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Dec, 7 2011


Real NoSQL
Applications in the
Enterprise Today.
    Apache

                      Cassandra
                      Jonathan Ellis, CTO DataStax
                      Matt Aslett, 451 Group
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
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.
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	
  	
  
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	
  	
  
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	
  	
  
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	
  	
  
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	
  	
  
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	
  	
  
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	
  	
  
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	
  	
  
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	
  	
  
Photo credit:
Foxtongue on Flickr
http://www.flickr.com/photos/foxtongue/
4844016087/



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




                                ©	
  2011	
  by	
  The	
  451	
  Group.	
  All	
  rights	
  reserved	
  	
  
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	
  	
  
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	
  	
  
Requirements	
  




                                                                                                  Data	
  analysis	
  
                                                                                                  • 	
  read	
  heavy	
  	
  
                                                                                                  • 	
  batch	
  processing	
  
                                                                                                  • 	
  analyAcs-­‐opAmized	
  	
  	
  
                                                                                                  • 	
  data	
  locality	
  model	
  




                   ©	
  2011	
  by	
  The	
  451	
  Group.	
  All	
  rights	
  reserved	
  	
  
Use	
  cases	
  –	
  new	
  soluAons	
  




Data	
  analysis	
  
• 	
  read	
  heavy	
  	
  
• 	
  batch	
  processing	
  
• 	
  analyAcs-­‐opAmized	
  	
  	
  
• 	
  data	
  locality	
  model	
  




                                        ©	
  2011	
  by	
  The	
  451	
  Group.	
  All	
  rights	
  reserved	
  	
  
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	
  	
  
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	
  	
  
Target	
  markets	
  




                                                                                                       Web	
  applicaAons	
  
                                                                                                       • 	
  social	
  games	
  
                                                                                                       • 	
  SaaS	
  
                                                                                                       • 	
  e-­‐commerce	
  systems	
  
                                                                                                       • 	
  clickstream	
  analysis	
  
                                                                                                       • 	
  ad	
  and	
  offer	
  targeAng	
  




                        ©	
  2011	
  by	
  The	
  451	
  Group.	
  All	
  rights	
  reserved	
  	
  
Real NoSQL
Applications in the
Enterprise Today.     APACHE
                      CASSANDRA
                      JONATHAN ELLIS
                                       2
                                       8
Today’s Database Challenge
Navigating the NoSQL waters
  Distributed
  Horizontally scalable
  Eventually consistent
  Non-relational 
    Column  store
    Document stores
    Key-value
    Graph
    … and more
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
Bigtable, 2006
                   Dynamo, 2007




                          OSS, 2008




      Incubator, 2009
                         TLP, 2010
                         1.0, October 2011
Cassandra Highlights
  Multi-master,    multi-DC
  Linearly scalable
  Larger-than-memory datasets
  High performance
  Full durability
  Integrated caching
  Tuneable consistency
Performance




A single four-core machine; one million inserts + one million updates
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.
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
“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
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).
Netflix on Cassandra
  Fast
  Cheap
  Scalable
  Flexible
  No   SPOF
“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
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
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
“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
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
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
“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
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
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
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
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
100+ customers
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
DataStax Enterprise
1.  DataStax Enterprise
    Database Server

2.  OpsCenter
    Enterprise
    Management
    solution

3.  Expert production
    support &
    consultative
    services
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
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
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
DataStax Enterprise Compared

                         Scalable
    Operational
     Cost
      Real-Time +
                       Performance
      Ease
       Effective
    Analytics


DataStax Enterprise
       ✔

            ✔

          ✔

            ✔

Oracle Exadata
             ✔

           ✔

           ✖

           ✔

MySQL
                     ✖

            ✔

           ✔

           ✖

MongoDB
                   ✖

            ✔

           ✔

           ✖

Sharding
                  ✔

            ✖

           ✔

           ✖

HBase
                     ✔

            ✖

           ✔

           ✖

Oracle NoSQL DB
           ✔

            ✖

          ?              ✔
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!

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DataStax & 451 Group Webinar - Real NoSQL Applications in the Enterprise Today

  • 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
  • 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
  • 35.
  • 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
  • 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!