data infrastructure and hadoop at linkedin
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Big data and Hadoop
September 2012
Hari Shankar Menon
Software engineer
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LinkedIn Engineering Data warehouse team
Previously, Software engineer @Clickable– Worked on building the reporting and analytics platform on
Hadoop and HBase.
Hadoop and Open-source enthusiast
About me
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About LinkedIn Data Infrastructure overview Hadoop@LinkedIn Challenges
Agenda
Our mission
Connect the world’s professionals to make them more productive and successful
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*as of Nov 4, 2011**as of June 30, 2011
2004 2005 2006 2007 2008 2009 2010
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32
55
90
LinkedIn Members (Millions)
175M+
85%Fortune 100 Companies use LinkedIn to hire
Company Pages
>2M
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New Members joining
~2/sec
Professional searches in 2011
~4.2B
LinkedIn by numbers
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About LinkedIn Data Infrastructure overview Hadoop@LinkedIn Challenges
* Chart from Philip Russom- Research Director: TDWI
What is big data?
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Infrastructure technologies
Databus
Primary data store (Front-end)Distributed key-value store
Document-oriented store
Distributed PubSub messaging
Search technologies
Database change replication SenseiDB
Zoie Bobo
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http://data.linkedin.com/opensource
Open source
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About LinkedIn Data Infrastructure overview Hadoop@LinkedIn Challenges
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What is Hadoop Evolution of Hadoop Impact
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Recommendation systems– Generating recommendations– Modeling– A/B Testing– Grandfathering
Data warehouse/ETL– Raw data storage– Aggregations– Heavy lifting
Data sciences– Strategic analyses– Experimentation sandbox
@
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Pandora Search for People
Events YouMay BeInterested In
Groups browse maps
The Recommendations opportunity
• Relevance/Latency
• Offline computation
• Caching
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Improving recommendations
• Mathematical modeling
• A/B Testing
• Grandfathering
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Hadoop in the Data warehouse
• Source of truth• Lower retention• Ad-hoc analysis
• Longer retention• Complex
transformations• Algorithmic
computations
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Hadoop in Data Sciences
• Deep dives
• Sandbox
• Hackday projects
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Data Insights - 1
Job migration after financial collapse
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Data Insights - 2
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Data Insights - 3
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About LinkedIn Data Infrastructure overview Hadoop@LinkedIn Challenges
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1. User adoption of new technologies
2. Real-time processing
3. Graph/Network algorithms
4. Making data accessible
Challenges
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User adoption
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• Challenges• Random reads/writes• Warm-up time
• Solutions• Parts of the problem that can be moved offline?• HBase, Voldemort
Real-time processing
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• Graph problems• Traditional joins
Map-reduce-incompatible problems
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• Hadoop Tons of data
Making data accessible
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Finally!
No Silver bullet
Hadoop Offline processing
Scalability by design
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www.linkedin.com/in/harisreekumar
www.linkedin.com/company/linkedin/careers