Physical Database Design and TuningIntroductionUnderstanding the WorkloadCreating an ISUD ChartDecisions to MakeIndex SelectionExample 1Example 2Examples of ClusteringIndex-Only PlansHorizontal DecompositionsHorizontal Decompositions (Contd.)Masking Conceptual Schema ChangesIndex Tuning “Wizards”Tuning Queries and ViewsMore Guidelines for Query TuningGuidelines for Query Tuning (Contd.)Slide 23Points to remember (Contd.)Physical Database Design and TuningR&G - Chapter 20Although the whole of this life were said to be nothing but a dream and the physical world nothing but a phantasm, I should call this dream or phantasm real enough, if, using reason well, we were never deceived by it. Baron Gottfried Wilhelm von LeibnizIntroduction•We will be talking at length about “database design”–Conceptual Schema: info to capture, tables, columns, views, etc.–Physical Schema: indexes, clustering, etc.•Physical design linked tightly to query optimization–So we’ll study this “bottom up”–But note: DB design is usually “top-down”•conceptual then physical. Then iterate.•We must begin by understanding the workload:–The most important queries and how often they arise.–The most important updates and how often they arise.–The desired performance for these queries and updates.Understanding the Workload•For each query in the workload:–Which relations does it access?–Which attributes are retrieved?–Which attributes are involved in selection/join conditions? How selective are these conditions likely to be? •For each update in the workload:–Which attributes are involved in selection/join conditions? How selective are these conditions likely to be?–The type of update (INSERT/DELETE/UPDATE), and the attributes that are affected.Creating an ISUD Chart Employee TableTransaction Frequency % table Name Salary AddressPayroll Run monthly 100 S S SAdd Emps daily 0.1 I I IDelete Emps daily 0.1 D D DGive Raises monthly 10 S UInsert, Select, Update, Delete FrequenciesDecisions to Make•What indexes should we create?–Which relations should have indexes? What field(s) should be the search key? Should we build several indexes?•For each index, what kind of an index should it be?–Clustered? Dynamic/static? •Should we make changes to the conceptual schema?–More on this later…•Horizontal partitioning, replication, views ...Index Selection•One approach:–Consider most important queries in turn. –Consider best plan using the current indexes, and see if better plan is possible with an additional index. –If so, create it.•Before creating an index, must also consider the impact on updates in the workload!–Trade-off: indexes can make queries go faster, updates slower. Require disk space, too.Example 1•B+ tree index on D.dname supports ‘Toy’ selection.–Given this, index on D.dno is not needed.•B+ tree index on E.dno allows us to get matching (inner) Emp tuples for each selected (outer) Dept tuple.•What if WHERE included: `` ... AND E.age=25’’ ?–Could retrieve Emp tuples using index on E.age, then join with Dept tuples satisfying dname selection. Comparable to strategy that used E.dno index. –So, if E.age index is already created, this query provides much less motivation for adding an E.dno index.SELECT E.ename, D.mgrFROM Emp E, Dept DWHERE E.dno=D.dno AND D.dname=‘Toy’Example 2•All selections are on Emp so it should be the outer relation in any Index NL join.–Suggests that we build a B+ tree index on D.dno.•What index should we build on Emp?–B+ tree on E.sal could be used, OR an index on E.hobby could be used. Only one of these is needed, and which is better depends upon the selectivity of the conditions.•As a rule of thumb, equality selections more selective than range selections.•As both examples indicate, our choice of indexes is guided by the plan(s) that we expect an optimizer to consider for a query. Have to understand optimizers!SELECT E.ename, D.mgrFROM Emp E, Dept DWHERE E.sal BETWEEN 10000 AND 20000 AND E.hobby=‘Stamps’ AND E.dno=D.dnoExamples of Clustering•B+ tree index on E.age can be used to get qualifying tuples.–How selective is the condition?–Is the index clustered?•Consider the GROUP BY query.–If many tuples have E.age > 10, using E.age index and sorting the retrieved tuples may be costly.–Clustered E.dno index may be better!•Equality queries and duplicates:–Clustering on E.hobby helps!SELECT E.dnoFROM Emp EWHERE E.age>40SELECT E.dno, COUNT (*)FROM Emp EWHERE E.age>10GROUP BY E.dnoSELECT E.dnoFROM Emp EWHERE E.hobby=StampsIndex-Only Plans•A number of queries can be answered without retrieving any tuples from one or more of the relations involved if a suitable index is available.SELECT D.mgrFROM Dept D, Emp EWHERE D.dno=E.dnoSELECT D.mgr, E.eidFROM Dept D, Emp EWHERE D.dno=E.dnoSELECT E.dno, COUNT(*)FROM Emp EGROUP BY E.dnoSELECT E.dno, MIN(E.sal)FROM Emp EGROUP BY E.dnoSELECT AVG(E.sal)FROM Emp EWHERE E.age=25 AND E.sal BETWEEN 3000 AND 5000<E.dno><E.dno,E.eid><E.dno><E.dno,E.sal>B-tree trick!<E. age,E.sal> or<E.sal, E.age>Horizontal Decompositions•Usual Def. of decomposition: Relation is replaced by collection of relations that are projections. Most important case.–We will talk about this at length as part of Conceptual DB Design•Sometimes, might want to replace relation by a collection of relations that are selections. –Each new relation has same schema as original, but subset of rows.–Collectively, new relations contain all rows of the original. –Typically, the new relations are disjoint.Horizontal Decompositions (Contd.)•Contracts (Cid, Sid, Jid, Did, Pid, Qty, Val) •Suppose that contracts with value > 10000 are subject to different rules. –So queries on Contracts will often say WHERE val>10000. •One approach: clustered B+ tree index on the val field.•Second approach: replace contracts by two new relations, LargeContracts and SmallContracts, with the same attributes (CSJDPQV).–Performs like index on such queries, but no index overhead.–Can build clustered indexes on other attributes, in addition!Masking Conceptual Schema Changes•Horizonal Decomposition from above•Masked by a view.–NOTE: queries with condition val>10000 must be asked wrt LargeContracts for efficiency: so some users may have to be aware of change.•I.e. the users who were having performance problems•Arguably that’s OK --
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