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Preparing yourdataforcloud
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Transcript of Preparing yourdataforcloud
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Preparing Your Data for Cloud
Narinder Kumar 11/11/2010
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Agenda Relational DBMS's : Pros & Cons
Non-Relational DBMS's : Pros & Cons
Types of Non-Relational DBMS's
Current Market State
Applicability of Different Data-Bases in
different environments 2
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Relational DBMS - Pros Data Integrity
ACID Capabilities
High Level Query Model
Data Normalization
Data Independence
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Relational DBMS - Cons Scaling Issues
By Duplication (Master-Slave) By Sharding/Division (Not transparent)
Fixed Schema Mostly disk-oriented (Performance) May fair poorly with large data
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Non-Relational DBMS - Pros Scalability
Replication / Availability
Performance
Deployment Flexibility
Modelling Flexibility
Faster Development (?)5
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Non-Relational DBMS - Cons Lack of Transactional Support Data Integrity is Application's responsibility Data Duplication / Application Dependent Eventually Consistent (mostly) No Standardization New Technology
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RDBMS's and Cloud
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Cloud Capable RDBMS
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Almost every RDBMS can run in a IAAS Cloud Platform
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Cloud Native RDBMS's
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Types of Non-Relational DBMS Key Value Stores
Document Stores
Column Stores
Graph Stores
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Key Value Data-Bases Object is completely Opaque to DB Mostly GET, PUT & DELETE operations are
supported There may be limits on size of Objects
Inspired by Amazon Dynamo Paper
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Key Value DataBases & Cloud
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Project Voldemort
MemCachedDB Tokyo Tyrant
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Document Data-Bases Object is not completely opaque to DB Every Object has it's own schema
FirstName="Bob", Address="5 Oak St.", Hobby="sailing".
FirstName="Jonathan", Children=("Michael,10", "Jennifer,8")
Can perform queries based on Object's attributes Possible to describe relationships between Objects Joins and Transactions are not supported Good for XML or JSON objects
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Document DataBases & Cloud
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Column-Store Data-Bases Richer than Document Stores Multi-Dimensional Map
Tables Row Column Time-Stamp
Supports Multiple Data Types Usually use an Underlying DFS
Inspired by Google Big Table Paper15
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Column-Store Data-Bases & Cloud
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Key Factors while Making a Choice
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Application Architecture Requirements Platform choices Non-Functional Requirements
Consistency Availability Partition Security Data Redemption
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Different Requirements = Different Solutions
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Scenario 1
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Feature First Corporate Data Consistency Requirements Business Intelligence Legacy Application
RDBMS on Amazon Cloud, RackSpace (IaaS) or
Microsoft Azure/Amazon RDS (PaaS)
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Scenario 2
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Consumer Facing Application Big Files (Images, BLOB's, Files) Geographically Distributed Mostly writes Not heavy requirement on Rich Queries
Key-Value Data Stores (Amazon S3, Project Voldemort, Redis)
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Scenario 3
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Hundreds Of Government Documents with different schemas
Need to serve on Web Data Mining
Document Data-Stores (Amazon SimpleDB, Apache Couche DB, MongoDB)
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Scenario 4
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Scale First Huge Data-Set Analytical Requirements Consumer Facing High Availability over Consistency
Column Data-Stores (Google App Engine, Hbase, Cassandra)
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Mix & Match of Earlier Scenarios
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Polyglot Persistence RDBMS for low-
volume and high value Key-Value DB for large
files with little queries Memcached DB for
short-lived Data Column DB for
Analytics
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CAP Theorem
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Conclusions
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One Size does not Fit all
Many choices
No-SQL DB's providing Alternatives
RDBMS serve useful purpose
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www.inphina.com
http://thoughts.inphina.com
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References http://nosql-database.org/
http://www.drdobbs.com/database/218900502
http://perspectives.mvdirona.com/2009/11/03/OneSizeDoesNotFitAll.aspx
http://blog.nahurst.com/visual-guide-to-nosql-systems
http://blog.heroku.com/archives/2010/7/20/nosql/
http://www.vineetgupta.com/2010/01/nosql-databases-part-1-landscape.html
http://project-voldemort.com/
http://code.google.com/p/redis/
http://memcachedb.org/
http://aws.amazon.com/simpledb/
http://couchdb.apache.org/
http://www.mongodb.org
http://riak.basho.com