Column Stores For Wide and Sparse Data

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Presentation transcript:

Column Stores For Wide and Sparse Data Daniel Abadi * MIT *Graduating this year and seeking a job 1

Daniel Abadi - MIT - Talk at CIDR 2007 Row- vs. Column-Stores Row Store Column Store Last Name First E-mail Phone # Street Address Last Name First Name E-mail Phone # Street Address Easy to add a new record More data value locality Might waste time reading in unnecessary data Inserts and SELECT * might require multiple seeks 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Column-Store Applications Data Warehousing / DSS / OLAP Customer-relationship management IR (demo yesterday) But that’s not enough !!! 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Daniel Abadi - MIT - Talk at CIDR 2007 Two Observations Column-stores are good for: Wide Data (many columns) Sparse Data 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Column-Stores For Wide Data One block is 10 values 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Row-Store For Wide Data 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Column-Stores for Sparse Data Can use a column-specific NULL compression algorithm dependent on column sparsity 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Storage Option for Sparse Data Data Stored On Disk NULL 7 NULL StartPos EndPos #Vals 3 4 Header 1 11 5 NULL Non-NULL Values 7 3 4 NULL 8 1 8 1 Non-NULL Positions 01011001100 NULL NULL 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Wide, Sparse Applications Semantic Web GEM-Style Schemas XML (in paper) 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Daniel Abadi - MIT - Talk at CIDR 2007 Semantic Web/RDF Data Semantic Web goal is to enable integration an sharing of data across different applications and organizations Resource Description Framework (RDF) is data model Typically stored in triples format 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Daniel Abadi - MIT - Talk at CIDR 2007 Semantic Web/RDF Data Subject Property Object David rdf:type grad student Age 26 Rachel post-doc Year 3rd Office 925 29 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Daniel Abadi - MIT - Talk at CIDR 2007 Semantic Web/RDF Data Subject rdf:type Age Year Office David student 26 3rd NULL Rachel post-doc 29 925 More columns results in fewer joins More columns results in more NULLs 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Databases with GEM-style schemas Extension of the relational model to include: generalized attributes set-valued attributes sparse attributes in same conceptual schema entity (tuple) Often results in wide, sparse schemas 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Conclusion and Questions Column-stores’ ability to handle wide, sparse tables opens many doors Questions: Is schema design constrained by performance considerations of row-stores? Is physical data independence a myth? 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Daniel Abadi - MIT - Talk at CIDR 2007 Back-up Slides 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Storing XML Data in Relational DBMS Usually: XML elements can be relations XML attributes are table columns Parent/child and sibling order information also table columns Path expressions require a join With column-store: Can use inlining where descendent elements can be included in the same element relation 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

C-Store Performance on Sparse Data 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

C-Store Sparse CPU Performance 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Row Store Performance on Sparse Data 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Row Store Sparse CPU Performance 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Storage for Very Sparse Data Data Stored On Disk NULL 7 NULL StartPos EndPos #Vals NULL 4 Header 1 11 3 NULL Non-NULL Values 7 4 1 NULL NULL Non-NULL Positions 2 5 9 1 NULL NULL 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007

Daniel Abadi - MIT - Talk at CIDR 2007 Storage for Dense Data Data Stored On Disk 7 7 StartPos EndPos #Vals 3 2 Header 1 11 9 4 Non-NULL Values 7 7 3 NULL 2 4 9 NULL 1 9 8 9 1 Non-NULL Positions 1 5 7 9 11 8 11/18/2018 Daniel Abadi - MIT - Talk at CIDR 2007