Real Time statistics - Part IV

Today’s post is about the effect of real-time statistics with Histograms in place. Let’s build a test case to demonstrate the effect.
 
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>
demo@PDB19> insert 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.
 
demo@PDB19> begin
  2     dbms_Stats.gather_table_stats(user,'T',
  3             no_invalidate=>false,
  4             method_opt=>'for all columns size auto for columns N2 size 2048');
  5  end;
  6  /
 
PL/SQL procedure successfully completed.
 
So we got a table created with small set of data in it and build  a histogram on the column N2. The number of distinct values in the column is 32, which is way less than the number of buckets (2048) we requested, so we easily ended up with a nice Frequency histogram.
 
demo@PDB19> select column_name,num_nulls,num_distinct,low_value,get_stats_val(data_type,low_value) low_val,
  2      high_value,get_stats_val(data_type,high_value) high_val, histogram
  3  from user_tab_columns
  4  where table_name ='T';
 
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  LOW_VAL    HIGH_VALUE   HIGH_VAL        HISTOGRAM
---------- ---------- ------------ ---------- ---------- ------------ --------------- ---------
N1                  0         1000 C102       1          C20B         1000            NONE
N2                  0           32 C102       1          C121         32              FREQUENCY
N3                  0         1000 6569676874 eight      74776F206875 two hundred two NONE
 
demo@PDB19> select endpoint_number, endpoint_value
  2  from user_tab_histograms
  3  where table_name ='T'
  4  and column_name ='N2'
  5  order by endpoint_value;
 
ENDPOINT_NUMBER ENDPOINT_VALUE
--------------- --------------
              1              1
              4              2
              9              3
             16              4
             25              5
             36              6
             49              7
             64              8
             81              9
            100             10
            121             11
            144             12
            169             13
            196             14
            225             15
            256             16
            289             17
            324             18
            361             19
            400             20
            441             21
            484             22
            529             23
            576             24
            625             25
            676             26
            729             27
            784             28
            841             29
            900             30
            961             31
           1000             32
 
32 rows selected.
 
Let’s do few more conventional load for the real-time statistics to kick in.
 
demo@PDB19> insert into t(n1,n2,n3)
  2  with rws as ( select rownum+10000 n from dual connect by level <= 10000 )
  3  select n, ceil(sqrt(n)), to_char(to_date(n,'j'),'jsp')
  4  from rws;
 
10000 rows created.
 
demo@PDB19> commit;
 
Commit complete.
 
With monitoring information not yet flushed we got ended up with Real-Time column level statistics
 
demo@PDB19> select num_rows,blocks,monitoring
  2  from user_tables
  3  where table_name ='T';
 
  NUM_ROWS     BLOCKS MON
---------- ---------- ---
      1000          5 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          5 YES
 
demo@PDB19> select column_name,num_nulls,num_distinct,low_value,get_stats_val(data_type,low_value) low_val,
  2      high_value,get_stats_val(data_type,high_value) high_val, histogram
  3  from user_tab_columns
  4  where table_name ='T';
 
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  LOW_VAL    HIGH_VALUE  HIGH_VAL        HISTOGRAM
---------- ---------- ------------ ---------- ---------- ----------- --------------- ---------
N1                  0         1000 C102       1          C20B        1000            NONE
N2                  0           32 C102       1          C121        32              FREQUENCY
N3                  0         1000 6569676874 eight      74776F20687 two hundred two NONE
 
demo@PDB19> select t1.column_name,t1.num_nulls,t1.num_distinct,t1.low_value,get_stats_val(t2.data_type,t1.low_value) low_val,
  2      t1.high_value,get_stats_val(t2.data_type,t1.high_value) high_val, t1.histogram , t1.notes
  3  from user_tab_col_statistics t1,
  4       user_tab_columns t2
  5  where t1.table_name ='T'
  6  and t1.table_name = t2.table_name
  7  and t1.column_name = t2.column_name;
 
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  LOW_VAL    HIGH_VALUE   HIGH_VAL        HISTOGRAM  NOTES
---------- ---------- ------------ ---------- ---------- ------------ --------------- ---------- -------------------------
N1                  0         1000 C102       1          C20B         1000            NONE
N2                  0           32 C102       1          C121         32              FREQUENCY
N3                  0         1000 6569676874 eight      74776F206875 two hundred two NONE
N1                  0              C102       1          C30264       19900                      STATS_ON_CONVENTIONAL_DML
N2                  0              C102       1          C2022B       142                        STATS_ON_CONVENTIONAL_DML
N3                  0              6569676874 eight      74776F206875 two hundred two            STATS_ON_CONVENTIONAL_DML
 
6 rows selected.
 
demo@PDB19> select endpoint_number, endpoint_value
  2  from user_tab_histograms
  3  where table_name ='T'
  4  and column_name ='N2'
  5  order by endpoint_value;
 
ENDPOINT_NUMBER ENDPOINT_VALUE
--------------- --------------
              1              1
              4              2
              9              3
             16              4
             25              5
             36              6
             49              7
             64              8
             81              9
            100             10
            121             11
            144             12
            169             13
            196             14
            225             15
            256             16
            289             17
            324             18
            361             19
            400             20
            441             21
            484             22
            529             23
            576             24
            625             25
            676             26
            729             27
            784             28
            841             29
            900             30
            961             31
           1000             32
 
32 rows selected.
 
Now parsing the sql against the table produces the plan like this
 
demo@PDB19> set serveroutput off
demo@PDB19> select /*+ gather_plan_statistics */ count(*) from t where n2 < 42;
 
  COUNT(*)
----------
      1000
 
demo@PDB19> select * from table( dbms_xplan.display_cursor(format=>'allstats last'));
 
PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------
SQL_ID  469b54saa49vu, 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 |
---------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |      |      1 |        |      1 |00:00:00.01 |      83 |
|   1 |  SORT AGGREGATE            |      |      1 |      1 |      1 |00:00:00.01 |      83 |
|*  2 |   TABLE ACCESS STORAGE FULL| T    |      1 |   1000 |   1000 |00:00:00.01 |      83 |
---------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   2 - storage("N2"<42)
       filter("N2"<42)
 
Note
-----
   - dynamic statistics used: statistics for conventional DML
 
All the values in the histogram are < 42, so the optimizer think it will return all rows from the table. Hence the estimated cardinality was reported as 1000 , that maps to the number of rows statistics available in the data dictionary.   
 
Lets flush the monitoring info manually and verify the statistics
 
demo@PDB19> exec dbms_stats.flush_database_monitoring_info;
 
PL/SQL procedure successfully completed.
 
demo@PDB19>
demo@PDB19> select num_rows,blocks,monitoring
  2  from user_tables
  3  where table_name ='T';
 
  NUM_ROWS     BLOCKS MON
---------- ---------- ---
      1000          5 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          5 YES
     11000         80         STATS_ON_CONVENTIONAL_DML
 
demo@PDB19>
demo@PDB19> select column_name,num_nulls,num_distinct,low_value,get_stats_val(data_type,low_value) low_val,
  2      high_value,get_stats_val(data_type,high_value) high_val, histogram
  3  from user_tab_columns
  4  where table_name ='T';
                                                                                                                                                               
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  LOW_VAL    HIGH_VALUE  HIGH_VAL     HISTOGRAM 
---------- ---------- ------------ ---------- ---------- ----------- -----------  ----------
N1                  0         1000 C102       1          C20B        1000         NONE      
N2                  0           32 C102       1          C121        32           FREQUENCY 
N3                  0         1000 6569676874 eight      74776F20687 two hundred  NONE      
 
demo@PDB19>
demo@PDB19> select t1.column_name,t1.num_nulls,t1.num_distinct,t1.low_value,get_stats_val(t2.data_type,t1.low_value) low_val,
  2      t1.high_value,get_stats_val(t2.data_type,t1.high_value) high_val, t1.histogram , t1.notes
  3  from user_tab_col_statistics t1,
  4       user_tab_columns t2
  5  where t1.table_name ='T'
  6  and t1.table_name = t2.table_name
  7  and t1.column_name = t2.column_name;
 
COLUMN_NAM  NUM_NULLS NUM_DISTINCT LOW_VALUE  LOW_VAL    HIGH_VALUE  HIGH_VAL    HISTOGRAM       NOTES
---------- ---------- ------------ ---------- ---------- ----------- ----------- --------------- -------------------------
N1                  0         1000 C102       1          C20B        1000        NONE
N2                  0           32 C102       1          C121        32          FREQUENCY
N3                  0         1000 6569676874 eight      74776F20687 two hundred NONE
N1                  0              C102       1          C3026222    19733                       STATS_ON_CONVENTIONAL_DML
N2                  0              C102       1          C2022A      141                         STATS_ON_CONVENTIONAL_DML
N3                  0              6569676874 eight      74776F20687 two hundred                 STATS_ON_CONVENTIONAL_DML
 
6 rows selected.
 
demo@PDB19>
demo@PDB19> select endpoint_number, endpoint_value
  2  from user_tab_histograms
  3  where table_name ='T'
  4  and column_name ='N2'
  5  order by endpoint_value;
 
ENDPOINT_NUMBER ENDPOINT_VALUE
--------------- --------------
              1              1
              4              2
              9              3
             16              4
             25              5
             36              6
             49              7
             64              8
             81              9
            100             10
            121             11
            144             12
            169             13
            196             14
            225             15
            256             16
            289             17
            324             18
            361             19
            400             20
            441             21
            484             22
            529             23
            576             24
            625             25
            676             26
            729             27
            784             28
            841             29
            900             30
            961             31
           1000             32
 
32 rows selected.
 
demo@PDB19>
 
Real time statistics update the table and column level statistics, but not the histograms, now parsing the sql against the fresh set of statistics will produce the plan like this
 
demo@PDB19> select /*+ gather_plan_statistics */ count(*) from t t1 where n2 < 42;
 
  COUNT(*)
----------
      1000
 
demo@PDB19> select * from table( dbms_xplan.display_cursor(format=>'allstats last'));
 
PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------------
SQL_ID  8bpt64wrnspbu, 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 |      83 |
|   1 |  SORT AGGREGATE            |      |      1 |      1 |      1 |00:00:00.01 |      83 |
|*  2 |   TABLE ACCESS STORAGE FULL| T    |      1 |  11000 |   1000 |00:00:00.01 |      83 |
---------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   2 - storage("N2"<42)
       filter("N2"<42)
 
Note
-----
   - dynamic statistics used: statistics for conventional DML
 
 
24 rows selected.
 
The optimizer knows that there are 11000 rows in the table, but the histogram still says they are all in the range 1 to 32, even though the column high value statistics represent 141. So it estimates the cardinality as the number of rows in the table.
 
The key thing to note here is Real time statistics do update the table and column level statistics but not the histograms & this will be a viable problem if your queries are heavily dependent on histograms for optimization.