Compressing Relations And Indexes

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Compressing Relations And Indexes

• Jonathan Goldstein Raghu Ramakrishnan
• Uri Shaft
• Department of Compter Sciences, University of
  Wisconsin-Madison
• June 18, 1997

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Agenda

• Introduction
• Compressing A Relation
• Compression Applied to Rectangle Base Indexes
• Performance Evaluation
• Questions and Remarks

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Introduction

• Page level Compression
• Performance Study
• Application to B-trees and R-trees
• Multidimensional bulk loading algorithm

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Introduction
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Introduction
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Compressing A relation

• Frames Of Reference
• Non numeric attributes
• File level compression

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Frames of Reference
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Lossy Compression

• Point approximation in lossy compression

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Compressing an indexing structure

• Compressing a B-tree
• Compressing a rectangle based indexing structure
• Compression oriented Bulk Loading

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Rectangle Based indexing qualities
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Changing the frame of reference
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Bulk-Loading Algorithm

• Input. A set of points in some
  n-dimentional space.
• Output. A partition of the inut into subsets.
• Requirements. The partition shuold group points
  that are close to each other in the same group as
  much as possiblg

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GB-Pack compression oriented bulk loading
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GB-Pack compression oriented bulk loading

• Qualities
• trading off some tree quality for increased
  compression.
• number of entries per page is data-dependent.
• cutting a dimension in a value boundary in the
  data.

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GB-Pack compression oriented bulk loading
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GB-Pack compression oriented bulk loading
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GB-Pack compression oriented bulk loading
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Performance Evaluation

• Relational Compression Experiments.
• CPU vs. I/O Costs.
• Comparison With Techniques in commercial systems.
• Importance of Tuple-Level Decompression.
• R-tree Compression Experiments.

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Synthetic Data Sets

• Size The number of tuples in the relation.
• Dimensionality The number of attributes of the
  relations.
• Range The range of values for the attributes.
• Distribution uniform(worst case) / exponential.
• Partition Strategy.
• Page size.

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Sales Data Set

• Sales data set. Compression Achieved versus
  dimensionality

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CPU vs. I/O Costs
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R-tree Compression Experiments

• Testing the quality of R-trees on Sales Data Set.

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Questions And Remarks