of
relaAonal
database
products
database
products
 RejecAon
of
fixed
table
schema
 Retain
SQL
and
ACID
and
join
operaAons
 Designed
to
meet
scalability
 Designed
to
meet
scalability
requirements
of
distributed
requirements
of
distributed
architectures
architectures
 Or
improve
performance
so
 And/or
schema-­‐less
data
horizontal
scalability
is
no
management
requirements
longer
a
necessity
…
and
Beyond
 In-­‐memory
data
grid/cache
products
 PotenAal
primary
pla`orm
for
distributed
data
management
©
2011
by
The
451
Group.
All
rights
reserved

10. NoSQL,
NewSQL
and
Beyond
NoSQL
 Big
tables
–
data
mapped
by
row
key,
column
key
and
Ame
stamp
 Key-­‐value
stores
-­‐
store
keys
and
associated
values
 Document
store
-­‐
stores
all
data
as
a
single
document
 Graph
databases
-­‐
use
nodes,
properAes
and
edges
to
store
data
and
the
relaAonships
between
enAAes
©
2011
by
The
451
Group.
All
rights
reserved

11. NoSQL,
NewSQL
and
Beyond
NoSQL
NewSQL
 Big
tables
–
data
mapped
by
row
 MySQL
storage
engines
-­‐
scale-­‐
key,
column
key
and
Ame
stamp
up
and
scale-­‐out
 Key-­‐value
stores
-­‐
store
keys
and
 Transparent
sharding
-­‐
reduce
to
associated
values
manual
effort
required
to
scale
 Document
store
-­‐
stores
all
data
as
 Appliances
-­‐
take
advantage
of
a
single
document
improved
hardware
 Graph
databases
-­‐
use
nodes,
performance,
solid
state
drives
properAes
and
edges
to
store
data
 New
databases
-­‐
designed
and
the
relaAonships
between
specifically
for
scale-­‐out
enAAes
©
2011
by
The
451
Group.
All
rights
reserved

12. NoSQL,
NewSQL
and
Beyond
NoSQL
NewSQL
 Big
tables
–
data
mapped
by
row
 MySQL
storage
engines
-­‐
scale-­‐
key,
column
key
and
Ame
stamp
up
and
scale-­‐out
 Key-­‐value
stores
-­‐
store
keys
and
 Transparent
sharding
-­‐
reduce
to
associated
values
manual
effort
required
to
scale
 Document
store
-­‐
stores
all
data
as
 Appliances
-­‐
take
advantage
of
a
single
document
improved
hardware
 Graph
databases
-­‐
use
nodes,
performance,
solid
state
drives
properAes
and
edges
to
store
data
 New
databases
-­‐
designed
and
the
relaAonships
between
specifically
for
scale-­‐out
enAAes
Data
grid/cache
 spectrum
of
data
management
capabiliAes,
from
non-­‐persistent
data
caching
to
persistent
caching,
replicaAon,
and
distributed
data
and
compute
grid
©
2011
by
The
451
Group.
All
rights
reserved

13. Photo credit:
Foxtongue on Flickr
http://www.flickr.com/photos/foxtongue/
4844016087/
©
2011
by
The
451
Group.
All
rights
reserved

14. SPRAIN
Scalability
-­‐
Hardware
economics
 Example
project/service/vendor:
• BigTable,
HBase,
Riak,
MongoDB,
Couchbase,
Hadoop,
Cassandra
• Amazon
RDS,
Xeround,
SQL
Azure,
NuoDB
• Data
grid/cache
 Associated
use
case:
•
Large-­‐scale
distributed
data
storage
•
Analysis
of
conAnuously
updated
data
•
MulA-­‐tenant
PaaS
data
layer
©
2011
by
The
451
Group.
All
rights
reserved

15. SPRAIN
Performance
-­‐
MySQL
limitaAons
 Example
project/service/vendor:
• Hypertable,
Couchbase,
Riak,
Membrain,
MongoDB,
Redis
• Data
grid/cache
• VoltDB,
Clustrix
 Associated
use
case:
• Real
Ame
data
processing
of
mixed
read/write
workloads
• Data
caching
• Large-­‐scale
data
ingesAon
©
2011
by
The
451
Group.
All
rights
reserved

16. SPRAIN
Relaxed
consistency
-­‐
CAP
Theorem
 Example
project/service/vendor:
• Dynamo,
Voldemort,
Cassandra,
Riak
• Amazon
SimpleDB
 Associated
use
case:
• MulA-­‐data
center
replicaAon
• Service
availability
• Non-­‐transacAonal
data
off-­‐load
©
2011
by
The
451
Group.
All
rights
reserved

17. SPRAIN
Agility
-­‐
polyglot
persistence,
schema-­‐less
 Example
project/service/vendor:
• MongoDB,
CouchDB,
Cassandra,
Riak
• Google
App
Engine,
SimpleDB,
SQL
Azure
 Associated
use
case:
• Mobile/remote
device
synchronizaAon
• Agile
development
• Data
caching
©
2011
by
The
451
Group.
All
rights
reserved

18. SPRAIN
Intricacy
-­‐
big
data,
total
data
 Example
project/service/vendor:
• Neo4j,
GraphDB,
InfiniteGraph
• Apache
Cassandra,
Hadoop,
Riak
• VoltDB,
Clustrix
 Associated
use
case:
• Social
networking
applicaAons
• Geo-­‐locaAonal
applicaAons
• ConfiguraAon
management
database
©
2011
by
The
451
Group.
All
rights
reserved

19. SPRAIN
Necessity
-­‐
open
source
 The
failure
of
exisAng
suppliers
to
address
emerging
requirements
 Example
projects:
• BigTable:
Google
• Dynamo:
Amazon
• Cassandra:
Facebook
• HBase:
Powerset
• Voldemort:
LinkedIn
• Hypertable:
Zvents
• Neo4j:
Windh
Technologies
©
2011
by
The
451
Group.
All
rights
reserved

20. Use
cases
–
database
types
©
2011
by
The
451
Group.
All
rights
reserved

21. Use
cases
–
new
applicaAons
Web
applicaAons
•
social
games
•
SaaS
•
e-­‐commerce
systems
•
clickstream
analysis
•
ad
and
offer
targeAng
©
2011
by
The
451
Group.
All
rights
reserved

22. Use
cases
–
new
requirements
Web
applicaAons
•
social
games
•
SaaS
•
e-­‐commerce
systems
•
clickstream
analysis
•
ad
and
offer
targeAng
©
2011
by
The
451
Group.
All
rights
reserved

23. Requirements
Data
analysis
•
read
heavy
•
batch
processing
•
analyAcs-­‐opAmized
•
data
locality
model
©
2011
by
The
451
Group.
All
rights
reserved

24. Use
cases
–
new
soluAons
Data
analysis
•
read
heavy
•
batch
processing
•
analyAcs-­‐opAmized
•
data
locality
model
©
2011
by
The
451
Group.
All
rights
reserved

25. Requirements
Data
analysis
•
batch
processing
•
aggregaAon
of
mixed
data
sources
•
structured
and
un/semi-­‐
structured
data
•
transform
and
load
©
2011
by
The
451
Group.
All
rights
reserved

26. Use
cases
Data
analysis
•
batch
processing
•
aggregaAon
of
mixed
data
sources
•
structured
and
un/semi-­‐
structured
data
•
transform
and
load
©
2011
by
The
451
Group.
All
rights
reserved

27. Target
markets
Web
applicaAons
•
social
games
•
SaaS
•
e-­‐commerce
systems
•
clickstream
analysis
•
ad
and
offer
targeAng
©
2011
by
The
451
Group.
All
rights
reserved

28. Real NoSQL
Applications in the
Enterprise Today. APACHE
CASSANDRA
JONATHAN ELLIS
2
8

29. Today’s Database Challenge

30. Navigating the NoSQL waters
 Distributed
 Horizontally scalable
 Eventually consistent
 Non-relational
 Column store
 Document stores
 Key-value
 Graph
 … and more

31. Cassandra: the best for “big data”
 Elegant architecture
 Operational flexibility
 Industry-leading performance
 Youshould be using Cassandra for
applications requiring
 high-performance, realtime queries
 scalability past one machine
 bulletproof reliability

32. Bigtable, 2006
Dynamo, 2007
OSS, 2008
Incubator, 2009
TLP, 2010
1.0, October 2011

33. Cassandra Highlights
 Multi-master, multi-DC
 Linearly scalable
 Larger-than-memory datasets
 High performance
 Full durability
 Integrated caching
 Tuneable consistency

34. Performance
A single four-core machine; one million inserts + one million updates

36. The Cassandra Difference
Scalable
Operational
Cost
Performance
Ease
Effective
Cassandra
*
✔
✔
✔
Oracle Exadata
✔
✔
✖
MySQL
✖
✔
✔
MongoDB
✖
✔
✔
Sharding
✔
✖
✔
HBase
✔
✖
✔
*
And when it comes to Performance, we’re unmatched.

37. Why Businesses Choose Cassandra
Vertical
Big-Data
Never
Very Easy to
Non-
Flexible
Multi- Cost
Scale
Down
Fast
Operate
Structured
Schema
DC /
Effective
Data
Cloud
Media /
Advertising
✔
✔
✔
✔
✔
✔
✔
Telecomm
✔
✔
✔
✔
✔
✔
✔
Financial
✔
✔
✔
✔
✔
✔
Social
✔
✔
✔
✔
✔
✔
✔
✔
IT (DaaS)
✔
✔
✔
✔
✔
✔
✔
✔
Healthcare
✔
✔
✔
✔
✔
Online Retail
✔
✔
✔
✔
✔
✔
The most popular types of applications that use Cassandra are those that…
• Are web/SaaS-based, and/or
• Collect high volumes of “Data Exhaust” from machine-generated sources

38. “With Cassandra, we get better business
agility, and we don’t have to plan capacity in
advance, we don’t need to ask permission of
other people to build things for us, and we
don’t worry about running out of space or
power.”
 Adrian Cockcroft, Cloud Architect

39. Netflix’s problems
 Could not build datacenters fast enough
 Made decision to go to cloud (AWS)
 Cassandra on AWS is a key infrastructure
component of its globally distributed
streaming product.
 Applications include Netflix’s subscriber
system, AB testing, and viewing history
service (including positions at which
members stopped watching a streaming
program).

40. Netflix on Cassandra
 Fast
 Cheap
 Scalable
 Flexible
 No SPOF

41. “Without Cassandra, our engineers would’ve
had to create something that could scale to
our needs, that would’ve prevented us from
focusing on building product and solving
problems for Backupify’s users, which are far
more important tasks.”
 Matt Conway, VP Engineering

42. Backupify’s problem
 Cloud-based utility that enables
businesses and consumers to backup,
search and restore the content of popular
online applications such as Google Apps,
Gmail, Facebook, Twitter, and Blogger
 Needs:
 Horizontal scaling
 Ability to handle high write loads
 Elasticity with no manual sharding

43. Backupify on Cassandra
 Ease of scale enabled engineers to focus
on building great applications
 DataStax OpsCenter made it easy to
monitor the health and perf of their cluster
 Reliable, redundant and scalable low-
balance data storage helped eliminate
down-time
 Ability to offer both backup and storage,
but also analysis of data eventually

44. “You can seamlessly add new nodes and
expand your total capacity without
deteriorating the performance of the data
store. Cassandra has allowed us to scale very
effectively.”
 Harry Robertson, Tech Lead

45. Ooyala’s problem
 Ooyala provides a suite of technologies
and services that support content owners
in managing, analyzing and monetizing
the digital video they publish online
 Needs:
 Elasticity,to respond to spikes in data scale
 Ability to respond to increasingly
sophisticated analytic needs of customers

46. Ooyala on Cassandra
 Classic “Big Data” problem did not require
re-architecting
 Application agility was enabled –
developers spend time building cool apps,
not figuring out how to scale
 Enabled more powerful and granular
analytics to their customers

47. “Cassandra has allowed us to build bigger
features faster and more reliably, while using
less money and without needing to expand
our staff.”
 Kyle Ambroff, Sr. Engineer

48. Formspring’ problem
 Usersof Formspring engage with and learn
more about each other by asking and
responding to questions. With close to 4B
responses in the system and 30M unique
users, they needed:
 To support explosive growth
 To seamlessly syndicate user content
 To avoid sharding
 Application flexiblity

49. Formspring on Cassandra
 No sharding needed – just add nodes to
scale
 Performance – the popular users with
many followers saw no speed reduction.
 No more memcached!
 Flexibility of a schema-optional
architecture is very developer friendly

50. Why DataStax?
DataStax delivers database products and services
based on Apache Cassandra from experts who are
at the forefront of today's data revolution.
Database Software & Tools
Support & Services
 DataStax Enterprise
 Production Support
 DataStax Community
 Consultative Help
 DataStax OpsCenter
 Professional Training
 Drivers & Connectors
 Online Documentation

51. DataStax Overview
 Founded in April 2010
 Commercial leader in Apache Cassandra™, the popular
open-source “big data” database
 Headquartered in San Francisco Bay area
 100+ customers
 35+ employees (split between San Fran and Austin)
 Home to Apache Cassandra Chair & most committers
 Secured $11M in Series B funding in Sep 2011

52. 100+ customers

53. DataStax Value
 The simplest way to get started with Apache
Cassandra: DataStax Community Edition
 A smart, integrated platform that provides
Analytics and Real-Time capabilities in the
same database, without any resource
contention: DataStax Enterprise
 The backing of the Cassandra Experts

54. DataStax Enterprise
1. DataStax Enterprise
Database Server
2. OpsCenter
Enterprise
Management
solution
3. Expert production
support &
consultative
services

55. Enterprise Database Server
Enterprise-class database built to handle
today’s big-data needs in a cost-effective, easy,
and reliable way.
 Leverages resources on-premise or in the
cloud
 Guarantees uptime with a master-less
distributed architecture
 Allows for fast application changes via
flexible schemas
2 3
 Handles structured, semi-structured, and Real-Time
unstructured data
Replication
1 4
 Provides advanced security
 Eliminates the need for separate analytics Analytics
system
6 5

56. OpsCenter Enterprise
OpsCenter Enterprise supplies management,
monitoring, and control over DataStax Enterprise
 Visual, browser-based user  Proactive alerts that warn
interface
of impending issues
 Administration tasks  Built-in external
carried out in point-and- notification abilities
click fashion
 Allows for visual rebalance
of data across a cluster
when new nodes are added

57. Expert Production Support
DataStax Enterprise includes production support
and consultative services from the Cassandra
experts.
 Support service level
agreements that range from
business hours to 24x7x365
 Consultative support for
assistance on architecture,
design, and tuning
 Certified quarterly service
packs
 Hot-fix support

58. DataStax Enterprise Compared
Scalable
Operational
Cost
Real-Time +
Performance
Ease
Effective
Analytics
DataStax Enterprise
✔
✔
✔
✔
Oracle Exadata
✔
✔
✖
✔
MySQL
✖
✔
✔
✖
MongoDB
✖
✔
✔
✖
Sharding
✔
✖
✔
✖
HBase
✔
✖
✔
✖
Oracle NoSQL DB
✔
✖
? ✔

59. DataStax – Your One-Stop Shop
 DataStax Enterprise and Community Editions
 Professional Training, Expert Consulting
 Documentation and Dev Center
 http://www.datastax.com/docs
 http://www.datastax.com/dev
 Whitepapers, Case Studies, FAQ’s and more
 http://www.datastax.com/resources/whitepapers
 http://www.datastax.com/resources/casestudies
Thank you!