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Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 Research
 

Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 Research

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Slides from Matt Aslett's presentation at Cassandra Europe 2012.

Slides from Matt Aslett's presentation at Cassandra Europe 2012.

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    Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 Research Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 Research Presentation Transcript

    • NoSQL, NewSQL, and BeyondThe answer to SPRAINed relational databasesMatthew AslettResearch Manager, Data Management and Analytics 1 © 2012 by The 451 Group. All rights reserved
    • 451 Research Matthew Aslett • Research manager, data management and analytics • With The 451 Group since 2007 • www.twitter.com/maslettInformation Management Commercial Adoption of Open Source Operational databases (CAOS) Data warehousing  Open source projects Data caching  Adoption of open source software Event processing  Vendor strategies © 2012 by The 451 Group. All rights reserved
    • The 451 Group 3 © 2012 by The 451 Group. All rights reserved
    • Relevant reports NoSQL, NewSQL and Beyond • Assessing the drivers behind the development and adoption of NoSQL and NewSQL databases, as well as data grid/caching technologies • Released April 2011 • Role of open source in driving innovation • sales@the451group.com © 2012 by The 451 Group. All rights reserved
    • NoSQL, NewSQL and Beyond NoSQL NewSQL  New breed of non-relational  New breed of relational database products database products  Rejection of fixed table  Retain SQL and ACID schema and join operations  Designed to meet scalability  Designed to meet scalability requirements of distributed requirements of distributed architectures architectures  Or improve performance so  And/or schema-less data horizontal scalability is no management requirements longer a necessity … and Beyond  In-memory data grid/cache products  Potentialprimary platform for distributed data management © 2012 by The 451 Group. All rights reserved
    • The NoSQL landscape Key Value Store Graph •Citrusleaf Document • InfiniteGraph • HandlerSocket* •RavenDB • Neo4j • Redis • Riak • MongoDB • DEX • Voldemort •CouchBase • CouchDB •OrientDB • Membrain • Mongo Labs • Cloudant •NuvolaBase • Oracle NoSQL • Mongo HQ • Iris Couch • Castle •DynamoDB •RethinkDB • Redis-to-go •LevelDB • SimpleDB • Cassandra • DataStax EE •Acunu • HBase • App Engine Datastore -as-a-Service • Hypertable Big Tables © 2012 by The 451 Group. All rights reserved
    • The NewSQL ecosystem-as-a-Service New databases •Drizzle •NuoDB• Xeround •Drizzle • VoltDB •SQLFire• Tokutek • Akiban • JustOne DB • Translattice • GenieDB • Clustrix • Schooner SQL •ScaleDB •ParElastic • ScaleBaseStorage engines • MySQL Cluster •Continuent •ScaleArc •Zimory Scale •Galera • CodeFutures Clustering/sharding © 2012 by The 451 Group. All rights reserved
    • SPRAINED RELATIONAL DATABASESPhoto credit: Foxtongue on Flickrhttp://www.flickr.com/photos/foxtongue/4844016087/ © 2012 by The 451 Group. All rights reserved
    • SPRAIN Scalability - Hardware economics  Example project/service/vendor: • BigTable, HBase, Riak, MongoDB, Couchbase, Hadoop • Xeround, NuoDB • Data grid/cache  Associated use case: • Large-scale distributed data storage • Analysis of continuously updated data • Multi-tenant PaaS data layer © 2012 by The 451 Group. All rights reserved
    • SPRAIN Scalability  Netflix: • 37X growth in requests Jan 2010-Jan 2011 • “We had to get out of the datacenter business”  Dachis Group: • Specifically wanted a horizontally scalable data store  Spotify: • Importance (and challenges) of cross-datacenter replication  Tellybug: • Peak scalability – mission critical for two hours per week. Elastic scalability still not solved. © 2012 by The 451 Group. All rights reserved
    • SPRAIN Performance – RDBMS limitations  Example project/service/vendor: • Hypertable, Couchbase, Riak, Membrain, MongoDB, Redis • Data grid/cache • VoltDB, Clustrix  Associated use case: • Real time data processing of mixed read/write workloads • Data caching • Large-scale data ingestion © 2012 by The 451 Group. All rights reserved
    • SPRAIN Performance  Tellybug: • Having won the contract to deliver app for Britain’s Got Talent realised that its MySQL/Django/Python stack couldn’t deliver the anticipated load  Spotify: • Major upgrades without service interruptions, not possible with sharded SQL databases  Rackspace: • Ability to monitor a million different things, ability to withstand the failure of 1/3 data centers © 2012 by The 451 Group. All rights reserved
    • SPRAIN Relaxed consistency - CAP Theorem  Example project/service/vendor: • Dynamo, Voldemort, Cassandra, Riak • Amazon DynamoDB  Associated use case: • Multi-data center replication • Service availability • Non-transactional data off-load © 2012 by The 451 Group. All rights reserved
    • SPRAIN Relaxed consistency  Netflix: • “We value availability over consistency. We don’t need full consistency.”  Tellybug: • Soft production data – the ability to ignore failures • Spotify: • Flexibility to combine different consistency levels for different column families in a single application © 2012 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,  Associated use case: • Mobile/remote device synchronization • Agile development • Data caching © 2012 by The 451 Group. All rights reserved
    • SPRAIN Agility  Tellybug: • Had 4-5 weeks to ship a production system to meet contract • Ability to re-build entire infrastructure from scratch in 10-15 mins  Netflix: • Single SQL database had to be brought down to change the schema. • Put the logic into the Web services and employ distributed key stores to enable agile development and schema changes  Spotify: • Cassandra is a platform for quickly developing new applications © 2012 by The 451 Group. All rights reserved
    • SPRAIN Intricacy – volume, velocity, variety  Example project/service/vendor: • Neo4j, GraphDB, InfiniteGraph • Apache Cassandra, Hadoop, Riak • VoltDB, Clustrix  Associated use case: • Social networking applications • Geo-locational applications • Configuration management database © 2012 by The 451 Group. All rights reserved
    • SPRAIN Intricacy  Dachis Group: • Combining social media data for 2,000 brands with sentiment, relationship, conversations, social graph  Spotify: • More than half a billion playlists, about 10 thousand requests per second at peak  Rackspace:  One row per customer with thousands of columns – potentially millions of columns  Tellybug: • Ability to cope with 10,000 interactions/sec and integrate that with the social graph for thousands of concurrent users © 2012 by The 451 Group. All rights reserved
    • SPRAIN Necessity - open source  The failure of existing suppliers to address emerging requirements  Example projects: • BigTable: Google • Dynamo: Amazon • Cassandra: Facebook • HBase: Powerset • Voldemort: LinkedIn • Hypertable: Zvents • Neo4j: Windh Technologies © 2012 by The 451 Group. All rights reserved
    • SPRAIN Necessity  Spotify: • Created own backup and restore technology  Rackspace: • Distributed secondary indexing, blob storage, many other projects  Tellybug: • Created sharded counter in memcached, wrote several tools to figure out closeness to the ‘truth’  Netflix: • Developed multiple tools including multi-datacenter replication, automated configuration and backup, provisioning interface etc © 2012 by The 451 Group. All rights reserved
    • Relevant reports MySQL, NoSQL and NewSQL • Assessing the competitive dynamic between the MySQL ecosystem, NoSQL and NewSQL technologies • Due May 2012 • Including market sizing of the three database segments • Survey of 200+ database users • sales@the451group.com © 2012 by The 451 Group. All rights reserved
    • Thank you. Questions? Comments? matt.aslett@451research.com @maslett © 2012 by The 451 Group. All rights reserved