SampleClean: Bringing Data Cleaning into the BDAS Stack
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SampleClean: Bringing Data Cleaning into the BDAS Stack Sanjay Krishnan and Daniel Haas In Collaboration With: Juan Sanchez, Wenbo Tao, Jiannan Wang, Tim Kraska, Michael Franklin, Tova Milo, Ken Goldberg
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"SampleClean: Bringing Data Cleaning into the BDAS Stack" presentation at AMPCamp 5 by Sanjay Krishnan and Daniel Haas
Transcript of SampleClean: Bringing Data Cleaning into the BDAS Stack
SampleClean: Bringing Data Cleaning into the BDAS Stack!
Sanjay Krishnan and Daniel Haas!In Collaboration With: Juan Sanchez, Wenbo Tao, Jiannan
Wang, Tim Kraska, Michael Franklin, Tova Milo, Ken Goldberg !
Who publishes more? !
!!
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Microsoft Academic Search!
!! Paper Id! Affiliation!
16! Computer Science Division--University of California Berkeley CA!
101! University of California at Berkeley!
102! Department of Physics Stanford !University California!
116! Lawrence Berkeley National Labs!<ref>California</ref>!
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Microsoft Academic Search!
!! Paper Id! Affiliation!
16! Computer Science Division--University of California Berkeley CA!
101! University of California at Berkeley!
102! Department of Physics Stanford !University California!
116! Lawrence Berkeley National Labs!<ref>California</ref>!
X
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Microsoft Academic Search!
!!
University of California at Berkeley!Computer Science Division!University of California at Berkeley!Computer Science Division!University of California at Berkeley!
Department of Physics Stanford !University California!
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• Data cleaning in BDAS.!– Problem 1. Scale!– Problem 2. Latency!!
• Sampling to cope with scale.!• Asynchrony to cope with latency.!
!!
Enter SampleClean!
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Now it’s your turn!!
!!
Be the crowd and help us decide!
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Dirty Data is Ubiquitous!
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Example: Missing, incomplete, inconsistent data!
Data Cleaning is Hard!
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Time consuming!
Data Cleaning is Hard!
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Costly!Time consuming!
Data Cleaning is Hard!
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Costly!Domain-specific!
Time consuming!
Data Cleaning is Hard!
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Costly!Domain-specific!
Time consuming!
Analy0cs
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A New Data Cleaning Architecture!
Data Cleaning Data
Analy0cs
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A New Data Cleaning Architecture!
Data Cleaning
Data
Can it Scale?!
Crowd Machine Learning
Regex
Time
People are slow and expensive!
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Insight 1: Asynchrony Hides Latency!
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Insight 2: Sampling Hides Scale!
Query !Error!
Time!
BlinkDB!
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Query !Error!
Time!
Data Error
BlinkDB!
Insight 2: Sampling Hides Scale!
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Query !Error!
Time!
Data Error
SampleClean!
Insight 2: Sampling Hides Scale!
BlinkDB!
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SampleClean Data Flow!
Dirty Data Dirty Sample
Clean Sample
Query
Data Cleaning
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Sampling Asynchrony
SampleClean Data Flow!
Clean Sample
Query
Data Cleaning
Asynchrony
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The SampleClean Architecture!
Data Cleaning Library
Approximate Query Processing Asynchronous
Pipelines
Clean Sample
Issue Queries, ! Get Results!
Declare Cleaning !Operations!
Dirty Sample
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The SampleClean Architecture!
Data Cleaning Library
Approximate Query Processing Asynchronous
Pipelines
Clean Sample
Issue Queries, ! Get Results!
Declare Cleaning !Operations!
Dirty Sample
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Approximate Query Processing!• Estimate early results and bound with
error bars!
SampleClean: Fast and Accurate Query Processing on Dirty Data. SIGMOD 2014!!BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data. EuroSys 2013!
Query !Error!
Time!
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The SampleClean Architecture!
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Approximate Query Processing Asynchronous
Pipelines
Clean Sample
Issue Queries, ! Get Results!
Declare Cleaning !Operations!
Dirty Sample
Data Cleaning Library
Crowds and Machines Work Together!
• Extensible library of data cleaning tools!• Tools are:!– Automated!– Human-powered!– Hybrid!
!
Crowd Machine Learning
Regex
Time
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Active Learning and Crowds!• Choose informative training points!
Not !Informative!
Are these the same?!Stanford Department of IEOR!!UC Berkeley Stats!! ¢ Yes ! ¤ No!
Informative!
Are these the same?!Department of Mathematics Stanford University!!University of California Berkeley Department of Mathematics!! ¢ Yes ! ¤ No!
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Active Learning and Crowds!• Choose informative training points!
Not !Informative!
Are these the same?!Stanford Department of IEOR!!UC Berkeley Stats!! ¢ Yes ! ¤ No!
Informative!
Are these the same?!Department of Mathematics Stanford University!!University of California Berkeley Department of Mathematics!! ¢ Yes ! ¤ No!
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The SampleClean Architecture!
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Data Cleaning Library
Clean Sample
Issue Queries, ! Get Results!
Declare Cleaning !Operations!
Dirty Sample
Approximate Query Processing Asynchronous
Pipelines
Putting it all together: Asynchronous Pipelines!
• Users group data cleaning operations into pipelines!
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The SampleClean Architecture!
Data Cleaning Library
Approximate Query Processing Asynchronous
Pipelines
Clean Sample
Issue Queries, ! Get Results!
Declare Cleaning !Operations!
Dirty Sample
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Great, Now What?!• Prototype implementation complete!!
• Significant research challenges remain:!• Crowd worker performance and quality!• Pipeline semantics and optimization!• Programming model and interface!!
• Open source release targeted for next year!
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Summary!• Data Cleaning is slow, costly, and
domain-specific!
• SampleClean brings data cleaning into the BDAS stack !
• SampleClean uses asynchrony to hide latency, and sampling to hide scale!
• SampleClean combines Algorithms, Machines, and People, all in one system! 33
Asynchrony in Spark!• The Spark abstraction: blocking BSP!
• So how do we achieve asynchrony?!• Multithreaded master!• Intermediate results materialized in
Hive!• Standalone Finagle HTTP server for
crowd work!
!34
