Development

I wrote a couple of blogs in the past that talked about creating dynamic UIs based on a model layer that changes (example1 example2). Well in 12c there is a new ADF Faces component af:dynamicComponent that makes dynamic forms even more powerful. This component can be displayed as various UI components at runtime. This allows us to create Forms and tables with full functionality in a dynamic way.

In fact, we use this when you create either a form or a table component in your JSF page dragging over a data control. We now allow you to not specify each field in your UI but just say that you want to show all the fields in the data control.

In the demo below I show you how this is done, and then review how your UI automatically updates when you add fields in your model layer. For example if your DB changed and you used the "Synchronize with DB" and added the field to the VO - that's it no more need to go to every page and add the new field.

Check it out:

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Funny that oracle can easily cast ‘nan’,'inf’,'infinity’,'-inf’,'-infinity’ to corresponding binary_float_infinity,binary_double_nan, but there is no any format models for to_char(binary_float_infinity,format) or to_binary_***(text_expr,format) that can output the same as to_char(binary_float_infinity)/to_binary_float(‘inf’) without format parameter:

If a BINARY_FLOAT or BINARY_DOUBLE value is converted to CHAR or NCHAR, and the input is either infinity or NaN (not a number), then Oracle always returns the pound signs to replace the value.

Little example:

SQL> select to_binary_float('inf') from dual;

TO_BINARY_FLOAT('INF')
----------------------
                   Inf

SQL> select to_binary_float('inf','9999') from dual;
select to_binary_float('inf','9999') from dual
                       *
ERROR at line 1:
ORA-01722: invalid number

SQL> select
  2     to_char(binary_float_infinity)         without_format
  3    ,to_char(binary_float_infinity,'99999') with_format
  4    ,to_char(1e6d,'99999')                  too_large
  5  from dual;

WITHOUT_F WITH_FORMAT        TOO_LARGE
--------- ------------------ ------------------
Inf       ######             ######

SQL> select to_char(0/0f) without_format, to_char(0/0f,'tme') with_format from dual;

WITHOUT_F WITH_FORMAT
--------- --------------------------------------------------------------------------
Nan       ################################################################

ps. it’s just crossposting of my old blog.

In previous posts about caching mechanism of determinstic functions I wrote that cached results are kept only between fetch calls, but there is one exception from this rule: if all function parameters are literals, cached result will not be flushed every fetch call.
Little example with difference:

SQL> create or replace function f_deterministic(p varchar2)
  2     return varchar2
  3     deterministic
  4  as
  5  begin
  6     dbms_output.put_line(p);
  7     return p;
  8  end;
  9  /
SQL> set arrays 2 feed on;
SQL> set serverout on;
SQL> select
  2     f_deterministic(x) a
  3    ,f_deterministic('literal') b
  4  from (select 'not literal' x
  5        from dual
  6        connect by level<=10
  7       );

A                              B
------------------------------ ------------------------------
not literal                    literal
not literal                    literal
not literal                    literal
not literal                    literal
not literal                    literal
not literal                    literal
not literal                    literal
not literal                    literal
not literal                    literal
not literal                    literal

10 rows selected.

not literal
literal
not literal
not literal
not literal
not literal
not literal

As you can see, ‘literal’ was printed once, but ‘not literal’ was printed 6 times, so it was returned from cache 4 times.

Also i want to show the differences in consistency between:
1. Calling a function with determinstic and result_cache;
2. Calling an operator for function with result_cache;
3. Calling an operator for function with deterministic and result_cache;

In this example I will do updates in autonomouse transactions to emulate updates in another session during query execution:
Spoiler:: Tables and procedures with updates SelectShow

drop table t1 purge;
drop table t2 purge;
drop table t3 purge;

create table t1 as select 1 id from dual;
create table t2 as select 1 id from dual;
create table t3 as select 1 id from dual;

create or replace procedure p1_update as
  pragma autonomous_transaction;
begin
   update t1 set id=id+1;
   commit;
end;
/
create or replace procedure p2_update as
  pragma autonomous_transaction;
begin
   update t2 set id=id+1;
   commit;
end;
/
create or replace procedure p3_update as
  pragma autonomous_transaction;
begin
   update t3 set id=id+1;
   commit;
end;
/

Spoiler:: Variant 1 SelectShow

create or replace function f1(x varchar2) return number result_cache deterministic
as
  r number;
begin
   select id into r from t1;
   p1_update;
   return r;
end;
/

Spoiler:: Variant 2 SelectShow

create or replace function f2(x varchar2) return number result_cache
as
  r number;
begin
   select id into r from t2;
   p2_update;
   return r;
end;
/
create or replace operator o2
binding(varchar2)
return number
using f2
/

Spoiler:: Variant 3 SelectShow

create or replace function f3(x varchar2) return number result_cache deterministic
as
  r number;
begin
   select id into r from t3;
   p3_update;
   return r;
end;
/
create or replace operator o3
binding(varchar2)
return number
using f3
/

SQL> set arrays 2;
SQL> select
  2     f1(dummy) variant1
  3    ,o2(dummy) variant2
  4    ,o3(dummy) variant3
  5  from dual
  6  connect by level<=10;

  VARIANT1   VARIANT2   VARIANT3
---------- ---------- ----------
         1          1          1
         2          1          1
         2          1          1
         3          1          1
         3          1          1
         4          1          1
         4          1          1
         5          1          1
         5          1          1
         6          1          1

10 rows selected.

SQL> /

  VARIANT1   VARIANT2   VARIANT3
---------- ---------- ----------
         7         11         11
         8         11         11
         8         11         11
         9         11         11
         9         11         11
        10         11         11
        10         11         11
        11         11         11
        11         11         11
        12         11         11

10 rows selected.

All tests scripts: tests.zip

Craig Shallahamer wrote excellent article “When is v$sesstat really updated?”.
And my today post just a little addition and correction about the difference of updating ‘Db time’ and ‘CPU used by this session’ statistics.

In this test I want to show that the statistics will be updated after every fetch call.
I have set arraysize=2, so sql*plus will fetch by 2 rows:
(full script)

-- Result will be fetched by 2 rows:
set arraysize 2;
-- this query generates CPU consumption 
-- in the scalar subquery on fetch phase,
-- so CPU consumption will be separated 
-- into several periods between fetch calls:
with gen as (
            select/*+ materialize */
               level n, lpad(level,400) padding
            from dual
            connect by level<=200
            )
    ,stat as (
            select/*+ inline */
               sid,name,value 
            from v$mystat st, v$statname sn
            where st.statistic#=sn.statistic#
              and sn.name in ('DB time'
                             ,'CPU used by this session'
                             ,'user calls'
                             ,'recursive calls')
            )
--first rows just for avoiding SQL*Plus effect with fetching 1 row at start,
-- others will be fetched by "arraysize" rows:
select null rn,null cnt,null dbtime,null cpu,null user_calls, null recursive_calls from dual
union all -- main query:
select
   rownum rn
  ,(select count(*) from gen g1, gen g2, gen g3 where g1.n>g2.n and g1.n*0=main.n*0) cnt
  ,(select value from stat where sid*0=n*0 and name = 'DB time'                    ) dbtime
  ,(select value from stat where sid*0=n*0 and name = 'CPU used by this session'   ) cpu
  ,(select value from stat where sid*0=n*0 and name = 'user calls'                 ) user_calls
  ,(select value from stat where sid*0=n*0 and name = 'recursive calls'            ) recursive_calls
from gen main
where rownum<=10;
set arraysize 15;

Test results:

SQL> @tests/dbtime

        RN        CNT     DBTIME        CPU USER_CALLS RECURSIVE_CALLS
---------- ---------- ---------- ---------- ---------- ---------------

         1    3980000      12021      11989        200             472
         2    3980000      12021      11989        200             472
         3    3980000      12121      12089        201             472
         4    3980000      12121      12089        201             472
         5    3980000      12220      12186        202             472
         6    3980000      12220      12186        202             472
         7    3980000      12317      12283        203             472
         8    3980000      12317      12283        203             472
         9    3980000      12417      12383        204             472
        10    3980000      12417      12383        204             472

As you can see the statistics are updated after every fetch call.

Now since we already tested simple sql query, I want to do a little bit more complicated test with PL/SQL:
I’m going to write single PL/SQL block with next algorithm:
1. Saving stats
2. Executing some pl/sql code with CPU consumption
3. Getting statistics difference
4. Starting query from first test
5. Fetch 10 rows
6. Getting statistics difference
7. Fetch next 10 rows
8. Getting statistics difference
9. Fetch next 10 rows
10. Getting statistics difference
And after executing this block, i want to check statistics.

Full script:

set feed off;

-- saving previous values
column st_dbtime      new_value prev_dbtime      noprint;
column st_cpu_time    new_value prev_cputime     noprint;
column st_user_calls  new_value prev_user_calls  noprint;
column st_recur_calls new_value prev_recur_calls noprint;

select   max(decode(sn.NAME,'DB time'                  ,st.value))*10 st_dbtime
        ,max(decode(sn.NAME,'CPU used by this session' ,st.value))*10 st_cpu_time
        ,max(decode(sn.NAME,'user calls'               ,st.value))    st_user_calls
        ,max(decode(sn.NAME,'recursive calls'          ,st.value))    st_recur_calls
from v$mystat st, v$statname sn
where st.statistic#=sn.statistic# 
  and sn.name in ('DB time','CPU used by this session'
                 ,'user calls','recursive calls'
                 )
/
-- variable for output from pl/sql block: 
var output varchar2(4000);

prompt Executing test...;
----- main test:
declare
   cnt int;
   st_dbtime      number; 
   st_cpu_time    number; 
   st_user_calls  number; 
   st_recur_calls number; 
   cursor c is 
      with gen as (select/*+ materialize */
                     level n, lpad(level,400) padding
                   from dual
                   connect by level<=200)
      select
          rownum rn
        , (select count(*) from gen g1, gen g2, gen g3 where g1.n>g2.n and g1.n*0=main.n*0) cnt
      from gen main
      where rownum<=60;
   
   type ctype is table of c%rowtype;
   c_array ctype;
   
   procedure SnapStats(descr varchar2:=null)
   is
      st_new_dbtime      number;
      st_new_cpu_time    number;
      st_new_user_calls  number;
      st_new_recur_calls number;
   begin
      select   max(decode(sn.NAME,'DB time'                 ,st.value))*10 st_dbtime
              ,max(decode(sn.NAME,'CPU used by this session',st.value))*10 st_cpu_time
              ,max(decode(sn.NAME,'user calls'              ,st.value))    st_user_calls
              ,max(decode(sn.NAME,'recursive calls'         ,st.value))    st_recur_calls
          into st_new_dbtime,st_new_cpu_time,st_new_user_calls,st_new_recur_calls
      from v$mystat st, v$statname sn
      where st.statistic#=sn.statistic#
        and sn.name in ('DB time','CPU used by this session'
                       ,'user calls','recursive calls'
                       );
      if descr is not null then
         :output:= :output || descr ||':'||chr(10)
                || 'sesstat dbtime:     ' || (st_new_dbtime      - st_dbtime      )||chr(10)
                || 'sesstat cputime:    ' || (st_new_cpu_time    - st_cpu_time    )||chr(10)
                || 'sesstat user calls: ' || (st_new_user_calls  - st_user_calls  )||chr(10)
                || 'sesstat recur calls:' || (st_new_recur_calls - st_recur_calls )||chr(10)
                || '======================================'||chr(10);
      end if;
      st_dbtime      := st_new_dbtime     ;
      st_cpu_time    := st_new_cpu_time   ;
      st_user_calls  := st_new_user_calls ;
      st_recur_calls := st_new_recur_calls;
   end;
   
begin
   -- saving previous stats:
   SnapStats;

   -- generating cpu load:
   for i in 1..1e7 loop
      cnt:=cnt**2+cnt**1.3-cnt**1.2;
   end loop;
   -- getting new stats:
   SnapStats('After pl/sql loop');
   
   open c;
   SnapStats('After "open c"');
   fetch c bulk collect into c_array limit 10;
   SnapStats('After fetch 10 rows');
   fetch c bulk collect into c_array limit 10;
   SnapStats('After fetch 20 rows');
   fetch c bulk collect into c_array limit 10;
   SnapStats('After fetch 30 rows');
   close c;
   SnapStats('After close c');
end;
/ 

prompt 'Delta stats after statement(ms):';
select   max(decode(sn.NAME,'DB time'                 ,st.value))*10
          - &&prev_dbtime      as delta_dbtime
        ,max(decode(sn.NAME,'CPU used by this session',st.value))*10
          - &&prev_cputime     as delta_cpu_time
        ,max(decode(sn.NAME,'user calls'              ,st.value))  
          - &&prev_user_calls  as delta_user_calls
        ,max(decode(sn.NAME,'recursive calls'         ,st.value))  
          - &&prev_recur_calls as delta_recur_calls
from v$mystat st, v$statname sn
where st.statistic#=sn.statistic# 
  and sn.name in ('DB time','CPU used by this session'
                 ,'user calls','recursive calls'
                 )
/
prompt 'Test results:';
col output format a40;
print output;
set feed off;

Output:

SQL> @tests/dbtime2

Executing test...
'Delta stats after statement(ms):'

DELTA_DBTIME DELTA_CPU_TIME DELTA_USER_CALLS DELTA_RECUR_CALLS
------------ -------------- ---------------- -----------------
       18530          18460                5                33

Test results:
OUTPUT
----------------------------------------
After pl/sql loop:
sesstat dbtime:     0
sesstat cputime:    4350
sesstat user calls: 0
sesstat recur calls:2
======================================
After "open c":
sesstat dbtime:     0
sesstat cputime:    20
sesstat user calls: 0
sesstat recur calls:4
======================================
After fetch 10 rows:
sesstat dbtime:     0
sesstat cputime:    4680
sesstat user calls: 0
sesstat recur calls:2
======================================
After fetch 20 rows:
sesstat dbtime:     0
sesstat cputime:    4680
sesstat user calls: 0
sesstat recur calls:2
======================================
After fetch 30 rows:
sesstat dbtime:     0
sesstat cputime:    4690
sesstat user calls: 0
sesstat recur calls:2
======================================
After close c:
sesstat dbtime:     0
sesstat cputime:    0
sesstat user calls: 0
sesstat recur calls:3
======================================

We can notice that “CPU time” is updated at the same time as recursive calls, but “DB time” is updated only with “User calls”. Although this difference is not so important(because in most cases we can use other statistics in sum), but i think, if you want to instrument some code, it gives reason to check out desirable statistics for update time.

1. Go to /agent_inst/sysman/config
2. Open emd.properties file and
3. Change this line EMAGENT_PERL_TRACE_LEVEL=INFO to EMAGENT_PERL_TRACE_LEVEL=DEBUG
4. Restart agent
5. Run discovery again and send us /agent_inst/sysman/log/emagent_perl.trc file.

• Security both at data and UI layer.
  Grant read and write access at the query, column and row level. Control editable, enable, visible attributes of every UI component.
  
• Security aware UI components. 
  Formspider automatically detects the read&write privileges defined at the data layer and restricts access to data bound UI objects.
  
• Incredibly easy to manage. 
  Build and maintain your security model quickly with the point and click graphical user interface.
  
• Create reports on your security model easily with SQL. 
  No LDAP, no XML, no files. Formspider security model is implemented in relational database tables.

Everyone knows Tom Kyte’s mantra:

You should do it in a single SQL statement if at all possible.

But we all know that “Every rule has an exception”
There are many different cases when pl/sql with sql can be more efficient than only sql, and i dont want to catalog them. I just want to show a couple examples of such exceptions:

Simple example from forum:

select dt,
       dim1,
       dim2,
       val,
       sum(val) over(partition by dim1 order by dt) dim1_cumulative_sum,
       sum(val) over(partition by dim2 order by dt) dim2_cumulative_sum,
       sum(val) over(partition by dim1, dim2 order by dt) dim1_dim2_cumulative_sum
  from mg_t
 order by dt;

This query will be very hard for big data sets, so we can do it efficiently with pl/sql:

create or replace function xf_to_drop return xt2_to_drop pipelined
is
   type tt  is table of number index by pls_integer;
   type tt2 is table of tt index by pls_integer;
   dim1_c tt;
   dim2_c tt;
   dim12_c tt2;
begin
   for r in (
            select dt,
                   dim1,
                   dim2,
                   val
              from mg_t
             order by dt
   )
   loop
      dim1_c(r.dim1):=case when dim1_c.exists(r.dim1) then dim1_c(r.dim1) else 0 end + r.val;
      dim2_c(r.dim1):=case when dim2_c.exists(r.dim1) then dim2_c(r.dim1) else 0 end + r.val;
      dim12_c(r.dim1)(r.dim2):=case
                                  when dim12_c.exists(r.dim1)
                                   and dim12_c(r.dim1).exists(r.dim2)
                                  then dim12_c(r.dim1)(r.dim2)
                                  else 0
                               end + r.val;
      pipe row (xt1_to_drop( r.dt
                            ,r.dim1
                            ,r.dim2
                            ,r.val
                            ,dim1_c(r.dim1)
                            ,dim1_c(r.dim1)
                            ,dim12_c(r.dim1)(r.dim2)
                           ));
   end loop;
end;
/

create table mg_t as
select trunc(sysdate) + level/1440 dt,
       trunc(3 * dbms_random.value()) dim1,
       trunc(3 * dbms_random.value()) dim2,
       trunc(100 * dbms_random.value()) val
  from dual
connect by level <= 3e6;

create type xt1_to_drop is object(
       dt                  date
      ,dim1                number
      ,dim2                number
      ,val                 number
      ,dim1_cumulative_sum number
      ,dim2_cumulative_sum number
      ,dim1_dim2_cumulative_sum number
);
create type xt2_to_drop as table of xt1_to_drop;

create or replace function xf_to_drop return xt2_to_drop pipelined
is
   type tt is table of number index by pls_integer;
   type tt2 is table of tt index by pls_integer;
   dim1_c tt;
   dim2_c tt;
   dim12_c tt2;
begin
   for r in (
            select dt,
                   dim1,
                   dim2,
                   val
              from mg_t
             order by dt
   )
   loop
      dim1_c(r.dim1):=case when dim1_c.exists(r.dim1) then dim1_c(r.dim1) else 0 end + r.val;
      dim2_c(r.dim1):=case when dim2_c.exists(r.dim1) then dim2_c(r.dim1) else 0 end + r.val;
      dim12_c(r.dim1)(r.dim2):=case
                                  when dim12_c.exists(r.dim1)
                                   and dim12_c(r.dim1).exists(r.dim2)
                                  then dim12_c(r.dim1)(r.dim2)
                                  else 0
                               end + r.val;
      pipe row (xt1_to_drop( r.dt,r.dim1,r.dim2,r.val,dim1_c(r.dim1),dim1_c(r.dim1),dim12_c(r.dim1)(r.dim2)));
   end loop;
end;
/
exec for r in (select * from table(xf_to_drop)) loop null; end loop;

create table test (clientid NUMBER(10), accountid NUMBER(10));

This example also taken from our russian forum and there was very good and simple sql-only solution, but it’s not efficient on big data sets:

select min(group_member_id) as group_max_id, accountid, clientid
  from  (select clientid as group_member_id
         , connect_by_root accountid as accountid
         , connect_by_root clientid  as clientid
      from test
      connect by nocycle decode(accountid, prior accountid, 1, 0)
                       + decode(clientid,  prior clientid,  1, 0)
                       = 1
  ) a
  group by accountid, clientid
  order by group_max_id, accountid
/

We can try to remember algorithms courses and adopt one of the several algorithms for connected components:
Spoiler:: Based on weighted quick-union algorithm SelectShow

declare
   type int_array    is table of pls_integer index by pls_integer;
   type arr_elems    is table of sys.ku$_objnumset index by pls_integer;
   root              int_array;
   root_elems        arr_elems;

   n        int;
   clients  int_array;
   accounts int_array;

    l integer:=dbms_utility.get_time();

    procedure print(v in varchar2) is
    begin
      dbms_output.put_line(to_char((dbms_utility.get_time-l)/100,'0999.99')||' '||v);
      l:=dbms_utility.get_time();
    end;

   
   function get_root(n int) return pls_integer is
   begin
      if root.exists(n) then 
         return root(n);
      else 
         return null;
      end if;
   end;
   
   procedure update_root(old_root pls_integer,new_root pls_integer) is
      i       pls_integer;
      elem    pls_integer;
      cnt_old pls_integer;
      cnt_new pls_integer;
   begin
      if old_root!=new_root then 
         --root_elems(new_root):=root_elems(new_root) multiset union all root_elems(old_root);
         cnt_old:=root_elems(old_root).count;
         cnt_new:=root_elems(new_root).count;
         root_elems(new_root).extend(cnt_old);
         for i in 1..cnt_old
         loop
            elem := root_elems(old_root)(i);
            root(elem):=new_root;
            root_elems(new_root)(cnt_new+i):=elem;
         end loop;
         root_elems(old_root).delete;
      end if;
   end;
   
   procedure add_elem(p_root pls_integer, p_elem pls_integer) is
   begin
      if not root_elems.exists(p_root) then
         root_elems(p_root):=sys.ku$_objnumset(p_elem);
      else
         root_elems(p_root).extend();
         root_elems(p_root)(root_elems(p_root).count):=p_elem;
      end if;
   end;
   
   procedure add_link(clientid pls_integer,accountid pls_integer) is
      r1       pls_integer;
      r2       pls_integer;
      new_root pls_integer;
   begin
      r1:=get_root(clientid);
      r2:=get_root(accountid);
      
      if r1 is null or r2 is null then
         new_root := coalesce(r1,r2,clientid);
         if r1 is null then add_elem(new_root,clientid ); root(clientid) :=new_root; end if;
         if r2 is null then add_elem(new_root,accountid); root(accountid):=new_root; end if;
      else
         new_root := least(r1,r2);
         root(clientid) :=new_root;
         root(accountid):=new_root;
         update_root(greatest(r1,r2),new_root);
      end if;
   end;
   
   function str_format(p int) return varchar2 is
   begin
      return utl_lms.format_message('(%d, %d) = group #%d'
                                   ,clients(p)
                                   ,accounts(p)
                                   ,get_root(clients(p))
                                   );
   end;
begin
   print('start');
   select clientid,accountid 
          bulk collect into clients,accounts
   from test 
--   where rownum<=1000
   ;
   print('fetched');
   n:=clients.count;
   dbms_output.put_line('count='||n);
   for i in 1..n loop
      add_link(clients(i),accounts(i));
   end loop;
   print('processed');
---
/*
   for i in 1..n loop
      dbms_output.put_line(str_format(i));
   end loop;
--   */
end;

We can also try even more interesting special algorithms for parallel processing: CONNECTED COMPONENTS ALGORITHMS
FOR MESH-CONNECTED PARALLEL COMPUTERS