Choosing a Next Gen Database: the New World Order of NoSQL, NewSQL, and MySQL

Choosing a Next-Gen Database
The New World Order of NoSQL, NewSQL and MySQL

Matthew Aslett, 451 Research
Doron Levari & Paul Campaniello, ScaleBase

© 2012 by The 451 Group. All rights reserved

Agenda

1. 451 Research – Choosing a next-gen database

2. The New World Order of NoSQL, NewSQL and MySQL

3. ScaleBase - How to Scale Out your existing MySQL DB

4. Customer ROI/Case Studies

5. Q & A
(please type questions directly into the GoToWebinar side panel)


The 451 Group


451 Research
 Matthew Aslett
• Research manager, data management and analytics
• With The 451 Group since 2007
• www.twitter.com/maslett

Information Management Commercial Adoption of Open Source
 Operational databases (CAOS)
 Data warehousing  Open source projects
 Data caching  Adoption of open source software
 Event processing  Vendor strategies


In a nutshell
 The database landscape has changed massively in the last 5 years

 Database users – particularly MySQL users – have never had so
much choice

 And they are more prepared than ever to look at alternatives to the
traditional incumbents

 We have moved into an era of polyglot persistence
(and polyglot analytics)

 Choosing the right database for the right workload is critical

 And the choice ever been so confusing…


The database landscape – 5ish years ago

Relational
Non-relational Analytic Aster Netezza ParAccel SAP Sybase IQ
Infobright Greenplum IBM InfoSphere
Teradata Calpont Vertica

MarkLogic Oracle IBM DB2

Versant MySQL PostgreSQL SQL Server
McObject
SAP Sybase ASE Ingres
Progress Lotus Notes
Objectivity InterSystems EnterpriseDB
Operational

© 2012 by 451 Research. AllAll rights reserved
© 2012 by The 451 Group. rights reserved

The database landscape – less than 5 years ago

Relational
Non-relational Analytic Hadoop Teradata Aster IBM Netezza ParAccel SAP Sybase IQ
Piccolo Infobright EMC Greenplum IBM InfoSphere
HPCC Teradata Calpont Actian VectorWise HP Vertica

MarkLogic Oracle SAP HANA Percona IBM DB2 MariaDB
Versant SkySQL MySQL PostgreSQL SQL Server
McObject
SAP Sybase ASE Actian Ingres
Progress Lotus Notes
Objectivity InterSystems EnterpriseDB
Operational


Non-relational Relational
Analytic Hadoop Teradata Aster Netezza ParAccel SAP Sybase IQ

SAP HANA
Oracle Percona IBM DB2 MariaDB
MarkLogic SkySQL MySQL PostgreSQL SQL Server

Versant Actian Ingres
EnterpriseDB
SAP Sybase ASE
McObject

Progress

Objectivity

Lotus Notes
Operational
InterSystems



NoSQL SAP HANA
DataStax Enterprise Oracle Percona IBM DB2 MariaDB
MarkLogic Castle Acunu Neo4J
SkySQL MySQL PostgreSQL SQL Server
Citrusleaf Graph
Hypertable
Versant BerkeleyDB Cassandra HBase InfiniteGraph Actian Ingres
OrientDB
Oracle NoSQL Big tables EnterpriseDB
RethinkDB App Engine DEX
HandlerSocket* Datastore NuvolaBase SAP Sybase ASE
McObject Riak Redis-to-go -as-a-Service
SimpleDB
LevelDB DynamoDB
Progress Redis Iris Mongo Mongo Cloudant
Membrain
Couch Lab HQ
Voldemort RavenDB
Couchbase
Key value MongoDB CouchDB
Objectivity

Lotus Notes Document
Operational
Starcounter InterSystems



NoSQL SAP HANA
DataStax Enterprise Oracle Percona IBM DB2 MariaDB
MarkLogic Castle Acunu Neo4J
SkySQL MySQL PostgreSQL
SQL Server
Citrusleaf Graph
Hypertable
Versant BerkeleyDB Cassandra HBase InfiniteGraph -as-a-Service FathomDB
Actian Ingres
OrientDB Amazon RDS Database.com
Oracle NoSQL Big tables Postgres Plus Cloud ClearDB EnterpriseDB
Rackspace MySQL Cloud
HandlerSocket* Datastore NuvolaBase SAP Sybase ASE
Google Cloud SQL SQL Azure
McObject Riak Redis-to-go -as-a-Service
SimpleDB NewSQL
LevelDB DynamoDB
Progress Redis NuoDB VoltDB New databases
Membrain Iris Mongo Mongo Cloudant -as-a-Service MemSQL JustOneDB SQLFire
Couch Lab HQ StormDB Drizzle Akiban Translattice
Voldemort RavenDB
Couchbase Xeround SchoonerSQL Clustrix
GenieDB
Key value MongoDB CouchDB ScaleArc ParElastic
Tokutek ScaleDB Zimory Scale Continuent
Objectivity
Storage MySQL Cluster Galera CodeFutures
Lotus Notes Document engines ScaleBase Clustering/sharding
Operational
Starcounter InterSystems


NoSQL, NewSQL and Beyond
NoSQL
 New breed of non-relational
database products
 Rejection of fixed table
schema and join operations
 Designed to meet scalability
requirements of distributed
architectures
 And/or schema-less data
management requirements


NoSQL NewSQL
 New breed of non-relational  New breed of relational
database products database products
 Rejection of fixed table  Retain SQL and ACID
schema and join operations  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


Relevant reports
 NoSQL, NewSQL and Beyond
• Assessing the drivers behind the development and adoption
of NoSQL and NewSQL databases, as well as data
grid/caching technologies

• Released April 2011

• Role of open source in driving innovation

• sales@the451group.com


NoSQL NewSQL
 New breed of non-relational  New breed of relational
database products database products
 Rejection of fixed table  Retain SQL and ACID
schema and join operations  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

MySQL in the headlights
 MySQL was once the default database for new Web applications.
Now it faces a competitive challenge from alternative databases


SPRAINED RELATIONAL DATABASES

Photo credit: Foxtongue on Flickr http://www.flickr.com/photos/foxtongue/4844016087/


SPRAIN
 The traditional relational database has been stretched beyond its
normal capacity by the needs of high-volume, highly distributed or
highly complex applications.

 There are workarounds – such as DIY sharding – but manual,
homegrown efforts can result in database administrators being
stretched beyond their normal capacity in terms of managing
complexity.

 Scalability
 Performance
 Relaxed consistency Increased willingness to look towards
 Agility emerging alternatives
 Intricacy
 Necessity


Alternatives
 NoSQL
• *IF* suitable for the application and workload in terms of consistency,
data model, and developer skillset
NoSQL
DataStax Enterprise
Castle Acunu Neo4J
Citrusleaf Graph
Hypertable
BerkeleyDB Cassandra HBase InfiniteGraph
OrientDB
Oracle NoSQL Big tables
HandlerSocket* Datastore NuvolaBase
Riak Redis-to-go -as-a-Service
SimpleDB
LevelDB DynamoDB
Redis Iris Mongo Mongo Cloudant
Membrain
Couch Lab HQ
Voldemort RavenDB
Couchbase
Key value MongoDB CouchDB

Document


Alternatives
 NewSQL
• New databases
• Advanced storage engines, particularly for MySQL
• Advanced clustering/shard management approaches

-as-a-Service • Datomic • MemSQL New databases
• StormDB • Akiban • Drizzle • NuoDB
• Xeround • VoltDB • SQLFire
• Tokutek • JustOneDB • Translattice
• GenieDB • Clustrix
• SchoonerSQL
• ScaleDB • ParElastic • ScaleBase
Storage engines • MySQL Cluster • Continuent • ScaleArc
• Zimory Scale • Galera • CodeFutures
Advanced clustering/sharding


NewSQL approaches
 New databases
• Pros: Designed specifically to support distributed architecture
• Cons: May lack compatibility with existing applications

 Advanced storage engines, particularly for MySQL
• Pros: Retain familiarity with with MySQL skills, tools
• Cons: Re-architecting from the inside out.

 Advanced clustering/shard management approaches
• Pros: Retain application compatibility while adding scalability
• Cons: An extra layer of complexity?

 Issues to consider:
• Does it require a forklift move of your entire application ecosystem
• Can you continue to leverage your existing MySQL skill set?
• Is there a risk for your data, e.g. memory reliability?


Spotlight on ScaleBase
 Creates a shared nothing architecture from standard databases

 Elastic load balancing for MySQL (other databases on the roadmap)

 Scale Out via read/write splitting or automatic data distribution

 Data Traffic Manager serves as a proxy between the apps and DB

 Provides a single point for administering the shared nothing cluster
(for performance, HA, change management)

 And the ability to add scalability without the need to migrate to a
new database architecture or make any changes to existing apps.


NewSQL and MySQL
 Many NewSQL offerings are designed to complement MySQL, and
can also be considered part of the MySQL ecosystem

-as-a-Service • Datomic • MemSQL New databases
• StormDB • Akiban • Drizzle • NuoDB
• Xeround • VoltDB • SQLFire
• Tokutek • JustOneDB • Translattice
• SchoonerSQL


NewSQL and MySQL
 Many NewSQL offerings are designed to complement MySQL, and
can also be considered part of the MySQL ecosystem

-as-a-Service
New databases
• Drizzle
• Xeround
• Tokutek
• SchoonerSQL


Relevant reports
MySQL vs NoSQL and NewSQL: 2011-2015

 Assessing the competitive
dynamic

 Released May 2012

 Including market sizing estimates
for all three sectors

 Survey of 200+ database users

 sales@the451group.com

 https://451research.com/report-long?icid=2289
 http://blogs.the451group.com/information_management/?p=1740


Conclusions
 NoSQL and NewSQL pose a long-term threat to MySQL’s position as
the default database for Web applications, given their use for new
development projects.

 NewSQL technologies are, at this stage, largely being adopted to
improve the performance and scalability of existing databases,
particularly MySQL.

 The MySQL ecosystem is arguably more healthy and vibrant than
ever, while, the options for MySQL users have never been greater.

 And there is a significant portion of the MySQL user base that is
willing to consider alternatives.


Choosing a Next-Gen Database
How to Scale Out your MySQL Database
October 23, 2012

Who We Are

Presenters: Paul Campaniello,
VP of Global Marketing
25 year technology veteran with
marketing experience at Mendix,
Lumigent, Savantis and Precise.

Doron Levari, Founder
A technologist and long-time
veteran of the database industry.
Prior to founding ScaleBase, Doron
was CEO to Aluna.

26

Pain Points – The Scalability Problem

• Thousands of new online and mobile
apps launching every day
• Demand climbs for these apps and
databases can’t keep up
• App must provide uninterrupted
access and availability
• Database performance and
scalability is critical

27

Big Data = Big Scaling Needs

Big Data = Transactions + Interactions + Observations
Sensors/RFID/Devices Mobile Web User Generated Content Spatial & GPS Coordinates

BIG DATA
Petabytes User Click Stream Sentiment Social Interactions & Feeds

Web Logs Dynamic Pricing Search Marketing

WEB
Offer History A/B Testing Affiliate Networks
Terabytes External
Demographics
Segmentation Customer Touches

CRM
Business Data
Offer Details Support Contacts Feeds

Gigabytes
HD Video, Audio, Images
Behavioral
ERP

Purchase Detail
Targeting Speech to Text
Purchase Record
Product/Service Logs
Payment Record Dynamic
Funnels
SMS/MMS
Megabytes

Increasing Data Variety and Complexity

28
The 451 Group & Teradata

SPRAIN

• The traditional relational database has been stretched beyond
its normal capacity by the needs of high-volume, highly
distributed or highly complex applications.

• There are workarounds – such as sharding – but manual,
homegrown efforts can result in database administrators
being stretched beyond their normal capacity in terms of
managing complexity.

– Scalability
– Performance
– Relaxed consistency Increased willingness to look towards
– Agility emerging scale out alternatives
– Intricacy
– Necessity

29

The Real $prain Pain

Infrastructure
Cost $
Large You just lost
Capital customers
Expenditure

Predicted
Demand

Opportunity Traditional
Cost Hardware

Actual
Demand

Dynamic
Scaling

time

30

Fix the $prain Pain: Scale-Out Your MySQL

Don’t throw out the baby with the bath water!

• Keep your MySQL - keep your InnoDB
• Ecosystem compatibility, preserve skills
• 100% Application compatibility
– MySQL is the starting point...
it can only get better from there…

• Your data is safe!
• Smoother, no down-time, no forklift
• No “in-memory” magic
• No “in-memory” size limit

31

Scale Out (two methods)
Read

Write

Read/Write
1
Splitting

Replication

Automatic Data
2
Distribution

32

Scale Out via Read/Write Splitting

• Excellent solution for scaling high session-volume reads
• Helps with writes too as master is freed up!
• With ScaleBase:
– Ensure data consistency with replication monitoring and lag-based load-
balancing
– Transaction aware, improved data consistency and isolation thru master
stickiness
– Simplify management, reduce TCO with real-time monitoring and alerts

33

Scale Out via Automatic Data Distribution

• The ultimate way to scale
• Delivers significant performance improvements
• Good for scaling high data-volume and session-volume reads and writes
• With ScaleBase:
– Best data-distribution policy to optimize database utilization

– Guarantee system-wide data consistency

– Improved performance with parallel query execution

– No downtime

– Reconstruct query results in real time
– Maintain unified view, support for ORDER BY, GROUP BY, LIMIT, Aggregate functions…

– Simplify management, reduce TCO with real-time monitoring and alerts

34

Scale Out Provides Immediate & Tangible Value

Application Server Database A Standby A

Application Server Database B Standby B

Database C Standby C
BI

Database D Standby D
Management

35

Choose Your Scale-out Path

Data Distribution
(Reads and writes)

Database Size

Read/Write Splitting
(Reads)

1 DB?
Good for me!

# of concurrent sessions
36

Detailed Scale Out Case Studies

Nokia AppDynamics Mozilla Solar Edge
• Device Apps App • Next gen APM • New Product/ • Next Gen
• Availability company Next Gen App/ Monitoring App
• Scalability • Scalability for the AppStore • Massive Scale
• Geo-clustering Netflix • Scalability • Monitors real
implementation • Geo-sharding time data from
• 100 Apps
thousands of
• 300 MySQL DB
distributed
systems

37

Summary

• Database scalability is a significant problem (SPRAIN)

– App explosion, Big Data and mobile compound it

• The MySQL ecosystem is more healthy and vibrant than ever

• ScaleBase provides long term, cost-effective Scale Out solutions
(R/W splitting & data distribution)
– No ecosystem forklift

– 100% application compatibility
(i.e. no app rewrites)

– Leverage your existing MySQL skill set

– Data is never at risk

38

Questions (please enter directly into the GTW side panel)

matt.aslett@451research.com paul.campaniello@scalebase.com

doron.levari@scalebase.com
@maslett
@scalebase
@451research
www.ScaleBase.com

www.451research.com 617.630.2800

39

Choosing a Next Gen Database: the New World Order of NoSQL, NewSQL, and MySQL

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