Query optimisation

1. Query optimisation

2. Example - hospital database Doctors 100 tuples Patients 2500 tuples

3. Query 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

4. 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

5. Evaluation #2 • 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: 2500 + 1000 = 3500; write intermediate result (too big) to disk: 2500; • restrict above result • read 2500; result: estimated 50 tuples; • project • cost: 8500 (read and write)

6. 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

7. 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

8. 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, ...

9. 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

10. 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

11. 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]

12. Expression transformation • distributivity • commutativity and associativity • idempotence • scalar expressions • conditional expressions • semantic transformation

13. 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)

14. 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

15. 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

16. 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 end end

17. Index lookup index X on Patients.D_name

18. 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] * PK[j]) to result /* PK[j] represents the tuple of P that K[j] points to */ end end

19. 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

20. 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 • … • ...

21. 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) ...

22. 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’ ;