1. Query Processing and Optimization

2. One View of Basic Query Processing Steps
© Dr. Philip Cannata
Data Management

3. Relational Algebra Relational Algebra Operation
Relational Algebra Operation Name
Equivalent SQL Operation
A1, A2, …, Ak (r)
Project
select A1, A2, … Ak from r
p (r)
Select
… from r where p
r x s
Cartesian-Product
… from r, s
A=C (r x s)
Equijoin
… from r, s where A = C
 (r x s)
Theta Join
… from r, s where A  C
r s
Natural Join
… from r natural join s
 x (E)
Rename (Alias)
… from E x …
x (A1, A2, An) (E)
Rename
select x.x A1, x. y A2, x. z An from
E x
table 
Assignment
create table tmp as (select … )
© Dr. Philip Cannata
Data Management

4. Selection and Projection Rules
Relational Algebra Rules
Selection and Projection Rules
Break complex selection into simpler ones:
Cond1Cond2 (R)  Cond1 (Cond2 (R) )
Break projection into stages:
attr (R)   attr ( attr (R)), if attr  attr
Commute projection and selection:
 attr (Cond(R))  Cond ( attr (R)),
if attr  all attributes in Cond
© Dr. Philip Cannata
Data Management

5. Commutativity and Associativity of Join
Relational Algebra Rules
Commutativity and Associativity of Join
Join commutativity: R S  S R
used to reduce cost of nested loop evaluation strategies (smaller relation should be in outer loop)
Join associativity: R (S T)  (R S) T
used to reduce the size of intermediate relations in computation of multi-relational join – first compute the join that yields smaller intermediate result
N-way join has T(N) N! different evaluation plans
T(N) is the number of parenthesized expressions
N! is the number of permutations
Query optimizer cannot look at all plans (might take longer to find an optimal plan than to compute query brute-force). Hence it does not necessarily produce optimal plan
© Dr. Philip Cannata
Data Management

6. Pushing Selections and Projections
Relational Algebra Rules
Pushing Selections and Projections
Cond (R  S)  R  Cond S
Cond relates attributes of both R and S
Reduces size of intermediate relation since rows can be discarded sooner
Cond (R  S)  Cond (R)  S
Cond involves only the attributes of R
Reduces size of intermediate relation since rows of R are discarded sooner
attr(R  S)  attr(attr (R)  S),
if attributes(R)  attr  attr
reduces the size of an operand of product
© Dr. Philip Cannata
Data Management

7. Oracle Join Methods
Before showing query processing examples, we need to discuss some Oracle Join Methods.
Nested loops join
The nested loop iterates over all rows of the outer table. If there are conditions in the where clause of the SQL statement that apply to the outer table only, it checks whether those apply. If they do, the corresponding rows (from the where condition) in the joined inner table are searched. These rows from the inner table are either found using an index (if a suitable index exists) or by doing a full table scan.
Hash join
Hash joins are used when the joining large tables. The optimizer uses the smaller of the 2 tables to build a hash table in memory and the scans the large table and compares the hash value (of rows from large table) with this hash table to find the joined rows.
Merge join (also called sort merge join)
Sort merge join is used to join two independent data sources. They perform better than nested loop joins when the volume of data is big in tables but not as good as hash joins in general.
They perform better than hash join when the join condition columns are already sorted or there is no sorting required.
© Dr. Philip Cannata
Data Management

8. Another View of Basic Query Processing Steps
Example of a Bind Variable:
select * from emp where sal = :salary
© Dr. Philip Cannata
Data Management

9. Another View of Basic Query Processing Steps
© Dr. Philip Cannata
Data Management

10. Query Processing Example – SQL, Rational Algebra, and Query Tree
select * from s_dept d join s_region r on d.region_id = r.id join s_warehouse w on r.id = w.region_id;
* (r.id=w.region_id (d.region_id=r.id ( d (s_dept) x  r (s_region)) x  w (s_warehouse))) *
r.id=w.region_id x
d.region_id=r.id x
 w (s_warehouse)
 d (s_dept)
 r (s_region)
© Dr. Philip Cannata
Data Management

11. Query Processing Example
select * from s_dept d join s_region r on d.region_id = r.id
join s_warehouse w on r.id = w.region_id;
Estimation on the number of rows of each operation
© Dr. Philip Cannata
Data Management

12. Do the same for the S_Region and S_Dept Tables.
Query Processing Example
So let’s gather statistics for the S_Warehouse Table.
Do the same for the S_Region and S_Dept Tables.
© Dr. Philip Cannata
Data Management

13. Query Processing Example
select * from s_dept d join s_region r on d.region_id = r.id
join s_warehouse w on r.id = w.region_id;
© Dr. Philip Cannata
Data Management

14. © Dr. Philip Cannata
Data Management

15. Query Processing Example
Run these. Then get the Execution Plan for the query.
create index dept_index on s_dept(region_id);
create index warehouse_index on s_warehouse(region_id);
select * from s_dept d join s_region r on d.region_id = r.id join s_warehouse w on r.id = w.region_id;
© Dr. Philip Cannata
Data Management

16. SQL, Rational Algebra, Query Tree, and Optimized Query Tree
select * from s_dept d join s_region r on d.region_id = r.id join s_warehouse w on r.id = w.region_id where w.country = 'US’
d.name, r.name, w.city (w.county=‘US’ (r.id=w.region_id (d.region_id=r.id ( d (s_dept) x  r (s_region)) x  w (s_warehouse))))
d.name, r.name, w.city
d.name, r.name, w.city
w.county=‘US’
Optimized Query Tree
r.id=w.region_id
r.id=w.region_id
Query Tree x x
d.region_id=r.id
d.region_id=r.id
w.county=‘US’ x
 w (s_warehouse) x
 d (s_dept)
 r (s_region)
 w (s_warehouse)
 d (s_dept)
 r (s_region)
© Dr. Philip Cannata
Data Management

17. Query Processing Example
select * from s_dept d join s_region r on d.region_id = r.id
join s_warehouse w on r.id = w.region_id where w.country = 'US’
© Dr. Philip Cannata
Data Management

18. (this only works on Dr. Cannata machine)
A Really Hairy Query Processing Example – see the next page for the Execution Plan
(this only works on Dr. Cannata machine)
select null link, round(log(2, message_size)) label,
((obytes*8)/ )/nvl(kstat_dur, 10) "kstat 64 Stream"
from
(SELECT distinct eid, rid,
NTH_VALUE(obytes, 2) OVER (PARTITION BY rid ORDER BY snaptime
ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) obytes from
(SELECT eid, rid, snaptime,
avg(obytes) OVER (PARTITION BY rid ORDER BY snaptime
ROWS BETWEEN 0 PRECEDING AND UNBOUNDED FOLLOWING) obytes from
obytes - LAG(obytes, 1, 0) OVER (ORDER BY snaptime) AS obytes
FROM (SELECT to_number(ltrim(eid, ':')) eid,
to_number(ltrim(rid, ':')) rid,
to_number(ltrim(snaptime, ':')) snaptime,
to_number(ltrim(obytes, ':')) obytes
FROM TABLE(SEM_MATCH(
'(?sub :rid ?rid)
(?sub :eid ?eid)
(?sub :snaptime ?snaptime)
(?sub :obytes ?obytes)
(?sub :name :2c903000ac562-1_data_stats)
(?sub :class :hca)',
SEM_Models('OBSERV_RDF_MODEL'),
null,
SEM_ALIASES(SEM_ALIAS('',':')),
null))) where eid = :P10_EXP) order by rid, snaptime)) k,
ibdatarun d
where d.eid = :P10_EXP and d.rid = k.rid
and streams = 64
order by label
© Dr. Philip Cannata
Data Management

19. © Dr. Philip Cannata
Data Management