Mohamed Houri’s Oracle Notes

September 12, 2018

Troubleshooting via indexing

Filed under: Oracle — hourim @ 6:27 pm

The damage that can be inflicted by a filter predicate applied on a big table is difficult to calculate. But it is clear that it has been almost always a tragic mistake to filter a huge amount of rows from a very large table. This article outlines one of those practical cases where the creation of a chirurgical index overcame the waste of time and energy spent at the table level in a very critical third party software query.

To begin with here’s a SQL monitoring report of this critical query taken from a 12cR2 release:

SELECT
    conRef h,
    vamisin h
FROM 
  TABLEHIST h
WHERE h.typeisin <> 4
AND h.optionv NOT IN
  (SELECT  vwx.ident 
   FROM AGGREGATE_VW vwx 
   WHERE (vwx.I1 = 2298535)
  )
AND h.backclient NOT IN (106, 28387)
AND h.vamisin        IN (91071113)

Global Information
------------------------------
 Status              :  DONE (ALL ROWS)       
 Instance ID         :  1                     
 Session             :  XXXXXX (2198:9848) 
 SQL ID              :  7v8109fkuusw3         
 SQL Execution ID    :  17529954              
 Execution Started   :  08/01/2018 04:58:31   
 First Refresh Time  :  08/01/2018 04:58:35   
 Last Refresh Time   :  08/01/2018 04:59:16   
 Duration            :  45s                   
 Module/Action       :  JDBC Thin Client/-    
 Service             :  sphpre1               
 Program             :  JDBC Thin Client      
 Fetch Calls         :  6                     

Global Stats
=================================================================
| Elapsed |   Cpu   |    IO    | Fetch | Buffer | Read  | Read  |
| Time(s) | Time(s) | Waits(s) | Calls |  Gets  | Reqs  | Bytes |
=================================================================
|      44 |    2.35 |       42 |     6 |  81930 | 39157 | 311MB |
=================================================================

SQL Plan Monitoring Details (Plan Hash Value=4122899908)
========================================================================================================
| Id |               Operation                |          Name          |   Time    | Start  |   Rows   |
|    |                                        |                        | Active(s) | Active | (Actual) |
========================================================================================================
|  0 | SELECT STATEMENT                       |                        |        42 |     +4 |    50549 |
|  1 |   HASH JOIN ANTI                       |                        |        42 |     +4 |    50549 |
|  2 |    TABLE ACCESS BY INDEX ROWID BATCHED |TABLEHIST               |        42 |     +4 |    50549 |
|  3 |     INDEX RANGE SCAN                   |TABLEHIST_IND4          |        42 |     +4 |     281K |
|  4 |    VIEW                                |                        |         1 |    +45 |    12883 |
|  5 |     CONNECT BY WITH FILTERING (UNIQUE) |                        |         1 |    +45 |    21028 |
|  6 |      INDEX RANGE SCAN                  |IDX_AGGREGAT_VIEW_IDENT |         1 |    +45 |       40 |
|  7 |      HASH JOIN                         |                        |         1 |    +45 |        0 |
|  8 |       NESTED LOOPS                     |                        |         1 |    +45 |    20988 |
|  9 |        STATISTICS COLLECTOR            |                        |         1 |    +45 |        9 |
| 10 |         CONNECT BY PUMP                |                        |         1 |    +45 |    21028 |
| 11 |        INDEX RANGE SCAN                |IDX_AGGREGAT_VIEW_IDENT |         1 |    +45 |    20988 |
| 12 |       INDEX FAST FULL SCAN             |IDX_AGGREGAT_VIEW_IDENT |           |        |          |
========================================================================================================

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("H"."OPTIONV"="IDENT")
   2 - filter(("H"."BACKCLIENT"<>:SYS_B_3 AND "H"."BACKCLIENT"<>:SYS_B_2 
           AND "H"."TYPEISIN"<>:SYS_B_0))
   3 - access("H"."VAMISIN"=:1)
   4 - filter((TO_NUMBER(REPLACE(REGEXP_SUBSTR 
                  ("IDENT_PATH",'@@(.+?)(@@|$)',1,1,HEXTORAW('5824EBC2000')),'@@',NULL))=:SYS_B_1 
              AND "IDENT">0))
   5 - access("F"."MGR"=PRIOR NULL)
   6 - access("F"."MGR"=1)
   9 - access("connect$_by$_pump$_005"."prior f.ident "="F"."MGR")
 
Note
-----
   - this is an adaptive plan 

In their attempt to fix this performance pain, DBAs and developers were focusing their efforts in reviewing the part of the execution plan that involves the hierarchical query (operation n°4 to 12).

But whoever knows how to interpret correctly the multitude information provided by the SQL monitor report would have immediately pointed out where most of the query time is spent. Look at the Start Active column of operations n°0 to 3: they all shows +4 seconds. This means that those operations started to be active 4 seconds after the beginning of the query. And whoever knows how to interpret an execution plan would have pointed out that the parent-child operation n°2-3 is the first to be started by Oracle. Look now to the Time Active(s) column of these two operations. They both indicate 42 seconds. This means that the table/index operation 2-3 took 42 seconds. Since the total duration of this query is 45 seconds we know then clearly what part of the query we have to address.

The index at operations n°3 supplied its parent table at line n°2 with 281K of rowids which has thrown away all but only 50549 using the filter predicate n°2

   2 - filter(("H"."BACKCLIENT"<>:SYS_B_3 AND "H"."BACKCLIENT"<>:SYS_B_2 
           AND "H"."TYPEISIN"<>:SYS_B_0))

Most of the 81930 buffer gets come from the TABLEHIST table at operation line n°2. We need then to engineer an index starting by the column in the predicate on which an equality is applied (VAMISIN) followed by the other three columns from the other predicates on which an inequality is applied (OPTIONV, BACKCLIENT, TYPEISIN).

SQL> create index TABLEHIST_IND_MHO on TABLEHIST 
       (VAMISIN, OPTIONV, BACKCLIENT, TYPEISIN)

Since the select part of this critical query involves only two columns of which one (VAMISIN) is already included in the new index then why not add the second selected column to this index and avoid visiting the table at all?

SQL> create index TABLEHIST_IND_MHO on TABLEHIST 
       (VAMISIN, OPTIONV, BACKCLIENT, TYPEISIN, CONREF)

Although this will increase the size of the index it has, however, the potentiality to eliminate 96% of I/O.
Indeed, following the creation of the new index, Oracle has used the following execution plan:

-------------------------------------------------------------------------------
| Id  | Operation                            | Name                   | Rows  |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |                        |       |
|*  1 |  HASH JOIN ANTI                      |                        |     1 |
|*  2 |   INDEX RANGE SCAN                   |TABLEHIST_IND_MHO       |    49 |
|*  3 |   VIEW                               |                        |    89 |
|*  4 |    CONNECT BY WITH FILTERING (UNIQUE)|                        |       |
|*  5 |     INDEX RANGE SCAN                 |IDX_AGGREGAT_VIEW_IDENT |     5 |
|   6 |     NESTED LOOPS                     |                        |    84 |
|   7 |      CONNECT BY PUMP                 |                        |       |
|*  8 |      INDEX RANGE SCAN                |IDX_AGGREGAT_VIEW_IDENT |    17 |
-------------------------------------------------------------------------------


SQL> @sqlstats
Enter value for sql_id: 7v8109fkuusw3

     CHILD SQL_PROFILE          PLAN_HASH_VALUE   AVG_GETS    EXECS
---------- -------------------- --------------- ---------- --------
1	                           860019752       3651      204

As you can see Oracle is not visiting anymore the TABLEHIST table and the reduction of the logical I/O consumption is very remarkable as it went from 81930 to 3651.

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September 11, 2018

Exploded index causes a wrong execution plan

Filed under: Oracle — hourim @ 6:57 pm

A couple of days ago I have been asked to look at performance issue which occurred in a very simple delete statement. Below is this delete with its good and bad execution plan respectively:

SQL_ID gq3khfnctny4h
--------------------
DELETE FROM table_t1 WHERE evt_id = :1  AND eng_id = :2
 
Plan hash value: 1362912592 
-----------------------------------------------------------------
| Id  | Operation          | Name          | Rows  | Cost (%CPU)|
-----------------------------------------------------------------
|   0 | DELETE STATEMENT   |               |       |     3 (100)|
|   1 |  DELETE            | TABLE_T1      |       |            |
|   2 |   INDEX UNIQUE SCAN| PK_TABLE_T117 |     1 |     3   (0)| -- cost = 3
-----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
    2 - access("EVT_ID"=:1 AND "ENG_ID"=:2)
SQL_ID gq3khfnctny4h
--------------------
DELETE FROM table_t1 WHERE evt_id = :1  AND eng_id = :2
 
Plan hash value: 3057265474 
-------------------------------------------------------------------
| Id  | Operation             | Name          | Rows  |Cost (%CPU)|
-------------------------------------------------------------------
|   0 | DELETE STATEMENT      |               |       |    2 (100)|
|   1 |  DELETE               | TABLE_T1      |       |           |
|   2 |   INDEX FAST FULL SCAN| PK_TABLE_T117 |     1 |    2   (0)| -- cost = 2
-------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
    2 - access("EVT_ID"=:1 AND "ENG_ID"=:2)

The statistics of the different executions of this delete over the last couple of weeks are:

SQL> @HistStats
Enter value for sql_id: gq3khfnctny4h
Enter value for from_date: 01092018

SNAP_BEGIN                PLAN_HASH_VALUE    EXECS END_OF_FETCH  AVG_ETIME  AVG_LIO   AVG_ROWS
------------------------- --------------- -------- ------------ ---------- -------- ----------
04-SEP-18 01.00.23.666 PM      1362912592     5109         5109          0     19.3          1 -- good execution plan
04-SEP-18 02.00.32.471 PM      1362912592      574          574          0       20          1
04-SEP-18 03.00.48.319 PM      1362912592     1472         1472          0       20          1
05-SEP-18 08.00.10.847 AM      1362912592     3378         3378          0     19.4          1
05-SEP-18 05.00.04.444 PM      1362912592    17759        17759          0     19.7          1
05-SEP-18 08.00.43.235 PM      1362912592   201040       201040          0     18.6          1
05-SEP-18 09.00.30.871 PM      1362912592    59826        59826          0     19.7          1
05-SEP-18 10.01.28.933 PM      1362912592   140850       140850          0     17.7          1
06-SEP-18 12.01.11.664 AM      1362912592    17951        17951          0     22.9          1
06-SEP-18 09.00.31.336 AM      3057265474    22899        22899         19   2136.5          1 -- bad plan switch 
06-SEP-18 10.00.44.721 AM      3057265474    45736        45736         14     1315          1
06-SEP-18 11.00.48.929 AM      3057265474    56729        56729         11  37105.8          1
06-SEP-18 05.00.43.993 PM      3057265474    48568        48568         12    11275          1

As you can see, when the delete statement switched from an execution plan using a INDEX UNIQUE SCAN (1362912592) to an execution plan using an INDEX FAST FULL SCAN (3057265474) of the same primary key index, the average elapsed time of the delete went from 0 to 19 seconds.

The AVG_ROWS column above clearly shows that we are always deleting a single row. This is because the two columns of the predicate part represent the two primary key columns (evt_id, eng_id). So why on earth would Oracle FAST FULL scan an entire index to end up by locating a single row and delete it?

If you look back to the above execution plan you will realize that Oracle has completely messed up its estimations as it has considered that FAST FULL scanning the PK_TABLE_T117 primary key index will cost only 2 while the cost of unique scanning the same index would be 3. This is why it has switched for the bad execution plan causing a serious performance issue.

The statistics of this unique index are:

SQL> select
        index_name
      , blevel
      , leaf_blocks
      , distinct_keys
      , num_rows
      , clustering_factor
      , sample_size    
    from user_indexes
    where index_name = 'PK_TABLE_T117';

INDEX_NAME     BLEVEL LEAF_BLOCKS DISTINCT_KEYS   NUM_ROWS CLUSTERING_FACTOR SAMPLE_SIZE 
-------------- ------ ----------- ------------- ---------- ----------------- ----------- 
PK_TABLE_T117       3       28860        524234     524234            348959      524234   

Since I have at my disposable a PRE-PRODUCTION copy of this database I decided to get the same index statistics from this database:

SQL> select
        index_name
      , blevel
      , leaf_blocks
      , distinct_keys
      , num_rows
      , clustering_factor
      , sample_size    
    from user_indexes
    where index_name = 'PK_TABLE_T117';

INDEX_NAME      BLEVEL LEAF_BLOCKS DISTINCT_KEYS   NUM_ROWS CLUSTERING_FACTOR SAMPLE_SIZE 
-------------- ------- ----------- ------------- ---------- ----------------- ----------- 
PK_TABLE_T117        2       21470        617240     617240            190479      617240 

How could a BLEVEL of an index be equal to 3 in PRODUCTION and the BLEVEL of the same index with much more rows be equal to 2 in another database?

That’s probably an indication of an exploded index due to the delete and its pattern. Let’s check this using a little bit enhanced Jonathan Lewis script

SQL> @IndexEfficiency2
Enter value for m_schemaname: xxxx
../..
PK_TABLE_T117
      Current Index Size GB: 1.44         Expected Index size GB: .07

This index certainly needs a rebuild

SQL> alter index PK_TABLE_T117 rebuild online;
SQL> select
            index_name
          , blevel
          , leaf_blocks
          , distinct_keys
          , num_rows
          , clustering_factor
          , sample_size      
     from user_indexes
     where index_name = 'PK_TABLE_T117';

INDEX_NAME      BLEVEL LEAF_BLOCKS DISTINCT_KEYS   NUM_ROWS CLUSTERING_FACTOR SAMPLE_SIZE 
-------------- ------- ----------- ------------- ---------- ----------------- ----------- 
PK_TABLE_T117        2        1461        524202     524202            348956      524202

SQL> @sizeBySegName
Enter value for segment_name: PK_TABLE_T117
Enter value for owner: xxxx

SEGMENT_TYPE       TABLESPACE_NAME  SEGMENT_NAME      GB
------------------ ---------------- --------------- -----
INDEX              IDX              PK_TABLE_T117   .0117
                                                    ------
Total Segment Size                                  .0117 

And here’s below the new execution plan:

SQL> explain plan for
     DELETE FROM table_t1 WHERE evt_id = :1  AND eng_id = :2;
  
SQL> select * from table(dbms_xplan.display);

--------------------------------------------------------------------
| Id  | Operation          | Name          | Rows  | Bytes | Cost  |
--------------------------------------------------------------------
|   0 | DELETE STATEMENT   |               |     1 |    31 |     2 |
|   1 |  DELETE            | TABLE_T1|     |       |       |
|   2 |   INDEX UNIQUE SCAN| PK_TABLE_T117 |     1 |    31 |     2 | -- cost = 2
--------------------------------------------------------------------

SQL> explain plan for 
    delete /*+ index_ffs(TABLE_T1, PK_TABLE_T117) */ table_t1 
    WHERE evt_id = :1  AND eng_id = :2;

SQL> select * from table(dbms_xplan.display);

----------------------------------------------------------------------
| Id  | Operation             | Name          | Rows  | Bytes | Cost  |
-----------------------------------------------------------------------
|   0 | DELETE STATEMENT      |               |     1 |    31 |   402 |
|   1 |  DELETE               | TABLE_T1      |       |       |       |
|   2 |   INDEX FAST FULL SCAN| PK_TABLE_T117 |     1 |    31 |   402 | -- cost = 402
-----------------------------------------------------------------------

CONCLUSION

I have never considered rebuilding an index to be a reliable tuning strategy. This is why, I have already ousted this operation from my list of tuning steps. But, as Tom Kyte says “always is never true, never is always false”, it is then interesting to see practical real life cases where rebuilding index reveals to be a fix for a wrong execution plan choice.

May 19, 2018

Merge join in modern Relational Database Management Systems

Filed under: Oracle — hourim @ 2:07 pm

There are many posts out there explaining what a MERGE JOIN is, how it works and why it is less popular than NESTED LOOPS and HASH JOIN physical operations. In a nutshell, MERGE JOIN compares two sets of sorted data on the merge column and outputs matched rows. It reads both data sets only once. This is why it is known as an unrelated combined operation as explained by Christian Antognini in his last book.

1. Oracle merge join

Here’s a simple Oracle example illustrating the different subtleties of the MERGE JOIN using Oracle 12cR2:

SQL> create table t1 as 
        select rownum*2 n1, rownum*5 n2, rownum n3
     from dual
     connect by level <=2e1; 

SQL> create table t2 as 
        select rownum*3 n1, rownum*5 n2, rownum n3
     from dual
     connect by level <=1e2; 

SQL> select
        /*+ use_merge(t1,t2) */
        t1.n1 t1n1
       ,t2.n1 t2n1
     from t1
        join t2
     on t1.n1 = t2.n1;

      T1N1       T2N1
---------- ----------
         6          6
        12         12
        18         18
        24         24
        30         30
        36         36

6 rows selected.

---------------------------------------------------------------
| Id  | Operation           | Name | Starts | E-Rows | A-Rows |
---------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |      1 |        |      6 |
|   1 |  MERGE JOIN         |      |      1 |     20 |      6 |
|   2 |   SORT JOIN         |      |      1 |     20 |     20 |
|   3 |    TABLE ACCESS FULL| T1   |      1 |     20 |     20 |
|*  4 |   SORT JOIN         |      |     20 |    100 |      6 | 
|   5 |    TABLE ACCESS FULL| T2   |      1 |    100 |    100 |
---------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("T1"."N1"="T2"."N1")
       filter("T1"."N1"="T2"."N1")

The number of rows (A-Rows=20) generated by the first child operation(Line Id n°2) of the MERGE JOIN operation at line id n° 1 and the number of times its second child operation (line id n°4) has been executed ( Starts=20) suggest that the MERGE JOIN is somehow using a NESTED LOOPS kind of algorithm. But we see clearly that, in contrast to NESTED LOOPS, the two merge join inputs (T1 and T2) are scanned only once (Starts = 1 at lines id n°3 and 5). So what does this Starts=20 of operation n°4 mean exactly? Clearly we haven’t made 20 separate sorts as the following proves:

set autotrace on stat

select
        /*+ use_merge(t1,t2) */
        t1.n1 t1n1
       ,t2.n1 t2n1
     from t1
        join t2
     on t1.n1 = t2.n1;

Statistics
---------------------------------------------------
          0  recursive calls
          4  db block gets
         14  consistent gets
          0  physical reads
          0  redo size
        708  bytes sent via SQL*Net to client
        608  bytes received via SQL*Net from client
          2  SQL*Net roundtrips to/from client
          2  sorts (memory)          --> only two sorts corresponding to operations n°2 and n°4 
          0  sorts (disk)                
          6  rows processed

In effect, the MERGE JOIN parent operation gets a row from each sorted input and compares them. Typically it takes 20 rows from T1 and 100 rows from T2. It then gets the first row of each input and compare them using predicate n°4; if they join their corresponding rows are returned. If not, the MERGE JOIN will then discard the lower value and gets the next row from the lower input data set and continue the comparison process until there is no anymore rows to process. This algorithm can be simplified as follows:

Get 20  sorted rows from T1
Get 100 sorted rows from T2
LOOP until no rows to compare
  if  join value of T1 =  join value of T2
  then
      output the joined rows 
      discard  join value of T2
      get next join value of T1
      get next join value of T2      
  elsif join value of T1 < join value of T2 
      discard join value of T1 
      get next join value of T1 
  elsif join value of T1 >  join value of T2
      discard  join value of T2
      get next join value of T2
  end if;
END LOOP;

So, we can infer that the Starts = 20 of operation at line Id n° 4 represents Oracle comparing each of the 20 join column values of T1 with their equivalent ordered join column of T2 (first rows from T1 with first row from T2 and so on until there is no more rows in T1 to compare).

But let’s now change the order of the join so that table T2 will be the first data set input of the merge join operation:

select
  /*+ leading (t2, t1) use_merge(t2,t1) */
   t1.n1 t1n1
  ,t2.n1 t2n1
from t1
join t2
on t1.n1 = t2.n1;

      T1N1       T2N1
---------- ----------
         6          6
        12         12
        18         18
        24         24
        30         30
        36         36

6 rows selected.
---------------------------------------------------------------
| Id  | Operation           | Name | Starts | E-Rows | A-Rows |
---------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |      1 |        |      6 |
|   1 |  MERGE JOIN         |      |      1 |     20 |      6 |
|   2 |   SORT JOIN         |      |      1 |    100 |     14 | -- why 14 rows?
|   3 |    TABLE ACCESS FULL| T2   |      1 |    100 |    100 |
|*  4 |   SORT JOIN         |      |     14 |     20 |      6 |
|   5 |    TABLE ACCESS FULL| T1   |      1 |     20 |     20 |
---------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("T1"."N1"="T2"."N1")
       filter("T1"."N1"="T2"."N1")

We are still generating the same number of rows, 6, but this time the operation at line id n°4 is started 14 times. Why 14 and not 100 the totality of the T2 rows?

The answer to this question is : the merge join doesn’t necessarily need to scan every row from both inputs. It stops scanning as soon as:

  • it reaches the end of either input
  • or it reaches a join value from the first input that is greater than the highest join value from the second input.

When I made T2 the driving table of the merge join, Oracle declared the end of the join process as soon as it reached the 14th row of T2 (A-Rows=14). This is simply because the 14th ordered row of T2 is greater than any join value from T1 as the following proves:

-- get the minimum value of t2.n1 that is greater than the max value of t1.n1
SQL> select min(n1) from t2 where n1 > (select max(n1) from t1);

   MIN(N1)
----------
        42

This means that starting from t2.n1= 42 there will be no join possibility since 42 is greater than all join column values from the lesser input (T1). And, as such, remaining join values from T1 should be discarded according to the join algorithm(T2 being the first input of the merge join)

elsif merge value of T2 >  merge value of T1
      discard merge value of T1
      get next merge value of T1
end if;

And how many rows the merge join shoud have already processed when it reaches this merge stop point? 14 naturally:

SQL> select 
         count(1) 
     from t2 
     where 
         n1 <= (select min(n1) from t2 where n1 > (select max(n1) from t1) 
                );
 COUNT(1)
---------
       14

If we redo the same demonstration for the case where T1 will be the driving table we will obviously find that the merge join has to go through the entire number of rows in T1 table (A-Rows = 20) because there is no join value in T1 that is greater than the largest join value in T2.

SQL> select n1 from t1 where n1 > (select max(n1) from t2);
no rows selected

2. One-to-many and many-to-many merge join

In the above setup we unofficially examined a one-to-many join version of the merge join. This type of join occurs when the optimizer knows that there are no duplicate join column values in the first input data set. Although I know t1.n1 is unique I didn’t supply any such extra information like a unique index for the optimizer to opt for a one-to-many join option.This is why officially we have been using a many-to-many merge join in the preceding examples.

       2.1. MS-SQL Server
In contrast to Oracle, MS SQL server execution plan makes a clear distinction between these two types of merge join as the following shows:

create table t1(n1 int, n2 int);

insert into t1(n1,n2)
       select top 20
	     2* row_number() over (order by a.name) 
		,abs(row_number() over (order by a.name) -1) 
from sys.all_objects a cross join sys.all_objects;

create table t2(n1 int, n2 int) ;

insert into t2(n1,n2)
       select top 100
	     3* row_number() over (order by a.name) 
		,abs(row_number() over (order by a.name) -1) 
from sys.all_objects a cross join sys.all_objects;

-- many to many
select
   t1.n1 t1n1
  ,t2.n1 t2n1
from
   t1
join t2
on t1.n1 = t2.n1
option (merge join);

But if I create a unique index in T1 indicating to the optimizer the absence of duplicate rows in the join column I will obviously obtain a one-to-many merge join type as the following execution plan shows:

create unique index t1_uk on t1(n1);

-- one-to-many join
select
   t1.n1 t1n1
  ,t2.n1 t2n1
from
   t1
join t2
on t1.n1 = t2.n1
option (merge join);


In a one-to-many join, when two rows join, the optimizer outputs them, discards the join value from the second input (T2), gets the next join value from the first input (T1) and continue the merge process. The optimizer can safely discard the joined value from T2 because it knows that there will be no duplicate rows in T1 that will ever join with the T2 discarded row.

In a many-to-many join, the merge join algorithm, very probably, keeps track of the discarded T2 row somewhere in a memory structure. If the next iteration finds that the current row is duplicated it will then compare it with the saved inmemory row. If, instead, the next row from T1 reveals to be a new one, the optimizer can then safely delete the inmemory T2 saved row. This approach can be backed up by the merge join algorithm displayed above which shows that the merge process goes always forward. It never needs to step backward in the data set. In the complex many-to-many join case this “always walk down” can be ensured by looking for a previous compared join row stored in memory and probably not by stepping backward. If an extra filter is present in the query it will be replayed back to ensure that the saved joined row satisfy the filter predicate or not.

     2.2. PostgreSQL
Using PostgreSQL we can have both textual and graphical execution plan. But instead of a many-to-many or a one-to-many merge join, PostgreSQL uses a different terminology which is Inner Unique (True and False) respectively as illustrated below:
First the data model:

create table t1 (n1 int, n2 int, n3 int);
create table t2 (n1 int, n2 int, n3 int);	

with got_my_data (j)
as
 (select generate_series(1, 20)
 )
 insert into t1(n1, n2, n3)
 select 
    j*2
   ,j*5
   ,j
  from 
   got_my_data;
   
with got_my_data (j)
as
 (select generate_series(1, 100)
 )
 insert into t2(n1, n2, n3)
 select 
    j*3
   ,j*5
   ,j
  from 
   got_my_data;

Since there is no hint in PostgreSQL with which I can force a merge join operation, I will cancel the hash join possibility, run the query and get the graphical execution plan using pgAdmin4

postgres=# set enable_hashjoin=false;
SET

 explain analyze
 select
        t1.n1 t1n1
       ,t2.n1 t2n1
     from t1
        join t2
     on t1.n1 = t2.n1;


As you can see this is a many-to-many join as indicated by the Inner Unique set to false. If I create a unique index on T2, re-query and get the corresponding execution plan this is what I will observe:

postgres=# create unique index t2_uk on t2(n1);
CREATE INDEX

explain analyze verbose select
       t1.n1 t1n1
      ,t2.n1 t2n1
    from t1
       join t2
    on t1.n1 = t2.n1;

I don’t have enough experience in reading PostgreSQL execution plans but according to the actual rows generated by the second sort in the plan (rows = 14) it seems that, very probably, the query planner has used T2 table as the first input of the merge join. This is why the unique index on this table has triggered a one-to-many join while a unique index on T1 hasn’t(not show here but tested). For a one-to-many merge join to occur uniquensess is required for the join column of the first input.

2.3. Oracle
Let’s add a duplicate row in T1 and re-execute the same merge join query using Oracle database

SQL> insert into t1 values (6, -1, -1);
1 row created.

SQL> commit;

select
    /*+ use_merge(t1,t2) */
  t1.n1 t1n1
 ,t2.n1 t2n1
from t1
join t2
on t1.n1 = t2.n1;

      T1N1       T2N1
---------- ----------
         6          6
         6          6
        12         12
        18         18
        24         24
        30         30
        36         36

7 rows selected.
---------------------------------------------------------------
| Id  | Operation           | Name | Starts | E-Rows | A-Rows |
---------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |      1 |        |      7 |
|   1 |  MERGE JOIN         |      |      1 |     20 |      7 |
|   2 |   SORT JOIN         |      |      1 |     20 |     21 |
|   3 |    TABLE ACCESS FULL| T1   |      1 |     20 |     21 |
|*  4 |   SORT JOIN         |      |     21 |    100 |      7 |
|   5 |    TABLE ACCESS FULL| T2   |      1 |    100 |    100 |
---------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("T1"."N1"="T2"."N1")
       filter("T1"."N1"="T2"."N1")

As expected, the duplicated join value from T1 has been returned by the merge join. But there is no clue in the execution plan about whether this is a many-to-many or a a one-to-many join. Even if I delete the inserted duplicate row and create a unique index on t1.n1, I will still find nothing related to the type of merge join in the corresponding execution plan as shown below:

SQL> delete from t1 where n1=6 and n2 =-1;

SQL> create unique index t1_uk on t1(n1);

select
    /*+ use_merge(t1,t2) */
  t1.n1 t1n1
 ,t2.n1 t2n1
from t1
join t2
on t1.n1  = t2.n1;

      T1N1       T2N1
---------- ----------
         6          6
        12         12
        18         18
        24         24
        30         30
        36         36

6 rows selected.

----------------------------------------------------------------
| Id  | Operation           | Name  | Starts | E-Rows | A-Rows |
----------------------------------------------------------------
|   0 | SELECT STATEMENT    |       |      1 |        |      6 |
|   1 |  MERGE JOIN         |       |      1 |     20 |      6 |
|   2 |   INDEX FULL SCAN   | T1_UK |      1 |     20 |     20 |
|*  3 |   SORT JOIN         |       |     20 |    100 |      6 |
|   4 |    TABLE ACCESS FULL| T2    |      1 |    100 |    100 |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."N1"="T2"."N1")
       filter("T1"."N1"="T2"."N1")

There is no clue about the type of merge join after I have made unique the join column of the first input. However, we can observe that, thanks to the INDEX FULL SCAN operation, rows from the first input are acquired pre-sorted and don’t need the usual extra SORT JOIN operation.

Finally, we are not going to finish this merge join investigation without creating a unique index on the second data set input and see what this will change in the execution plan:

SQL> create unique index t2_uk on t2(n1);

SQL> select
        /*+ use_merge(t1,t2) */
        t1.n1 t1n1
       ,t2.n1 t2n1
    from t1
    join t2
    on t1.n1 = t2.n1;

      T1N1       T2N1
---------- ----------
         6          6
        12         12
        18         18
        24         24
        30         30
        36         36

6 rows selected.
--------------------------------------------------------------
| Id  | Operation         | Name  | Starts | E-Rows | A-Rows |
--------------------------------------------------------------
|   0 | SELECT STATEMENT  |       |      1 |        |      6 |
|   1 |  MERGE JOIN       |       |      1 |     20 |      6 |
|   2 |   INDEX FULL SCAN | T2_UK |      1 |    100 |     14 |
|*  3 |   SORT JOIN       |       |     14 |     20 |      6 |
|   4 |    INDEX FULL SCAN| T1_UK |      1 |     20 |     20 |
--------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."N1"="T2"."N1")
       filter("T1"."N1"="T2"."N1")

If you look closely to the above execution plan you will immediately spot out two important points:

  • Oracle has inverted the join order as T2 becomes the first input and T1 the second one

The Cost Based optimizer is very clever since, by switching the join order, it can declare the end of the query much earlier by stop scanning T2 as soon as it reaches the 14th row of the first input (T2) as explained above.

  • The second point is related to the extra SORT JOIN operation at line n°3.

This operation receives an already pre-sorted data set via the INDEX FULL SCAN operation at line n°4. So why Oracle needs to apply an extra SORT on an ordered data?

In fact, as already explained in section 1 above, the SORT JOIN operation at line n°3, as its name doesn’t suggest, is responsible for applying the join condition (access and filter predicate n°3) on the right input for each row produced by the left input (14 rows in the current case). This is why, regardless of how the rows are acquired, the SORT JOIN operation is always needed to be applied on the right data set input. The same doesn’t apply for the first input data set where the SORT JOIN operation can be skipped whenever this data is retrieved already sorted.

3. Summary

In this article I tried to explain how the merge join algorithm has been implemented in modern relational database systems. I demonstrated that the merge join doesn’t necessarily need to scan every row from both inputs. It stops scanning as soon as it reaches the end of either input or it reaches a join value from the first input that is greater than the highest join value from the second input. I have outlined, using MS-SQL Sever and PostgreSQL the concept of one-to-many and many-to-many join and how a unique index on the first input data set of the join can switch from a costly many-to-many to a less aggressive one-to-many form of the join.Although I have shown it here, a merge join can work with inequality join predicate and it supports outer (MERGE JOIN OUTER), semi(MERGE JOIN SEMI) and anti (MERGE JOIN ANTI) logical join operations.

March 20, 2018

DDL optimisation is not working in 12cR2: really?

Filed under: Oracle — hourim @ 7:32 pm

If you want to know what DDL optimisation is then you can read this article I wrote a couple of years ago.

As the title already suggests, I have been surprised during the preparation of a database upgrade script from 11gR2 to 12cR2 to see the following alter table taking 5 hours instead of what should have been an instantaneous operation:

SQL> select banner from v$version;
BANNER
--------------------------------------------------------------------------------
Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 - 64bit Production


SQL> alter table t1 add
  	(col1 number  default 0 null
       ,col2 number default 0 null
       ,col3 number default 0 null
       ,col4 number default 0 null
       ,col5 number default 0 null
       ,col6 number default 0 null
       ,col7 number default 0 null
      )
      /
Table altered.

Elapsed: 04:51:24.48

Oracle should have used the DDL optimisation technique which has been extended to nullable columns in 12cR1.

So what the heck is this? From where am I going to start troubleshooting this issue?

The first thing that came to my mind was to check the value of the hidden parameter, _add_col_optim_enabled, driven this feature. It might be possible that the DBA has un-set its default value during the database upgrade:

SQL> select
         n.ksppinm
        ,c.ksppstvl
        ,n.ksppdesc
       from
        sys.x$ksppi n
        ,sys.x$ksppcv c
       where n.indx=c.indx
       and n.ksppinm = '_add_col_optim_enabled';

KSPPINM                KSPPSTVL   KSPPDESC
---------------------- ---------- -----------------------------------
_add_col_optim_enabled TRUE       Allows new add column optimization

The DDL optimisation parameter is correctly set. If this parameter has been changed to FALSE the DDL optimisation feature would have, naturally, been cancelled as the following demonstrates:

create table t as select rownum n1 from dual connect by level <=10;

-- the following alter table will use the DDL optimisation
-- as the predicate of the execution plan confirms

alter table t add col1 number default 0 null;

select count(1) from t where col1=42;

select * from table(dbms_xplan.display_cursor);					   

---------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes |
---------------------------------------------------
|   0 | SELECT STATEMENT   |      |       |       |
|   1 |  SORT AGGREGATE    |      |     1 |     3 |
|*  2 |   TABLE ACCESS FULL| T    |     1 |     3 |
---------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(DECODE(TO_CHAR(SYS_OP_VECBIT("SYS_NC00002$",0)),NULL
             ,NVL("COL1",0),'0',NVL("COL1",0),'1',"COL1")=42)

-- change the hidden parameter driving the DDL optimisation
alter session set "_add_col_optim_enabled"=false;

-- and add a new equivalent column
alter table t add col2 number default 0 null;

select count(1) from t where col2=42;

select * from table(dbms_xplan.display_cursor);	

---------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes |
---------------------------------------------------
|   0 | SELECT STATEMENT   |      |       |       |
|   1 |  SORT AGGREGATE    |      |     1 |     3 |
|*  2 |   TABLE ACCESS FULL| T    |     1 |     3 |
---------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 – filter("COL2"=42) -- DDL optimisation didn't occur.

After having ruled out the hidden parameter hypothesis, I embarked on a bibliography research checking the Oracle official documentation to see whether the DDL optimisation has actually been withdrawn in 12cR2 for nullable added columns having a default value or not. It is useless to say that this effort has been pointless.

Nowadays when all else fails Twitter might be there for a quick help:

Unfortunately not everyone is lucky. No answer 🙂

There was, nevertheless, something in the overnight SQL batch log that should have given me a hint about what was happeing under the hood:

SQL> alter table t1 add (colX numeric default 0 not null);

Table altered.

Elapsed: 00:500:00.06

While adding a nullable column with default value was taking an eternity, adding a not null column with default value to the same table was instantaneous. This database is reacting exactly as if I were running under an 11gR2 version. But I’ve completely neglected this option and continued exploring other possibilities until I decided to close my laptop and leave for the week end.

On Monday of the next week I shared this issue with one of my friends who suggested me to check the compatible parameter. No sooner said than done:

SQL> show parameter compatible

NAME                                 TYPE        VALUE
------------------------------------ ----------- ----------
compatible                           string      11.2.0.4.0

Now that I have this value in front of my eyes, bringing together the pieces of the puzzle becomes easy. I provisionned a new fresh copy of Production database, upgraded it to 12.2.0.1.0, checked the compatible parameter:

SQL> show parameter compatible

NAME                                 TYPE        VALUE
------------------------------------ ----------- --------
compatible                           string      12.2.0.1

and launched again the same alter table which obviously completed in less a second:

SQL> alter table t1 add
  	(col1 number  default 0 null
       ,col2 number default 0 null
       ,col3 number default 0 null
       ,col4 number default 0 null
       ,col5 number default 0 null
       ,col6 number default 0 null
       ,col7 number default 0 null
      )
      /
Table altered.

Elapsed: 00:00:00.10

Bottom Line

It doesn’t matter what Oracle version you are running, the only features that you will be allowed to use are those enabled by the Oracle version you will set in the compatible parameter. Thus don’t be surprised to see new features not kicking in if you set the compatible mode to a prior release value where these intended features were not implemented yet.

March 17, 2018

Library cache lock

Filed under: Oracle — hourim @ 7:55 am

This is a very simple note, as much for my own documentation as anything else, showing how I have extremely rapidly identified what was causing a database application to wait on a library cache lock wait event.

The ASH of my customer case was showing the following list of predominant wait events:

select event, count(1)
from gv$active_session_history
where
    sample_time between to_date('09032018 00:46:00', 'ddmmyyyy hh24:mi:ss')
                and     to_date('09032018 10:44:00', 'ddmmyyyy hh24:mi:ss')
group by event
order by 2 desc;

EVENT                                  COUNT(1)
------------------------------------ ----------
library cache lock                       350174
library cache: mutex X                    54474
cursor: pin S wait on X                   10896
                                           2964

Naturally I wanted to know what sql_id is responsible of these library cache wait events first via ASH

select sql_id, count(1)
from gv$active_session_history
where
    sample_time between to_date('09032018 00:46:00', 'ddmmyyyy hh24:mi:ss')
                and     to_date('09032018 10:44:00', 'ddmmyyyy hh24:mi:ss')
and event in 
 ('library cache lock','library cache: mutex X','cursor: pin S wait on X')
group by sql_id
order by 2 desc;

SQL_ID            COUNT(1)
----------------- --------
6tcs65pchhp71       147902
9mqzzppbpa64j         7373
                         3	

And then via classical dbms_xplan to get the corresponding execution plan and v$sql to get the SQL text respectively:

SQL> select * from table(dbms_xplan.display_cursor('6tcs65pchhp71',null));

PLAN_TABLE_OUTPUT
----------------------------------------------------------------------------
SQL_ID: 6tcs65pchhp71 cannot be found
SQL> select sql_fulltext, executions, end_of_fetch_count
    from gv$sql
    where sql_id = '6tcs65pchhp71';

no rows selected

Let’s summarize: there is a sql_id which is responsible of a dramatic library cache wait event that I am monitoring at real time basis and which

  • has no execution plan in memory
  • and is not present in gv$sql

The above two points manifestly are symptoms of a sql query which hasn’t gone beyond the parse phase. In other words it might be a query which Oracle is not able to soft parse and thereby it has never reached the hard parse phase nor the execution step. Hopefully ASH can clearly show this:

select 
     in_parse
    ,in_hard_parse
    ,in_sql_execution
    ,count(1)
from gv$active_session_history
where
    sample_time between to_date('09032018 00:46:00', 'ddmmyyyy hh24:mi:ss')
                and     to_date('09032018 10:44:00', 'ddmmyyyy hh24:mi:ss')
and
   sql_id = '6tcs65pchhp71'
group by 
     in_parse
    ,in_hard_parse
    ,in_sql_execution
order by 4 desc;

I I I   COUNT(1)
- - - ----------
Y N N     162758
Y Y N        385

Indeed this query is almost always in the “parse” phase. So the initial question of what is causing this dramatic library cache lock turned to be : why this query is sticking at the parse phase?

I don’t know why I decided to look at the dba_hist_sqltext but it made my day:

SQL> select sql_text from dba_hist_sqltext where sql_id = '6tcs65pchhp71';

SQL_TEXT
--------------------------------------------------------------------------------
select col1, col2,...,coln from t1;

I was again lucky that this query has no where clause and no bind variable value to use so that I tried to run it as is and here what I found:

select col1, col2,...,coln 
from t1;
     *
ERROR at line 15:
ORA-00600 : internal error code, arguments: [qksvcGetGuardCol:2], [50574], [0], [],
[],[],[],[],[],[],[],[],[],

The multi-user concurrent system was repeating the same query several times and since this query was always failing during the parse phase we observed this library cache lock at the top of the wait events.

March 5, 2018

SEMI JOIN in modern relational databases

Filed under: Oracle — hourim @ 8:17 pm

A semi join returns rows only from the first table provided at least one matching row is found in the second table. The difference between a conventional join and a semi join is that rows from the first table are returned once at most even when the join column of the second table contains duplicate rows. There is no ANSI SQL way to invoke a semi join. The following syntax doesn’t exist:

  SQL>  select 
          t1.* 
        from t1 
          semi join t2 
        on t1.n1= t2.n1

In almost all modern relational databases, the semi join logical operation is triggered via the predicate IN, ANY, SOME, EXISTS and DISTINCT. MS-SQL Server has an extra particularity which makes it using a semi join during an INTERSECT operation as we will see later in this article.

The following queries illustrate these diferent semi-join cases in a 12.2.0.1 Oracle release:

SQL> create table t1
     as
        select
            rownum n1,
            mod(rownum,5) n2,
            trunc( (rownum - 1 / 3) ) n3
        from
            dual
        connect by
            level <= 1e4; SQL> create table t2
     as
        select
            rownum n1,
            mod(rownum,3) n2,
            trunc( (rownum - 1 / 5) ) n3
        from
            dual
        connect by
        level <= 5;

1. Using IN, ANY and SOME

SQL> select 
       t1.n1
     from
       t1
     where
       t1.n1 in (select t2.n1 from t2);

SQL> select 
       t1.n1
     from
       t1
     where
      t1.n1 = any (select t2.n1 from t2);

SQL> select 
       t1.n1
     from
       t1
     where
    t1.n1 = some (select t2.n1 from t2);
   
Plan hash value: 1275841967
----------------------------------------------------------------
| Id  | Operation            | Name | Starts | E-Rows | A-Rows |
----------------------------------------------------------------
|   0 | SELECT STATEMENT     |      |      1 |        |      5 |
|*  1 |  HASH JOIN RIGHT SEMI|      |      1 |      5 |      5 |
|   2 |   TABLE ACCESS FULL  | T2   |      1 |      5 |      5 |
|   3 |   TABLE ACCESS FULL  | T1   |      1 |  10000 |  10000 |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("T1"."N1"="T2"."N1")

2. Using EXISTS

select 
   t1.n1
 from
   t1
 where
   exists (select null from t2 where t2.n1 = t1.n1);

Plan hash value: 1275841967
----------------------------------------------------------------
| Id  | Operation            | Name | Starts | E-Rows | A-Rows |
----------------------------------------------------------------
|   0 | SELECT STATEMENT     |      |      1 |        |      5 |
|*  1 |  HASH JOIN RIGHT SEMI|      |      1 |      5 |      5 |
|   2 |   TABLE ACCESS FULL  | T2   |      1 |      5 |      5 |
|   3 |   TABLE ACCESS FULL  | T1   |      1 |  10000 |  10000 |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("T2"."N1"="T1"."N1")

3. Using DISTINCT in INNER join and the effect of the 12cR1 Partial join

select 
  distinct
   t1.n1
 from
   t1
 inner join t2
 on (t1.n1 = t2.n1);

Plan hash value: 3703458891
---------------------------------------------------------------
| Id  | Operation           | Name | Starts | E-Rows | A-Rows |
---------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |      1 |        |      5 |
|   1 |  HASH UNIQUE        |      |      1 |      5 |      5 |
|*  2 |   HASH JOIN SEMI    |      |      1 |      5 |      5 |
|   3 |    TABLE ACCESS FULL| T2   |      1 |      5 |      5 |
|   4 |    TABLE ACCESS FULL| T1   |      1 |  10000 |    256 | –- only 256 rows
---------------------------------------------------------------

Outline Data
-------------

  /*+
      BEGIN_OUTLINE_DATA
      IGNORE_OPTIM_EMBEDDED_HINTS
      OPTIMIZER_FEATURES_ENABLE('12.2.0.1')
      DB_VERSION('12.2.0.1')
      ALL_ROWS
      OUTLINE_LEAF(@"SEL$58A6D7F6")
      MERGE(@"SEL$1" >"SEL$2")
      OUTLINE(@"SEL$2")
      OUTLINE(@"SEL$1")
      FULL(@"SEL$58A6D7F6" "T2"@"SEL$1")
      FULL(@"SEL$58A6D7F6" "T1"@"SEL$1")
      LEADING(@"SEL$58A6D7F6" "T2"@"SEL$1" "T1"@"SEL$1")
      USE_HASH(@"SEL$58A6D7F6" "T1"@"SEL$1")
      USE_HASH_AGGREGATION(@"SEL$58A6D7F6")
      PARTIAL_JOIN(@"SEL$58A6D7F6" "T1"@"SEL$1")
      END_OUTLINE_DATA
  */

Predicate Information (identified by operation id):
---------------------------------------------------
   2 – access("T1"."N1"="T2"."N1")

The apparition of the semi join in the above execution plan is the result of the partial join transformation introduced by Oracle as from 12cR1. Earlier versions were not able to use this partial join transformation scanning, as such, the entire content of t1 table is scanned instead of tiny 256 rows as shown below:

select 
   /*+ NO_PARTIAL_JOIN(@"SEL$58A6D7F6" "T1"@"SEL$1") */
  distinct
   t1.n1
 from
   t1
 inner join t2
 on (t1.n1 = t2.n1);

Plan hash value: 975306333

---------------------------------------------------------------
| Id  | Operation           | Name | Starts | E-Rows | A-Rows |
---------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |      1 |        |      5 |
|   1 |  HASH UNIQUE        |      |      1 |      5 |      5 |
|*  2 |   HASH JOIN         |      |      1 |      5 |      5 |
|   3 |    TABLE ACCESS FULL| T2   |      1 |      5 |      5 |
|   4 |    TABLE ACCESS FULL| T1   |      1 |  10000 |  10000 | -- 10,000 rows
---------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("T1"."N1"="T2"."N1")

So far all the semi join logical operations have been executed via a HASH JOIN physical operation. This doesn’t mean that a semi joins can’t be used in a NESTED LOOPS physical operation. Here’s below the demonstratation:

SQL> create index t2_idx on t2(n1);
SQL> select 
       t1.n1
     from
       t1
     where
      t1.n1 in (select t2.n1 from t2);

----------------------------------------------------------------
| Id  | Operation          | Name   | Starts | E-Rows | A-Rows |
----------------------------------------------------------------
|   0 | SELECT STATEMENT   |        |      1 |        |      5 |
|   1 |  NESTED LOOPS SEMI |        |      1 |      5 |      5 |
|   2 |   TABLE ACCESS FULL| T1     |      1 |  10000 |  10000 |
|*  3 |   INDEX RANGE SCAN | T2_IDX |  10000 |      1 |      5 |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."N1"="T2"."N1")

4. SEMI join and uniqueness
I started this article by specifying that a semi join guaranties at most one row will be returned (per join column) from the left table even if there are duplicate rows in the join column of the second table. An example being worth a thousand words so here we go:

SQL> -- insert first a duplicate row in the rigth table
SQL> insert into t2 values(5,42,42);
SQL> commit;

-- first we will start using a conventional join
SQL> select 
   t1.n1
 from
   t1
 inner join t2
 on (t1.n1 = t2.n1);
        N1
----------
         1
         2
         3
         4
         5
         5
6 rows selected

----------------------------------------------------------------
| Id  | Operation          | Name   | Starts | E-Rows | A-Rows |
----------------------------------------------------------------
|   0 | SELECT STATEMENT   |        |      1 |        |      6 |
|   1 |  NESTED LOOPS      |        |      1 |      5 |      6 |
|   2 |   TABLE ACCESS FULL| T1     |      1 |  10000 |  10000 |
|*  3 |   INDEX RANGE SCAN | T2_IDX |  10000 |      1 |      6 |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 – access("T1"."N1"="T2"."N1")

-- and now a semi join
SQL> select 
       t1.n1
     from
       t1
     where
      t1.n1 in (select t2.n1 from t2);

        N1
----------
         1
         2
         3
         4
         5

5 rows selected
----------------------------------------------------------------
| Id  | Operation          | Name   | Starts | E-Rows | A-Rows |
----------------------------------------------------------------
|   0 | SELECT STATEMENT   |        |      1 |        |      5 |
|   1 |  NESTED LOOPS SEMI |        |      1 |      5 |      5 |
|   2 |   TABLE ACCESS FULL| T1     |      1 |  10000 |  10000 |
|*  3 |   INDEX RANGE SCAN | T2_IDX |  10000 |      1 |      5 |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."N1"="T2"."N1")

It is for this reason that the semi join has been implemented: do not duplicate rows from the outer table when they multiple join with rows from the inner table.

Since now we know the semi join main goal we can infer that, as long as, Oracle is sure that the right table has no duplicate values in the join column, it will fall back to a simple inner join. Let’s check:

SQL> drop index t2_idx;
SQL> create unique index t2_uq_idx on t2(n1);
SQL> select
       t1.n1
     from
       t1
     where
       exists (select null from t2 where t2.n1 = t1.n1);

        N1
     ------
         1
         2
         3
         4
         5
-------------------------------------------------------------------
| Id  | Operation          | Name      | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------
|   0 | SELECT STATEMENT   |           |      1 |        |      5 |
|   1 |  NESTED LOOPS      |           |      1 |      5 |      5 |
|   2 |   TABLE ACCESS FULL| T1        |      1 |  10000 |  10000 |
|*  3 |   INDEX UNIQUE SCAN| T2_UQ_IDX |  10000 |      1 |      5 |
-------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T2"."N1"="T1"."N1")

Notice that we have no semi join anymore. Thanks to the unique index on t2(n1) table, Oracle knows that there are no duplicate rows in the join column of the outer table. It can then reliably use an inner join because rows from the outer table will at most join exactly once with rows from the inner table.

There exists another way to make Oracle falling to an inner join instead of a semi join even when there are duplicate rows in the join column of the outer table. In fact whenever Oracle uses an operation that eliminates duplicate rows from the outer table (for example sort unique or group by) it can confidently use an inner join in place of a semi join. Here’s below one case demontrated:

SQL> drop index t2_uq_idx;
SQL> insert into t2 values(5,42,42);

SQL> create unique index t1_uq_idx on t1(n1);

SQL> select
       t1.n1
     from
       t1
     where
       exists (select null from t2 where t2.n1 = t1.n1);

        N1
     ------
         1
         2
         3
         4
         5
--------------------------------------------------------------------
| Id  | Operation           | Name      | Starts | E-Rows | A-Rows |
--------------------------------------------------------------------
|   0 | SELECT STATEMENT    |           |      1 |        |      5 |
|   1 |  NESTED LOOPS       |           |      1 |      5 |      5 |
|   2 |   SORT UNIQUE       |           |      1 |      5 |      5 |
|   3 |    TABLE ACCESS FULL| T2        |      1 |      5 |      6 |
|*  4 |   INDEX UNIQUE SCAN | T1_UQ_IDX |      5 |      1 |      5 | -- 5 rows
--------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("T2"."N1"="T1"."N1")

As you can see Oracle didn’t use a semi join because it has scanned all rows from the outer table, ordered them and eliminated duplicate rows via the SORT UNIQUE operation. As such when joining with the t1 table Oracle knows that it can join t2 rows at most exactly once. Notice in passing that while the semi join has full scanned the t1 table (10,000 rows) the inner join touched only 5 distinct index keys.

5. SEMI join and foreign key integrity

Suppose now that we want to select rows from a child table matching their parent rows in the parent table.

–- drop and recreate table t1 and t2
SQL> create index t1_idx on t1(n1);
SQL> alter table t1 add constraint t1_uq unique (n1);
SQL> alter table t2 add constraint t2_t1_fk foreign key (n1) references t1(n1);

The setup is now done. But let’s disable first the foreign key, the unique constraint and try a semi join query:

SQL> alter table t1 disable constraint t1_uq;
SQL> alter table t2 disable constraint t2_t1_fk;

SQL> select
        t2.n1
     from
        t2
     where
       exists (select null from t1 where t1.n1 = t2.n1);

-----------------------------------------------------------------------
|   Id  | Operation               | Name   | Starts | E-Rows | A-Rows |
-----------------------------------------------------------------------
|     0 | SELECT STATEMENT        |        |      1 |        |      5 |
|- *  1 |  HASH JOIN SEMI         |        |      1 |      5 |      5 |
|     2 |   NESTED LOOPS SEMI     |        |      1 |      5 |      5 |
|-    3 |    STATISTICS COLLECTOR |        |      1 |        |      5 |
|     4 |     TABLE ACCESS FULL   | T2     |      1 |      5 |      5 |
|  *  5 |    INDEX RANGE SCAN     | T1_IDX |      5 |  10000 |      5 |
|-    6 |   INDEX FAST FULL SCAN  | T1_IDX |      0 |  10000 |      0 |
-----------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("T1"."N1"="T2"."N1")
   5 - access("T1"."N1"="T2"."N1")

Note
-----
   - this is an adaptive plan (rows marked '-' are inactive)

As expected a classical semi join operation.

Let’s now enable the unique key of the parent table t1;

SQL> alter table t1 enable constraint t1_uq;

SQL> select
        t2.n1
     from
        t2
     where
       exists (select null from t1 where t1.n1 = t2.n1);

----------------------------------------------------------------
| Id  | Operation          | Name   | Starts | E-Rows | A-Rows |
----------------------------------------------------------------
|   0 | SELECT STATEMENT   |        |      1 |        |      5 |
|   1 |  NESTED LOOPS      |        |      1 |      5 |      5 |
|   2 |   TABLE ACCESS FULL| T2     |      1 |      5 |      5 |
|*  3 |   INDEX RANGE SCAN | T1_IDX |      5 |      1 |      5 |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."N1"="T2"."N1")

The semi join has gone because Oracle knows that there is a unique constraint on the join column from the outer table t1, it can then safely use an inner join.

Finally let’s enable the foreign key and re-execute the semi join query:

SQL> alter table t2 enable constraint t2_t1_fk;

SQL> select
        t2.n1
     from
        t2
     where
       exists (select null from t1 where t1.n1 = t2.n1);

-------------------------------------------------------------
| Id  | Operation         | Name | Starts | E-Rows | A-Rows |
-------------------------------------------------------------
|   0 | SELECT STATEMENT  |      |      1 |        |      5 |
|*  1 |  TABLE ACCESS FULL| T2   |      1 |      5 |      5 |
-------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter("T2"."N1" IS NOT NULL)

Oracle can use a trusted foreign key relationship to remove a logical semi-join and accesses only the t2 table.

5. SEMI join and INTERSECT

In SQL Server an intersect set operation can also be done via a semi-join as the following proves:

create table t1(n1 int, n2 int,n3 int) ;
create table t2(n1 int, n2 int, n3 int) ;

insert into t1(n1,n2,n3)
select top 10000
  row_number() over (order by a.name)
 ,row_number() over (order by a.name)%5
 ,(row_number() over (order by a.name) -1) /3
from sys.all_objects a cross join sys.all_objects;

insert into t2(n1,n2,n3)
select top 5
  row_number() over (order by a.name )
 ,row_number() over (order by a.name)%3
 ,(row_number() over (order by a.name)-1)/5
from sys.all_objects a cross join sys.all_objects;

select t2.n1 from t2 intersect select t1.n1 from t1;


But neither Oracle nor PostgreSQL use a semi join when evaluating an intersect set operation as the followings prove respectively:

–- Oracle 12cR2
SQL> select t2.n1 from t2 intersect select t1.n1 from t1;

---------------------------------------------------------------
| Id  | Operation           | Name | Starts | E-Rows | A-Rows |
---------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |      1 |        |      5 |
|   1 |  INTERSECTION       |      |      1 |        |      5 |
|   2 |   SORT UNIQUE       |      |      1 |      6 |      6 |
|   3 |    TABLE ACCESS FULL| T2   |      1 |      6 |      6 |
|   4 |   SORT UNIQUE       |      |      1 |  10000 |  10000 |
|   5 |    TABLE ACCESS FULL| T1   |      1 |  10000 |  10000 |
---------------------------------------------------------------
-- PostgreSQL 10.1
create table t1 as
  select 
     generate_series(1, 1e4) n1
	,generate_series(1, 1e4)%5 n2
	,(generate_series(1, 1e4)-1)/3 n3;
	
create table t2 as
  select 
     generate_series(1, 5) n1
	,generate_series(1, 5)%3 n2
	,(generate_series(1, 5)-1)/5 n3;
	
select t2.n1 from t2 intersect select t1.n1 from t1;


postgres=# explain analyze select t2.n1 from t2 intersect select t1.n1 from t1;
                                                           QUERY PLAN
------------------------------------------------------------------------------------------------------------------------
 HashSetOp Intersect  (cost=0.00..347.00 rows=200 width=36) (actual time=13.186..13.189 rows=5 loops=1)
   ->  Append  (cost=0.00..316.90 rows=12040 width=36) (actual time=0.363..9.571 rows=10005 loops=1)
         ->  Subquery Scan on "*SELECT* 1"  (cost=0.00..55.90 rows=2040 width=36) (actual time=0.362..0.369 rows=5 loops=1)
               ->  Seq Scan on t2  (cost=0.00..30.40 rows=2040 width=4) (actual time=0.050..0.053 rows=5 loops=1)
         ->  Subquery Scan on "*SELECT* 2"  (cost=0.00..261.00 rows=10000 width=9) (actual time=0.031..7.743 rows=10000 loops=1)
               ->  Seq Scan on t1  (cost=0.00..161.00 rows=10000 width=5) (actual time=0.029..4.478 rows=10000 loops=1)
 Planning time: 0.555 ms
 Execution time: 13.911 ms

6. SUMMARY

We have seen in this article how a semi join logical operation is triggered when using IN, ANY, SOME, EXISTS and DISTINCT in several modern relational databases. We’ve outlined as well that, in contrast to Oracle and PostgreSQL, SQL-Server can use a semi join during an INTERSECT set operation. We’ve demonstrated that while a regular joins can duplicate rows, semi joins do not produce any duplicates. They are defined to return (or not) rows only from one table of the join. We’ve also shown that, as long as the CBO is sure that the right table has no duplicate values in the join column, it will fall back to a simple inner join where extra transformations become possible and where the left table might not be fully scanned.

March 1, 2018

Diagnosing the past : CPU bound

Filed under: Oracle — hourim @ 6:24 pm

Few weeks ago I have been confronted to a weird real life system performance issue which, I believe is worth a sharing blog post. It started by a customer complaint about a dramatic slow in inserts of commands and products so that the application in its entirety appeared frozen. A very quick look-up over ASH shows no particular wait events or any new TOP SQL that I am not aware of. I know this application to such an extent that I can recognize its top queries via their sql_id. This is why my first impression was that there is no particular unusual wait event to point out except a high CPU consumption.

But the Load Profile of the corresponding one hour AWR report looks very odd:

Load Profile                    Per Second   Per Transaction  Per Exec  Per Call
~~~~~~~~~~~~~~~            ---------------   --------------- --------- ---------
             DB Time(s):              95.2               4.7      0.02     0.02
              DB CPU(s):               7.7               0.1      0.00     0.00
      Background CPU(s):               0.2               0.0      0.00     0.00
      Redo size (bytes):         439,216.3          18,666.4
  Logical read (blocks):       1,774,174.9          75,401,1
          Block changes:           2,465.0             104.8
 Physical read (blocks):           3,609.2             153.4
Physical write (blocks):             135.2               5.8
       Read IO requests:           2,436.8             103.6
      Write IO requests:              53.6               2.3
           Read IO (MB):              28.2               1.2
          Write IO (MB):               1.1               0.0
           IM scan rows:               0.0               0.0
Session Logical Read IM:               
             User calls:           5,423.4             230.5
           Parses (SQL):             244.4              10.4
      Hard parses (SQL):               0.5               0.0
     SQL Work Area (MB):              28.3               1.2
                 Logons:               0.7               0.0
         Executes (SQL):           4,470.6             190.0
              Rollbacks:               0.2               0.0
           Transactions:              23.5

Have you ever seen such a dramatic Load Profile? The foreground sessions spend collectively 95 seconds of time for each wall clock second. All those sessions were either actively working or waiting. They burned 7.7 of CPU per wall clock second. For a machine of 24 cores this seems to be a CPU constrained application. The overall workload is generating 1,77 million of logical blocks read per second which represent a rate of approximatively 6GB of logical blocks read per hour. And only a tiny percentage of those block reads are changed (2,465/1,774,174 = 0,13%).

I was really intrigued by this abnormal situation since I didn’t spot any unusual wait event or SQL query. While I was drilling down the different sections of the AWR report, cross-checking properly the performance key indicators they deliver, something in the TOP 10 Foreground Events captured my attention:

Top 10 Foreground Events by Total Wait Time
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
                                           Total Wait      Avg   % DB    Wait
Event                                Waits Time (sec)    Wait(ms)  time Class
------------------------------ ----------- ---------- --------- ------ --------
db file sequential read          2,813,888        32K     11.37    9.3 User I/O
DB CPU                                          27,9K              8.1
db file parallel read              278,494      10,8K     38.67    3.1 User I/O
read by other session              444,473       4785     10.77    1.4 User I/O
direct path read                   311,471     4718,6     15.15    1.4 User I/O
gc cr block 2-way                  577,081     2366,4      4.10     .7 Cluster
log file sync                       89,040     2254,7     25.32     .7 Commit
db file scattered read              138,798    2136,8     15.40     .6 User I/O
gc current block 2-way              368,907    1994,1      5.41     .6 Cluster
gc buffer busy acquire              329,900    1776,8      5.39     .5 Cluster
  • The % DB time column doesn’t sum up to 100% (or near this number) totalling only 26,4%
  • The disk is very slow servicing the physical I/O and background events with a dramatic average time wait.

When the %DB time column doesn’t add up to nearly 100% it generally indicates a symptom of a CPU bounded application. Indeed this is confirmed a little bit upward in the same AWR report via the HOST CPU section:

Host CPU
~~~~~~~~                  Load Average
 CPUs Cores Sockets     Begin       End     %User   %System  %WIO  %Idle
----- ----- ------- --------- --------- --------- --------- ------ -----
   48    24       4     55.57   219.13       92.0       2.3    2.3   5.0

The application was already over CPU bounded at the beginning (55) and completely frozen at the end of the load (219 CPU). For a machine with 24 cores, the DB CPU load is indeed very high

 DB CPU Load = DB CPU time/Elapsed Time
            = 27900/3600 = 7.7

And so is the DB time load


              Snap Id      Snap Time      Sessions Curs/Sess  Instance
            --------- ------------------- -------- --------- ---------
Begin Snap:     28812 06-Feb-18 13:00:01       365      12.5         2
  End Snap:     28813 06-Feb-18 14:00:15       421      12.5         2
   Elapsed:               60.23 (mins)
   DB Time:            5,734.18 (mins)

DB time Load = DB time/Elapsed Time
              = 5734.1/60.23 = 95.2

So what else?

I asked the storage team to check the performance of the underlying disk. The confirmation of the dramatic disk latency has been obviously confirmed by the storage team. But the fundamental and legitimate question is what has changed per regards to the yesterday normal functioning of the application that might explain this performance deterioration?

While I was trying to figure out a solution I asked a help from a DBA who is the primary owner of another critical application. To my surprise this DBA told me that one of his applications is also suffering from similar performance deterioration and that this application is hosted by the same server as the one hosting the application I am trying to troubleshoot. What a coincidence.

A couple of minutes later we found that a critical batch has been triggered in the other DBA application. This batch was extensively executing a parallel query using a very high degree of parallelism (DOP) which was consuming the whole SAN bandwidth. Needless to say that when this batch job has been stopped the initial problem I was trying to fix vanishes by itself and the database returned to its normal functioning

Bottom Line

When you are troubleshooting an application which seems to be completely frozen and where the disk is very badly servicing the database and where the DB time % column of the AWR Top 10 foreground Events section is not summing up to 100% then you are probably CPU bounded. In such case, don’t forget to consider, among the panoply of your check list, whether others applications hosted in the same server are not stressing the disk with very aggressive parallel queries.

January 27, 2018

Adaptive Cursor Sharing and parallel execution plan

Filed under: Oracle — hourim @ 7:33 am

I have shown and demonstrated at different places that there exist three pre-requisites for a cursor, using natural or forced bind variable, to be bind sensitive:

  • it uses a range (inequality) predicate
  • it uses an equality predicate and histogram
  • it uses a partition key in its predicate

I think that from now on we can add a fourth pre-requisite which is:

  • the very first execution of this cursor should not be run via a parallel execution plan.

Let’s demonstrate this point (the model should be taken from this post):

alter system flush shared_pool;
alter session set cursor_sharing= force;
alter table t_acs parallel;

select count(1) from t_acs where n2 = 1;

SQL_ID  7ck8k47bnqpnv, child number 0
-------------------------------------
------------------------------------------------
| Id  | Operation         | Name       | Rows  |
------------------------------------------------
|   0 | SELECT STATEMENT  |            |       |
|   1 |  SORT AGGREGATE   |            |     1 |
|*  2 |   INDEX RANGE SCAN| T_ACS_IDX1 |     1 |
------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("N2"=:SYS_B_1)

select
    child_number
   ,is_bind_sensitive
   ,is_bind_aware
 from 
   gv$sql
 where
    sql_id ='7ck8k47bnqpnv';

CHILD_NUMBER I I
------------ - -
           0 Y N

The very first execution of the above cursor used a serial execution plan and an equality predicate on a skewed column having Frequency histogram. This is why it has been marked bind sensitive.

But what would have happened to the cursor if its very first execution has been ran parallely?

alter system flush shared_pool;
select count(1) from t_acs where n2 = 1e6;
-----------------------------------------------------------
| Id  | Operation              | Name     | Rows  | Bytes |
-----------------------------------------------------------
|   0 | SELECT STATEMENT       |          |       |       |
|   1 |  SORT AGGREGATE        |          |     1 |     3 |
|   2 |   PX COORDINATOR       |          |       |       |
|   3 |    PX SEND QC (RANDOM) | :TQ10000 |     1 |     3 |
|   4 |     SORT AGGREGATE     |          |     1 |     3 |
|   5 |      PX BLOCK ITERATOR |          |  1099K|  3220K|
|*  6 |       TABLE ACCESS FULL| T_ACS    |  1099K|  3220K|
-----------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   6 - access(:Z>=:Z AND :Z<=:Z)
       filter("N2"=:SYS_B_1)

Note
-----
   - Degree of Parallelism is 8 because of table property

select
    child_number
   ,is_bind_sensitive
   ,is_bind_aware
 from 
   gv$sql
 where
    sql_id ='7ck8k47bnqpnv';

CHILD_NUMBER I I
------------ - -
           0 N N

Since the very first execution of the cursor uses a parallel execution plan Oracle refuses to set its bind sensitive property. And from this stage and on, until a cursor is flushed out from the library cache, all cursor executions will share the same parallel execution plan.

But what would have happened if the very first cursor execution would have used a serial plan?

alter system flush shared_pool;

select count(1) from t_acs where n2 = 100;
select count(1) from t_acs where n2 = 100;
select count(1) from t_acs where n2 = 100;
------------------------------------------------
| Id  | Operation         | Name       | Rows  |
------------------------------------------------
|   0 | SELECT STATEMENT  |            |       |
|   1 |  SORT AGGREGATE   |            |     1 |
|*  2 |   INDEX RANGE SCAN| T_ACS_IDX1 |   100 |
------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 – access("N2"=:SYS_B_1)

select
    child_number
   ,is_bind_sensitive
   ,is_bind_aware
   ,executions
 from 
   gv$sql
 where
    sql_id ='7ck8k47bnqpnv';

CHILD_NUMBER I I EXECUTIONS
------------ - - ----------
           0 Y N          3

As expected the cursor is bind sensitive. Let’s now make it bind aware :

select count(1) from t_acs where n2 = 1000;
select count(1) from t_acs where n2 = 1000;
select count(1) from t_acs where n2 = 1000;
select count(1) from t_acs where n2 = 1000;

select
    child_number
   ,is_bind_sensitive
   ,is_bind_aware
   ,executions
 from 
   gv$sql
 where
    sql_id ='7ck8k47bnqpnv';

CHILD_NUMBER I I EXECUTIONS
------------ - - ----------
           0 Y N          6
           1 Y Y          1

And here where the serious stuff starts. I will show you how Oracle will unset the bind sensitive and bind awareness property of the above cursor whenever the execution plan triggered by the ECS layer code and produced by CBO is a parallel plan:

select count(1) from t_acs where n2 = 1e6;

SQL_ID  7ck8k47bnqpnv, child number 2
-------------------------------------
-----------------------------------------------------------
| Id  | Operation              | Name     | Rows  | Bytes |
-----------------------------------------------------------
|   0 | SELECT STATEMENT       |          |       |       |
|   1 |  SORT AGGREGATE        |          |     1 |     3 |
|   2 |   PX COORDINATOR       |          |       |       |
|   3 |    PX SEND QC (RANDOM) | :TQ10000 |     1 |     3 |
|   4 |     SORT AGGREGATE     |          |     1 |     3 |
|   5 |      PX BLOCK ITERATOR |          |  1099K|  3219K|
|*  6 |       TABLE ACCESS FULL| T_ACS    |  1099K|  3219K|
-----------------------------------------------------------

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

   6 - access(:Z>=:Z AND :Z<=:Z)
       filter("N2"=:SYS_B_1)

Note
-----
   - Degree of Parallelism is 8 because of table property

select
    child_number
   ,is_bind_sensitive
   ,is_bind_aware
   ,executions
 from 
   gv$sql
 where
    sql_id ='7ck8k47bnqpnv';

CHILD_NUMBER I I EXECUTIONS
------------ - - ----------
           0 Y N          6
           1 Y Y          1
           2 N N          1

The new child number 2 is not anymore bind sensistive nor bind aware. That’s the most important conclusion of this article : Oracle doesn’t allow ACS to work with parallelism.

Summary

ACS has been implemented for very frequently used queries having different set of bind variables values each of which generating different amount of I/O. In this context, Oracle decided to cancel ACS whenever a parallel plan is triggred by ECS. Before 12cR2 there was a bug identified by Bug 16226575 in which ACS was disabled for query having decorated parallel object irrespective of the plan being chosen serial or parallel. As we saw in this article this has been fixed in 12cR2. ACS will be disabled only if its produced execution plan is parallel.

December 10, 2017

Compress enable novalidate

Filed under: Oracle — hourim @ 7:47 pm

We knew here that we can’t DDL optimise a BASIC compressed table. So I uncompressed my table in a 12.2.0.1.0 and DDL optimise it as in the following:

SQL> select 
        pct_free
	,compression
	,compress_for
    from 
	 user_tables
    where 
	   table_name = 'T1';

  PCT_FREE COMPRESS COMPRESS_FOR
---------- -------- -------------
         0 DISABLED

SQL> alter table t1 add c_ddl number default 42 not null;

Table altered.

But I changed my mind and finally decided to revert back and drop this newly added column:

SQL> alter table t1 drop column c_ddl;
alter table t1 drop column c_ddl
                           *
ERROR at line 1:
ORA-39726: unsupported add/drop column operation on compressed tables

Damned database!!!

What’s going on here? My table is not compressed. Is it?

In fact I didn’t show you how I have uncompressed the t1 table:

SQL> alter table t1 nocompress;

Table altered.

Compressing or un-compressing a table without moving it will do nothing to its data. When the COMPRESSION column of a table is DISABLED this doesn’t necessarily mean that this table doesn’t contain compressed data . And this is probably the reason why the above bizarre error pops up out from nowhere.
In fact in order to have a significant meaning, the COMPRESSION column should normally always be considered with the PCT_FREE column (notice in passing that this is why I printed above the pct_free value):

SQL> select
         table_name
        ,compression
        ,pct_free
        ,compress_for
     from 
     user_tables
    where table_name = 'T1';

TABLE_NAME COMPRESS   PCT_FREE COMPRESS_FOR
---------- -------- ---------- --------------
T1         DISABLED          0

When PCT_FREE equals zero this is generally a hint that Oracle is considering BASIC compression since it thinks that the table is not considered for updates anymore. The definition given by Oracle to the COMPRESSION column goes in the same direction as well:

COMPRESSION	 	Indicates whether table compression 
                    is enabled (ENABLED) or not (DISABLED)

This is crystal clear: COMPRESSION referes to the status of the current compression. It doesn’t tell anything about the compression history of the table if any.

Compressing/uncompressing a table without moving it is nothing else than implementing or desimplementing compression but only for newly inserted rows. This is why I think that Oracle could have avoided the confusion by using the same command as when dealing with table constraints. Instead of this:

SQL> alter table t1 nocompress;

We would have this :

SQL> alter table t1 nocompress enable novalidate;

And of course the COMPRESSION column should have had a status indicating whether it is validated or not.

In the 10046 file of a compressed table with a move of its data and without a move we can see this respectively:

alter table t1 move compress basic

call     count       cpu    elapsed       disk      query    current        rows
------- ------  -------- ---------- ---------- ---------- ----------  ----------
Parse        1      0.01       0.02          0          0          1           0
Execute      1      1.43       1.44          3       1865       2548     1000000
Fetch        0      0.00       0.00          0          0          0           0
------- ------  -------- ---------- ---------- ---------- ----------  ----------
total        2      1.45       1.46          3       1865       2549     1000000

Misses in library cache during parse: 1
Optimizer mode: ALL_ROWS
Parsing user id: 106  
Number of plan statistics captured: 1

Rows (1st) Rows (avg) Rows (max)  Row Source Operation
---------- ---------- ----------  ---------------------------------------------------
         0          0          0  LOAD AS SELECT  T1 (cr=1905 pr=1 pw=1382 time=1436345 us
   1000000    1000000    1000000   TABLE ACCESS FULL T1 (cr=1797 pr=0 pw=0 time=104378 

Elapsed times include waiting on following events:
  Event waited on                             Times   Max. Wait  Total Waited
  ----------------------------------------   Waited  ----------  ------------
  PGA memory operation                         1140        0.00          0.01
  Disk file operations I/O                        1        0.00          0.00
  db file sequential read                         3        0.00          0.00
  direct path write                               1        0.00          0.00
  reliable message                                4        0.00          0.00
  enq: RO - fast object reuse                     2        0.00          0.00
  enq: CR - block range reuse ckpt                2        0.00          0.00
  SQL*Net message to client                       1        0.00          0.00
  SQL*Net message from client                     1        5.93          5.93
********************************************************************************
alter table t1 nocompress


call     count       cpu    elapsed       disk      query    current        rows
------- ------  -------- ---------- ---------- ---------- ----------  ----------
Parse        1      0.00       0.00          0          0          0           0
Execute      1      0.00       0.00          0          0          2           0
Fetch        0      0.00       0.00          0          0          0           0
------- ------  -------- ---------- ---------- ---------- ----------  ----------
total        2      0.00       0.01          0          0          2           0

Misses in library cache during parse: 1
Optimizer mode: ALL_ROWS
Parsing user id: 106  

Elapsed times include waiting on following events:
  Event waited on                             Times   Max. Wait  Total Waited
  ----------------------------------------   Waited  ----------  ------------
  Compression analysis                            8        0.00          0.00
  SQL*Net message to client                       1        0.00          0.00
  SQL*Net message from client                     1        6.65          6.65
********************************************************************************

Spot that whileOracle managed to rework a million rows of in the first case it didn’t touch any in the second case proving that nocompress/compress without a move doesn’t concern existing data.

Since it concerns only new data then an inserted row should be compressed/non-compressed. Let’s see:

SQL> create table t1 as select
      rownum n1
     ,mod(rownum,10) n2
     from dual
     connect by level <=1e6;

Table created.

SQL> @sizeBysegNameMB
Enter value for segment_name: t1
Enter value for owner: c##mhouri


SEGMENT_TYPE  TABLESPACE_NAME  SEGMENT_NAME PARTITION_NAME          MB
------------- ---------------- ------------ --------------- ----------
TABLE         USERS            T1                                   15
                                                            ----------
Total Segment Size                                                  15

Let’s compress this table without moving its data and get its new size:

SQL> alter table t1 compress;

Table altered.

SQL> @sizeBysegNameMB
Enter value for segment_name: t1
Enter value for owner: c##mhouri


SEGMENT_TYPE  TABLESPACE_NAME  SEGMENT_NAME PARTITION_NAME          MB
------------- ---------------- ------------ --------------- ----------
TABLE         USERS            T1                                   15
                                                            ----------
Total Segment Size                                                  15

As you can see the size of the table remains intact indicating again that compressing without moving does nothing to the existing data.

Let’s now direct path load a single row, dump the corresponding block and analyse the dump to see if compression has been activated or not:

SQL> select rowid, n1,n2 from t1 where n1=1;

ROWID                      N1         N2
------------------ ---------- ----------
AAATYNAAHAAAdpTAAA          1          1

SQL> insert /*+ append */ into t1 select 1,42 from dual;

1 row created.

SQL> select rowid, n1,n2 from t1 where n1=1;
select rowid, n1,n2 from t1 where n1=1
                         *
ERROR at line 1:
ORA-12838: cannot read/modify an object after modifying it in parallel


SQL> commit;

Commit complete.

SQL> select rowid, n1,n2 from t1 where n1=1;

ROWID                      N1         N2
------------------ ---------- ----------
AAATYNAAHAAAdpTAAA          1          1
AAATYNAAHAADOgcAAA          1         42

SQL> select
  2    dbms_rowid.rowid_relative_fno('AAATYNAAHAADOgcAAA') file_no
  3  , dbms_rowid.rowid_block_number('AAATYNAAHAADOgcAAA') block_no
  4  from dual;

   FILE_NO   BLOCK_NO
---------- ----------
         7     845852


SQL> alter session set tracefile_identifier='DumpingDirectLoadRowInsert';

Session altered.

SQL> alter system dump datafile 7 block 845852;

System altered.

Unfortunately I couldn’t spot anything interesting in the dump file which indicates that the inserted row has been compressed. I will probably come back to this article in a near future.

October 28, 2017

Cursor selectivity cube -Part II

Filed under: Oracle — hourim @ 1:40 pm

In the same vein as the preceding blog post, in this second and last post pf the series I will provide three differents scripts to a bind aware cursor that owes its bind sensitiveness property from a bind variable value having a Hybrid histogram. The first one gives the selectivity cube of a popular Hybrid histogram value. The second script do the same thing for a non-popular Hybrid histogram having an endpoint number. The third and last script gives the selectivity cube of a non captured Hybrid histogram value.

1. Cursor Selectivity cube for a popular Hybrid histogram

In order to put all this in action I am going to use the model I have found in this article:

SQL> desc acs_test_tab
 Name                Null?    Type
 ------------------- -------- -------------
 ID                  NOT NULL NUMBER
 RECORD_TYPE                  NUMBER
 DESCRIPTION                  VARCHAR2(50)

SQL> alter session set cursor_sharing=force;

SQL> select
       column_name
      ,num_distinct
      ,num_buckets
      ,sample_size
      ,histogram
    from
       user_tab_col_statistics
    where table_name = 'ACS_TEST_TAB'
    and column_name  = 'RECORD_TYPE';

COLUMN_NAME  NUM_DISTINCT NUM_BUCKETS SAMPLE_SIZE HISTOGRAM
------------ ------------ ----------- ----------- ------------
RECORD_TYPE         50080         254        5407 HYBRID

As you can see the RECORD_TYPE column has a HYBRID histogram with the following popular and non-popular values distribution:

select
         endpoint_number
        ,endpoint_actual_value
        ,endpoint_repeat_count
        --,bucket_size
        ,case when Popularity > 0 then 'Pop'
                   else 'Non-Pop'
          end Popularity
    from
   (
     select
         uth.endpoint_number
        ,uth.endpoint_actual_value
        ,uth.endpoint_repeat_count
        ,ucs.sample_size/ucs.num_buckets bucket_size      
        ,(uth.endpoint_repeat_count - ucs.sample_size/ucs.num_buckets) Popularity
    from
        user_tab_histograms uth
       ,user_tab_col_statistics ucs
   where
        uth.table_name   = ucs.table_name
    and uth.column_name   = ucs.column_name
    and uth.table_name    = 'ACS_TEST_TAB'
    and uth.column_name   = 'RECORD_TYPE'
    )
   order by endpoint_number;

ENDPOINT_NUMBER ENDPOINT_A ENDPOINT_REPEAT_COUNT POPULAR
--------------- ---------- --------------------- -------
              1 1                              1 Non-Pop
           2684 2                           2683 Pop     -- we will use this
           2695 569                            1 Non-Pop -- we wiil use this
           2706 1061                           1 Non-Pop
           2717 1681                           1 Non-Pop
           2727 1927                           1 Non-Pop
../..
           5364 98501                          1 Non-Pop
           5375 98859                          1 Non-Pop
           5386 99187                          1 Non-Pop
           5396 99641                          1 Non-Pop
           5407 99999                          1 Non-Pop

254 rows selected.

There are 254 endpoint actual values of which only one value is popular(2). The following query will be used all over this article to show how we can compute the cursor selectivity cube for the three types of Hybrid histogram mentioned in the introduction:

-- as the ambassador of popular values
SELECT MAX(id) FROM acs_test_tab WHERE record_type = 2;

We will start by getting the selectivity cube of the popular value 2 as shown below (script can be found at the end of this blog post):

SQL> @CurSelCubeHybridPop

PL/SQL procedure successfully completed.

Enter value for bind: 2

BIND              LOW       HIGH
---------- ---------- ----------
2             ,446588    ,545829

Now that we have figured out what will be the value of the cursor selectivity cube of a bind aware cursor using the popular value 2, let’s see whether we have been right or wrong:

SELECT MAX(id) FROM acs_test_tab WHERE record_type = 1;-- only for ACS warmup

SELECT MAX(id) FROM acs_test_tab WHERE record_type = 2;
SELECT MAX(id) FROM acs_test_tab WHERE record_type = 2;                            

SQL_ID  9vu42gjuudpvj, child number 1
-------------------------------------
---------------------------------------------------
| Id  | Operation          | Name         | Rows  |
---------------------------------------------------
|   0 | SELECT STATEMENT   |              |       |
|   1 |  SORT AGGREGATE    |              |     1 |
|*  2 |   TABLE ACCESS FULL| ACS_TEST_TAB | 49621 |
---------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 – filter("RECORD_TYPE"=:SYS_B_0)

select
    child_number
    ,range_id
   ,low
   ,high
 from
   gv$sql_cs_selectivity
 where
    sql_id ='9vu42gjuudpvj';

CHILD_NUMBER   RANGE_ID LOW        HIGH
------------ ---------- ---------- ----------
           1          0 0.446588   0.545829

Spot how the low-high value of child cursor n°1 matches perfectly the low and high value given by the CurSelCubeHybridPop.sql script.

2. Cursor Selectivity cube for a non-popular Hybrid histogram value having an endpoint number

Let’s now consider a non-popular value having an endpoint number in the histogram table and let’s figure out what would be the selectivity cube of its underlying bind aware cursor:

SQL> @CurSelCubeHybridNonPop

PL/SQL procedure successfully completed.

PL/SQL procedure successfully completed.

Enter value for bind: 569

BIND              LOW       HIGH
---------- ---------- ----------
569           ,000166    ,000203

And now we are ready to execute the corresponding query, get its execution plan and present the low and high value of the bind aware cursor when ran against this Hybrid non-popular value:

SELECT MAX(id) FROM acs_test_tab WHERE record_type = 569;

SQL_ID  9vu42gjuudpvj, child number 2 –- new execution plan
-------------------------------------
-----------------------------------------------------------------------------------
| Id  | Operation                            | Name                       | Rows  |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |                            |       |
|   1 |  SORT AGGREGATE                      |                            |     1 |
|   2 |   TABLE ACCESS BY INDEX ROWID BATCHED| ACS_TEST_TAB               |    18 |
|*  3 |    INDEX RANGE SCAN                  | ACS_TEST_TAB_RECORD_TYPE_I |    18 |
-----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 – access("RECORD_TYPE"=:SYS_B_0)

select
    child_number
    ,range_id
   ,low
   ,high
 from
   gv$sql_cs_selectivity
 where
    sql_id ='9vu42gjuudpvj';

CHILD_NUMBER   RANGE_ID LOW        HIGH
------------ ---------- ---------- ----------
           2          0 0.000166   0.000203  –- new execution plan
           1          0 0.446588   0.545829

Spot again the precision of the forecast. The low and high value of child cursor n°2 correspond exactly to the selectivity cube of the Hybrid non-popular value anticipated by the CurSelCubeHybridNonPop.sql script.

3. Cursor Selectivity cube for a non-popular Hybrid histogram value without an endpoint number

A Hybrid histogram value without an endpoint number is a value that exists for the column but which has not been captured by the Histogram gathering program for reasons I am not going to expose here. We can get all those values via an appropriate query. 41 is one value among the not captured ones. Let’s use it in the following demonstration:

Firt we will get its expected selectivity cube:

SQL> @CurSelCubeHybridNonPopWithoutEndPoint

PL/SQL procedure successfully completed.

PL/SQL procedure successfully completed.

Enter value for bind: 41

        41        LOW       HIGH
---------- ---------- ----------
        41    ,000009    ,000011

This selectivity range is not included in the selectivity range of child cursor n°1 nor in that of child cursor n°2. This is why if we use it in the following query it will certainly force ECS to hard parse a new execution plan as the following proves:

SQL> SELECT MAX(id) FROM acs_test_tab WHERE record_type = 41;

SQL_ID  9vu42gjuudpvj, child number 3 -- new execution plan
-------------------------------------
-----------------------------------------------------------------------------------
| Id  | Operation                            | Name                       | Rows  |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |                            |       |
|   1 |  SORT AGGREGATE                      |                            |     1 |
|   2 |   TABLE ACCESS BY INDEX ROWID BATCHED| ACS_TEST_TAB               |     1 |
|*  3 |    INDEX RANGE SCAN                  | ACS_TEST_TAB_RECORD_TYPE_I |     1 |
-----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("RECORD_TYPE"=:SYS_B_0)

As it has been again correctly expected a new execution plan (child cursor n°3) has been compiled. But since the new execution plan is identical to an existing one (child cursor n°2) Oracle has merged the selectivities of these two cursors, kept shareable the last compiled one and deleted the old plan as the following proves:

select
    child_number
    ,range_id
   ,low
   ,high
 from
   gv$sql_cs_selectivity
 where
    sql_id ='9vu42gjuudpvj';

CHILD_NUMBER   RANGE_ID LOW        HIGH
------------ ---------- ---------- ----------
           3          0 0.000009   0.000203   -- new plan with merge selectivity
           2          0 0.000166   0.000203
           1          0 0.446588   0.545829

select
   child_number
  ,is_shareable
from
   gv$sql
where
   sql_id = '9vu42gjuudpvj';

CHILD_NUMBER I
------------ -
           0 N
           1 Y
           2 N –- deleted 
           3 Y – selectivity cube merged

4. Conclusion

The first blog post of this series provided a script with which we can anticipate the cursor selectivity cube range of a bind aware cursor when its bind sensitivenes is due to a predicate having a Frequency histogram. In this article we presented three new scripts giving the same anticipation for a bind aware cursor that owes its bind sensitivenes respectively to a popular Hybrid histogram, a non-popular Hybrid histogram having and endpoint number and a non captured Hybrid histogram. This concludes the series
CurSelCubeHybridNonPop

CurSelCubeHybridNonPopWithoutEndPoint

CurSelCubeHybridPop

Next Page »

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