This presentation shows why NoSQL is a good fit for Cloud applications.

1. NoSQL in der Cloud – Why?
Andreas Hartmann
19.11.2012

2. Quelle: http://res.sys-con.com/story/mar12/2188748/CloudBigData_0_0.jpg
19.11.2012 2 NoSQL in der Cloud – Why?

3. Why Cloud???
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4. What does Big Data mean???
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5. Why NoSQL???
BigData Connectivity P2P Knowledge
Concurrencey Diversity Cloud
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6. What is the Problem with Big Data
► Caching
► Master/Slave
► Master/Master
► Cluster
► Table Partitioning
► Federated Tables
► Sharding
Quelle: http://www.codefutures.com/database-sharding/
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7. NoSQL Basics – CAP Theorem
Consistency
Each client always
has the same view of
the data
Availability
All clients can always
read and write
Partition tolerance
The System continues
to operate despite
arbitrary message
loss
Quelle: http://blog.nahurst.com/visual-guide-to-nosql-systems
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8. ACID-BASE
ACID BASE
Atomicity Basically Available
► all or nothing rule ► the system does guarantee availability, in
terms of the CAP theorem
Consistency
Soft-State
► any transaction the database performs
will take it from one consistent state to ► the state of the system may change over
another time, even without input
Isolation Eventual Consistency
► the system will become consistent over
► no transaction should be able to interfere
time, given that the system doesn't
with another transaction at all
receive input during that time
Durability
► once a transaction has been committed,
it will remain so Quelle:
http://stackoverflow.com/questions/3342497/explanation-of-
Quelle: http://en.wikipedia.org/wiki/ACID base-terminology
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9. Things to think about!!!
► Scalability
► High Availability
► Caching
► Architecture
► Load Balancing
► Queuing
► Data Handling
► Federation
► Serving Files
► Storing Files
Quelle: http://cloudscaling.com/blog/cloud-computing/up-out-centralized-and-decentralized
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10. Open Your Mind;-)
Quelle: http://images.tribe.net/tribe/upload/photo/deb/074/deb074db-81fc-4b8a-bfbd-b18b922885cb
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11. NoSQL Tagcloud
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12. Datastore Types
Key/Value Document
Store Store
Extensible
Record
Graph
Stores (Wide-
Database
column
Stores)
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13. What ist means
Query Developer
Data is easily and quickly read/stored More technologies to have fun with
using primary key
Broader choice of persistence stores
Denormalize data for commonly used
queries
Probably Cross Store Persistence
► Store name, firstname etc in RDBMS
► Shema Design is optimized for the most
common Use-Cases ► Store followers in Graph database
► Store Content in RDBMS
► Store User Generated Content in
Document database
Quelle: http://www.slideshare.net/adessoAG/no-sql-9355109
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14. Wich is the right one
Quelle: http://www.slideshare.net/emileifrem/nosql-east-a-nosql-overview-and-the-benefits-of-graph-databases
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15. Wich is the right one
Quelle: http://martinfowler.com/articles/nosql-intro.pdf
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16. Architecture Case Study
Business Critical
Data Read/Write
Data should be
consistent
Rarley Changed Data /
Various Reads / Slight
inconsistencies are OK
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17. mongoDB as an Example
Document-
JSON like
oriented &
documents
schemaless
Open Source
written in C++ with a AGPL-
Licence v.3.0
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18. mongoDB in Detail
MongoDB Basics
Security and Authentication – Indexes
Replication – Scaling
Map/Reduce – Binary Data Sets
Monitoring – Backup
Schema Design – Connectivity – Ecosystem
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19. mongoDB – Replication
Replica Sets
► A replica set consists of two or more nodes that are copies of each other
► The replica set automatically selects a primary (master).
► Drivers can automatically detect when a replica set primary changes and will
begin sending writes to the new primary
Why Replica Sets
► Automated Failover
► Read Scaling (slaveOkay Method)
► Maintenance
► Disaster Recovery
Quelle: http://www.mongodb.org/display/DOCS/Replica+Sets
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20. mongoDB – Scaling
Sharding
► Horizontal scaling across multiple nodes
Sharding Key
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21. mongoDB – Scaling
Replica Sets comes together with Sharding
Quelle: http://www.mongodb.org/display/DOCS/Sharding+Introduction
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22. mongoDB – Map/Reduce
Parallel processing huge datasets on distributed systems
Data Data Data Data
MAP
REDUCE
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23. mongoDB – Map/Reduce
Map Reduce Execute
Map
► XXX
var map = function() {
emit( this.author,
{ pages: this.pages } );
};
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24. mongoDB – Map/Reduce
Map Reduce Execute
Reduce
var reduce = function( key, values ) {
var sum = 0;
values.forEach( function( doc ) {
sum += doc.pages;
} );
return { "pages": sum };
};
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25. mongoDB – Map/Reduce
Map Reduce Execute
Execute
db.bookstore.mapReduce( map, reduce,
{ out: "myresultcollection" } );
{
"result" : "myresultcollection",
"timeMillis" : 156,
"counts" : {
"input" : 7,
"emit" : 7,
"reduce" : 3,
"output" : 3
},
"ok" : 1,
}
19.11.2012 25 NoSQL in der Cloud – Why?

26. Quelle: http://res.sys-con.com/story/mar12/2188748/CloudBigData_0_0.jpg
Quelle: http://www.techweekeurope.co.uk/wp-content/uploads/2011/03/Big-Data.jpg
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27. Thank you for your attention!
info@adesso.de
www.adesso.de