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Query optimisation

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**Example - hospital database**

Doctors 100 tuples Patients 2500 tuples

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**Query SELECT Doctors.name FROM Doctors, Patients**

Get the name of the doctors who treat patients suffering from the prk11 disease SELECT Doctors.name FROM Doctors, Patients WHERE Disease = ‘prk11’ AND D_name = Doctors.Name

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**Evaluation #1 restrict Patients to those who suffer from prk11**

read: 2500 tuples; result: estimated 50 tuples; no need to write intermediate result - sufficiently small join above result with Doctors read: 100 tuples (Doctors); result 50 tuples; no need to write to disk intermediate result project result over Doctors.name the desired result is in the memory estimated cost (read and write) 2600

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**Evaluation #2 join Patients with Doctors restrict above result project**

suppose the internal memory allows only some 350 tuples join Patients with Doctors read Patients in batches of 250 tuples; therefore read Doctors 10 times; read: = 3500; write intermediate result (too big) to disk: 2500; restrict above result read 2500; result: estimated 50 tuples; project cost: 8500 (read and write)

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**Intermediate conclusions**

the evaluation strategy (procedural aspect) can lead to very big differences in computation time, for the same query computation time: read from and write to disk (quintessential) processor time the actual evaluation procedures are far more complex than in the previous introductory example

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Optimisation - what deciding upon the best strategy of evaluating a query it is performed automatically by the optimiser of the DBMS not just for data retrieval operations, but for updating operations as well (e.g. UPDATE) not guaranteed to give the best result

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Optimisation - how based on statistical information about the specific database (not necessarily, though) perform expression transformation (cast query in some internal form and convert to respective canonical form candidate low level procedures selection query plans generation and selection statistical information - could you think of examples? cardinality of base relations, indexes, ...

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**Cast (transform) query in some internal form**

internal format more suitable for automatic processing trees (syntax tree or query tree) from a conceptual point of view is is easier to assume that the internal format is relational algebra

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**Convert to canonical form**

the initial expression is transformed into an equivalent but more efficient form “efficient form” = efficient when executed these transformation are performed independently from actual data values and access paths

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**Expression transformation**

examples (A WHERE condition#1) WHERE condition#2 (A WHERE condition#1 AND condition#2) (A [projection#1] ) [projection#2] A [projection#2] (A [projection]) WHERE condition (A WHERE condition) [restriction]

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**Expression transformation**

distributivity commutativity and associativity idempotence scalar expressions conditional expressions semantic transformation

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**Set level operations the operators of relational algebra are set level**

i.e. they manipulate sets (relations) and not individual tuples however, these operators are implemented by internal (DBMS) procedures these procedures, inherently, need tuple-access (in fact, they need access to scalar values)

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**Choose candidate low-level procedures**

the optimiser decides how to execute the query (expressed in canonical form) access paths are relevant at this stage in the main, each basic operation (join , restriction, …) has a set of procedures that implement it e.g. RESTRICTION - (1) on candidate key; (2) on indexed key; (3) on other attributes … each procedure has associated a cost function (usually based on the required I/O disk operations); these functions are used in the next stage

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**Implementing JOIN - examples**

R and P - two relations to be joined J - the attribute on which the (natural) join is performed R[i] and P[j] mean the i-th tuple of R and the j-th tuple of P, respectively R[i].J means the value of the attribute J for the i-th tuple of the relation R R has M and P has N tuples, respectively

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**Implementing JOIN - brute force**

for i:=1 to M for j := 1 to N do if R[i].J = P[j].J then add joined tuple R[i]*P[j] to result end

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Index lookup index X on Patients.D_name

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**Implementing JOIN - index lookup**

/* index X on P.J */ for i:=1 to M for j := 1 to K[i] do add joined tuple (R[i] * PK[j]) to result /* PK[j] represents the tuple of P that K[j] points to */ end

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**Choose the cheapest query plan**

construct query plans (query evaluation plan) combine candidate low level procedures choose the cheapest total cost = the sum of individual costs individual costs depend on the actual data values; estimates are used instead, based on statistical data usually not all possible evaluation procedures are generated; the search space is reduced by applying heuristics

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**Database statistics - in the data dictionary**

for each base table cardinality space occupied etc. for each column of each base table no of distinct values maximum, minimum and average values histogram of values … ...

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**An optimiser is never perfect**

the following example is a real life example suppose a Postgres definition for base relation: Treatment(Patient, Drug, Disease, …) the query get all the drugs that are taken by patients that suffer from prk11 (all the drugs, not only those for prk11) SELECT DISTINCT Drug FROM Treatment WHERE Patient IN (SELECT Patient FROM Treatment WHERE Disease = ‘prk11’) ; the query is far slower that the equivalent one (next) ...

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**An optimiser is never perfect**

/* this query is faster than the previous one, even though it seems to be performing more computations - Patient is not unique! */ CREATE VIEW V_Treatment AS SELECT * FROM Treatment SELECT DISTINCT Treatment.Drug FROM Treatment, V_Treatment WHERE Treatment.Patient = V_Treatment.Patient AND Disease = ‘prk11’ ;

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Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 16- 1.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 16- 1.

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