Query processing and Query Optimization
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The document discusses query processing and optimization, detailing how high-level SQL queries are transformed into efficient lower-level relational algebra expressions through parsing and translation. It highlights the roles of optimizers in selecting lower-cost query evaluation plans and compares two relational queries. Additionally, it covers methods such as materialization and pipelining for query execution, as well as measures for query cost evaluation.
Query processing and Query Optimization
- 1. QUERY PROCESSING AND QUERY OPTIMIZATION By NIRAJ GANDHA
- 2. What is Query Processing? It is a 3 step process that transforms a high level query (sql) into an equivalent and more efficient lower-level query (of relational algebra).
- 3. Query Query Query is the statement written by the user in high language using pl/sql.
- 4. Parser & Translator Query Parser & Translator Parser: Checks the syntax and verifies the relation. Translator: Translates the query into an equivalent relational algebra. Example: SQL> select name from customer; RA:=∏name(customer)
- 5. Relational Algebra Query Parser & Translator Relational Algebra It is the query converted in algebraic form from pl/ sql by translator. Example: SQL>SELECT ENAME FROM EMP,ASG WHERE EMP.ENO=ASG.ENO AND DUR>37; RA:1) ΠENAME(σDUR>37∧EMP.ENO=ASG.ENO(EMP × ASG)) 2) ΠENAME(EMP ENO (σDUR>37(ASG)))
- 6. Optimizer Query Parser & Translator Relational Algebra Optimizer It will select the query which has low cost. Example: 1) ΠENAME(σDUR>37∧EMP.ENO=ASG.ENO(EMP × ASG)) 2) ΠENAME(EMP ENO (σDUR>37(ASG))) Optimizer will select Expression2 as it avoids the expensive and large intermediate Cartesian product, and therefore typically is better.
- 7. Comparison of two relational queries ΠENAME(σDUR>37∧EMP.ENO=ASG.ENO (EMP × ASG)) ΠENAME(EMP ENO(σDUR>37(ASG))) EMP x ASG Temp as EMP.ENO=ASG.ENO ΠENAME ENO(σDUR>37(ASG) EMP ENO ΠENAME σDUR>37 ∧temp
- 8. Query Parser & Translator Relational Algebra Optimizer Statistical Data Statistical Data A Statical Data is a database used for statistical analysis purposes. It is an OLAP(Online Analytical Processing), instead of OLTP(Online Transaction Processing) system
- 9. Evaluation Plan Query Parser & Translator Relational Algebra Optimizer Statistical Data Evaluation Plan Relational Algebra annotated with instructions on how to evaluate it is called an evaluation primitive. Sequence of primitive operations that can be used to evaluate a query is a query evaluation plan.
- 10. EVALUATION & DATA The evaluation engine takes the evaluation plan as condition and applies it on the data. Query Parser & Translator Relational Algebra Optimizer Statistical Data Evaluation Plan Evaluation The information on which the query has to be performed is called data.Data
- 11. OUTPUT Query Parser & Translator Relational Algebra Optimizer Statistical Data Evaluation Plan EvaluationOutput Data After the evaluation of plan on data, processed information is showed in output.
- 12. Diagram of Query Processing Query Parser & Translator Relational Algebra Optimizer Statistical Data Evaluation Plan EvaluationOutput Data
- 13. Measures of Query Cost The cost of query evaluation can be measured in terms of different resources, including disk accesses CPU time to execute a query in a distributed or parallel database system the cost of communication.
- 14. Materialization In materialization approach, output of every single operation is saved in temporary relation for the subsequent use. It starts from the lowest-level operations in the expression. Ex: Πcustomer(σbalance<2500(account) customer) Πcustomer σbalance<2500 customer account
- 15. Pipelining In pipelining approach, output of every single operation is not necessary to save in temporary relation for the subsequent use. In this the operations take place simultaneously or in background It starts from the lowest-level operations in the expression. Ex: Πcustomer(σbalance<2500(account) customer) Πcustomer σbalance<2500 customer account
- 16. Query Optimization It is the process of selecting the most efficient query- evaluation plan from among the many strategies usually possible for processing a given query, especially if the query is complex.
- 17. Example of Optimization ∏customer(σbranch_city=”Brooklyn”(branch (account depositor))) ∏customer σbranch_city=”Brooklyn” branch account depositor ∏customer((σbranch_city=”Brooklyn”(branc h)) (account depositor)) ∏customer σbranch_city=”Brooklyn” branch account depositor
- 18. The end