Real Time statistics - Part III

Having discussed enough about real time statistics in the previous blog post, it is the time to look about mixed workload in the context of real time statistics.
 
demo@PDB19> show parameter optimizer_real_time_statistics
 
NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
optimizer_real_time_statistics       boolean     FALSE
demo@PDB19> alter session set optimizer_real_time_statistics=true;
 
Session altered.
demo@PDB19> create table t(
  2     n1 number primary key,
  3     n2 number ,
  4     n3 varchar2(80) );
 
Table created.
demo@PDB19> insert /*+ append */ into t(n1,n2,n3)
  2  with rws as ( select rownum n from dual connect by level <= 1000 )
  3  select n, ceil(sqrt(n)),
  4     to_char(to_date(n,'j'),'jsp')
  5  from rws;
 
1000 rows created.
 
demo@PDB19> commit;
 
Commit complete.
 
We got an initial table created and did some direct path load on it, to get the basic statistics available. 
 
demo@PDB19> select num_rows,blocks,stale_stats,notes
  2  from user_tab_statistics
  3  where table_name ='T';
 
  NUM_ROWS     BLOCKS STALE_S NOTES
---------- ---------- ------- -------------------------
      1000          8 NO
 
Did some conventional path dml, to get the real-time statistics to kick in.
 
demo@PDB19> insert into t(n1,n2,n3)
  2  with rws as ( select rownum+1000 n from dual connect by level <= 10000 )
  3  select n, ceil(sqrt(n)),
  4     to_char(to_date(n,'j'),'jsp')
  5  from rws;
 
10000 rows created.
 
demo@PDB19> commit;
 
Commit complete.
 
demo@PDB19> select num_rows,blocks,monitoring
  2  from user_tables
  3  where table_name ='T';
 
  NUM_ROWS     BLOCKS MON
---------- ---------- ---
      1000          8 YES
 
demo@PDB19> select num_rows,blocks,stale_stats,notes
  2  from user_tab_statistics
  3  where table_name ='T';
 
  NUM_ROWS     BLOCKS STALE_S NOTES
---------- ---------- ------- -------------------------
      1000          8 YES
 
demo@PDB19> select column_name,num_nulls,num_distinct,low_value,high_value
  2  from user_tab_columns
  3  where table_name ='T';
 
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  HIGH_VALUE
---------- ---------- ------------ ---------- ----------
N1                  0         1000 C102       C20B
N2                  0           32 C102       C121
N3                  0         1000 6569676874 74776F2068
                                              756E647265
                                              642074776F
demo@PDB19> select column_name,num_nulls,num_distinct,low_value,high_value,notes
  2  from user_tab_col_statistics
  3  where table_name ='T';
 
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  HIGH_VALUE NOTES
---------- ---------- ------------ ---------- ---------- -------------------------
N1                  0         1000 C102       C20B       STATS_ON_LOAD
N2                  0           32 C102       C121       STATS_ON_LOAD
N3                  0         1000 6569676874 74776F2068 STATS_ON_LOAD
                                              756E647265
                                              642074776F
 
N1                  0              C102       C3020A02   STATS_ON_CONVENTIONAL_DML
N2                  0              C102       C20206     STATS_ON_CONVENTIONAL_DML
N3                  0              6569676874 74776F2074 STATS_ON_CONVENTIONAL_DML
                                              686F757361
                                              6E64207477
                                              6F2068756E
                                              6472656420
                                              6569676874
 
So without flushing database monitoring info we got only the additional column level stats, but no information about the table level stats available yet.
 
demo@PDB19> set serveroutput off
demo@PDB19> select /*+ gather_plan_statistics */ count(*) from t where n2 <= 42;
 
  COUNT(*)
----------
      1764
 
demo@PDB19> select * from table(dbms_xplan.display_cursor(format=>'allstats last'));
 
PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------------
SQL_ID  99qz8xwvgmh1s, child number 0
-------------------------------------
select /*+ gather_plan_statistics */ count(*) from t where n2 <= 42
 
Plan hash value: 2966233522
 
------------------------------------------------------------------------------------------------------
| Id  | Operation                  | Name | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |
------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |      |      1 |        |      1 |00:00:00.01 |      75 |      5 |
|   1 |  SORT AGGREGATE            |      |      1 |      1 |      1 |00:00:00.01 |      75 |      5 |
|*  2 |   TABLE ACCESS STORAGE FULL| T    |      1 |    425 |   1764 |00:00:00.01 |      75 |      5 |
------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   2 - storage("N2"<=42)
       filter("N2"<=42)
 
Note
-----
   - dynamic statistics used: statistics for conventional DML
 
 
24 rows selected.
 
Column stats for conventional dml, but number of rows on table wrong. The above sql got optimized using real time statistics available at the column level stats.
 
The column N2 got low value and high value recorded as C102 and  C20206, which translates to the numerical value of 1 and 105.
 
demo@XEPDB1> variable n number
demo@XEPDB1> exec dbms_stats.convert_raw_value('C102',:n);
 
PL/SQL procedure successfully completed.
 
demo@XEPDB1> print n
 
         N
----------
         1
 
demo@XEPDB1> exec dbms_stats.convert_raw_value('C20206',:n);
 
PL/SQL procedure successfully completed.
 
demo@XEPDB1> print n
 
         N
----------
       105
 
The value of 425 reported as cardinality in the above plan is due to mix of table and column level statistics.
 
Cardinality = Selectivity * Num of rows
Selectivity = required range / available range + 1 /NDV (since it got unbounded, closed)
 
Selectivity = (42 – 1) / (105 -1) + 1/32 = 0 .425480769
Cardinality = 0 .425480769 * 1000 = 425.48
 
We could wait for the monitoring information to flush naturally, or we can flush it manually.
 
demo@PDB19> exec dbms_stats.flush_database_monitoring_info;
 
PL/SQL procedure successfully completed.
 
demo@PDB19> select num_rows,blocks,monitoring
  2  from user_tables
  3  where table_name ='T';
 
  NUM_ROWS     BLOCKS MON
---------- ---------- ---
      1000          8 YES
 
demo@PDB19>
demo@PDB19> select num_rows,blocks,stale_stats,notes
  2  from user_tab_statistics
  3  where table_name ='T';
 
  NUM_ROWS     BLOCKS STALE_S NOTES
---------- ---------- ------- -------------------------
      1000          8 YES
     11000         76         STATS_ON_CONVENTIONAL_DML
 
demo@PDB19> select column_name,num_nulls,num_distinct,low_value,high_value
  2  from user_tab_columns
  3  where table_name ='T';
 
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  HIGH_VALUE
---------- ---------- ------------ ---------- ----------
N1                  0         1000 C102       C20B
N2                  0           32 C102       C121
N3                  0         1000 6569676874 74776F2068
                                              756E647265
                                              642074776F
 
demo@PDB19> select column_name,num_nulls,num_distinct,low_value,high_value,notes
  2  from user_tab_col_statistics
  3  where table_name ='T';
 
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  HIGH_VALUE NOTES
---------- ---------- ------------ ---------- ---------- -------------------------
N1                  0         1000 C102       C20B       STATS_ON_LOAD
N2                  0           32 C102       C121       STATS_ON_LOAD
N3                  0         1000 6569676874 74776F2068 STATS_ON_LOAD
                                              756E647265
                                              642074776F
 
N1                  0              C102       C3020A02   STATS_ON_CONVENTIONAL_DML
N2                  0              C102       C20206     STATS_ON_CONVENTIONAL_DML
N3                  0              6569676874 74776F2074 STATS_ON_CONVENTIONAL_DML
                                              686F757361
                                              6E64207477
                                              6F2068756E
                                              6472656420
                                              6569676874
 
Now the sql against this fresh set of statistics produced new estimates.
 
demo@PDB19> select /*+ gather_plan_statistics */ count(*) from t t1 where n2 <= 42;
 
  COUNT(*)
----------
      1764
 
demo@PDB19> select * from table(dbms_xplan.display_cursor(format=>'allstats last'));
 
PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------------
SQL_ID  f7d7dtpdauwcb, child number 0
-------------------------------------
select /*+ gather_plan_statistics */ count(*) from t t1 where n2 <= 42
 
Plan hash value: 2966233522
 
---------------------------------------------------------------------------------------------
| Id  | Operation                  | Name | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
---------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |      |      1 |        |      1 |00:00:00.01 |      75 |
|   1 |  SORT AGGREGATE            |      |      1 |      1 |      1 |00:00:00.01 |      75 |
|*  2 |   TABLE ACCESS STORAGE FULL| T    |      1 |   4680 |   1764 |00:00:00.01 |      75 |
---------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   2 - storage("N2"<=42)
       filter("N2"<=42)
 
Note
-----
   - dynamic statistics used: statistics for conventional DML
 
Creation of real-time statistics does not invalidate cursors. Application requires to reparse the new sql’s, still there are several opportunities for execution plan to change as the state of statistics change.
 
Stale statistics – underestimate rows.
Real time column statistics – still underestimate rows
Real time table statistics – over estimate rows.