SlideShare a Scribd company logo
Massively scalable NoSQL
with Apache Cassandra!
Jonathan Ellis
Project Chair, Apache Cassandra
CTO, DataStax
@spyced
Big data



           Analytics        Realtime
                       ?
           (Hadoop)        (“NoSQL”)




©2012 DataStax
Some Casandra users




 ©2012 DataStax
eBay
                     Application/Use Case
                     • Social Signals: like/want/own features for
                       eBay product and item pages
                     • Hunch taste graph for eBay users and items
                     • Many time series use cases


                     Why Cassandra?
                     • Multi-datacenter
                     • Scalable
                     • Write performance
                     • Distributed counters
                     • Hadoop support




©2012 DataStax ACE
Time series data




©2012 DataStax
Multi-datacenter support




©2012 DataStax
Distributed counters




©2012 DataStax
Hadoop support




©2012 DataStax
Disney
                     Application/Use Case
                     • Meet the data management needs of user
                       facing applications across The Walt Disney
                       Company with a single platform


                     Why Cassandra?
                     • DataStax Enterprise can tackle real-time
                       and search functions in the same cluster
                     • Scalability
                     • 24x7 uptime




©2012 DataStax NDI
Multitenancy




©2012 DataStax
Multitenancy




©2012 DataStax
Enterprise search




©2012 DataStax
SimpleReach
                     Application/Use Case
                     • SimpleReach tracks social actions for
                       content creators, from Twitter and
                       Facebook to Pinterest and Reddit, to deliver
                       detailed insights and clear metrics around
                       social behavior.

                     Why Cassandra?
                     • Very high velocity data ingest rate and
                       large data volumes
                     • Workload separation between realtime
                       and batch applications




©2012 DataStax NDE
SourceNinja
                     Application/Use Case
                     • SourceNinja notifies you to performance,
                       security, and bug fixes for the software you
                       depend on


                     Why Cassandra?
                     • Previous database system could not
                       handle load; HBase has too many points
                       of failure and was too slow
                     • Fast real time capabilities, batch analytics
                       on that data, and enterprise search




©2012 DataStax RDE
Netflix
                     Application/Use Case
                     • General purpose backend for large scale
                       highly available cloud based web services
                       supporting Netflix Streaming


                     Why Cassandra?
                     • Highly available, highly robust and no
                       schema change downtime
                     • Highly scalable, optimized for SSD
                     • Much lower cost than previous Oracle and
                       SimpleDB implementations
                     • Flexible data model
                     • Ability to directly influence/implement
                       OSS feature set
                     • Supports local and wide area distributed
                       operations, spanning US and Europe

©2012 DataStax RCE
Optimized for SSD




©2012 DataStax
Open source




©2012 DataStax
Use case patterns
  • Massively scalable
  • High performance
  • Reliable/Available




©2012 DataStax
©2012 DataStax
reads/s            writes/s

                                                                       35000



                                                                      30000


                                                                     25000


                                                                    20000


                                                                   15000


                                                                   10000

                                                               5000
                 Cassandra 0.6
                                                               0
©2012 DataStax
                                           Cassandra 1.0
©2012 DataStax
Classic partitioning with SPOF
                 partition 1   partition 2      partition 3   partition 4




                                         router


                                             client
©2012 DataStax
Availability
  • “High availability implies that a single fault will not bring
            down your system. Not ‘we’ll recover quickly.’”
            -- Ben Coverston: DataStax

     •      “The biggest problem with failover is that you're almost
            never using it until it really hurts. It's like backups that
            you never test.”
            -- Rick Branson: Instagram




©2012 DataStax
Fully distributed, no SPOF
                 client




                          p3
                                p6        p1
                           p1




                                     p1




©2012 DataStax
Partitioning



                  jim     age: 36   car: camaro   gender: M

                 carol    age: 37   car: subaru   gender: F

                 johnny   age:12    gender: M

                 suzy     age:10     gender: F

©2012 DataStax
Partitioning
           Primary key determines placement*



                  jim     age: 36   car: camaro   gender: M

                 carol    age: 37   car: subaru   gender: F

                 johnny   age:12    gender: M

                 suzy     age:10     gender: F

©2012 DataStax
PK      MD5 Hash



                  jim     5e02739678...
                                             MD5* hash
                 carol    a9a0198010...   operation yields a
                                           128-bit number
                 johnny   f4eb27cea7...       for keys
                                             of any size.
                 suzy     78b421309e...




©2012 DataStax
The “token ring”




                 Node A   Node B




                 Node D   Node C



©2012 DataStax
Start            End
                 A   0xc000000000..1 0x0000000000..0

                 B   0x0000000000..1 0x4000000000..0

                 C   0x4000000000..1 0x8000000000..0

                 D   0x8000000000..1 0xc000000000..0




                      jim          5e02739678...


                     carol         a9a0198010...


                     johnny        f4eb27cea7...


                     suzy          78b421309e...


©2012 DataStax
Start            End
                 A   0xc000000000..1 0x0000000000..0

                 B   0x0000000000..1 0x4000000000..0

                 C   0x4000000000..1 0x8000000000..0

                 D   0x8000000000..1 0xc000000000..0




                      jim          5e02739678...


                     carol         a9a0198010...


                     johnny        f4eb27cea7...


                     suzy          78b421309e...


©2012 DataStax
Start            End
                 A   0xc000000000..1 0x0000000000..0

                 B   0x0000000000..1 0x4000000000..0

                 C   0x4000000000..1 0x8000000000..0

                 D   0x8000000000..1 0xc000000000..0




                      jim          5e02739678...


                     carol         a9a0198010...


                     johnny        f4eb27cea7...


                     suzy          78b421309e...


©2012 DataStax
Start            End
                 A   0xc000000000..1 0x0000000000..0

                 B   0x0000000000..1 0x4000000000..0

                 C   0x4000000000..1 0x8000000000..0

                 D   0x8000000000..1 0xc000000000..0




                      jim          5e02739678...


                     carol         a9a0198010...


                     johnny        f4eb27cea7...


                     suzy          78b421309e...


©2012 DataStax
Start            End
                 A   0xc000000000..1 0x0000000000..0

                 B   0x0000000000..1 0x4000000000..0

                 C   0x4000000000..1 0x8000000000..0

                 D   0x8000000000..1 0xc000000000..0




                      jim          5e02739678...


                     carol         a9a0198010...


                     johnny        f4eb27cea7...


                     suzy          78b421309e...


©2012 DataStax
Replication




                                 Node A   Node B




                                 Node D   Node C


       carol     a9a0198010...
©2012 DataStax
Node A   Node B




                                 Node D   Node C


       carol     a9a0198010...
©2012 DataStax
Node A   Node B




                                 Node D   Node C


       carol     a9a0198010...
©2012 DataStax
Highlights
 • Adding capacity is application-transparent and requires
            no downtime
     •      No SPOF, not even temporarily
           •     No “primary” replica

     •      Configurable synchronous/asynchronous
     •      Tolerates node failure; never have to restart replication
            “from scratch”
     •      “Smart” replication avoids correlated failures



©2012 DataStax
CQL: You got SQL in my NoSQL!
 CREATE TABLE users (
    id uuid PRIMARY KEY,
    name text,
    state text,
    birth_date int
 );



 CREATE INDEX ON users(state);

 SELECT * FROM users WHERE state=‘Texas’ AND birth_date > 1950;




©2012 DataStax
Strictly “realtime” focused
  • No joins
  • No subqueries
  • No aggregation functions* or GROUP BY
  • ORDER BY?




©2012 DataStax
Clustered data in in CFS




©2012 DataStax
Clustered data in in CFS




©2012 DataStax
Clustering in CQL3
 CREATE TABLE sblocks (
     block_id uuid,
     subblock_id uuid,
     data blob,
                                 block_id   subblock_id    data
     PRIMARY KEY (block_id,
                  subblock_id)
                                 Block1     subblock A    data A
 );
                                 Block1     subblock B    data B
                                   ...          ...         ...


                                 Block2     subblock C    data C
                                 Block2     subblock D    data D
                                   ...          ...         ...


                                 Block3     subblock E    data E
                                 Block3     subblock F    data F
                                   ...          ...         ...
©2012 DataStax
Collections
 CREATE TABLE users (
    id uuid PRIMARY KEY,
    name text,
    state text,
    birth_date int
 );

 CREATE TABLE users_addresses (
    user_id uuid REFERENCES users,
    email text
 );

 SELECT *
 FROM users NATURAL JOIN users_addresses;




©2012 DataStax
Collections
 CREATE TABLE users (
    id uuid PRIMARY KEY,
    name text,
    state text,




                 X
    birth_date int
 );

 CREATE TABLE users_addresses (
    user_id uuid REFERENCES users,
    email text
 );

 SELECT *
 FROM users NATURAL JOIN users_addresses;




©2012 DataStax
Collections
 CREATE TABLE users (
    id uuid PRIMARY KEY,
    name text,
    state text,
    birth_date int,
    email_addresses set<text>
 );

 UPDATE users
 SET email_addresses = email_addresses + {‘jbellis@gmail.com’,
 ‘jbellis@datastax.com’};




©2012 DataStax
Big data



           Analytics        Realtime
                       ?
           (Hadoop)        (“NoSQL”)




©2012 DataStax
The evolution of Analytics




                 Analytics + Realtime
©2012 DataStax
The evolution of Analytics


                             replication




                 Analytics                 Realtime

©2012 DataStax
The evolution of Analytics


                 ETL




©2012 DataStax
Big data



           Analytics    Datastax     Realtime
           (Hadoop)    Enterprise   (Cassandra)




©2012 DataStax
©2012 DataStax
Better Hadoop than Hadoop
  • “Vanilla” Hadoop
           •     8+ services to setup, monitor, backup, and recover
                 (NameNode, SecondaryNameNode, DataNode, JobTracker, TaskTracker,
                 Zookeeper, Region Server,...)

           •     Single points of failure
           •     Can't separate online and offline processing


     •      DataStax Enterprise
           •     Single, simplified component
           •     Self-organizes based on workload
           •     Peer to peer
           •     JobTracker failover
©2012 DataStax
Enterprise search with Solr
 SELECT title FROM solr WHERE solr_query='title:natio*';

  title
 --------------------------------------------------------------------------
                                       Bolivia national football team 2002
  List of French born footballers who have played for other national teams
                     Lithuania national basketball team at Eurobasket 2009
                                       Bolivia national football team 2000
                                     Kenya national under-20 football team
                                       Bolivia national football team 1999
                                  Israel men's national inline hockey team
                                       Bolivia national football team 2001




©2012 DataStax
Managing & Monitoring Big Data




 ©2012 DataStax
Questions?
     •      http://www.datastax.com/docs
     •      http://www.datastax.com/dev/blog/whats-new-in-
            cassandra-1-1
     •      http://www.datastax.com/dev/blog/schema-in-
            cassandra-1-1
     •      http://www.datastax.com/products/enterprise




©2012 DataStax

More Related Content

What's hot

The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012
The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012
The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012
Big Data Spain
 
Microsoft SQL Server Data Warehouses for SQL Server DBAs
Microsoft SQL Server Data Warehouses for SQL Server DBAsMicrosoft SQL Server Data Warehouses for SQL Server DBAs
Microsoft SQL Server Data Warehouses for SQL Server DBAs
Mark Kromer
 
DataStax 6 and Beyond
DataStax 6 and BeyondDataStax 6 and Beyond
DataStax 6 and Beyond
David Jones-Gilardi
 
Breakthrough performance with MySQL Cluster (2012)
Breakthrough performance with MySQL Cluster (2012)Breakthrough performance with MySQL Cluster (2012)
Breakthrough performance with MySQL Cluster (2012)
Frazer Clement
 
OSSCube MySQL Cluster Tutorial By Sonali At Osspac 09
OSSCube MySQL Cluster Tutorial By Sonali At Osspac 09OSSCube MySQL Cluster Tutorial By Sonali At Osspac 09
OSSCube MySQL Cluster Tutorial By Sonali At Osspac 09
OSSCube
 
MySQL 开发
MySQL 开发MySQL 开发
MySQL 开发
YUCHENG HU
 
MySQL User Camp: MySQL Cluster
MySQL User Camp: MySQL ClusterMySQL User Camp: MySQL Cluster
MySQL User Camp: MySQL Cluster
Shivji Kumar Jha
 
MySQL Cluster 8.0 tutorial
MySQL Cluster 8.0 tutorialMySQL Cluster 8.0 tutorial
MySQL Cluster 8.0 tutorial
Frazer Clement
 
NewSQL - Deliverance from BASE and back to SQL and ACID
NewSQL - Deliverance from BASE and back to SQL and ACIDNewSQL - Deliverance from BASE and back to SQL and ACID
NewSQL - Deliverance from BASE and back to SQL and ACID
Tony Rogerson
 
DataStax | Data Science with DataStax Enterprise (Brian Hess) | Cassandra Sum...
DataStax | Data Science with DataStax Enterprise (Brian Hess) | Cassandra Sum...DataStax | Data Science with DataStax Enterprise (Brian Hess) | Cassandra Sum...
DataStax | Data Science with DataStax Enterprise (Brian Hess) | Cassandra Sum...
DataStax
 
MySQL Cluster NoSQL Memcached API
MySQL Cluster NoSQL Memcached APIMySQL Cluster NoSQL Memcached API
MySQL Cluster NoSQL Memcached API
Mat Keep
 
Oracle sharding : Installation & Configuration
Oracle sharding : Installation & ConfigurationOracle sharding : Installation & Configuration
Oracle sharding : Installation & Configuration
suresh gandhi
 
[Cassandra summit Tokyo, 2015] Cassandra 2015 最新情報 by ジョナサン・エリス(Jonathan Ellis)
[Cassandra summit Tokyo, 2015] Cassandra 2015 最新情報 by ジョナサン・エリス(Jonathan Ellis)[Cassandra summit Tokyo, 2015] Cassandra 2015 最新情報 by ジョナサン・エリス(Jonathan Ellis)
[Cassandra summit Tokyo, 2015] Cassandra 2015 最新情報 by ジョナサン・エリス(Jonathan Ellis)
datastaxjp
 
Enterprise Virtualization with Xen
Enterprise Virtualization with XenEnterprise Virtualization with Xen
Enterprise Virtualization with Xen
Frank Martin
 
DataStax | Effective Testing in DSE (Lessons Learned) (Predrag Knezevic) | Ca...
DataStax | Effective Testing in DSE (Lessons Learned) (Predrag Knezevic) | Ca...DataStax | Effective Testing in DSE (Lessons Learned) (Predrag Knezevic) | Ca...
DataStax | Effective Testing in DSE (Lessons Learned) (Predrag Knezevic) | Ca...
DataStax
 
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 MySQLChoosing 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
ScaleBase
 
MySQL 5.5&5.6 new features summary
MySQL 5.5&5.6 new features summaryMySQL 5.5&5.6 new features summary
MySQL 5.5&5.6 new features summaryLouis liu
 
MySQL Performance Best Practices
MySQL Performance Best PracticesMySQL Performance Best Practices
MySQL Performance Best Practices
Olivier DASINI
 
D Maeda Bi Portfolio
D Maeda Bi PortfolioD Maeda Bi Portfolio
D Maeda Bi Portfolio
DMaeda
 
Oracle Database appliance - Value proposition Webcast
Oracle Database appliance - Value proposition WebcastOracle Database appliance - Value proposition Webcast
Oracle Database appliance - Value proposition Webcast
Thanos TP
 

What's hot (20)

The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012
The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012
The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012
 
Microsoft SQL Server Data Warehouses for SQL Server DBAs
Microsoft SQL Server Data Warehouses for SQL Server DBAsMicrosoft SQL Server Data Warehouses for SQL Server DBAs
Microsoft SQL Server Data Warehouses for SQL Server DBAs
 
DataStax 6 and Beyond
DataStax 6 and BeyondDataStax 6 and Beyond
DataStax 6 and Beyond
 
Breakthrough performance with MySQL Cluster (2012)
Breakthrough performance with MySQL Cluster (2012)Breakthrough performance with MySQL Cluster (2012)
Breakthrough performance with MySQL Cluster (2012)
 
OSSCube MySQL Cluster Tutorial By Sonali At Osspac 09
OSSCube MySQL Cluster Tutorial By Sonali At Osspac 09OSSCube MySQL Cluster Tutorial By Sonali At Osspac 09
OSSCube MySQL Cluster Tutorial By Sonali At Osspac 09
 
MySQL 开发
MySQL 开发MySQL 开发
MySQL 开发
 
MySQL User Camp: MySQL Cluster
MySQL User Camp: MySQL ClusterMySQL User Camp: MySQL Cluster
MySQL User Camp: MySQL Cluster
 
MySQL Cluster 8.0 tutorial
MySQL Cluster 8.0 tutorialMySQL Cluster 8.0 tutorial
MySQL Cluster 8.0 tutorial
 
NewSQL - Deliverance from BASE and back to SQL and ACID
NewSQL - Deliverance from BASE and back to SQL and ACIDNewSQL - Deliverance from BASE and back to SQL and ACID
NewSQL - Deliverance from BASE and back to SQL and ACID
 
DataStax | Data Science with DataStax Enterprise (Brian Hess) | Cassandra Sum...
DataStax | Data Science with DataStax Enterprise (Brian Hess) | Cassandra Sum...DataStax | Data Science with DataStax Enterprise (Brian Hess) | Cassandra Sum...
DataStax | Data Science with DataStax Enterprise (Brian Hess) | Cassandra Sum...
 
MySQL Cluster NoSQL Memcached API
MySQL Cluster NoSQL Memcached APIMySQL Cluster NoSQL Memcached API
MySQL Cluster NoSQL Memcached API
 
Oracle sharding : Installation & Configuration
Oracle sharding : Installation & ConfigurationOracle sharding : Installation & Configuration
Oracle sharding : Installation & Configuration
 
[Cassandra summit Tokyo, 2015] Cassandra 2015 最新情報 by ジョナサン・エリス(Jonathan Ellis)
[Cassandra summit Tokyo, 2015] Cassandra 2015 最新情報 by ジョナサン・エリス(Jonathan Ellis)[Cassandra summit Tokyo, 2015] Cassandra 2015 最新情報 by ジョナサン・エリス(Jonathan Ellis)
[Cassandra summit Tokyo, 2015] Cassandra 2015 最新情報 by ジョナサン・エリス(Jonathan Ellis)
 
Enterprise Virtualization with Xen
Enterprise Virtualization with XenEnterprise Virtualization with Xen
Enterprise Virtualization with Xen
 
DataStax | Effective Testing in DSE (Lessons Learned) (Predrag Knezevic) | Ca...
DataStax | Effective Testing in DSE (Lessons Learned) (Predrag Knezevic) | Ca...DataStax | Effective Testing in DSE (Lessons Learned) (Predrag Knezevic) | Ca...
DataStax | Effective Testing in DSE (Lessons Learned) (Predrag Knezevic) | Ca...
 
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 MySQLChoosing 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
 
MySQL 5.5&5.6 new features summary
MySQL 5.5&5.6 new features summaryMySQL 5.5&5.6 new features summary
MySQL 5.5&5.6 new features summary
 
MySQL Performance Best Practices
MySQL Performance Best PracticesMySQL Performance Best Practices
MySQL Performance Best Practices
 
D Maeda Bi Portfolio
D Maeda Bi PortfolioD Maeda Bi Portfolio
D Maeda Bi Portfolio
 
Oracle Database appliance - Value proposition Webcast
Oracle Database appliance - Value proposition WebcastOracle Database appliance - Value proposition Webcast
Oracle Database appliance - Value proposition Webcast
 

Viewers also liked

Cassandra NoSQL Tutorial
Cassandra NoSQL TutorialCassandra NoSQL Tutorial
Cassandra NoSQL Tutorial
Michelle Darling
 
Introduction to Data Modeling in Cassandra
Introduction to Data Modeling in CassandraIntroduction to Data Modeling in Cassandra
Introduction to Data Modeling in Cassandra
Jim Hatcher
 
C*ollege Credit: An Introduction to Apache Cassandra
C*ollege Credit: An Introduction to Apache CassandraC*ollege Credit: An Introduction to Apache Cassandra
C*ollege Credit: An Introduction to Apache Cassandra
DataStax
 
durability, durability, durability
durability, durability, durabilitydurability, durability, durability
durability, durability, durability
Matthew Dennis
 
DZone Cassandra Data Modeling Webinar
DZone Cassandra Data Modeling WebinarDZone Cassandra Data Modeling Webinar
DZone Cassandra Data Modeling Webinar
Matthew Dennis
 
Introduction to data modeling with apache cassandra
Introduction to data modeling with apache cassandraIntroduction to data modeling with apache cassandra
Introduction to data modeling with apache cassandra
Patrick McFadin
 
Introduction to Real-Time Analytics with Cassandra and Hadoop
Introduction to Real-Time Analytics with Cassandra and HadoopIntroduction to Real-Time Analytics with Cassandra and Hadoop
Introduction to Real-Time Analytics with Cassandra and Hadoop
Patricia Gorla
 
From rdbms to cassandra without a hitch
From rdbms to cassandra without a hitchFrom rdbms to cassandra without a hitch
From rdbms to cassandra without a hitch
Duyhai Doan
 
Cassandra Data Model
Cassandra Data ModelCassandra Data Model
Cassandra Data Model
ebenhewitt
 
How Do I Cassandra?
How Do I Cassandra?How Do I Cassandra?
How Do I Cassandra?Rick Branson
 
An Overview of Apache Cassandra
An Overview of Apache CassandraAn Overview of Apache Cassandra
An Overview of Apache Cassandra
DataStax
 
Cassandra By Example: Data Modelling with CQL3
Cassandra By Example: Data Modelling with CQL3Cassandra By Example: Data Modelling with CQL3
Cassandra By Example: Data Modelling with CQL3
Eric Evans
 
HBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL database
HBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL databaseHBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL database
HBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL database
Edureka!
 
Visualising Data with Code
Visualising Data with CodeVisualising Data with Code
Visualising Data with Code
Ri Liu
 

Viewers also liked (14)

Cassandra NoSQL Tutorial
Cassandra NoSQL TutorialCassandra NoSQL Tutorial
Cassandra NoSQL Tutorial
 
Introduction to Data Modeling in Cassandra
Introduction to Data Modeling in CassandraIntroduction to Data Modeling in Cassandra
Introduction to Data Modeling in Cassandra
 
C*ollege Credit: An Introduction to Apache Cassandra
C*ollege Credit: An Introduction to Apache CassandraC*ollege Credit: An Introduction to Apache Cassandra
C*ollege Credit: An Introduction to Apache Cassandra
 
durability, durability, durability
durability, durability, durabilitydurability, durability, durability
durability, durability, durability
 
DZone Cassandra Data Modeling Webinar
DZone Cassandra Data Modeling WebinarDZone Cassandra Data Modeling Webinar
DZone Cassandra Data Modeling Webinar
 
Introduction to data modeling with apache cassandra
Introduction to data modeling with apache cassandraIntroduction to data modeling with apache cassandra
Introduction to data modeling with apache cassandra
 
Introduction to Real-Time Analytics with Cassandra and Hadoop
Introduction to Real-Time Analytics with Cassandra and HadoopIntroduction to Real-Time Analytics with Cassandra and Hadoop
Introduction to Real-Time Analytics with Cassandra and Hadoop
 
From rdbms to cassandra without a hitch
From rdbms to cassandra without a hitchFrom rdbms to cassandra without a hitch
From rdbms to cassandra without a hitch
 
Cassandra Data Model
Cassandra Data ModelCassandra Data Model
Cassandra Data Model
 
How Do I Cassandra?
How Do I Cassandra?How Do I Cassandra?
How Do I Cassandra?
 
An Overview of Apache Cassandra
An Overview of Apache CassandraAn Overview of Apache Cassandra
An Overview of Apache Cassandra
 
Cassandra By Example: Data Modelling with CQL3
Cassandra By Example: Data Modelling with CQL3Cassandra By Example: Data Modelling with CQL3
Cassandra By Example: Data Modelling with CQL3
 
HBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL database
HBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL databaseHBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL database
HBase Vs Cassandra Vs MongoDB - Choosing the right NoSQL database
 
Visualising Data with Code
Visualising Data with CodeVisualising Data with Code
Visualising Data with Code
 

Similar to Massively Scalable NoSQL with Apache Cassandra

Getting Big Value from Big Data
Getting Big Value from Big DataGetting Big Value from Big Data
Getting Big Value from Big DataDataStax
 
Toronto jaspersoft meetup
Toronto jaspersoft meetupToronto jaspersoft meetup
Toronto jaspersoft meetupPatrick McFadin
 
Top five questions to ask when choosing a big data solution
Top five questions to ask when choosing a big data solutionTop five questions to ask when choosing a big data solution
Top five questions to ask when choosing a big data solutionjbellis
 
DDN Accelerating-Decisions-Through-Enterprise-Hadoop-final
DDN Accelerating-Decisions-Through-Enterprise-Hadoop-finalDDN Accelerating-Decisions-Through-Enterprise-Hadoop-final
DDN Accelerating-Decisions-Through-Enterprise-Hadoop-finalIntelHealthcare
 
Designing Resilient Application Platforms with Apache Cassandra - Hayato Shim...
Designing Resilient Application Platforms with Apache Cassandra - Hayato Shim...Designing Resilient Application Platforms with Apache Cassandra - Hayato Shim...
Designing Resilient Application Platforms with Apache Cassandra - Hayato Shim...
jaxLondonConference
 
Demystifying Data Warehousing as a Service - DFW
Demystifying Data Warehousing as a Service - DFWDemystifying Data Warehousing as a Service - DFW
Demystifying Data Warehousing as a Service - DFW
Kent Graziano
 
Cassandra 2.0 to 2.1
Cassandra 2.0 to 2.1Cassandra 2.0 to 2.1
Cassandra 2.0 to 2.1
Johnny Miller
 
Scalability 09262012
Scalability 09262012Scalability 09262012
Scalability 09262012
Mike Miller
 
Demystifying Data Warehousing as a Service (GLOC 2019)
Demystifying Data Warehousing as a Service (GLOC 2019)Demystifying Data Warehousing as a Service (GLOC 2019)
Demystifying Data Warehousing as a Service (GLOC 2019)
Kent Graziano
 
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Denodo
 
Scaling DataStax in Docker
Scaling DataStax in DockerScaling DataStax in Docker
Scaling DataStax in Docker
DataStax
 
Datastax - Why Your RDBMS fails at scale
Datastax - Why Your RDBMS fails at scaleDatastax - Why Your RDBMS fails at scale
Datastax - Why Your RDBMS fails at scale
Ruth Mills
 
C* Summit 2013: Searching for a Needle in a Big Data Haystack by Jason Ruther...
C* Summit 2013: Searching for a Needle in a Big Data Haystack by Jason Ruther...C* Summit 2013: Searching for a Needle in a Big Data Haystack by Jason Ruther...
C* Summit 2013: Searching for a Needle in a Big Data Haystack by Jason Ruther...
DataStax Academy
 
Reporting from the Trenches: Intuit & Cassandra
Reporting from the Trenches: Intuit & CassandraReporting from the Trenches: Intuit & Cassandra
Reporting from the Trenches: Intuit & Cassandra
DataStax
 
The Top 5 Factors to Consider When Choosing a Big Data Solution
The Top 5 Factors to Consider When Choosing a Big Data SolutionThe Top 5 Factors to Consider When Choosing a Big Data Solution
The Top 5 Factors to Consider When Choosing a Big Data SolutionDATAVERSITY
 
Minnebar 2013 - Scaling with Cassandra
Minnebar 2013 - Scaling with CassandraMinnebar 2013 - Scaling with Cassandra
Minnebar 2013 - Scaling with Cassandra
Jeff Bollinger
 
Scalar, nimble, brocade, commvault, star trek into darkness, toronto, 05 16 2013
Scalar, nimble, brocade, commvault, star trek into darkness, toronto, 05 16 2013Scalar, nimble, brocade, commvault, star trek into darkness, toronto, 05 16 2013
Scalar, nimble, brocade, commvault, star trek into darkness, toronto, 05 16 2013
patmisasi
 
Data Con LA 2018 - Analyzing Movie Reviews using DataStax by Amanda Moran
Data Con LA 2018 - Analyzing Movie Reviews using DataStax by Amanda MoranData Con LA 2018 - Analyzing Movie Reviews using DataStax by Amanda Moran
Data Con LA 2018 - Analyzing Movie Reviews using DataStax by Amanda Moran
Data Con LA
 
implementation of a big data architecture for real-time analytics with data s...
implementation of a big data architecture for real-time analytics with data s...implementation of a big data architecture for real-time analytics with data s...
implementation of a big data architecture for real-time analytics with data s...
Joseph Arriola
 
Cloudify summit2012 pub
Cloudify summit2012 pubCloudify summit2012 pub
Cloudify summit2012 pub
Gary Berger
 

Similar to Massively Scalable NoSQL with Apache Cassandra (20)

Getting Big Value from Big Data
Getting Big Value from Big DataGetting Big Value from Big Data
Getting Big Value from Big Data
 
Toronto jaspersoft meetup
Toronto jaspersoft meetupToronto jaspersoft meetup
Toronto jaspersoft meetup
 
Top five questions to ask when choosing a big data solution
Top five questions to ask when choosing a big data solutionTop five questions to ask when choosing a big data solution
Top five questions to ask when choosing a big data solution
 
DDN Accelerating-Decisions-Through-Enterprise-Hadoop-final
DDN Accelerating-Decisions-Through-Enterprise-Hadoop-finalDDN Accelerating-Decisions-Through-Enterprise-Hadoop-final
DDN Accelerating-Decisions-Through-Enterprise-Hadoop-final
 
Designing Resilient Application Platforms with Apache Cassandra - Hayato Shim...
Designing Resilient Application Platforms with Apache Cassandra - Hayato Shim...Designing Resilient Application Platforms with Apache Cassandra - Hayato Shim...
Designing Resilient Application Platforms with Apache Cassandra - Hayato Shim...
 
Demystifying Data Warehousing as a Service - DFW
Demystifying Data Warehousing as a Service - DFWDemystifying Data Warehousing as a Service - DFW
Demystifying Data Warehousing as a Service - DFW
 
Cassandra 2.0 to 2.1
Cassandra 2.0 to 2.1Cassandra 2.0 to 2.1
Cassandra 2.0 to 2.1
 
Scalability 09262012
Scalability 09262012Scalability 09262012
Scalability 09262012
 
Demystifying Data Warehousing as a Service (GLOC 2019)
Demystifying Data Warehousing as a Service (GLOC 2019)Demystifying Data Warehousing as a Service (GLOC 2019)
Demystifying Data Warehousing as a Service (GLOC 2019)
 
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
 
Scaling DataStax in Docker
Scaling DataStax in DockerScaling DataStax in Docker
Scaling DataStax in Docker
 
Datastax - Why Your RDBMS fails at scale
Datastax - Why Your RDBMS fails at scaleDatastax - Why Your RDBMS fails at scale
Datastax - Why Your RDBMS fails at scale
 
C* Summit 2013: Searching for a Needle in a Big Data Haystack by Jason Ruther...
C* Summit 2013: Searching for a Needle in a Big Data Haystack by Jason Ruther...C* Summit 2013: Searching for a Needle in a Big Data Haystack by Jason Ruther...
C* Summit 2013: Searching for a Needle in a Big Data Haystack by Jason Ruther...
 
Reporting from the Trenches: Intuit & Cassandra
Reporting from the Trenches: Intuit & CassandraReporting from the Trenches: Intuit & Cassandra
Reporting from the Trenches: Intuit & Cassandra
 
The Top 5 Factors to Consider When Choosing a Big Data Solution
The Top 5 Factors to Consider When Choosing a Big Data SolutionThe Top 5 Factors to Consider When Choosing a Big Data Solution
The Top 5 Factors to Consider When Choosing a Big Data Solution
 
Minnebar 2013 - Scaling with Cassandra
Minnebar 2013 - Scaling with CassandraMinnebar 2013 - Scaling with Cassandra
Minnebar 2013 - Scaling with Cassandra
 
Scalar, nimble, brocade, commvault, star trek into darkness, toronto, 05 16 2013
Scalar, nimble, brocade, commvault, star trek into darkness, toronto, 05 16 2013Scalar, nimble, brocade, commvault, star trek into darkness, toronto, 05 16 2013
Scalar, nimble, brocade, commvault, star trek into darkness, toronto, 05 16 2013
 
Data Con LA 2018 - Analyzing Movie Reviews using DataStax by Amanda Moran
Data Con LA 2018 - Analyzing Movie Reviews using DataStax by Amanda MoranData Con LA 2018 - Analyzing Movie Reviews using DataStax by Amanda Moran
Data Con LA 2018 - Analyzing Movie Reviews using DataStax by Amanda Moran
 
implementation of a big data architecture for real-time analytics with data s...
implementation of a big data architecture for real-time analytics with data s...implementation of a big data architecture for real-time analytics with data s...
implementation of a big data architecture for real-time analytics with data s...
 
Cloudify summit2012 pub
Cloudify summit2012 pubCloudify summit2012 pub
Cloudify summit2012 pub
 

More from jbellis

Vector Search @ sw2con for slideshare.pptx
Vector Search @ sw2con for slideshare.pptxVector Search @ sw2con for slideshare.pptx
Vector Search @ sw2con for slideshare.pptx
jbellis
 
Data day texas: Cassandra and the Cloud
Data day texas: Cassandra and the CloudData day texas: Cassandra and the Cloud
Data day texas: Cassandra and the Cloud
jbellis
 
Cassandra Summit 2015
Cassandra Summit 2015Cassandra Summit 2015
Cassandra Summit 2015
jbellis
 
Cassandra summit keynote 2014
Cassandra summit keynote 2014Cassandra summit keynote 2014
Cassandra summit keynote 2014
jbellis
 
Cassandra 2.1
Cassandra 2.1Cassandra 2.1
Cassandra 2.1jbellis
 
Tokyo cassandra conference 2014
Tokyo cassandra conference 2014Tokyo cassandra conference 2014
Tokyo cassandra conference 2014jbellis
 
Cassandra Summit EU 2013
Cassandra Summit EU 2013Cassandra Summit EU 2013
Cassandra Summit EU 2013jbellis
 
Cassandra Summit 2013 Keynote
Cassandra Summit 2013 KeynoteCassandra Summit 2013 Keynote
Cassandra Summit 2013 Keynotejbellis
 
Pycon 2012 What Python can learn from Java
Pycon 2012 What Python can learn from JavaPycon 2012 What Python can learn from Java
Pycon 2012 What Python can learn from Javajbellis
 
Apache Cassandra: NoSQL in the enterprise
Apache Cassandra: NoSQL in the enterpriseApache Cassandra: NoSQL in the enterprise
Apache Cassandra: NoSQL in the enterprisejbellis
 
Dealing with JVM limitations in Apache Cassandra (Fosdem 2012)
Dealing with JVM limitations in Apache Cassandra (Fosdem 2012)Dealing with JVM limitations in Apache Cassandra (Fosdem 2012)
Dealing with JVM limitations in Apache Cassandra (Fosdem 2012)jbellis
 
Cassandra at High Performance Transaction Systems 2011
Cassandra at High Performance Transaction Systems 2011Cassandra at High Performance Transaction Systems 2011
Cassandra at High Performance Transaction Systems 2011jbellis
 
Cassandra 1.0 and the future of big data (Cassandra Tokyo 2011)
Cassandra 1.0 and the future of big data (Cassandra Tokyo 2011)Cassandra 1.0 and the future of big data (Cassandra Tokyo 2011)
Cassandra 1.0 and the future of big data (Cassandra Tokyo 2011)jbellis
 
What python can learn from java
What python can learn from javaWhat python can learn from java
What python can learn from javajbellis
 
State of Cassandra, 2011
State of Cassandra, 2011State of Cassandra, 2011
State of Cassandra, 2011jbellis
 
Brisk: more powerful Hadoop powered by Cassandra
Brisk: more powerful Hadoop powered by CassandraBrisk: more powerful Hadoop powered by Cassandra
Brisk: more powerful Hadoop powered by Cassandrajbellis
 
PyCon 2010 SQLAlchemy tutorial
PyCon 2010 SQLAlchemy tutorialPyCon 2010 SQLAlchemy tutorial
PyCon 2010 SQLAlchemy tutorial
jbellis
 
Cassandra 0.7, Los Angeles High Scalability Group
Cassandra 0.7, Los Angeles High Scalability GroupCassandra 0.7, Los Angeles High Scalability Group
Cassandra 0.7, Los Angeles High Scalability Group
jbellis
 
Cassandra devoxx 2010
Cassandra devoxx 2010Cassandra devoxx 2010
Cassandra devoxx 2010
jbellis
 
Cassandra FrOSCon 10
Cassandra FrOSCon 10Cassandra FrOSCon 10
Cassandra FrOSCon 10jbellis
 

More from jbellis (20)

Vector Search @ sw2con for slideshare.pptx
Vector Search @ sw2con for slideshare.pptxVector Search @ sw2con for slideshare.pptx
Vector Search @ sw2con for slideshare.pptx
 
Data day texas: Cassandra and the Cloud
Data day texas: Cassandra and the CloudData day texas: Cassandra and the Cloud
Data day texas: Cassandra and the Cloud
 
Cassandra Summit 2015
Cassandra Summit 2015Cassandra Summit 2015
Cassandra Summit 2015
 
Cassandra summit keynote 2014
Cassandra summit keynote 2014Cassandra summit keynote 2014
Cassandra summit keynote 2014
 
Cassandra 2.1
Cassandra 2.1Cassandra 2.1
Cassandra 2.1
 
Tokyo cassandra conference 2014
Tokyo cassandra conference 2014Tokyo cassandra conference 2014
Tokyo cassandra conference 2014
 
Cassandra Summit EU 2013
Cassandra Summit EU 2013Cassandra Summit EU 2013
Cassandra Summit EU 2013
 
Cassandra Summit 2013 Keynote
Cassandra Summit 2013 KeynoteCassandra Summit 2013 Keynote
Cassandra Summit 2013 Keynote
 
Pycon 2012 What Python can learn from Java
Pycon 2012 What Python can learn from JavaPycon 2012 What Python can learn from Java
Pycon 2012 What Python can learn from Java
 
Apache Cassandra: NoSQL in the enterprise
Apache Cassandra: NoSQL in the enterpriseApache Cassandra: NoSQL in the enterprise
Apache Cassandra: NoSQL in the enterprise
 
Dealing with JVM limitations in Apache Cassandra (Fosdem 2012)
Dealing with JVM limitations in Apache Cassandra (Fosdem 2012)Dealing with JVM limitations in Apache Cassandra (Fosdem 2012)
Dealing with JVM limitations in Apache Cassandra (Fosdem 2012)
 
Cassandra at High Performance Transaction Systems 2011
Cassandra at High Performance Transaction Systems 2011Cassandra at High Performance Transaction Systems 2011
Cassandra at High Performance Transaction Systems 2011
 
Cassandra 1.0 and the future of big data (Cassandra Tokyo 2011)
Cassandra 1.0 and the future of big data (Cassandra Tokyo 2011)Cassandra 1.0 and the future of big data (Cassandra Tokyo 2011)
Cassandra 1.0 and the future of big data (Cassandra Tokyo 2011)
 
What python can learn from java
What python can learn from javaWhat python can learn from java
What python can learn from java
 
State of Cassandra, 2011
State of Cassandra, 2011State of Cassandra, 2011
State of Cassandra, 2011
 
Brisk: more powerful Hadoop powered by Cassandra
Brisk: more powerful Hadoop powered by CassandraBrisk: more powerful Hadoop powered by Cassandra
Brisk: more powerful Hadoop powered by Cassandra
 
PyCon 2010 SQLAlchemy tutorial
PyCon 2010 SQLAlchemy tutorialPyCon 2010 SQLAlchemy tutorial
PyCon 2010 SQLAlchemy tutorial
 
Cassandra 0.7, Los Angeles High Scalability Group
Cassandra 0.7, Los Angeles High Scalability GroupCassandra 0.7, Los Angeles High Scalability Group
Cassandra 0.7, Los Angeles High Scalability Group
 
Cassandra devoxx 2010
Cassandra devoxx 2010Cassandra devoxx 2010
Cassandra devoxx 2010
 
Cassandra FrOSCon 10
Cassandra FrOSCon 10Cassandra FrOSCon 10
Cassandra FrOSCon 10
 

Recently uploaded

GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neo4j
 
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
SOFTTECHHUB
 
Elizabeth Buie - Older adults: Are we really designing for our future selves?
Elizabeth Buie - Older adults: Are we really designing for our future selves?Elizabeth Buie - Older adults: Are we really designing for our future selves?
Elizabeth Buie - Older adults: Are we really designing for our future selves?
Nexer Digital
 
RESUME BUILDER APPLICATION Project for students
RESUME BUILDER APPLICATION Project for studentsRESUME BUILDER APPLICATION Project for students
RESUME BUILDER APPLICATION Project for students
KAMESHS29
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Quotidiano Piemontese
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
Octavian Nadolu
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
SOFTTECHHUB
 
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIEnchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Vladimir Iglovikov, Ph.D.
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Safe Software
 
PCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase TeamPCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase Team
ControlCase
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Kari Kakkonen
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
Aftab Hussain
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems S.M.S.A.
 
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofszkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
Alex Pruden
 
Introduction to CHERI technology - Cybersecurity
Introduction to CHERI technology - CybersecurityIntroduction to CHERI technology - Cybersecurity
Introduction to CHERI technology - Cybersecurity
mikeeftimakis1
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Matthew Sinclair
 
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
KatiaHIMEUR1
 
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI supportEpistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
Alan Dix
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Neo4j
 

Recently uploaded (20)

GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
 
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
 
Elizabeth Buie - Older adults: Are we really designing for our future selves?
Elizabeth Buie - Older adults: Are we really designing for our future selves?Elizabeth Buie - Older adults: Are we really designing for our future selves?
Elizabeth Buie - Older adults: Are we really designing for our future selves?
 
RESUME BUILDER APPLICATION Project for students
RESUME BUILDER APPLICATION Project for studentsRESUME BUILDER APPLICATION Project for students
RESUME BUILDER APPLICATION Project for students
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
 
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIEnchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
 
PCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase TeamPCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase Team
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
 
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofszkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
 
Introduction to CHERI technology - Cybersecurity
Introduction to CHERI technology - CybersecurityIntroduction to CHERI technology - Cybersecurity
Introduction to CHERI technology - Cybersecurity
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
 
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
 
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI supportEpistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
 

Massively Scalable NoSQL with Apache Cassandra

  • 1. Massively scalable NoSQL with Apache Cassandra! Jonathan Ellis Project Chair, Apache Cassandra CTO, DataStax @spyced
  • 2. Big data Analytics Realtime ? (Hadoop) (“NoSQL”) ©2012 DataStax
  • 3. Some Casandra users ©2012 DataStax
  • 4. eBay Application/Use Case • Social Signals: like/want/own features for eBay product and item pages • Hunch taste graph for eBay users and items • Many time series use cases Why Cassandra? • Multi-datacenter • Scalable • Write performance • Distributed counters • Hadoop support ©2012 DataStax ACE
  • 9. Disney Application/Use Case • Meet the data management needs of user facing applications across The Walt Disney Company with a single platform Why Cassandra? • DataStax Enterprise can tackle real-time and search functions in the same cluster • Scalability • 24x7 uptime ©2012 DataStax NDI
  • 13. SimpleReach Application/Use Case • SimpleReach tracks social actions for content creators, from Twitter and Facebook to Pinterest and Reddit, to deliver detailed insights and clear metrics around social behavior. Why Cassandra? • Very high velocity data ingest rate and large data volumes • Workload separation between realtime and batch applications ©2012 DataStax NDE
  • 14. SourceNinja Application/Use Case • SourceNinja notifies you to performance, security, and bug fixes for the software you depend on Why Cassandra? • Previous database system could not handle load; HBase has too many points of failure and was too slow • Fast real time capabilities, batch analytics on that data, and enterprise search ©2012 DataStax RDE
  • 15. Netflix Application/Use Case • General purpose backend for large scale highly available cloud based web services supporting Netflix Streaming Why Cassandra? • Highly available, highly robust and no schema change downtime • Highly scalable, optimized for SSD • Much lower cost than previous Oracle and SimpleDB implementations • Flexible data model • Ability to directly influence/implement OSS feature set • Supports local and wide area distributed operations, spanning US and Europe ©2012 DataStax RCE
  • 18. Use case patterns • Massively scalable • High performance • Reliable/Available ©2012 DataStax
  • 20. reads/s writes/s 35000 30000 25000 20000 15000 10000 5000 Cassandra 0.6 0 ©2012 DataStax Cassandra 1.0
  • 22. Classic partitioning with SPOF partition 1 partition 2 partition 3 partition 4 router client ©2012 DataStax
  • 23. Availability • “High availability implies that a single fault will not bring down your system. Not ‘we’ll recover quickly.’” -- Ben Coverston: DataStax • “The biggest problem with failover is that you're almost never using it until it really hurts. It's like backups that you never test.” -- Rick Branson: Instagram ©2012 DataStax
  • 24. Fully distributed, no SPOF client p3 p6 p1 p1 p1 ©2012 DataStax
  • 25. Partitioning jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F ©2012 DataStax
  • 26. Partitioning Primary key determines placement* jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F ©2012 DataStax
  • 27. PK MD5 Hash jim 5e02739678... MD5* hash carol a9a0198010... operation yields a 128-bit number johnny f4eb27cea7... for keys of any size. suzy 78b421309e... ©2012 DataStax
  • 28. The “token ring” Node A Node B Node D Node C ©2012 DataStax
  • 29. Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
  • 30. Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
  • 31. Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
  • 32. Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
  • 33. Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
  • 34. Replication Node A Node B Node D Node C carol a9a0198010... ©2012 DataStax
  • 35. Node A Node B Node D Node C carol a9a0198010... ©2012 DataStax
  • 36. Node A Node B Node D Node C carol a9a0198010... ©2012 DataStax
  • 37. Highlights • Adding capacity is application-transparent and requires no downtime • No SPOF, not even temporarily • No “primary” replica • Configurable synchronous/asynchronous • Tolerates node failure; never have to restart replication “from scratch” • “Smart” replication avoids correlated failures ©2012 DataStax
  • 38. CQL: You got SQL in my NoSQL! CREATE TABLE users ( id uuid PRIMARY KEY, name text, state text, birth_date int ); CREATE INDEX ON users(state); SELECT * FROM users WHERE state=‘Texas’ AND birth_date > 1950; ©2012 DataStax
  • 39. Strictly “realtime” focused • No joins • No subqueries • No aggregation functions* or GROUP BY • ORDER BY? ©2012 DataStax
  • 40. Clustered data in in CFS ©2012 DataStax
  • 41. Clustered data in in CFS ©2012 DataStax
  • 42. Clustering in CQL3 CREATE TABLE sblocks (     block_id uuid,     subblock_id uuid,     data blob, block_id subblock_id data     PRIMARY KEY (block_id, subblock_id) Block1 subblock A data A ); Block1 subblock B data B ... ... ... Block2 subblock C data C Block2 subblock D data D ... ... ... Block3 subblock E data E Block3 subblock F data F ... ... ... ©2012 DataStax
  • 43. Collections CREATE TABLE users ( id uuid PRIMARY KEY, name text, state text, birth_date int ); CREATE TABLE users_addresses ( user_id uuid REFERENCES users, email text ); SELECT * FROM users NATURAL JOIN users_addresses; ©2012 DataStax
  • 44. Collections CREATE TABLE users ( id uuid PRIMARY KEY, name text, state text, X birth_date int ); CREATE TABLE users_addresses ( user_id uuid REFERENCES users, email text ); SELECT * FROM users NATURAL JOIN users_addresses; ©2012 DataStax
  • 45. Collections CREATE TABLE users ( id uuid PRIMARY KEY, name text, state text, birth_date int, email_addresses set<text> ); UPDATE users SET email_addresses = email_addresses + {‘jbellis@gmail.com’, ‘jbellis@datastax.com’}; ©2012 DataStax
  • 46. Big data Analytics Realtime ? (Hadoop) (“NoSQL”) ©2012 DataStax
  • 47. The evolution of Analytics Analytics + Realtime ©2012 DataStax
  • 48. The evolution of Analytics replication Analytics Realtime ©2012 DataStax
  • 49. The evolution of Analytics ETL ©2012 DataStax
  • 50. Big data Analytics Datastax Realtime (Hadoop) Enterprise (Cassandra) ©2012 DataStax
  • 52. Better Hadoop than Hadoop • “Vanilla” Hadoop • 8+ services to setup, monitor, backup, and recover (NameNode, SecondaryNameNode, DataNode, JobTracker, TaskTracker, Zookeeper, Region Server,...) • Single points of failure • Can't separate online and offline processing • DataStax Enterprise • Single, simplified component • Self-organizes based on workload • Peer to peer • JobTracker failover ©2012 DataStax
  • 53. Enterprise search with Solr SELECT title FROM solr WHERE solr_query='title:natio*'; title -------------------------------------------------------------------------- Bolivia national football team 2002 List of French born footballers who have played for other national teams Lithuania national basketball team at Eurobasket 2009 Bolivia national football team 2000 Kenya national under-20 football team Bolivia national football team 1999 Israel men's national inline hockey team Bolivia national football team 2001 ©2012 DataStax
  • 54. Managing & Monitoring Big Data ©2012 DataStax
  • 55. Questions? • http://www.datastax.com/docs • http://www.datastax.com/dev/blog/whats-new-in- cassandra-1-1 • http://www.datastax.com/dev/blog/schema-in- cassandra-1-1 • http://www.datastax.com/products/enterprise ©2012 DataStax