CPSC-608 Database Systems Fall 2011 Instructor: Jianer Chen Office: HRBB 315C Phone: Notes #9

Where we were: 2 SQL program SQL Compiler Logic query plan LQP Optimizer Improved logic query plan SELECT a1, b1, c1 FROM A, B, C WHERE a2=1 AND b2=2 AND c2=3 × A B C × σ π a1, b1, c1 a2=1, b2 =2, c2=3 × A B C × σ π a1, b1, c1 a2=1 σ σ b2 =2 c2=3

Construction of Physical Query Plan

Input: an optimized LQP T, and a main memory constraint M × ∩ π σ σ σ G F E D C B A

Construction of Physical Query Plan Input: an optimized LQP T, and a main memory constraint M 1.Replacing each leaf R of T by “scan(R)”; × ∩ π σ σ σ scan(G) scan(F) scan(E) scan(D) scan(C) scan(B) scan(A)

Construction of Physical Query Plan Input: an optimized LQP T, and a main memory constraint M 1.Replacing each leaf R of T by “scan(R)”; 2.Combining the “scan’s” with other operations; × ∩ π σ σ σ scan(G) scan(F) scan(E) scan(D) scan(C) scan(B) scan(A) index-scan

Construction of Physical Query Plan Input: an optimized LQP T, and a main memory constraint M 1.Replacing each leaf R of T by “scan(R)”; 2.Combining the “scan’s” with other operations; 3.Replacing each internal node v of T by a proper algorithm; × ∩ π σ σ σ scan(G) scan(F) scan(E) scan(D) scan(C) scan(B) scan(A) index-scan J2P J1P CJ I1P

Construction of Physical Query Plan Input: an optimized LQP T, and a main memory constraint M 1.Replacing each leaf R of T by “scan(R)”; 2.Combining the “scan’s” with other operations; 3.Replacing each internal node v of T by a proper algorithm; 4.For each edge e in T, decide if e should be “materialized”; × ∩ π σ σ σ scan(G) scan(F) scan(E) scan(D) scan(C) scan(B) scan(A) index-scan J2P J1P CJ I1P

Construction of Physical Query Plan Input: an optimized LQP T, and a main memory constraint M 1.Replacing each leaf R of T by “scan(R)”; 2.Combining the “scan’s” with other operations; 3.Replacing each internal node v of T by a proper algorithm; 4.For each edge e in T, decide if e should be “materialized”; × ∩ π σ σ σ scan(G) scan(F) scan(E) scan(D) scan(C) scan(B) scan(A) index-scan J2P J1P CJ I1P

Construction of Physical Query Plan Input: an optimized LQP T, and a main memory constraint M 1.Replacing each leaf R of T by “scan(R)”; 2.Combining the “scan’s” with other operations; 3.Replacing each internal node v of T by a proper algorithm; 4.For each edge e in T, decide if e should be “materialized”; 5.Cut all materialized edges; × ∩ π σ σ σ scan(G) scan(F) scan(E) scan(D) scan(C) scan(B) scan(A) index-scan J2P J1P CJ I1P

Construction of Physical Query Plan Input: an optimized LQP T, and a main memory constraint M 1.Replacing each leaf R of T by “scan(R)”; 2.Combining the “scan’s” with other operations; 3.Replacing each internal node v of T by a proper algorithm; 4.For each edge e in T, decide if e should be “materialized”; 5.Cut all materialized edges; 6.Each subtree is a call to the subroutine at the root of the subtree. The order of the calls follows the bottom-up order in the structure. × ∩ π σ σ σ scan(G) scan(F) scan(E) scan(D) scan(C) scan(B) scan(A) index-scan J2P J1P CJ I1P 1 2 3

Construction of Physical Query Plan Input: an optimized LQP T, and a main memory constraint M 1.Replacing each leaf R of T by “scan(R)”; 2.Combining the “scan’s” with other operations; 3.Replacing each internal node v of T by a proper algorithm; 4.For each edge e in T, decide if e should be “materialized”; 5.Cut all materialized edges; 6.Each subtree is a call to the subroutine at the root of the subtree. The order of the calls follows the bottom-up order in the structure. × ∩ π σ σ σ scan(G) scan(F) scan(E) scan(D) scan(C) scan(B) scan(A) index-scan J2P J1P CJ I1P This produces an executable code for the input DB program

Physical Query Plan: Summary Replacing internal nodes of a LQP by proper algorithms; Deciding if a subroutine call should be pipelined or materialized; Many optimization techniques are involved here; In practice, heuristic optimization techniques are used to construct good physical query plans; The resulting physical query plan is an executable code.

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secondary storage (disks) in tables (relations) database administrator DDL language database programmer DML (query) language DBMS file manager buffer manager main memory buffers index/file manager DML complier DDL complier query execution engine transaction manager concurrency control lock table logging & recovery graduate database

secondary storage (disks) in tables (relations) database administrator DDL language database programmer DML (query) language DBMS file manager buffer manager main memory buffers index/file manager DML complier DDL complier query execution engine transaction manager concurrency control lock table logging & recovery graduate database