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Big Data and Me Bhupesh Bansal Feb 3, 2012
Relational Model Architecture Reference :  http:// www.slideshare.net / adorepump / voldemort-nosql
Linkedin 2006 Reference :  http://www.slideshare.net/linkedin/linked-in-javaone-2008-tech-session-comm
Relational model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Specialized Systems Architecture Reference :  http:// www.slideshare.net / adorepump / voldemort-nosql
Linkedin 2007 Reference :  http://www.slideshare.net/linkedin/linked-in-javaone-2008-tech-session-comm
Specialized systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Batch Driven Architecture Reference :   http:// www.slideshare.net / bhupeshbansal /hadoop-user-group-jan2010
Motivation I : Big Data  02/06/12 Reference :  algo2.iti.kit.edu/.../fopraext/index.html
Motivation II: Data Driven Features
Motivation III: Makes Money  02/06/12 Proprietary & Confidential
Motivation IV: Big Data is cool 02/06/12
Reference : http:// www.slideshare.net / BenSiscovick /the-business-of-big-data-ia-ventures-8577588
Big Data Challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why is this hard ? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Some good news !! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What works !! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What doesn’t works !! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Open source ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Open source: Storage ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Open source: Publish/Subscribe ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Open source: Real time analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Open source: Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Open source: Batch computation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Open source: Other ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
My personal picks !! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Jeff Dean’s Thoughts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Reference  : http:// www.slideshare.net / xlight /google-designs-lessons-and-advice-from-building-large-distributed-systems
How Voldemort was born ? Reference : 1)  http://www.slideshare.net/bhupeshbansal/hadoop-user-group-jan2010 2)  http://www.slideshare.net/adorepump/voldemort-nosql
Why NoSQL ? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inspired By Amazon Dynamo & Memcached ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ACID Vs CAP ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Proprietary & Confidential 02/06/12
Consistent Hashing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Proprietary & Confidential 02/06/12
R+W > N  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Proprietary & Confidential 02/06/12
Versioning & Conflict Resolution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Vector Clock ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Proprietary & Confidential 02/06/12
Client API ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Voldemort Physical Deployment
 
Read-only storage engine ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Reference :   http:// www.slideshare.net / bhupeshbansal /hadoop-user-group-jan2010
What do we use Hadoop/Voldemort for ? Proprietary & Confidential 02/06/12
Batch Driven Architecture Reference :   http:// www.slideshare.net / bhupeshbansal /hadoop-user-group-jan2010
Data Flow Driven Architecture Reference : http:// sna-projects.com /blog/2011/08/ kafka /
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Big Data Architecture Systems Open Source

Hinweis der Redaktion

  1. Example: member data--does not make sense to repeatedly join positions, emails, groups, etc. Explain about joins How to better model in java? Json like data model
  2. Example: member data--does not make sense to repeatedly join positions, emails, groups, etc. Explain about joins How to better model in java? Json like data model
  3. Statistical learning as the ultimate agile development tool (Peter Norvig), “business logic” through data rather than code
  4. No Joins Across data domains due to APIs Within data domains due to performance Natural operation: getAll(id…) Latency: if you want to call 30 services on your main pages, they better be quick (30 * 20ms = 600ms)
  5. - Strong Consistency: all clients see the same view, even in presence of updates - High Availability: all clients can find some replica of the data, even in the presence of failures Partition-tolerance: the system properties hold even when the system is partitioned high availability : Mantra for websites Better to deal with inconsistencies, because their primary need is to scale well to allow for a smooth user experience.
  6. Hashing .. Why do we need it ?? Basic problem : Clients need to know which data is where ?? Many ways of solving it Central configuration Hashing Linear hashing works : issue is when cluster is dynamic ?? KeyHash –node IDmapping change for a lot of entries When you add new slots Consistent hashing : preserves key –Node mapping for most of the keys and only change the minimal amount needed How to do it ?? Number of partitions ---------------------------- Arbitrary , each node is allocated many partitions (better load balancing and fault tolerance) Few hundreds to few thousands .. Key  partition mapping is fixed and only ownership of partitions can change
  7. Give example of read and writes with vector clocks Pros and cons vs paxos and 2pc User can supply strategy for handling cases where v1 and v2 are not comparable.
  8. Fancy way of doing Optimistic locking
  9. Very simple APIS NO Range Scans .. . No iterator on KeySet / Entry SET : Very hard to fix performance Have plans to provide such an iterator
  10. Explain about partitions Make things fast by removing slow things, not by tuning HTTP client not performant Separate caching layer
  11. Transfer time: 30 minutes Can max out a gb network, so be careful
  12. Example: member data--does not make sense to repeatedly join positions, emails, groups, etc. Explain about joins How to better model in java? Json like data model
  13. Questions, comments, etc