In this post, I will demonstrate a new feature introduced in 12c : In database archiving. It enables you to archive rows within a table by marking them as invisible. This is accomplshed  by means of a hidden column ORA_ARCHIVE_STATE. These invisible rows are not visible to the queries but if needed, can be viewed , by setting a session parameter ROW ARCHIVAL VISIBILITY.

Overview:

-- Create test user uilm, tablespace ilmtbs
-- Connect as user uilm
-- create and populate test table (5 rows) ilmtab with row archival clause
-- Note that the table has an additional column ORA_ARCHIVE_STATE automatically created   and has the default value of 0 (indicates that row is active)
-- Note that this column is not visible when we describe the table or simply issue select * from ...
-- We need to access data dictionary to view the column
-- Make two  rows in the table inactive by setting ORA_ARCHIVE_STATE column to a non zero value.
-- Check that inactive rows are not visible to query
-- Set the parameter ROW ARCHIVAL VISIBILITY  = all to see inactive rows also
-- Set the parameter ROW ARCHIVAL VISIBILITY  = active to hide inactive rows
-- Issue an insert into ... select * and check that only 3 visible rows are inserted
-- Set the parameter ROW ARCHIVAL VISIBILITY  = all to see inactive rows also
-- Issue an insert into ... select * and check that all the rows are inserted but ORA_ARCHIVE_STATE    is not propagated in inserted rows
-- Disable row archiving in the table and check that column ORA_ARCHIVE_STATE is automatically dropped
-- drop tablespace ilmtbs and user uilm

Implementation :

-- Create test user, tablespace and test table
SQL> conn sys/oracle@em12c:1523/pdb1 as sysdba
sho con_name

CON_NAME
------------------------------
PDB1

SQL> set sqlprompt PDB1>

PDB1>create tablespace ilmtbs datafile '/u02/app/oracle/oradata/cdb1/pdb1/ilmtbs01.dbf' size 1m;
grant connect, resource, dba  to uilm identified by oracle;
alter user uilm default tablespace ilmtbs;

conn uilm/oracle@em12c:1523/pdb1
sho con_name

CON_NAME
------------------------------
PDB1
-- create table with "row archival clause"
PDB1>drop table ilmtab purge;
create table ilmtab (id number, txt char(15)) row archival;
insert into ilmtab values (1, 'one');
insert into ilmtab values (2, 'two');
insert into ilmtab values (3, 'three');
insert into ilmtab values (4, 'four');
insert into ilmtab values (5, 'five');
commit;
-- Note that the table has an additional column ORA_ARCHIVE_STATE automatically created    and has the default value of 0 (indicates that row is active)
PDB1>col ora_archive_state for a20
select id, txt, ora_archive_state from ilmtab;

ID TXT             ORA_ARCHIVE_STATE
---------- --------------- --------------------
1 one             0
2 two             0
3 three           0
4 four            0
5 five            0
-- Note that this column is not visible when we describe the table or simply issue select * from ...
PDB1>desc ilmtab
Name                                      Null?    Type
----------------------------------------- -------- ----------------------------
ID                                                 NUMBER
TXT                                                CHAR(15)

PDB1>select * from ilmtab;

ID TXT
---------- ---------------
1 one
2 two
3 three
4 four
5 five
-- Since the column is invisible, let me try and make it visible
-- Note that Since the column is maintained by oracle itself, user can't modify its attributes
PDB1>alter table ilmtab modify (ora_archive_state visible);
alter table ilmtab modify (ora_archive_state visible)
*
ERROR at line 1:
ORA-38398: DDL not allowed on the system ILM column
-- We need to access data dictionary to view the column
-- Note that this column is shown as hidden and has not been generated by user
PDB1>col hidden for a7
col USER_GENERATED for 20
col USER_GENERATED for a20

select TABLE_NAME, COLUMN_NAME, HIDDEN_COLUMN, USER_GENERATED
from user_tab_cols where table_name='ILMTAB';

TABLE_NAME  COLUMN_NAME          HID USER_GENERATED
----------- -------------------- --- --------------------
ILMTAB      ORA_ARCHIVE_STATE    YES NO
ILMTAB      ID                   NO  YES
ILMTAB      TXT                  NO  YES
-- We can make selected rows in the table inactive by setting ORA_ARCHIVE_STATE column to a non zero value.
This can be accomplished using update table... set ORA_ACRHIVE_STATE =
. <non-zero value>
. dbms_ilm.archivestatename(1)

-- Let's update row with id =1 with ORA_ARCHIVE_STATE=2
     and update row with id =2 with dbms_ilm.archivestatename(2)
PDB1>update ilmtab set ora_archive_state=2 where id=1;

update ilmtab set ora_archive_state= dbms_ilm.archivestatename(2) where id=2;
-- Let's check whether updates have been successful and hidden rows are not visible
PDB1>select id, txt, ORA_ARCHIVE_STATE from ilmtab;

ID TXT             ORA_ARCHIVE_STATE
---------- --------------- --------------------
3 three           0
4 four            0
5 five            0
-- The updated rows are not visible!!
-- Quite logical since we have made the rows active and by default only active rows are visible

-- To see inactive rows also, we need to set the parameter ROW ARCHIVAL VISIBILITY  = all at session level
-- Note that the column ORA_ARCHIVE_STATE has been set to 1 for id =2 although we had set it to 2 using
dbms_ilm.archivestatename(2)
PDB1>alter session set ROW ARCHIVAL VISIBILITY  = all;
select id, txt, ORA_ARCHIVE_STATE from ilmtab;

ID TXT             ORA_ARCHIVE_STATE
---------- --------------- --------------------
1 one             2
2 two             1
3 three           0
4 four            0
5 five            0
-- Note that the column ORA_ARCHIVE_STATE has been set to 1 for id =2 although we had set it to 2 using    dbms_ilm.archivestatename(2)

-- Let's find out why
-- Note that The function dbms_ilm.archivestatename(n) returns only two values    0 for n=0 and 1 for  n <> 0
PDB1>col state0 for a8
col state1 for a8
col state2 for a8
col state3 for a8

select dbms_ilm.archivestatename(0) state0 ,dbms_ilm.archivestatename(1) state1,
dbms_ilm.archivestatename(2) state2,dbms_ilm.archivestatename(3) state3  from dual;

STATE0   STATE1   STATE2   STATE3
-------- -------- -------- --------
0        1        1        1
-- In order to make the inactive rows (id=1,2) hidden again, we need to set the parameter ROW ARCHIVAL VISIBILITY  = Active
PDB1>alter session set row archival visibility = active;
select id, txt, ORA_ARCHIVE_STATE from ilmtab;

ID TXT             ORA_ARCHIVE_STATE
---------- --------------- --------------------
3 three           0
4 four            0
5 five            0
-- Let's issue an insert into ... select *
-- Note that only 3 new rows are visible
PDB1>insert into ilmtab select * from ilmtab;

select id, txt, ora_archive_state from ilmtab;

ID TXT             ORA_ARCHIVE_STATE
---------- --------------- --------------------
3 three           0
4 four            0
5 five            0
3 three           0
4 four            0
5 five            0

6 rows selected.
-- I want to check if hidden rows were also inserted
-- Let's check by making  hidden rows visible again
-- Note that only visible rows(id=3,4,5) were inserted
PDB1>alter session set row archival visibility=all;
select id, txt, ora_archive_state from ilmtab;

ID TXT             ORA_ARCHIVE_STATE
---------- --------------- --------------------
1 one             2
2 two             1
3 three           0
4 four            0
5 five            0
3 three           0
4 four            0
5 five            0

8 rows selected.
-- Let's set row archival visibility = all and then again insert rows from ilmtab
-- Note that all the 8 rows are inserted but ORA_ARCHIVE_STATE ha not been copied    ORA_ARCHIVE_STATE <> 0 in only 2 records (id = 1,2) even now.
PDB1>alter session set row archival visibility=all;
insert into ilmtab select * from ilmtab;
select id, txt, ora_archive_state from ilmtab order by id;

ID TXT             ORA_ARCHIVE_STATE
---------- --------------- --------------------
1 one             0
1 one             2
2 two             0
2 two             1
3 three           0
3 three           0
3 three           0
3 three           0
4 four            0
4 four            0
4 four            0
4 four            0
5 five            0
5 five            0
5 five            0
5 five            0

16 rows selected.
-- Disable row level archiving for the table
-- Note that as soon as row archiving is disabled, pseudo column ora_archive_state is dropped automatically
PDB1>alter table ilmtab no row archival;
select id, txt, ORA_ARCHIVE_STATE from ilmtab;

ERROR at line 1:
ORA-00904: "ORA_ARCHIVE_STATE": invalid identifier

PDB1>col hidden for a7
col USER_GENERATED for 20
col USER_GENERATED for a20

select TABLE_NAME, COLUMN_NAME, HIDDEN_COLUMN, USER_GENERATED
from user_tab_cols where table_name='ILMTAB';

TABLE_NAME  COLUMN_NAME          HID USER_GENERATED
----------- -------------------- --- --------------------
ILMTAB      ID                   NO  YES
ILMTAB      TXT                  NO  YES
Note : Had we created this table using sys, we could not have disabled row archiving .

-- cleanup --
PDB1>conn sys/oracle@em12c:1523/pdb1 as sysdba
drop tablespace ilmtbs including contents and datafiles;
drop user uilm cascade;
References:

http://docs.oracle.com/cd/E16655_01/server.121/e17613/part_lifecycle.htm#VLDBG14154

----------------------------------------------------------------------------------------------------

Oracle 12c Index

----------------------------------------------------------------------------------------------

 

The inverted table format can deliver fast and flexible query capabilities, but is not widely used. ADABAS is probably the most successful implementation, but how often do you see that nowadays? Following is a description of how to implement inverted structures within a relational database. All code run on Oracle Database 12c, release 12.1.0.1.

Consider this table and a few rows, that describe the contents of my larder:

create table food(id number,capacity varchar2(10),container varchar2(10),item varchar2(10));
insert into food values(1,'large','bag','potatoes');
insert into food values(2,'small','box','carrots');
insert into food values(3,'medium','tin','peas');
insert into food values(4,'large','box','potatoes');
insert into food values(5,'small','tin','carrots');
insert into food values(6,'medium','bag','peas');
insert into food values(7,'large','tin','potatoes');
insert into food values(8,'small','bag','carrots');
insert into food values(9,'medium','box','peas');

The queries I run against the table might be "how many large boxes have I?" or "give me all the potatoes, I don't care about how they are packed". The idea is that I do not know in advance what columns I will be using in my predicate: it could be any combination. This is a common issue in a data warehouse.
So how do I index the table to satisfy any possible query? Two obvious possibilities:
First, build an index on each column, and the optimizer can perform an index_combine operation on whatever columns happen to be listed in the predicate. But that means indexing every column - and the table might have hundreds of columns. No way can I do that.
Second, build a concatenated index across all the columns: in effect, use an IOT. That will give me range scan access if any of the predicated columns are in the leading edge of the index key followed by filtering on the rest of the predicate. Or if the predicate does not include the leading column(s), I can get skip scan access and filter. But this is pretty useless, too: there will be wildly divergent performance depending on the predicate.
The answer is to invert the table:

create table inverted(colname varchar2(10),colvalue varchar2(10),id number);
insert into inverted select 'capacity',capacity,id from food;
insert into inverted select 'container',container,id from food;
insert into inverted select 'item',item,id from food;

Now just one index on each table can satisfy all queries:

create index food_i on food(id);
create index inverted_i on inverted(colname,colvalue);

To retrieve all the large boxes:

orclz> set autotrace on explain
orclz> select * from food where id in
  2  (select id from inverted where colname='capacity' and colvalue='large'
  3  intersect
  4  select id from inverted where colname='container' and colvalue='box');

        ID CAPACITY   CONTAINER  ITEM
---------- ---------- ---------- ----------
         4 large      box        potatoes


Execution Plan
----------------------------------------------------------
Plan hash value: 1945359172

---------------------------------------------------------------------------------
| Id  | Operation                                | Name       | Rows  | Bytes | C
---------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                         |            |     3 |   141 |
|   1 |  MERGE JOIN                              |            |     3 |   141 |
|   2 |   TABLE ACCESS BY INDEX ROWID            | FOOD       |     9 |   306 |
|   3 |    INDEX FULL SCAN                       | FOOD_I     |     9 |       |
|*  4 |   SORT JOIN                              |            |     3 |    39 |
|   5 |    VIEW                                  | VW_NSO_1   |     3 |    39 |
|   6 |     INTERSECTION                         |            |       |       |
|   7 |      SORT UNIQUE                         |            |     3 |    81 |
|   8 |       TABLE ACCESS BY INDEX ROWID BATCHED| INVERTED   |     3 |    81 |
|*  9 |        INDEX RANGE SCAN                  | INVERTED_I |     3 |       |
|  10 |      SORT UNIQUE                         |            |     3 |    81 |
|  11 |       TABLE ACCESS BY INDEX ROWID BATCHED| INVERTED   |     3 |    81 |
|* 12 |        INDEX RANGE SCAN                  | INVERTED_I |     3 |       |
---------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   4 - access("ID"="ID")
       filter("ID"="ID")
   9 - access("COLNAME"='capacity' AND "COLVALUE"='large')
  12 - access("COLNAME"='container' AND "COLVALUE"='box')

Note
-----
   - dynamic statistics used: dynamic sampling (level=2)

orclz>

Or all the potatoes:

orclz> select * from food where id in
  2  (select id from inverted where colname='item' and colvalue='potatoes');

        ID CAPACITY   CONTAINER  ITEM
---------- ---------- ---------- ----------
         1 large      bag        potatoes
         4 large      box        potatoes
         7 large      tin        potatoes


Execution Plan
----------------------------------------------------------
Plan hash value: 762525239

---------------------------------------------------------------------------------
| Id  | Operation                              | Name       | Rows  | Bytes | Cos
---------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                       |            |     3 |   183 |
|   1 |  NESTED LOOPS                          |            |       |       |
|   2 |   NESTED LOOPS                         |            |     3 |   183 |
|   3 |    SORT UNIQUE                         |            |     3 |    81 |
|   4 |     TABLE ACCESS BY INDEX ROWID BATCHED| INVERTED   |     3 |    81 |
|*  5 |      INDEX RANGE SCAN                  | INVERTED_I |     3 |       |
|*  6 |    INDEX RANGE SCAN                    | FOOD_I     |     1 |       |
|   7 |   TABLE ACCESS BY INDEX ROWID          | FOOD       |     1 |    34 |
---------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   5 - access("COLNAME"='item' AND "COLVALUE"='potatoes')
   6 - access("ID"="ID")

Note
-----
   - dynamic statistics used: dynamic sampling (level=2)
   - this is an adaptive plan

orclz>

Of course, consideration needs to be given to handling more complex boolean expressions; maintaining the inversion is going to take resources; and a query generator has to construct the inversion code and re-write the queries. But In principle, this structure can deliver indexed access for unpredictable predicates of any number of any columns, with no separate filtering operation. Can you do that with a normalized star schema? I don't think so.
I hope this little thought experiment has stimulated the little grey cells, and made the point that relational structures are not always optimal for all problems.
--
John Watson
Oracle Certified Master DBA
http://skillbuilders.com

I recently had the opportunity to talk with Tom Kyte (!), and in the course of our conversation, he really made me face up to the fact that the SQL syntax I use every day is frozen in time: I’m not making much use of the analytic functions and other syntax that Oracle has introduced since 8i.

Here’s a brief history of these additions to Oracle SQL, from Keith Laker, Oracle’s Product Manager for Analytical SQL:

8i	Window functions
9i	Rollup, grouping sets, cube, enhanced window functions
10g	SQL Model clause, statistical functions, partition outer join
11g	SQL Pivot clause, Recursive WITH, Listagg, Nth value
12c	Pattern matching, Top N

Not only do these make complex queries much, much simpler and easier, they are also much faster for the same result than non-analytic SQL, as Tom Kyte has shown repeatedly on his blog and in his books.

So, I was sold and I wanted to jump in with Recursive WITH. The WITH clause lets you define inline views to use across an entire query, and the coolest thing about this is that you can define the subquery recursively – so that the inline view calls itself.

Recursive WITH basic syntax:

WITH Tablename (col1, col2, ...) AS
(SELECT A, B, C... FROM dual                   --anchor member
UNION ALL
SELECT A', B', C'... from Tablename where...   --recursive member
)
select ... from Tablename where ...

Refactoring the Factorial

One fun thing about recursive WITH, aka recursive subquery refactoring, is the ease with which we can implement a recursive algorithm in SQL. Let’s warm up with a classic example of recursion: finding the factorial of a number. Factorial(n) = n! = 1*2*3*…*n . It’s a classic example because Factorial(n) can be defined recursively as:

Factorial(0) = 1
Factorial(n) = Factorial(n-1) * n

Here’s a first pass at implementing that directly in SQL to find the factorial of 5, using a recursive WITH subquery:

WITH Factorial (operand,total_so_far) AS
(SELECT 5 operand, 5 total_so_far FROM dual    -- Using anchor member to pass in "5"
UNION ALL
SELECT operand-1, total_so_far * (operand-1) FROM Factorial
WHERE operand > 1)
SELECT * FROM Factorial;

   OPERAND TOTAL_SO_F
---------- ----------
         5          5
         4         20
         3         60
         2        120
         1        120

and to display just the result, we select it from Factorial:

WITH Factorial (operand,total_so_far) AS
(SELECT 5 operand, 5 total_so_far FROM dual    -- Find the factorial of 5
UNION ALL
SELECT operand-1, total_so_far * (operand-1) FROM Factorial
WHERE operand > 1)
SELECT MAX(operand) || '! = ' || MAX(total_so_far) AS RESULT FROM Factorial;

RESULT
-----------------
5! = 120

Ahem! I have cheated a little for simplicity here. The query doesn’t take into account that Factorial(0) = 1:

WITH Factorial (operand,total_so_far) AS
(SELECT 0 operand, 0 total_so_far FROM dual    -- Find the factorial of 0
UNION ALL
SELECT operand-1, total_so_far * (operand-1) FROM Factorial
WHERE operand > 1)                             -- This is going to get me nowhere fast...
SELECT * FROM Factorial;

  OPERAND TOTAL_SO_F
---------- ----------
         0          0

To do it properly, we need to include Factorial(0) = 1 in the recursive subquery:

WITH Factorial (operand,total_so_far) AS
(SELECT 0 operand, 0 total_so_far FROM dual    -- Find the factorial of 0
UNION ALL
SELECT operand-1, 
CASE                                           -- Factorial (0) = 1
  WHEN operand=0 THEN 1
  ELSE (total_so_far * (operand-1))
  END
FROM Factorial
WHERE operand >= 0)
SELECT MAX(operand) || '! = ' || MAX(total_so_far) AS RESULT FROM Factorial;

RESULT
------------------------------------------------------------------------------------
0! = 1

We can also reverse direction and recursively build a table of factorials, multiplying as we go.
That’s the approach Lucas Jellema takes in his excellent blog post on factorials in SQL.

WITH Factorial (operand,output) AS
(SELECT 0 operand, 1 output FROM dual
UNION ALL
SELECT operand+1, output * (operand+1) FROM Factorial
WHERE operand < 5)
SELECT * FROM Factorial;

   OPERAND     OUTPUT
---------- ----------
         0          1
         1          1
         2          2
         3          6
         4         24
         5        120

There are two nice things about this approach: first, every row of the subquery result contains n and n! , and second, the rule that 0! = 1 is elegantly captured in the anchor member.

denrael ev’ew tahw gniylppA

Now let’s do something more interesting – reversing a string. Here’s some sample code in C from the CS 211 course at Cornell:

 public String reverseString(String word) {
     if(word == null || word.equals(""))
        return word;
     else
        return reverseString(word.substring(1, word.length())) + 
            word.substring(0,1);
  }

Let’s run through an example word to see how it works. For simplicity I’ll write reverseString(“word”) as r(word). Using “cat” as the word, stepping through the algorithm gives:

r(cat) = r(r(at))+c = r(r(r(t))+a+c = r(r(r(r())+t+a+c = ''+t+a+c = tac

Now to rewrite the same function in SQL. Using the same example string, “cat,” I want my recursively defined table to look like this:

in   out
--------
cat 
at   c
t    ac
     tac

In C, the initial letter in the word is the 0th letter, and in SQL, it’s the 1st letter. So the C expression word.substring(1,N) corresponds to SQL expression substr(word,2,N-1) . With that in mind, it’s easy to rewrite the C algorithm in SQL:

WITH WordReverse (INPUT, output) AS
  (SELECT 'CAT' INPUT, NULL output FROM dual
   UNION ALL
   SELECT substr(INPUT,2,LENGTH(INPUT)-1), substr(INPUT,1,1) || output
   FROM wordReverse
   WHERE LENGTH(INPUT) > 0
  )
SELECT * FROM wordReverse;

INPUT    OUTP
-------- ----
CAT
AT       C
T        AC
         TAC

NOTE: if using 11.2.0.3 or earlier, you might get “ORA-01489: result of string concatenation is too long” when reversing anything longer than a few letters. This is due to Bug 13876895: False ora-01489 on recursive WITH clause when concatenating columns. The bug is fixed in 11.2.0.4 and 12.1.0.1, and there’s an easy workaround: Cast one of the inputs to the concatenation as a varchar2(4000).

We could make this query user-friendlier by using a sql*plus variable to hold the input string. Another approach is to add an additional subquery to the with block to “pass in” parameters. I picked this up from Lucas Jellema’s post mentioned above, and wanted to give it a try, so I’ll add it in to my WordReverse query here.

Let’s use this to reverse a word that’s really quite atrocious:

WITH
params AS
  (SELECT 'supercalifragilisticexpialidocious' phrase FROM dual),
WordReverse (inpt, outpt) AS
  (SELECT phrase inpt, CAST(NULL AS varchar2(4000)) outpt FROM params
   UNION ALL
   SELECT substr(inpt,2,LENGTH(inpt)-1), substr(inpt,1,1) || outpt
   FROM wordReverse
   WHERE LENGTH(inpt) > 0
  )
SELECT phrase,outpt AS reversed FROM wordReverse, params
WHERE LENGTH(outpt) = LENGTH(phrase) ;

PHRASE                             REVERSED
---------------------------------- ----------------------------------------
supercalifragilisticexpialidocious suoicodilaipxecitsiligarfilacrepus

Now you might not have needed to know how to spell “supercalifragilisticexpialidocious” backwards, but one recursive requirement that does come up often is querying hierarchical data. I wrote a series of posts on hierarchical data recently, using Oracle’s CONNECT BY syntax. But recursive WITH can also be used to query hierarchical data. That’ll be the subject of my next post.

---

Republished with permission. Original URL: http://rdbms-insight.com/wp/?p=94

---

In my last post, I looked at using recursive WITH to implement simple recursive algorithms in SQL. One very common use of recursion is to traverse hierarchical data. I recently wrote a series of posts on hierarchical data, using Oracle’s CONNECT BY syntax and a fun example. In this post, I’ll be revisiting the same data using recursive WITH.

There are dozens of examples of hierarchical data, from the EMP table to the Windows Registry to binary trees, but I went with something more fun: the skeleton from the old song “Dem Dry Bones”.

Since every bone has only one ancestor, and there is a root bone with no ancestor, this is hierarchical data and we can stick it in a table and query it.

SELECT * FROM skeleton;
BONE                                     CONNECTED_TO_THE
---------------------------------------- ----------------------------------------
shoulder                                 neck
back                                     shoulder
hip                                      back
thigh                                    hip
knee                                     thigh
leg                                      knee
foot                                     heel
head
neck                                     head
toe                                      foot
arm                                      shoulder
wrist                                    arm
ankle                                    leg
heel                                     ankle
finger                                   wrist
a rib                                    back
b rib                                    back
c rib                                    back

You can see that I added some ribs and an arm to make the skeleton more complete!

Using Oracle’s CONNECT BY syntax:

SQL> col bone FOR a10
SQL> col connected_to_the FOR a9
SQL> col level FOR 99
SQL> col bone_tree FOR a27
SQL> col path FOR a65
 
SELECT bone, connected_to_the, level, 
lpad(' ',2*level, ' ') || bone AS bone_tree , 
ltrim(sys_connect_by_path(bone,'>'),'>') AS path
FROM skeleton
START WITH connected_to_the IS NULL
CONNECT BY prior bone=connected_to_the 
ORDER siblings BY 1

BONE       CONNECTED LEVEL BONE_TREE                   PATH
---------- --------- ----- --------------------------- -----------------------------------------------------------------
head                     1   head                      head
neck       head          2     neck                    head>neck
shoulder   neck          3       shoulder              head>neck>shoulder
arm        shoulder      4         arm                 head>neck>shoulder>arm
wrist      arm           5           wrist             head>neck>shoulder>arm>wrist
finger     wrist         6             finger          head>neck>shoulder>arm>wrist>finger
back       shoulder      4         back                head>neck>shoulder>back
a rib      back          5           a rib             head>neck>shoulder>back>a rib
b rib      back          5           b rib             head>neck>shoulder>back>b rib
c rib      back          5           c rib             head>neck>shoulder>back>c rib
hip        back          5           hip               head>neck>shoulder>back>hip
thigh      hip           6             thigh           head>neck>shoulder>back>hip>thigh
knee       thigh         7               knee          head>neck>shoulder>back>hip>thigh>knee
leg        knee          8                 leg         head>neck>shoulder>back>hip>thigh>knee>leg
ankle      leg           9                   ankle     head>neck>shoulder>back>hip>thigh>knee>leg>ankle
heel       ankle        10                     heel    head>neck>shoulder>back>hip>thigh>knee>leg>ankle>heel
foot       heel         11                       foot  head>neck>shoulder>back>hip>thigh>knee>leg>ankle>heel>foot
toe        foot         12                         toe head>neck>shoulder>back>hip>thigh>knee>leg>ankle>heel>foot>toe

The above CONNECT BY query uses the LEVEL pseudocolumn and the SYS_CONNECT_BY_PATH function. With recursive WITH, there’s no need for these built-ins because these values fall naturally out of the recursion.

Let’s start with the basic hierarchical query rewritten in recursive WITH.
The hierarchical relationship in our table is:
Parent(row.bone) = row.connected_to_the

WITH skellarchy (bone, parent) AS
 ( SELECT bone, connected_to_the FROM skeleton 
   WHERE bone = 'head'                         -- Start with the root
 UNION ALL
   SELECT s.bone, s.connected_to_the 
   FROM skeleton s, skellarchy r
   WHERE r.bone = s.connected_to_the           -- Parent(row.bone) = row.connected_to_the
 )
SELECT * FROM skellarchy;

BONE       PARENT
---------- ----------------------------------------
head
neck       head
shoulder   neck
back       shoulder
arm        shoulder
hip        back
wrist      arm
a rib      back
b rib      back
c rib      back
thigh      hip
finger     wrist
knee       thigh
leg        knee
ankle      leg
heel       ankle
foot       heel
toe        foot

Because we built up the SKELLARCHY table recursively, it’s easy to make an equivalent to the LEVEL pseudocolumn; it falls right out of the recursion:

WITH skellarchy (bone, parent, the_level) AS
 ( SELECT bone, connected_to_the, 0 FROM skeleton 
   WHERE bone = 'head'                         
 UNION ALL
   SELECT s.bone, s.connected_to_the , r.the_level + 1
   FROM skeleton s, skellarchy r
   WHERE r.bone = s.connected_to_the           
 )
SELECT * FROM skellarchy;

BONE       PARENT      THE_LEVEL
---------- ---------- ----------
head                           0
neck       head                1
shoulder   neck                2
back       shoulder            3
arm        shoulder            3
hip        back                4
wrist      arm                 4
a rib      back                4
b rib      back                4
c rib      back                4
thigh      hip                 5
finger     wrist               5
knee       thigh               6
leg        knee                7
ankle      leg                 8
heel       ankle               9
foot       heel               10
toe        foot               11

and it’s also easy to build up a path from root to the current node like the “SYS_CONNECT_BY_PATH” function does for CONNECT BY queries:

WITH skellarchy (bone, parent, the_level, the_path) AS
 ( SELECT bone, connected_to_the, 0, CAST(bone AS varchar2(4000)) FROM skeleton 
   WHERE bone = 'head'                         
 UNION ALL
   SELECT s.bone, s.connected_to_the , r.the_level + 1, r.the_path || '->' || s.bone
   FROM skeleton s, skellarchy r
   WHERE r.bone = s.connected_to_the           
 )
SELECT * FROM skellarchy;

BONE       PARENT     THE_LEVEL THE_PATH
---------- ---------- --------- --------------------------------------------------------------------------------
head                          0 head
neck       head               1 head->neck
shoulder   neck               2 head->neck->shoulder
back       shoulder           3 head->neck->shoulder->back
arm        shoulder           3 head->neck->shoulder->arm
hip        back               4 head->neck->shoulder->back->hip
wrist      arm                4 head->neck->shoulder->arm->wrist
a rib      back               4 head->neck->shoulder->back->a rib
b rib      back               4 head->neck->shoulder->back->b rib
c rib      back               4 head->neck->shoulder->back->c rib
thigh      hip                5 head->neck->shoulder->back->hip->thigh
finger     wrist              5 head->neck->shoulder->arm->wrist->finger
knee       thigh              6 head->neck->shoulder->back->hip->thigh->knee
leg        knee               7 head->neck->shoulder->back->hip->thigh->knee->leg
ankle      leg                8 head->neck->shoulder->back->hip->thigh->knee->leg->ankle
heel       ankle              9 head->neck->shoulder->back->hip->thigh->knee->leg->ankle->heel
foot       heel              10 head->neck->shoulder->back->hip->thigh->knee->leg->ankle->heel->foot
toe        foot              11 head->neck->shoulder->back->hip->thigh->knee->leg->ankle->heel->foot->toe

and we can use our generated the_level column to make a nice display just as we used the level pseudocolumn with CONNECT BY:

WITH skellarchy (bone, parent, the_level) AS
 ( SELECT bone, connected_to_the, 0  FROM skeleton 
   WHERE bone = 'head'                         
 UNION ALL
   SELECT s.bone, s.connected_to_the , r.the_level + 1
   FROM skeleton s, skellarchy r
   WHERE r.bone = s.connected_to_the           
 )
SELECT lpad(' ',2*the_level, ' ') || bone AS bone_tree FROM skellarchy;

BONE_TREE
---------------------------
head
  neck
    shoulder
      back
      arm
        hip
        wrist
        a rib
        b rib
        c rib
          thigh
          finger
            knee
              leg
                ankle
                  heel
                    foot
                      toe

Now, the bones are coming out in a bit of a funny order for a skeleton. Instead of this:

    shoulder
      back
      arm
        hip
        wrist
        a rib
        b rib
        c rib
          thigh
          finger

I want to see this:

    shoulder
      arm
        wrist
          finger
      back
        a rib
        b rib
        c rib
        hip
          thigh

The rows are coming out in BREADTH FIRST ordering – meaning all siblings of ‘shoulder’ are printed before any children of ‘shoulder’. But I want to see them in DEPTH FIRST: going from shoulder to finger before we start on the backbone.

WITH skellarchy (bone, parent, the_level) AS
 ( SELECT bone, connected_to_the, 0  FROM skeleton 
   WHERE bone = 'head'                         
 UNION ALL
   SELECT s.bone, s.connected_to_the , r.the_level + 1
   FROM skeleton s, skellarchy r
   WHERE r.bone = s.connected_to_the           
 )
SEARCH DEPTH FIRST BY bone SET bone_order
SELECT lpad(' ',2*the_level, ' ') || bone AS bone_tree FROM skellarchy
ORDER BY bone_order;

BONE_TREE
---------------------------
head
  neck
    shoulder
      arm
        wrist
          finger
      back
        a rib
        b rib
        c rib
        hip
          thigh
            knee
              leg
                ankle
                  heel
                    foot
                      toe

And now the result looks more like a proper skeleton.

Now on to cycles. A cycle is a loop in the hierarchical data: a row is its own ancestor. To put a cycle in the example data, I made the skeleton bend over and connect the head to the toe:

UPDATE skeleton SET connected_to_the='toe' WHERE bone='head';

And now if we try to run the query:

ERROR at line 2:
ORA-32044: cycle detected while executing recursive WITH query

With the CONNECT BY syntax, we can use CONNECT BY NOCYCLE to run a query even when cycles exist, and the pseudocolumn CONNECT_BY_IS_CYCLE to help detect cycles. For recursive WITH, Oracle provides a CYCLE clause, which is a bit more powerful as it allows us to name the column which is cycling.

WITH skellarchy (bone, parent, the_level) AS
 ( SELECT bone, connected_to_the, 0  FROM skeleton 
   WHERE bone = 'head'                         
 UNION ALL
   SELECT s.bone, s.connected_to_the , r.the_level + 1
   FROM skeleton s, skellarchy r
   WHERE r.bone = s.connected_to_the           
 )
SEARCH DEPTH FIRST BY bone SET bone_order
CYCLE bone SET is_a_cycle TO 'Y' DEFAULT 'N'
SELECT lpad(' ',2*the_level, ' ') || bone AS bone_tree, is_a_cycle FROM skellarchy
--where is_a_cycle='N'
ORDER BY bone_order;

BONE_TREE                                                    I
------------------------------------------------------------ -
head                                                         N
  neck                                                       N
    shoulder                                                 N
      arm                                                    N
        wrist                                                N
          finger                                             N
      back                                                   N
        a rib                                                N
        b rib                                                N
        c rib                                                N
        hip                                                  N
          thigh                                              N
            knee                                             N
              leg                                            N
                ankle                                        N
                  heel                                       N
                    foot                                     N
                      toe                                    N
                        head                                 Y

The query runs until the first cycle is detected, then stops.

The CONNECT BY syntax does provide a nice pseudocolumn, CONNECT_BY_ISLEAF, which is 1 when a row has no further children, 0 otherwise. In my next post, I’ll look at emulating this pseudocolumn with recursive WITH.

---

Republished with permission. Original URL: http://rdbms-insight.com/wp/?p=103

---

The CONNECT BY syntax provides a useful pseudocolumn, CONNECT_BY_ISLEAF, which identifies leaf nodes in the data: it’s 1 when a row has no further children, 0 otherwise. In this post, I’ll look at emulating this pseudocolumn using recursive WITH.

Let’s continue with the example from my previous posts about hierarchical data: the skeleton from the old song “Dem Dry Bones”.

UPDATE skeleton SET connected_to_the=NULL WHERE bone='head';
SELECT * FROM skeleton;

BONE                                     CONNECTED_TO_THE
---------------------------------------- ----------------------------------------
shoulder                                 neck
back                                     shoulder
hip                                      back
thigh                                    hip
knee                                     thigh
leg                                      knee
foot                                     heel
head
neck                                     head
toe                                      foot
arm                                      shoulder
wrist                                    arm
ankle                                    leg
heel                                     ankle
finger                                   wrist
a rib                                    back
b rib                                    back
c rib                                    back

With CONNECT BY, we can use the CONNECT_BY_ISLEAF pseudocolumn to identify leaf nodes:

SELECT bone, level, 
ltrim(sys_connect_by_path(bone,' -> '),' -> ') AS path
FROM skeleton
WHERE connect_by_isleaf=1
START WITH connected_to_the IS NULL
CONNECT BY prior bone=connected_to_the 
ORDER siblings BY 1;

BONE      LEVEL PATH                                                                                            
--------- ----- ----------------------------------------------------------------------------------------------- 
finger        6 head -> neck -> shoulder -> arm -> wrist -> finger                                              
a rib         5 head -> neck -> shoulder -> back -> a rib                                                       
b rib         5 head -> neck -> shoulder -> back -> b rib                                                       
c rib         5 head -> neck -> shoulder -> back -> c rib                                                       
toe          12 head -> neck -> shoulder -> back -> hip -> thigh -> knee -> leg -> ankle -> heel -> foot -> toe

This pseudocolumn takes a little more thought to replicate using recursive WITH than the LEVEL pseudocolumn and the SYS_CONNECT_BY_PATH, which, as we saw in my last post, fall naturally out of the recursion.

We can imitate CONNECT_BY_ISLEAF by searching DEPTH FIRST and using the LEAD function to peek at the next row’s the_level value. If the next row’s level is higher than the current row, then it’s a child of the current row; otherwise, it’s not a child. Since, with DEPTH FIRST, all the children of a row come out before any siblings, if the next row isn’t a child, then the current row is a leaf.

WITH skellarchy (bone, parent, the_level) AS
 ( SELECT bone, connected_to_the, 0  FROM skeleton 
   WHERE bone = 'head'                         
 UNION ALL
   SELECT s.bone, s.connected_to_the , r.the_level + 1
   FROM skeleton s, skellarchy r
   WHERE r.bone = s.connected_to_the           
 )
SEARCH DEPTH FIRST BY bone SET bone_order
CYCLE bone SET is_a_cycle TO 'Y' DEFAULT 'N'
SELECT lpad(' ',2*the_level, ' ') || bone AS bone_tree , the_level,
  lead(the_level) OVER (ORDER BY bone_order) AS next_level,
  CASE 
    WHEN the_level < lead(the_level) OVER (ORDER BY bone_order) THEN NULL
    ELSE 'LEAF'
  END is_leaf
FROM skellarchy
ORDER BY bone_order;

BONE_TREE                                      THE_LEVEL NEXT_LEVEL IS_L
--------------------------------------------- ---------- ---------- ----
head                                                   0          1
  neck                                                 1          2
    shoulder                                           2          3
      arm                                              3          4
        wrist                                          4          5
          finger                                       5          3 LEAF
      back                                             3          4
        a rib                                          4          4 LEAF
        b rib                                          4          4 LEAF
        c rib                                          4          4 LEAF
        hip                                            4          5
          thigh                                        5          6
            knee                                       6          7
              leg                                      7          8
                ankle                                  8          9
                  heel                                 9         10
                    foot                              10         11
                      toe                             11            LEAF

Watch out for Cycles

The first point of caution about this solution concerns cycles. In my last post, I had created a cycle by making the ‘head’ node’s parent the ‘toe’ node. If I’d left the cycle in the data, the toe node wouldn’t be a leaf any more, but this query would falsely identify the head as a leaf:

UPDATE skeleton SET connected_to_the='toe' WHERE bone='head';

BONE_TREE                                      THE_LEVEL NEXT_LEVEL IS_L
--------------------------------------------- ---------- ---------- ----
head                                                   0          1
  neck                                                 1          2
    shoulder                                           2          3
      arm                                              3          4
        wrist                                          4          5
          finger                                       5          3 LEAF
      back                                             3          4
        a rib                                          4          4 LEAF
        b rib                                          4          4 LEAF
        c rib                                          4          4 LEAF
        hip                                            4          5
          thigh                                        5          6
            knee                                       6          7
              leg                                      7          8
                ankle                                  8          9
                  heel                                 9         10
                    foot                              10         11
                      toe                             11         12
                        head                          12            LEAF
 
19 rows selected.

This can be corrected for by adding WHERE IS_A_CYCLE=’N’ to the query.

Respect the order of evaluation…

A second point of caution: if I add a WHERE clause to the query that limits the number of levels, the last line of the resultset will always be identified as a leaf.

WITH skellarchy (bone, parent, the_level) AS
 ( SELECT bone, connected_to_the, 0  FROM skeleton 
   WHERE bone = 'head'                         
 UNION ALL
   SELECT s.bone, s.connected_to_the , r.the_level + 1
   FROM skeleton s, skellarchy r
   WHERE r.bone = s.connected_to_the           
 )
SEARCH DEPTH FIRST BY bone SET bone_order
CYCLE bone SET is_a_cycle TO 'Y' DEFAULT 'N'
SELECT lpad(' ',2*the_level, ' ') || bone AS bone_tree , the_level,
  lead(the_level) OVER (ORDER BY bone_order) AS next_level,
  CASE 
    WHEN the_level < lead(the_level) OVER (ORDER BY bone_order) THEN NULL
    ELSE 'LEAF'
  END is_leaf
FROM skellarchy
WHERE the_level < 8 
ORDER BY bone_order;

BONE_TREE                                                     THE_LEVEL NEXT_LEVEL IS_L
------------------------------------------------------------ ---------- ---------- ----
head                                                                  0          1
  neck                                                                1          2
    shoulder                                                          2          3
      arm                                                             3          4
        wrist                                                         4          5
          finger                                                      5          3 LEAF
      back                                                            3          4
        a rib                                                         4          4 LEAF
        b rib                                                         4          4 LEAF
        c rib                                                         4          4 LEAF
        hip                                                           4          5
          thigh                                                       5          6
            knee                                                      6          7
              leg                                                     7            LEAF      <<<=====

The leg is falsely identified as a leaf, and NEXT_LEVEL comes out as NULL, even though the ‘leg’ row has a child row. Why is that? It’s because this solution uses the LEAD analytic function. With analytic functions, WHERE clauses are evaluated before the analytic functions.

Highlighting the relevant bits from the query:

WITH skellarchy AS ...[recursive WITH subquery]...
SELECT ... LEAD(the_level) OVER (ORDER BY bone_order) AS next_level ... --analytic function
FROM skellarchy
WHERE the_level < 8 ...                                                 --where clause

To quote the documentation:

Analytic functions compute an aggregate value based on a group of rows…. The group of rows is called a window and is defined by the analytic_clause. For each row, a sliding window of rows is defined. The window determines the range of rows used to perform the calculations for the current row…. Analytic functions are the last set of operations performed in a query except for the final ORDER BY clause. All joins and all WHERE, GROUP BY, and HAVING clauses are completed before the analytic functions are processed.

In the query above, “where the_level < 8" will be evaluated before LEAD(the_level). The EXPLAIN PLAN shows this very clearly:

-----------------------------------------------------------------------------------------------------
| Id  | Operation                                | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                         |          |     2 |    76 |     8  (25)| 00:00:01 |
|   1 |  WINDOW BUFFER                           |          |     2 |    76 |     8  (25)| 00:00:01 |  <<=== LEAD
|*  2 |   VIEW                                   |          |     2 |    76 |     8  (25)| 00:00:01 |  <<=== filter("THE_LEVEL"<8)
|   3 |    UNION ALL (RECURSIVE WITH) DEPTH FIRST|          |       |       |            |          |
|*  4 |     TABLE ACCESS FULL                    | SKELETON |     1 |    24 |     2   (0)| 00:00:01 |
|*  5 |     HASH JOIN                            |          |     1 |    49 |     5  (20)| 00:00:01 |
|   6 |      RECURSIVE WITH PUMP                 |          |       |       |            |          |
|   7 |      TABLE ACCESS FULL                   | SKELETON |    18 |   432 |     2   (0)| 00:00:01 |
-----------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   2 - filter("THE_LEVEL"<8)
   4 - filter("BONE"='head')
   5 - access("R"."BONE"="S"."CONNECTED_TO_THE")

The WINDOW BUFFER (analytic window) is evaluated after the VIEW which filters on “THE_LEVEL”<8. So, "lead(the_level) over (order by bone_order)" will be null where the_level=7, and the 'leg' wrongly identified as a leaf node. What we actually want is for the analytic function LEAD to run over the whole resultset, and only then limit the results to show the levels 0-7. The obvious way to do this is to wrap the query in a second SELECT statement:

SELECT * FROM (
  WITH skellarchy (bone, parent, the_level) AS
   ( SELECT bone, connected_to_the, 0  FROM skeleton 
     WHERE bone = 'head'                         
   UNION ALL
     SELECT s.bone, s.connected_to_the , r.the_level + 1
     FROM skeleton s, skellarchy r
     WHERE r.bone = s.connected_to_the           
   )
  SEARCH DEPTH FIRST BY bone SET bone_order
  CYCLE bone SET is_a_cycle TO 'Y' DEFAULT 'N'
  SELECT lpad(' ',2*the_level, ' ') || bone AS bone_tree , the_level,
    lead(the_level) OVER (ORDER BY bone_order) AS next_level,
    CASE 
      WHEN the_level < lead(the_level) OVER (ORDER BY bone_order) THEN NULL
      ELSE 'LEAF'
    END is_leaf
  FROM skellarchy
  ORDER BY bone_order
) WHERE the_level < 8;

BONE_TREE                                                     THE_LEVEL NEXT_LEVEL IS_L
------------------------------------------------------------ ---------- ---------- ----
head                                                                  0          1
  neck                                                                1          2
    shoulder                                                          2          3
      arm                                                             3          4
        wrist                                                         4          5
          finger                                                      5          3 LEAF
      back                                                            3          4
        a rib                                                         4          4 LEAF
        b rib                                                         4          4 LEAF
        c rib                                                         4          4 LEAF
        hip                                                           4          5
          thigh                                                       5          6
            knee                                                      6          7
              leg                                                     7          8

Now, the analytic function in the inner query is evaluated first, before the WHERE clause in the outer query. We can see this in the EXPLAIN PLAN too, of course. Now the WINDOW BUFFER (analytic window) is evaluated before the VIEW with filter(“THE_LEVEL”<8) :

------------------------------------------------------------------------------------------------------
| Id  | Operation                                 | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                          |          |     2 |  4068 |     8  (25)| 00:00:01 |
|*  1 |  VIEW                                     |          |     2 |  4068 |     8  (25)| 00:00:01 |  <<=== filter("THE_LEVEL"<8)
|   2 |   WINDOW BUFFER                           |          |     2 |    76 |     8  (25)| 00:00:01 |  <<=== LEAD
|   3 |    VIEW                                   |          |     2 |    76 |     8  (25)| 00:00:01 |
|   4 |     UNION ALL (RECURSIVE WITH) DEPTH FIRST|          |       |       |            |          |
|*  5 |      TABLE ACCESS FULL                    | SKELETON |     1 |    24 |     2   (0)| 00:00:01 |
|*  6 |      HASH JOIN                            |          |     1 |    49 |     5  (20)| 00:00:01 |
|   7 |       RECURSIVE WITH PUMP                 |          |       |       |            |          |
|   8 |       TABLE ACCESS FULL                   | SKELETON |    18 |   432 |     2   (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   1 - filter("THE_LEVEL"<8)
   5 - filter("BONE"='head')
   6 - access("R"."BONE"="S"."CONNECTED_TO_THE")

This is one case of the general point that, as Tom Kyte explains in this Ask Tom answer,“select analytic_function from t where CONDITION” is NOT THE SAME AS “select * from (select analytic_function from t) where CONDITION”.

So, to sum up my last few posts, we can do everything that CONNECT BY can do with the 11g recursive WITH syntax. Plus, the recursive WITH syntax makes it easy to express simple recursive algorithms in SQL.

---

Republished with permission. Original URL: http://rdbms-insight.com/wp/?p=135

---