Progressive Optimization: A Robust Approach to Query Re-optimization, Slides of Database Management Systems (DBMS)

The concept of progressive query optimization (pop) and its contribution to robust query processing. Pop addresses the risks and opportunities of query re-optimization through the use of check operators and their variants. The document also includes performance analysis and real-world experiment results.

Typology: Slides

2012/2013

Uploaded on 04/27/2013

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Robust Query Processing through
Progressive Optimization
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Robust Query Processing through

Progressive Optimization

Motivation

  • Current optimizers depend heavily upon the cardinality estimations
  • What if there errors in those estimations?
  • Errors can occur due to …
    • Inaccurate statistics
    • Invalid assumptions (e.g. attribute independence)

Contribution

  • Concept of CHECK and its various flavors
  • Method for determining validity ranges for QEPs
  • Performance analysis of prototype of POP

Evaluating a Re-optimization Scheme

  • Risk Vs Opportunity
  • Risk:
    • Extent to which re-optimization is not worthwhile leads to performance regression.
    • Regression may occur when Re-optimization of query results in selection of same or even worse plan.
    • Regression may occur when Query execution needs to be repeated

Progressive Query Optimization(POP)

Architecture of POP 1

  • Find out valid ranges
  • Location of CHECKs
  • Executing CHECKs
  • Interpret CHECK
  • Exploit intermediate results

Computation on Validity Ranges

  • Validity range: is an upper and lower bound which when violated, guarantees that the current plan is sub-optimal wrt to the optimizers cost model
  • No need to enumerate all possible optimal plans beforehand
  • Uses modified Newton-Raphson method to find validity ranges

Exploiting Intermediate Results

  • All the intermediate results are stored as temporary MVs
  • Not necessarily written out to disk
  • In the end, all these temporary MVs needs to be deleted (extra overhead?)

Lazy Checking

  • Adding CHECKs above a materialization point (SORT, TEMP etc)
  • As, no results have been output yet
  • And materialized results can be re-used

Lazy checking with eager materialization

  • Insert materialization point if it does not exists already
  • Typically done only for nested-loop join

EC with Deferred Compensation

  • Only SPJ queries
  • Identifier of all rows returned to the user are stored in a table S, which is used later in the new plan for anti-join with the new-result stream

CHECK Placement

Risk Analysis

  • Risk Analysis

Opportunity Analysis