Query Processing B.Ramamurthy Chapter 12 11/27/2018 B.Ramamurthy

Introduction Query processing refers to activities including translation of HLL queries into operations at physical file level, query-optimization transformations, and actual evaluation of queries. Objective of most query processing algorithms is to reduce the number of disk access which dominates the cost of query processing. 11/27/2018 B.Ramamurthy

Query Processing Steps Parsing and translation: checks the validity and translates to relational algebraic expression. Optimization : analogous to algebraic simplification Evaluation : many ways of evaluation. 11/27/2018 B.Ramamurthy

Steps in Query Processing Parser & Translator Relational Algebra Expressions Query Stats about data Optimizer Query Output Evaluation Engine Execution Plan data data 11/27/2018 B.Ramamurthy

Query Processing Steps : Example Select balance From account Where balance < 2500 Can be translated into either of the following: s balance < 2500 ( p balance (account)) p balance (s balance< 2500 (account)) 11/27/2018 B.Ramamurthy

Evaluation To specify how to evaluate the query we need to provide relational algebra expressions as well as annotate it with how to evaluate each operation. This annotation is known as evaluation primitive. Several primitives together form a pipeline. A sequence of primitives together form a query-execution plan or query-evaluation plan. Query execution engine evaluates the query. 11/27/2018 B.Ramamurthy

A Query-evaluation Plan p balance s balance< 2500 account Different evaluation plans will have different costs. It is Upto the system to decide the most efficient plan. 11/27/2018 B.Ramamurthy

Query Optimization Query optimization is the process of selecting the most efficient query evaluation plan for a query. Optimizers use statistical data about relations such as relation sizes and index depths to make a good estimate of cost of a plan. We will look at cost model, cost of individual operations selection, sorting, join, and other operations. 11/27/2018 B.Ramamurthy

Catalog Information Relevant catalog information about relations includes: nr , number of tuples in the relation br , number of blocks containing relation r sr , size of a tuple of relation r in bytes fr , blocking factor, number of tuples/block V(A,r), number of distinct values that appear in the relation r for attribute A. SC(A,r), selection cardinality of attribute A of relation r. Average number of records that satisfy equality condition for attribute A. 1 if A is key attribute, A(A,r) / nr for one distributed evenly. 11/27/2018 B.Ramamurthy

Information about indices Information about the indices are also maintained in the statistical database: fi , average fan out of internal nodes in a B+ tree index. HTi , number of levels of the index tree. LBi , number of lowest level index blocks in the index tree. All stats are usually registered for during DB updates. 11/27/2018 B.Ramamurthy

Measures of Query Cost Disk access dominates the cost. Assume all transfers of blocks cost the same. Number of block transfers from the memory for query will be the measure we will use. Cost also depends on main memory buffer. We assume 1 block / relation main memory buffer. 11/27/2018 B.Ramamurthy

Selection Operation Selection using linear search Selection using binary search .. Using indices : primary and secondary Selection involving comparisons Implementation of complex selections 11/27/2018 B.Ramamurthy

Join Operation There are several algorithms available for computing join. Lets analyze and compare them. 11/27/2018 B.Ramamurthy

11/27/2018 B.Ramamurthy

11/27/2018 B.Ramamurthy