Here is how to get the distinct count of values across all columns in the table:select count(distinct value)from ( select c1 as value from country_test union all select c2 from country_test union all select c3 from country_test union all select c4 from country_test);This uses UNION ALL to unpivot the columns, then counts the distinct values

The Features That
maybe
You Didn’t Know About
Oren Nakdimon
www.db-oriented.com
 oren@db-oriented.com
 +972-54-4393763
@DBoriented

This presentation is available in http://db-oriented.com/presentations
©OrenNakdimon
WHO AM I?
A CHRONOLOGY BY “ORACLE YEARS”
Where: IAF
When: Oracle 6/7 [1991-1997]
What: Developer
Where: Golden Screens
When: Oracle 8 [1997-1998]
What: Server Group Manager
Where: TELEknowledge
When: Oracle 8i/9i [1998-2003]
What: DBA Group Manager
Where: Olista
When: Oracle 10g/11g [2004-2011]
What: VP R&D + Israel Site Manager
Where:
When: Oracle 11g/12c [2011-]
What: Freelance Consultant
Where:
When: 2012-
What: Database
Architect / Developer / DBA

©OrenNakdimon
@DBORIENTED

©OrenNakdimon
HTTP://DB-ORIENTED.COM

©OrenNakdimon
 Join ilOUG meetup group
 Download presentations
 Give us your feedback
 About this meeting
 What do you want to hear and see next?
 Initiate and participate in discussions
 Propose your presentation for upcoming
meetings
http://www.meetup.com/ilOUG-Oracle-Technologies-Meetup/
iloug16@gmail.com

©OrenNakdimon
SQL

©OrenNakdimon
Aggregate
Functions

©OrenNakdimon
select department_id,
MIN(salary)
from employees
group by department_id;
DEPARTMENT_ID EMPLOYEE_ID FIRST_NAME LAST_NAME SALARY
30 117Sigal Tobias 2800.00
30 119Karen Colmenares 2500.00
30 118Guy Himuro 2600.00
90 102Lex De Haan 17000.00
90 101Neena Kochhar 17000.00
30 115Alexander Khoo 3100.00
60 104Bruce Ernst 6000.00
30 116Shelli Baida 2900.00
60 106Valli Pataballa 4800.00
30 114Den Raphaely 11000.00
90 100Steven King 24000.00
60 103Alexander Hunold 9000.00
60 105David Austin 4800.00
60 107Diana Lorentz 4200.00

©OrenNakdimon
MIN(salary)
from employees
30 118Guy Himuro 2600.00
90 102Lex De Haan 17000.00
90 100Steven King 24000.00
DEPARTMENT_ID MIN(SALARY)
30 2500.00
60 4200.00
90 17000.00

©OrenNakdimon
30 118Guy Himuro 2600.00
90 102Lex De Haan 17000.00
90 100Steven King 24000.00

©OrenNakdimon
Aggregate
Functions
FIRST

©OrenNakdimon
THE FIRST FUNCTION
30 118Guy Himuro 2600.00
90 102Lex De Haan 17000.00
90 100Steven King 24000.00
select
department_id,
min(first_name) keep(dense_rank FIRST order by salary)
from employees

©OrenNakdimon
THE FIRST FUNCTION
30 118Guy Himuro 2600.00
90 102Lex De Haan 17000.00
90 100Steven King 24000.00
select
department_id,
min(first_name) keep(dense_rank FIRST order by salary)
from employees
DEPARTMENT_ID MIN(FIRST_NAME)…
30Karen
60Diana
90Lex

©OrenNakdimon
Aggregate
Functions
LAST

©OrenNakdimon
THE LAST FUNCTION
30 118Guy Himuro 2600.00
90 102Lex De Haan 17000.00
90 100Steven King 24000.00
select
department_id,
min(first_name) keep(dense_rank LAST order by salary)
from employees
DEPARTMENT_ID MIN(FIRST_NAME)…
30Den
60Alexander
90Steven

©OrenNakdimon
WHAT IS THE MOST COMMON JOB IN EACH DEPARTMENT?
min(job_id) keep(dense_rank last order by cnt)
from (select department_id,
job_id,
count(*) cnt
from employees
group by department_id,
job_id)
DEPARTMENT_ID JOB_ID CNT
------------- ---------------------------- ----------
90 AD_VP 2
30 PU_CLERK 5
60 IT_PROG 5
90 AD_PRES 1
30 PU_MAN 1

©OrenNakdimon
min(job_id) keep(dense_rank last order by cnt)
from (select department_id,
job_id,
count(*) cnt
from employees
group by department_id,
job_id)
DEPARTMENT_ID MIN(JOB_ID)KEEP(DENSE_RANKLAST
------------- ------------------------------
30 PU_CLERK
60 IT_PROG
90 AD_VP

©OrenNakdimon
Aggregate
Functions
STATS_MODE

©OrenNakdimon
stats_mode(job_id)
from employees
DEPARTMENT_ID MIN(JOB_ID)KEEP(DENSE_RANKLAST
------------- ------------------------------
30 PU_CLERK
60 IT_PROG
90 AD_VP

©OrenNakdimon
Collections

©OrenNakdimon
DISTINCT COUNT OF A VALUE ACROSS COLUMNS IN A TABLE
I have this table:
select * from country_test;
c1 c2 c3 c4
————— ————— ————— ——————
india us china uk
india india china uk
india china china uk
us us us uk
I need the distinct count of countries across the c1,c2,c3,c4 columns
of the table, so the output has to be
c1 c2 c3 c4 cnt
————— ————— ————— ——— ————
india us china uk 4
india india china uk 3
india china china uk 3
us us us uk 2
http://asktom.oracle.com/pls/apex/f?p=100:11:0::::P11_QUESTION_ID:8749607800346631637

©OrenNakdimon
ops$tkyte%ORA11GR2> with data(r)
2 as
3 (select 1 r from dual
4 union all
5 select r+1 from data where r < 4
6 )
7 select c1, c2, c3, c4, count(distinct c) cnt
8 from (
9 select rowid rid,
10 c1, c2, c3, c4,
11 decode(r,1,c1,2,c2,3,c3,4,c4) c
12 from data, country_test
13 )
14 group by rid, c1, c2, c3, c4
15 /
C1 C2 C3 C4 CNT
---------- ---------- ---------- ---------- ----------
india us china uk 4
us us us uk 2
http://asktom.oracle.com/pls/apex/f?p=100:11:0::::P11_QUESTION_ID:8749607800346631637

©OrenNakdimon
with data as (select rownum r,c1,c2,c3,c4
from ctest)
select listagg(rpad(val,21),'') within
group (order by column_list) orig
,count(distinct val) countries
from
(
select * from data
unpivot (val for column_list in
(c1,c2,c3,c4))
)
group by r
order by r;
select * from country_test,
lateral(
select count(distinct c) cnt from (
select c1 c from dual union all
select c2 from dual union all
select c3 from dual union all
select c4 from dual
)
);
select country_test.*, cnt_tab.cnt
from country_test,
(
select count(1) cnt, rid
from (
select rowid rid, c1 c from country_test
union select rowid, c2 from country_test
)
group by rid
) cnt_tab
where country_test.rowid = cnt_tab.rid
select * from country_test,
lateral(
select count(distinct val) cnt from (
select c1,c2,c3,c4 from dual
) unpivot(val for col in (c1,c2,c3,c4))
);

©OrenNakdimon
create type string_ntt as
table of varchar2(4000)

©OrenNakdimon
Collections
Default
Constructor

©OrenNakdimon
select
string_ntt('John','Paul','George','Ringo') beatles
from dual;
BEATLES
----------------------------------------------
STRING_NTT('John', 'Paul', 'George', 'Ringo')

©OrenNakdimon
select
c.*,
cardinality(set(string_ntt(c1,c2,c3,c4)))
from country_test c;
C1 C2 C3 C4 STRING_NTT(C1,C2,C3,C4)
----- ----- ----- ----- --------------------------------------------------
india us china uk STRING_NTT('india', 'us', 'china', 'uk')
india india china uk STRING_NTT('india', 'india', 'china', 'uk')
india china china uk STRING_NTT('india', 'china', 'china', 'uk')
us us us uk STRING_NTT('us', 'us', 'us', 'uk')

©OrenNakdimon
Collections
SET

©OrenNakdimon
select
c.*,
C1 C2 C3 C4 SET(STRING_NTT(C1,C2,C3,C4))
----- ----- ----- ----- ----------------------------------------
india india china uk STRING_NTT('india', 'china', 'uk')
india china china uk STRING_NTT('india', 'china', 'uk')
us us us uk STRING_NTT('us', 'uk')
----- ----- ----- ----- --------------------------------------------------

©OrenNakdimon
Collections
CARDINALITY

©OrenNakdimon
select
c.*,
C1 C2 C3 C4 CARDINALITY(SET(STRING_NTT(C1,C2,C3,C4)))
----- ----- ----- ----- -----------------------------------------
india us china uk 4
us us us uk 2
C1 C2 C3 C4 SET(STRING_NTT(C1,C2,C3,C4))
----- ----- ----- ----- ----------------------------------------
india india china uk STRING_NTT('india', 'china', 'uk')
india china china uk STRING_NTT('india', 'china', 'uk')
us us us uk STRING_NTT('us', 'uk')
----- ----- ----- ----- --------------------------------------------------

©OrenNakdimon
Collections
Unnesting

©OrenNakdimon
COLLECTION UNNESTING
select *
from
table(string_ntt('John','Paul','George','Ringo'));
COLUMN_VALUE
---------------------
John
Paul
George
Ringo

©OrenNakdimon
KAKURO
 Digits 1-9
 Sum = associated
clue
 No duplications
6 = 1+2+3
12 = 1+2+3+6
12 = 1+2+4+5
http://db-oriented.com/2016/06/10/kakuro-helper-using-sql-query-with-the-powermultiset-function/

©OrenNakdimon
> select *
from table(powermultiset(integer_ntt(1,2,3,4,5,6,7,8,9)));
COLUMN_VALUE
-------------------------
create type integer_ntt as table of integer

©OrenNakdimon
Collections
POWERMULTISET

©OrenNakdimon
> select *
from table(powermultiset(integer_ntt(1,2,3,4,5,6,7,8,9)));
COLUMN_VALUE
-------------------------
INTEGER_NTT(1)
INTEGER_NTT(2)
INTEGER_NTT(1, 2)
INTEGER_NTT(3)
INTEGER_NTT(1, 3)
INTEGER_NTT(2, 3)
INTEGER_NTT(1, 2, 3)
INTEGER_NTT(4)
INTEGER_NTT(1, 4)
INTEGER_NTT(2, 4)
INTEGER_NTT(3, 4)
INTEGER_NTT(1, 2, 3, 4)
INTEGER_NTT(5)
...
INTEGER_NTT(1, 3, 4, 5, 6, 7, 8, 9)
INTEGER_NTT(2, 3, 4, 5, 6, 7, 8, 9)
INTEGER_NTT(1, 2, 3, 4, 5, 6, 7, 8, 9)
511 rows selected.
create type integer_ntt as table of integer

©OrenNakdimon
break on x on num_of_elements skip 1
select sum(b.column_value) x,
a.num_of_elements,
listagg(b.column_value,'+') within group(order by b.column_value) expr
from (select rownum id ,
cardinality(column_value) num_of_elements,
column_value combination
from table(powermultiset(integer_ntt(1,2,3,4,5,6,7,8,9)))) a,
table(a.combination) b
where a.num_of_elements > 1
group by a.id,a.num_of_elements
order by x,num_of_elements,expr;
ID NUM_OF_ELEMENTS COMBINATION
---------- --------------- --------------------------------------------------------------
1 1 INTEGER_NTT(1)
2 1 INTEGER_NTT(2)
3 2 INTEGER_NTT(1, 2)
4 1 INTEGER_NTT(3)
7 3 INTEGER_NTT(1, 2, 3)
...
508 7 INTEGER_NTT(3, 4, 5, 6, 7, 8, 9)
509 8 INTEGER_NTT(1, 3, 4, 5, 6, 7, 8, 9)
510 8 INTEGER_NTT(2, 3, 4, 5, 6, 7, 8, 9)
511 9 INTEGER_NTT(1, 2, 3, 4, 5, 6, 7, 8, 9)
511 rows selected.

©OrenNakdimon
a.num_of_elements,
select a.*,b.column_value
ID NUM_OF_ELEMENTS COMBINATION COLUMN_VALUE
---------- --------------- -------------------- ------------
1 1 INTEGER_NTT(1) 1
3 2 INTEGER_NTT(1, 2) 1
7 3 INTEGER_NTT(1, 2, 3) 1
7 3 INTEGER_NTT(1, 2, 3) 2
7 3 INTEGER_NTT(1, 2, 3) 3
...
2304 rows selected.
1+2+3=6
1+2=3

©OrenNakdimon
a.num_of_elements,
X NUM_OF_ELEMENTS EXPR
---------- --------------- ----------
3 2 1+2
4 2 1+3
5 2 1+4
2+3
6 2 1+5
2+4
3 1+2+3
7 2 1+6
2+5
3+4
3 1+2+4
---------- --------------- --------------------
41 7 2+4+5+6+7+8+9
8 1+2+3+5+6+7+8+9
42 7 3+4+5+6+7+8+9
8 1+2+4+5+6+7+8+9
43 8 1+3+4+5+6+7+8+9
44 8 2+3+4+5+6+7+8+9
45 9 1+2+3+4+5+6+7+8+9
502 rows selected.

©OrenNakdimon
a.num_of_elements,
---------- --------------- ----------
3 2 1+2
4 2 1+3
5 2 1+4
2+3
6 2 1+5
2+4
3 1+2+3
7 2 1+6
2+5
3+4
3 1+2+4
---------- --------------- --------------------
41 7 2+4+5+6+7+8+9
8 1+2+3+5+6+7+8+9
42 7 3+4+5+6+7+8+9
8 1+2+4+5+6+7+8+9
43 8 1+3+4+5+6+7+8+9
44 8 2+3+4+5+6+7+8+9
45 9 1+2+3+4+5+6+7+8+9
502 rows selected.

©OrenNakdimon
PIVOT

©OrenNakdimon
break on x skip 1
select * from (
a.num_of_elements,
)
pivot (listagg(expr, chr(10)) within group (order by expr)
for num_of_elements in(2,3,4,5,6,7,8,9));
X 2 3 4 5 6 7 8 9
-- ---- ------ -------- ---------- ------------ -------------- ---------------- ---------
3 1+2
4 1+3
5 1+4
2+3

©OrenNakdimon
pivot (listagg(expr, chr(10)) within group (order by expr)
for num_of_elements in(2,3,4,5,6,7,8,9));
X 2 3 4 5 6 7 8 9
-- ---- ------ -------- ---------- ------------ -------------- ---------------- ---------
3 1+2
4 1+3
5 1+4
2+3
6 1+5 1+2+3
2+4
21 4+8+9 1+3+8+9 1+2+3+6+9 1+2+3+4+5+6
5+7+9 1+4+7+9 1+2+3+7+8
6+7+8 1+5+6+9 1+2+4+5+9
1+5+7+8 1+2+4+6+8
2+3+7+9 1+2+5+6+7
2+4+6+9 1+3+4+5+8
2+4+7+8 1+3+4+6+7
2+5+6+8 2+3+4+5+7
3+4+5+9
3+4+6+8
3+5+6+7
44 2+3+4+5+6+7+8+9
45 1+2+3+4+…

©OrenNakdimon
Recursive
Subquery
Factoring

©OrenNakdimon
RECURSIVE SUBQUERY FACTORING
with fib(x,f) as (
select 1 as x, 1 as f from dual
union all
select f, x+f from fib where x+f <= &n
)
select f
from fib;
anchor member

©OrenNakdimon
with fib(x,f) as (
union all
)
select f
from fib;
anchor member
recursive member

©OrenNakdimon
with fib(x,f) as (
union all
)
select f
from fib;
Enter value for n: 121
F
----------
1
2
3
5
8
13
21
34
55
89
10 rows selected.
anchor member
recursive member

©OrenNakdimon
Pattern
Matching

©OrenNakdimon
THE MOST BASIC SQL
 Row-level visibility
 Maximum one output row per input row
 WHERE clause

©OrenNakdimon
AGGREGATE FUNCTIONS
 Group-level visibility
 Strict definition of “group”
 Each input row belongs to exactly one group
 Maximum one output row per group
 GROUP BY clause
 HAVING clause

©OrenNakdimon
 Window-level visibility
 Strict definition of “window”
 Each input row has its own window
 Window-level aggregates are added to input
rows
 OVER
 PARTITION BY
 ORDER BY
ANALYTIC (WINDOW) FUNCTIONS

©OrenNakdimon
ANALYTIC (WINDOW) FUNCTIONS
 Window-level visibility
 Strict definition of “window”
 Each input row has its own window
 Window-level aggregates are added to input
rows
 OVER
 PARTITION BY
 ORDER BY

©OrenNakdimon
PATTERN MATCHING
 Enhanced analysis of row sequences
 Match-based output
 One row per match (similar to the “group by” concept)
or
 All the match’s input rows (similar to the “window” concept)
 Each input row may belong to 0, 1 or more
matches

©OrenNakdimon
PATTERN MATCHING
Zeckendorf's theorem
Every positive integer can be represented uniquely as the sum of distinct
non-consecutive Fibonacci numbers, and this representation can be found by
using a greedy algorithm, choosing the largest possible Fibonacci number at
each stage.
6 = 5 + 1 122 = 89 + 21 + 8 + 3 + 117 = 13 + 3 + 1 34 = 34
http://marogel.wordpress.com/2015/05/22/a-greedy-algorithm-using-recursive-subquery-factoring/

©OrenNakdimon
PATTERN MATCHING
with fib(x,f) as (
union all
)
select f
from fib
match_recognize(
order by f desc
all rows per match
pattern ((A|{-B-})+)
define A as sum(A.f) <= &n
)
F
1
2
3
5
8
13
21
34
55
89
n = 121

©OrenNakdimon
PATTERN MATCHING
with fib(x,f) as (
union all
)
select f
from fib
match_recognize(
order by f desc
all rows per match
)
F
89
55
34
21
13
8
5
3
2
1
n = 121

©OrenNakdimon
PATTERN MATCHING
with fib(x,f) as (
union all
)
select f
from fib
match_recognize(
order by f desc
all rows per match
)
F
89
55
34
21
13
8
5
3
2
1
A




n = 121

©OrenNakdimon
PATTERN MATCHING
with fib(x,f) as (
union all
)
select f
from fib
match_recognize(
order by f desc
all rows per match
)
F
89
21
8
3
A




n = 121

©OrenNakdimon
Oracle
Locator

©OrenNakdimon
create table stops (
stop_id varchar2(20) constraint stops_pk primary key,
stop_code varchar2(20),
stop_name varchar2(100),
stop_location mdsys.sdo_geometry
);
stops.txt
insert into user_sdo_geom_metadata
(table_name,column_name,diminfo,srid)
values ('STOPS',
'STOP_LOCATION',
mdsys.sdo_dim_array(
mdsys.sdo_dim_element('LONG', -180.0, 180.0, 0.05),
mdsys.sdo_dim_element('LAT', -90.0, 90.0, 0.05)),
8307);

©OrenNakdimon
CREATE TABLE STOPS_EXT (stop_id varchar2(20),
stop_code varchar2(20),
stop_name varchar2(100),
stop_desc varchar2(100),
stop_lat number,
stop_lon number,
location_type number(1),
parent_location varchar2(20),
zone_id number)
ORGANIZATION EXTERNAL (
TYPE ORACLE_LOADER
DEFAULT DIRECTORY EXT_TABLES_DIR
ACCESS PARAMETERS (
records delimited by 0x'0d0a'
characterset UTF8
skip 1
logfile EXT_TABLES_DIR:'stops_%p_%a.log'
badfile EXT_TABLES_DIR:'stops_%p_%a.txt'
fields terminated by ',' optionally enclosed by '"'
missing field values are null
reject rows with all null fields
)
LOCATION ('stops.txt')
)
REJECT LIMIT UNLIMITED;

©OrenNakdimon
insert /*+ append */ into stops (
stop_id,
stop_code,
stop_name,
stop_location)
select stop_id,
stop_code,
stop_name,
mdsys.sdo_geometry(2001, -- 2 dimensional point
8307, -- lat/long coordinate system
mdsys.sdo_point_type(stop_lon,
stop_lat,
null),
null, -- n/a for point type
null) -- n/a for point type
from stops_ext;
commit;

©OrenNakdimon
External
Tables

©OrenNakdimon
insert /*+ append */ into stops (
stop_id,
stop_code,
stop_name,
stop_location)
select stop_id,
stop_code,
stop_name,
mdsys.sdo_geometry(2001, -- 2 dimensional point
8307, -- lat/long coordinate system
mdsys.sdo_point_type(stop_lon,
stop_lat,
null),
null, -- n/a for point type
null) -- n/a for point type
from stops_ext;
commit;
create index stops_location_idx
on stops (stop_location)
indextype is mdsys.spatial_index
parameters ('layer_gtype=POINT');

©OrenNakdimon
(34.863356, 32.101307)

©OrenNakdimon
STOP_CODE STOP_NAME LONGITUDE LATITUDE
---------- -------------------------------- --------- ---------
33080 ‫השפלה‬/‫אימבר‬ 34.860736 32.097785
30990 ‫המושבות‬ ‫אם‬ ‫דרך‬/‫השפלה‬ 34.858287 32.101256
35378 ‫אריה‬ ‫קרית‬ ‫רכבת‬ ‫תחנת‬ 34.86375 32.104148
31424 ‫אריה‬ ‫קריית‬ ‫רכבת‬/‫וסע‬ ‫חנה‬ 34.863596 32.105353
38334 ‫המושבות‬ ‫אם‬ ‫דרך‬/‫המר‬ ‫זבולון‬ ‫דרך‬ 34.867824 32.10336
38143 ‫גיסין‬ ‫אבשלום‬/‫דיין‬ ‫משה‬ 34.86262 32.096893
32172 ‫גיסין‬ ‫אבשלום‬/‫בזל‬ 34.864442 32.097726
32231 ‫גיסין‬ ‫אבשלום‬/‫בזל‬ 34.866672 32.098263
8 rows selected.
Elapsed: 00:00:00.13
------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 275 | 16775 | 3 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID | STOPS | 275 | 16775 | 3 (0)| 00:00:01 |
|* 2 | DOMAIN INDEX (SEL: 0.100000 %)| STOPS_LOCATION_IDX | | | 3 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------
SELECT s.stop_code,
s.stop_name,
s.stop_location.sdo_point.x longitude,
s.stop_location.sdo_point.y latitude
FROM stops s
WHERE sdo_within_distance(
s.stop_location,
sdo_geometry(2001,8307,
sdo_point_type(34.863356, 32.101307, NULL), NULL, NULL),
'distance=500 unit=meter') = 'TRUE';
All the stops
within 500
meters

©OrenNakdimon
GEOMETRY AGGREGATION
SELECT sdo_util.to_wktgeometry(
sdo_aggr_union(sdoaggrtype(s.stop_location,0.05)))
FROM stops s
s.stop_location,

©OrenNakdimon
GEOMETRY AGGREGATION
SELECT sdo_util.to_wktgeometry(
sdo_aggr_union(sdoaggrtype(s.stop_location,0.05)))
FROM stops s
s.stop_location,
MULTIPOINT ((34.866672 32.098263), (34.864442 32.097726), (34.86262
32.096893), (34.867824 32.10336), (34.863596 32.105353), (34.86375
32.104148), (34.858287 32.101256), (34.860736 32.097785))

©OrenNakdimon
MULTIPOINT ((34.866672 32.098263), (34.864442 32.097726), (34.86262
32.096893), (34.867824 32.10336), (34.863596 32.105353), (34.86375
32.104148), (34.858287 32.101256), (34.860736 32.097785))

©OrenNakdimon
SELECT s.stop_code,
s.stop_name,
s.stop_location.sdo_point.x longitude,
s.stop_location.sdo_point.y latitude
FROM stops s
WHERE sdo_nn(
s.stop_location,
'sdo_num_res=3') = 'TRUE';
STOP_CODE STOP_NAME LONGITUDE LATITUDE
---------- ----------------------- --------- ----------
35378 ‫אריה‬ ‫קרית‬ ‫רכבת‬ ‫תחנת‬ 34.86375 32.104148
32172 ‫גיסין‬ ‫אבשלום‬/‫בזל‬ 34.864442 32.097726
31424 ‫אריה‬ ‫קריית‬ ‫רכבת‬/‫וסע‬ ‫חנה‬ 34.863596 32.105353
3 rows selected.
Elapsed: 00:00:00.21
------------------------------------------------------------------------------------------------------
------------------------------------------------------------------------------------------------------
| 1 | TABLE ACCESS BY INDEX ROWID | STOPS | 28 | 1708 | 3 (0)| 00:00:01 |
|* 2 | DOMAIN INDEX (SEL: 0.100000 %)| STOPS_LOCATION_IDX | | | 3 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------
3 nearest
stops

©OrenNakdimon
SELECT round(sdo_nn_distance(1)) dist_meters,
s.stop_code,
s.stop_name
FROM stops s
WHERE sdo_nn(
s.stop_location,
'sdo_num_res=3',1) = 'TRUE'
order by dist_meters;
DIST_METERS STOP_CODE STOP_NAME
----------- ---------- ---------------------------------------------------------------------------
317 35378 ‫אריה‬ ‫קרית‬ ‫רכבת‬ ‫תחנת‬
410 32172 ‫גיסין‬ ‫אבשלום‬/‫בזל‬
449 31424 ‫אריה‬ ‫קריית‬ ‫רכבת‬/‫וסע‬ ‫חנה‬
3 rows selected.
Elapsed: 00:00:00.06
3 nearest
stops + their
distance

©OrenNakdimon
COUNTRIES AND POLYGONS
> desc countries
Name Null? Type
----------------------------- -------- --------------------
COUNTRY_ID NUMBER
COUNTRY_NAME VARCHAR2(100)
POLYGON MDSYS.SDO_GEOMETRY

©OrenNakdimon
WHICH COUNTRY CONTAINS OUR POINT?
COUNTRY_NAME
----------------------------
Israel
1 row selected.
Elapsed: 00:00:00.03
---------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------
| 1 | TABLE ACCESS BY INDEX ROWID | COUNTRIES | 2 | 48 | 2 (0)| 00:00:01 |
|* 2 | DOMAIN INDEX (SEL: 0.100000 %)| COUNTRIES_POLYGON_IDX | | | 2 (0)| 00:00:01 |
---------------------------------------------------------------------------------------------------------
select c.country_name
from countries c
where sdo_relate(
c.polygon,
'mask=contains') = 'TRUE';

©OrenNakdimon
Online
Operations

©OrenNakdimon
Get ORA-54 due to active
transactions
Wait for active transactions to
end
Block new DML statements Do not block new DML
statements

©OrenNakdimon
THE KEYWORD ONLINE
 11g
 CREATE INDEX … [ONLINE]
 ALTER INDEX … REBUILD [ONLINE]
 12c
 ALTER TABLE … DROP CONSTRAINT … [ONLINE]
 ALTER TABLE … SET UNUSED … [ONLINE]
 DROP INDEX … [ONLINE]
 ALTER INDEX … UNUSABLE [ONLINE]

©OrenNakdimon
INVISIBLE INDEXES
 Maintained by DML
 Invisible to the optimizer
 Unless optimizer_use_invisible_indexes is true

©OrenNakdimon
ONLINE OPERATIONS
 Adding a new column to a non-empty table
 An optional column with no default
 As of 11g, adding a mandatory column with default is
a meta-data only operation:
 Fast
 No space
 No redo
 No undo
 Online
 As of 12c, the same is true also for optional columns

©OrenNakdimon
“ALMOST ONLINE” OPERATIONS
> drop index t_idx;
drop index t_idx
*
ERROR at line 1:
ORA-00054: resource busy and acquire with NOWAIT specified
or timeout expired
Elapsed: 00:00:00.00

©OrenNakdimon
> alter session set ddl_lock_timeout=2;
Session altered.
> drop index t_idx;
drop index t_idx
*
ERROR at line 1:
ORA-00054: resource busy and acquire with NOWAIT specified
or timeout expired
Elapsed: 00:00:02.02

©OrenNakdimon
> alter session set ddl_lock_timeout=2;
Session altered.
> drop index t_idx;
Index dropped.
Elapsed: 00:00:00.95

©OrenNakdimon
ALTER TABLE ADD CONSTRAINT
ALTER TABLE ADD CONSTRAINT ENABLE NOVALIDATE
ALTER TABLE ENABLE VALIDATE CONSTRAINT

©OrenNakdimon
Procedure P
Procedure P
My Schema
Edition1
Edition2
procedure p is
begin
-- do something
end p;
create or replace
procedure p as
begin
-- do something else
end p;

©OrenNakdimon
Procedure
P
My Schema
Edition1
procedure p is
...
select name
into ...
from people
...
Table
PEOPLE
- id
- name

©OrenNakdimon
Procedure
P
My Schema
Edition1
Edition2
Edition3
Procedure
P
procedure p is
...
select name
into ...
from people
...
Table
PEOPLE
- id
- name
- first_name
- last_nameprocedure p is
...
select
first_name,
last_name
into ...
from people
...

©OrenNakdimon
Procedure
P
My Schema
Edition1
Edition2
Edition3
Procedure
P
Editioning
View
PEOPLE
Editioning
View
PEOPLE
Table
PEOPLE$T
- id
- name
- first_name
- last_name
create editioning view people
as select id, name
from people$t
create editioning view people
as select id, first_name, last_name
from people$t
select name
into ...
from people
select
first_name, last_name
into ...
from people

©OrenNakdimon
I LOVE EBR BECAUSE…
 It enables to apply any change in an online
fashion
 The upgrade is performed in the privacy of a
new unexposed edition
 The upgrade can be done at any time
 Supported everywhere (since Oracle 11.2),
including standard edition

©OrenNakdimon
PARTITION VIEWS
T1
T2
T3
T4alter table add partition create table
create or replace view
drop tablealter table drop partition

©OrenNakdimon
PARTITION VIEWS
> desc events_01
Name Null? Type
--------------------------- -------- ----------------------------
EVENT_ID NOT NULL NUMBER
EVENT_TIME NOT NULL DATE
EVENT_TYPE_ID NOT NULL NUMBER
DETAILS VARCHAR2(100)
create or replace view events as
select * from events_01
where event_time >= date'2016-01-01'
and event_time < date'2016-02-01'
union all
union all

©OrenNakdimon
PARTITION VIEWS
select event_type_id,count(*)
from events
where event_time between date'2016-02-20' and date'2016-02-22'
group by event_Type_id;
-------------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------------
| 1 | HASH GROUP BY | | 7 | 21 | 19 (6)| 00:00:01 |
| 2 | VIEW | EVENTS | 2884 | 8652 | 18 (0)| 00:00:01 |
| 3 | UNION-ALL | | | | | |
|* 4 | FILTER | | | | | |
| 5 | TABLE ACCESS BY INDEX ROWID BATCHED| EVENTS_01 | 1 | 11 | 3 (0)| 00:00:01 |
|* 6 | INDEX RANGE SCAN | EVENT_01_TIME_IDX | 1 | | 2 (0)| 00:00:01 |
| 7 | TABLE ACCESS BY INDEX ROWID BATCHED | EVENTS_02 | 2882 | 31702 | 18 (0)| 00:00:01 |
|* 9 | FILTER | | | | | |
| 10 | TABLE ACCESS BY INDEX ROWID BATCHED| EVENTS_03 | 1 | 11 | 3 (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - filter(NULL IS NOT NULL)
6 - access("EVENT_TIME">=TO_DATE(' 2016-02-20 00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND
"EVENT_TIME“ <TO_DATE(' 2016-02-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
"EVENT_TIME"<=TO_DATE(' 2016-02-22 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))

©OrenNakdimon
PARTITION VIEWS
select count(details)
from events
and event_type_id = 1;
-------------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------------
| 1 | SORT AGGREGATE | | 1 | 74 | | |
| 2 | VIEW | EVENTS | 12366 | 893K| 79 (2)| 00:00:01 |
| 3 | UNION-ALL | | | | | |
|* 4 | TABLE ACCESS FULL | EVENTS_01 | 6367 | 99K| 40 (0)| 00:00:01 |
|* 5 | TABLE ACCESS FULL | EVENTS_02 | 5998 | 95968 | 38 (0)| 00:00:01 |
|* 6 | FILTER | | | | | |
|* 7 | TABLE ACCESS BY INDEX ROWID BATCHED| EVENTS_03 | 1 | 16 | 3 (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------------------
---------------------------------------------------
4 - filter("EVENT_TYPE_ID"=1 AND
"EVENT_TIME">=TO_DATE(' 2016-01-10 00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND
"EVENT_TIME"< TO_DATE(' 2016-02-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
"EVENT_TIME"<=TO_DATE(' 2016-02-22 00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND
"EVENT_TIME">=TO_DATE(' 2016-02-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
7 - filter("EVENT_TYPE_ID"=1)

©OrenNakdimon
PARTITION VIEWS
select count(details)
from events
and event_type_id = 6;
-------------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------------
| 1 | SORT AGGREGATE | | 1 | 74 | | |
| 2 | VIEW | EVENTS | 2418 | 174K| 42 (0)| 00:00:01 |
| 3 | UNION-ALL | | | | | |
|* 4 | TABLE ACCESS FULL | EVENTS_01 | 2388 | 38208 | 40 (0)| 00:00:01 |
|* 5 | TABLE ACCESS BY INDEX ROWID BATCHED | EVENTS_02 | 29 | 464 | 2 (0)| 00:00:01 |
|* 6 | INDEX RANGE SCAN | EVENT_02_TYPE_IDX | 41 | | 1 (0)| 00:00:01 |
|* 7 | FILTER | | | | | |
|* 8 | TABLE ACCESS BY INDEX ROWID BATCHED| EVENTS_03 | 1 | 16 | 2 (0)| 00:00:01 |
|* 9 | INDEX RANGE SCAN | EVENT_03_TYPE_IDX | 1 | | 1 (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------------------
---------------------------------------------------
"EVENT_TIME">=TO_DATE(' 2016-01-10 00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND
"EVENT_TIME“ <TO_DATE(' 2016-02-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
5 - filter("EVENT_TIME"<=TO_DATE(' 2016-02-22 00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND
6 - access("EVENT_TYPE_ID"=6)
8 - filter("EVENT_TIME"<=TO_DATE(' 2016-02-22 00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND
9 - access("EVENT_TYPE_ID"=6)

©OrenNakdimon
PARTITION VIEWS
 It’s our responsibility to insert into the right
partition
 EXCHANGE PARTITION is supported
 Indexing
 Local indexes are supported by definition
 Global indexes are not (easily) supported
 Partial indexes are supported by definition

THANK YOU
Oren Nakdimon
www.db-oriented.com
 oren@db-oriented.com
 +972-54-4393763
@DBoriented

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