Bitmap Index Design and Evaluation

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Bitmap Index Design and Evaluation
By Chee-Yong Chan Yannis E.Ioannidis
Ariel Noy Data representation and retrieval
seminar
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Introduction

• Query performance issues
• On Line Transaction Processing.
• Read write database.
• Decision Support System.
• Read mostly environments, with high selectivity
  factor.

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Bitmap In Simple Form
Value List Index

• Every value has its own column bitmap.

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Advantages

• Compact size.
• Efficient hardware support for bitmap operations
  (AND, OR, XOR, NOT).
• Fast search.
• Multiple differentiate bitmap indexes for
  different kind of queries.

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Selection queries.

• Queries of the form A op v
• A refers to indexed attribute.
• Op
• Range predicates
• Equality predicates

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Space time tradeoff of bitmap indexes, for
selection queries.

• Space optimal bitmap index.
• Time optimal bitmap index under a given space
  constraint.
• Bitmap index with
• optimal space
• time tradeoff.
• Time optimal bitmap
• index.

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Attribute Value Decomposition.
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Bitmap Encoding Scheme

• Equality Encoding
• bi bits one for each possible value, all 0, vi 1.
• Range Encoding
• vi right most bits 0, rest 1.

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Evaluation Algorithm for Range-Encoded Bitmap
Indexes.

• RangeEval - ONeil and Quass
• RangeEval-Opt
• number bitmap operation 50 off
• less bitmap scans for range predicate evaluation
• caluclating only the requested bitmap
• avoids the intermediate equality predicate
  evaluation by evaluating each range query in term
  only off lt based on
• A lt v Altv-1
• A gt v ! (Altv)
• Agtv Altv-1
• Working with only one bitmap B vs. working with
  at least two Beq and ( Blt or Bge)

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Example

• Alt864 using a 3 component base-10 index.
• RaneEval-Opt
• 4 operation 5 scans
• RangeEval
• 10 operations 6 scans

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Analytical Comparison
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Cost Model for Space-Time Tradeoff Analysis

• Space(I)
• Space metric is in term of number of bitmaps
  stored.
• Time(I)
• Time metric is in term of expected number of
  bitmap scans for a selection query evaluation.

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Comparison of Bitmap Encoding Scheme

• Equality encoded
• S(I) C
• T(I) nb/2

• Range encoded
• S(I) C-n
• T(I) 2n

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• Space Optimal
• number of bitmap in n-component space optimal
  n(b-2)
• b
• space efficiency is non-decreasing function of
  the number of components.
• The ultimate optimal is when nlog(C)
• Time Optimal
• the optimal base in n-component base is
• lt2,2,2,,C/2Ngt
• time efficiency is non-increasing function of the
  number of components.
• The ultimate optimal is when n1

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Optimal Space-Time Tradeoff (knee).

• Based on experimental, guessing and guts filling.
• 2 component index
• The base of the most time-efficient 2-component
  space-optimal index is given by

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Time Optimal Bitmap Index Under Space Constraint
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Bitmap Index Storage Schems

• Bitmap Level Storage (BS)
• each bitmap his own file
• Component Level Storage (CS)
• each index component has its own file
• Index Level Storage (IS)
• all together in one file

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Compression of each file

• CS has the best Space(I) tradeoff after
  compression.
• BS has the best Time(I) tradeoff after
  compression.