Dynamic Sample Selection for Approximate Query Processing Brian Babcock, Surajit Chaudari

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Dynamic Sample Selection for Approximate Query
Processing Brian Babcock, Surajit
Chaudari
Gautam Das

• Presented by
• Mariam John
  CSE 6392
• 02/14/2006

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Contents

• Introduction
• Dynamic Sample Selection
• Policies for Sample Selection
• Small Group Sampling
• Pre-Processing Phase
• Summary

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Why do we do Approximate Query Processing?

• Multi-gigabyte data repositories
• Data Analysis Application
• Data mining
• Decision Support Analysis
• Fast query response time
• Acceptability of inexact query response

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Problem

• Constructing an optimal sample that well
  represents the underlying data.
• Uniform sampling
• Non-uniform sampling

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Non-uniform sampling

• Purpose is to produce more accurate results
  across a particular set of queries.
• Produces more approximate results than uniform
  sampling.
• Optimal bias differs from query to query.

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Dynamic Sample Selection
Standard Sampling
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Dynamic Sample Selection

• Pre-Processing Phase

Query Workload
Sample Data
Select Strata
Build Sample
Data
Meta- Data
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Dynamic Sample Selection

• Runtime Phase

Query
Sample Data
Choose Samples
Rewrite Query
Meta- Data
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Dynamic Sample Selection

• How to identify the set of biased samples to be
  created?
• Occurs during pre-processing phase
• How to determine which of the various samples to
  use to answer a query?
• Occurs during runtime phase
• Simplest and most efficient strategy is when
  choice of samples is guided by the syntax of
  incoming query.

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Small Group Sampling

• Specific dynamic sample selection technique which
  targets aggregate queries with group-bys.
• Small group sampling approach
• Overall sample perform uniform sampling on
  large groups.
• Small group tables-one or more sample tables for
  smaller groups.

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Small group Sampling

• Set of small groups
• depends on
• grouping columns
• selection predicates

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Small Group Sampling

• Idea behind Small Group Sampling
• Determine for which values in each column to
  create small group tables.
• Create small group tables for each column of a
  table along with the overall sample.
• During runtime, choose a subset of sample tables
  to answer a query most accurately.
• Query is rewritten to run against the sample
  tables instead of the base tables.

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Pre-processing Phase

• For every column, identify the rare values within
  it and create small group tables.
• Pre-processing phase produces three
• outputs
• Overall sample table
• Small group tables
• Metadata table

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Pre-processing phase

• Rows can appear in multiple sample tables.
• Bitmask field is used to identify the set of
  sample tables to which a row was added.
• Avoids double counting of rows assigned to
  multiple sample tables.

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Summary

• Dynamic Sample Selection
• Takes advantage of available disk space
• Creates multiple biased sample tables during the
  pre-processing phase
• Picks best samples during runtime for query
  processing.
• Small Group Sampling
• Notion is to treat large and small groups
  differently
• Creates an overall sample table for large groups
  and a number of small group tables for each rare
  values in each column.