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Annoying Bug When Trying To Create Always Free TP Autonomous Database (“Supernaturally”) March 10, 2023

Posted by Richard Foote in Autonomous Database, Autonomous Transaction Processing, Create Autonomous Database Bug, Oracle, Oracle Blog, Oracle Bugs.
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I was having all sorts of problems with one of my Always Free Oracle Transaction Processing Autonomous Databases, mostly around space and configuration issues within its parent Container Database, that I just gave up, blew it away and just tried to create a fresh one. That’s the beauty of this great free capability, it’s so easy to just throw away and create new databases in only a few moments.

Well, usually it’s easy…

I now encounter an issue trying to create a new Always Free autonomous database via a really annoying bug. I thought I’ll share the issue (and the fix) in case anyone else encounters this issue (which was in the Australia East Sydney data centre).

So I go into OCI, then into the Autonomous Database screen and select Create Autonomous Database which gets you into the following screen (if you don’t know how to get into this screen, this post is likely to be of no interest to you):

 

So you fill in the new Database details and then scroll down:

 

 

Now if you select you want to create a Transaction Processing database and scroll down:

 

We now hit the damn bug. For some reason, when you select the Transaction Processing option, the OCPU auto scaling option gets selected and there’s no way to turn the thing off (you can’t just click on the greyed tick icon).

Which is unfortunate because this option is NOT allowed if you want to create an ALWAYS FREE Autonomous Database.

If you scroll down and continue to create your always free autonomous database by clicking on the Create Autonomous Database button:

You get an error saying you can’t because you’re using an option that’s not permitted with always free databases. The OCPU auto scaling option that you can’t now turn off!!!

However, there is a way to get out of this pickle and create the always free autonomous database. Firstly you need to scroll up again:

 

 

And unselect the Show only Always Free configuration options button.

This now makes the OCPU auto scaling tick button available to be unselected.

 

So now you can turn off the damn OCPU auto scaling option that you never wanted in the first place.

 

 

Now don’t forget to go back and select the Show only Always Free configuration options button again to ensure you don’t in fact use any options or sizings that prevents the creation of an Always Free autonomous database.

Alternatively, you can just toggle the Show only Always Free configuration options button, as turning it back on again will also remove the OCPU auto scaling tick. But if you do this, make sure you’ve selected which type of autonomous database you want, as changing the selection will automatically re-enable OCPU auto scaling and you’ll have to repeat the fixing process.

 

 

You can now finally click the Create Autonomous Database at the bottom of the screen and successfully create your free always free autonomous database.

So it’s a pain, but at least I found a relatively simple workaround.

As I mentioned previously, I encounter this issue when connected to the Australia East Sydney data centre. I have no idea if this occurs in other Oracle data centres as well?

I don’t currently have a Support Identifier, so if you do or you work for Oracle Corporation, feel free to raise a Service Request for this issue… 🙂

 

UPDATE 15 March 2023: This issue has now been resolved by Oracle. A huge thank you to Yasin Baskan for so very promptly addressing this issue.

Possible Impact To Clustering Factor Now ROWIDs Are Updated When Rows Migrate Part III (“Dancing With The Big Boys”) March 9, 2023

Posted by Richard Foote in 19c, 19c New Features, Attribute Clustering, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, CBO, Changing ROWID, Clustering Factor, Data Clustering, Full Table Scans, Index Access Path, Index Internals, Index Rebuild, Index statistics, Leaf Blocks, Migrated Rows, Oracle, Oracle 21c, Oracle Blog, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle19c, ROWID.
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In my previous post, I discussed how you can best reorg a table that has a significant number of migrated rows impact the Clustering Factor of important indexes, when such tables have the ENABLED ROW MOVEMENT disabled.

In this post I’ll discuss resolving similar issues, but when ROWIDs are updated on the fly when rows are migrated in Oracle Autonomous Databases.

As I discussed previously, by updating indexes with the new ROWIDs when rows migrate, such indexes can potentially increase in size as they store both old/new index entries concurrently AND due to the increased likelihood of associated index block splits. Additionally, such indexes can also have their Clustering Factor directly impacted when migrated rows disrupt the otherwise tight clustering of specific columns.

As such, we may want to address these issues to improve the performance of impacted queries.  But it’s important we address these issues appropriately…

To illustrate all this, I’m going to re-run the same demo as my previous post, but on a table with ENABLE ROW MOVEMENT enabled.

I’ll start by creating and populating a tightly packed table with ENABLE ROW MOVEMENT enabled and with data inserted in ID column order:

SQL> create table bowie2(id number, code1 number, code2 number, code3 number, code4 number, code5 number, code6 number, code7 number, code8 number, code9 number, code10 number, code11 number, code12 number, code13 number, code14 number, code15 number, code16 number, code17 number, code18 number, code19 number, code20 number, name varchar2(142)) PCTFREE 0 ENABLE ROW MOVEMENT;

Table BOWIE2 created.

SQL> insert into bowie2 SELECT rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, 'BOWIE' FROM dual CONNECT BY LEVEL <= 200000;

200,000 rows inserted.

SQL> commit;

Commit complete.

I’ll now create an index on this well ordered/clustered ID column:

SQL> create index bowie2_id_i on bowie2(id);

Index BOWIE2_ID_I created.

Next, I’ll update the table, increasing the size of the rows such that I generate a bunch of migrated rows:

SQL> update bowie2 set name='THE RISE AND FALL OF BOWIE STARDUST AND THE SPIDERS FROM MARS';

200,000 rows updated.

SQL> commit;

Commit complete.

 

If we check the number of migrated rows:

SQL> analyze table bowie2 compute statistics;

Table BOWIE2 analyzed.

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt from user_tables where table_name='BOWIE2';

   TABLE_NAME    NUM_ROWS    BLOCKS    EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
BOWIE2             200000      4654              82          367            169            0

We notice there are indeed 0 migrated rows. This is because in Oracle Autonomous Databases, the associated ROWIDs of migrated rows as updated on the fly in this scenario.

If we check the current Clustering Factor of the index:

SQL> execute dbms_stats.delete_table_stats(ownname=>null, tabname=>'BOWIE2');

PL/SQL procedure successfully completed.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE2', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE2';

   TABLE_NAME    NUM_ROWS    BLOCKS
_____________ ___________ _________
BOWIE2             200000      4654

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE2';

    INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
______________ _________ ______________ ____________________
BOWIE2_ID_I            2            945               109061

We can see that although the data was initially inserted in ID column order, we now have a relatively poor Clustering Factor at 109061 as the migrated rows have disrupted this previously perfect clustering.

We also notice that the BLEVEL has increased from 1 to now be 2 and the number of Leaf Blocks has increased to 945 from 473 after the rows migrated (as I discussed previously).

If we now run a query that returns 4200 rows from a 200,000 row table:

SQL> select * from bowie2 where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 4200

Plan hash value: 1495904576

----------------------------------------------------------------------------------------------
| Id | Operation                  | Name   | Starts | E-Rows | A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |        |      1 |        |   4200 |00:00:00.02 |    4572 |
|* 1 |  TABLE ACCESS STORAGE FULL | BOWIE2 |      1 |   4200 |   4200 |00:00:00.02 |    4572 |
----------------------------------------------------------------------------------------------

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

   1 - storage(("ID"<=4200 AND "ID">=1))
       filter(("ID"<=4200 AND "ID">=1))

Note
-----
    - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          4 CPU used by this session
          4 CPU used when call started
          4 DB time
      37101 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
          2 buffer is not pinned count
        325 bytes received via SQL*Net from client
     461965 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
         14 calls to kcmgcs
       4572 consistent gets
       4572 consistent gets from cache
       4572 consistent gets pin
       4572 consistent gets pin (fastpath)
          2 execute count
   37453824 logical read bytes from cache
       4560 no work - consistent read gets
         72 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          1 session cursor cache count
          1 session cursor cache hits
       4572 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4560 table scan blocks gotten
     252948 table scan disk non-IMC rows gotten
     252948 table scan rows gotten
          1 table scans (short tables)
          3 user calls

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'25qktyn35b662',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
______________________________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 4200

Plan hash value: 1495904576

-------------------------------------------------------------------------------------------------------------------
| Id | Operation                  | Name   | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |        |      1 |        |       |  1264 (100)|   4200 |00:00:00.02 |    4572 |
|* 1 |  TABLE ACCESS STORAGE FULL | BOWIE2 |      1 |   4200 |   684K|    1264 (1)|   4200 |00:00:00.02 |    4572 |
-------------------------------------------------------------------------------------------------------------------

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

   1 - storage(("ID"<=4200 AND "ID">=1))
       filter(("ID"<=4200 AND "ID">=1))

 

We can see that Oracle has decided to perform a Full Table Scan (FTS) and not use the index.

The Clustering Factor of the ID column is now so bad, that returning 4200 rows via such an index is just too expensive. The FTS is now deemed the cheaper option by the CBO.

We notice that the CBO cost of the FTS is 1264.

If we run a query that forces the use of the index:

SQL> select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________
SQL_ID bzm2vhchqpq7w, child number 0
-------------------------------------
select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

-------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |   4200 |00:00:00.01 |    2665 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   4200 |00:00:00.01 |    2665 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      21 |
-------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)


Statistics
-----------------------------------------------------------
          2 CPU used by this session
          2 CPU used when call started
          2 DB time
      14531 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
       2646 buffer is not pinned count
       5755 buffer is pinned count
        348 bytes received via SQL*Net from client
     462143 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
       2665 consistent gets
          2 consistent gets examination
          2 consistent gets examination (fastpath)
       2665 consistent gets from cache
       2663 consistent gets pin
       2663 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
   21831680 logical read bytes from cache
       2663 no work - consistent read gets
         73 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          3 process last non-idle time
          2 session cursor cache count
       2665 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4200 table fetch by rowid
          3 user calls

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'bzm2vhchqpq7w',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________________________________________
SQL_ID bzm2vhchqpq7w, child number 0

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

select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |       |  2314 (100)|   4200 |00:00:00.01 |    2665 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   684K|    2314 (1)|   4200 |00:00:00.01 |    2665 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |       |      22 (0)|   4200 |00:00:00.01 |      21 |
----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=4200)

 

The cost of the Index Range Scan plan has an overall cost of 2314, greater than the 1264 cost of the FTS plan.

Notice that the cost of using just the index within the plan is currently 22.

So the vast majority of the cost of this plan (2314 – 22 = 2292) is in Oracle having to access so many different table blocks due to the poor index Clustering Factor and NOT in the increased size of the index.

As I’ve discussed numerous times, you can potentially make an index smaller by rebuilding the index (if there’s free space within the index), but the impact on the Clustering Factor will be nothing but “disappointing”…

If we just rebuild the index:

SQL> alter index bowie2_id_i rebuild online;

Index BOWIE2_ID_I altered.

And now look at the new index related statistics:

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE2';

    INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
______________ _________ ______________ ____________________
BOWIE2_ID_I            1            473               109061

We notice that the index has indeed decreased in size, back to what is was before the row migrated following the Update (Blevel=1 and Leaf Blocks=473).

But the Clustering Factor remains unchanged at 109061.

If we now re-run the query:

 

SQL> select * from bowie2 where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 4200

Plan hash value: 1495904576

----------------------------------------------------------------------------------------------
| Id | Operation                  | Name   | Starts | E-Rows | A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |        |      1 |        |   4200 |00:00:00.02 |    4572 |
|* 1 |  TABLE ACCESS STORAGE FULL | BOWIE2 |      1 |   4200 |   4200 |00:00:00.02 |    4572 |
----------------------------------------------------------------------------------------------

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

   1 - storage(("ID"<=4200 AND "ID">=1))
       filter(("ID"<=4200 AND "ID">=1))

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          3 CPU used by this session
          3 CPU used when call started
          3 DB time
      31738 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
          2 buffer is not pinned count
        325 bytes received via SQL*Net from client
     461972 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
         14 calls to kcmgcs
       4572 consistent gets
       4572 consistent gets from cache
       4572 consistent gets pin
       4572 consistent gets pin (fastpath)
          2 execute count
   37453824 logical read bytes from cache
       4560 no work - consistent read gets
         73 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          3 process last non-idle time
          2 session cursor cache count
       4572 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4560 table scan blocks gotten
     252948 table scan disk non-IMC rows gotten
     252948 table scan rows gotten
          1 table scans (short tables)
          3 user calls

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'25qktyn35b662',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
______________________________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 4200

Plan hash value: 1495904576

-------------------------------------------------------------------------------------------------------------------
| Id | Operation                  | Name   | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |        |      1 |        |       |  1264 (100)|   4200 |00:00:00.02 |    4572 |
|* 1 |  TABLE ACCESS STORAGE FULL | BOWIE2 |      1 |   4200 |   684K|    1264 (1)|   4200 |00:00:00.02 |    4572 |
-------------------------------------------------------------------------------------------------------------------

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

   1 - storage(("ID"<=4200 AND "ID">=1))
       filter(("ID"<=4200 AND "ID">=1))

 

The CBO decides to still use a FTS instead of the index.

If we look at the cost now of using the index for this query:

SQL> select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________
SQL_ID bzm2vhchqpq7w, child number 0
-------------------------------------
select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

-------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |   4200 |00:00:00.01 |    2655 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   4200 |00:00:00.01 |    2655 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      11 |
-------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)

Note
-----
- automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          2 CPU used by this session
          2 CPU used when call started
          1 DB time
      13484 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
       2646 buffer is not pinned count
       5755 buffer is pinned count
        347 bytes received via SQL*Net from client
     461972 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
       2655 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
       2655 consistent gets from cache
       2654 consistent gets pin
       2654 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
   21749760 logical read bytes from cache
       2654 no work - consistent read gets
         73 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          1 session cursor cache count
          1 session cursor cache hits
       2655 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4200 table fetch by rowid
          3 user calls

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'bzm2vhchqpq7w',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________________________________________
SQL_ID bzm2vhchqpq7w, child number 0

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

select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |       |  2303 (100)|   4200 |00:00:00.01 |    2655 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   684K|    2303 (1)|   4200 |00:00:00.01 |    2655 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |       |      11 (0)|   4200 |00:00:00.01 |      11 |
----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=4200)

 

We notice the cost of the index has only moderately gone down to 2303 (previously it was 2314).

This reduction of 11 in the CBO cost is due entirely to the fact the index is now approximately 1/2 the size as it was before the index rebuild and has thus reduced the cost of reading the index blocks to 11 within the execution plan (previously it was 22).

But the vast majority of the cost within the Index Range Scan plan comes again with accessing the table blocks, which remains unchanged due to the unchanged Clustering Factor.

To reduce the Clustering Factor, we need to change the clustering of the data with the TABLE.

So, to improve the performance of this potentially important query, we need to re-cluster the data just as we did in the example in my previous post when we had migrated rows listed and ROWIDs were not updated on the fly.

We can now add an appropriate Clustering Attribute before we perform the table reorg:

SQL> alter table bowie2 add clustering by linear order (id);

Table BOWIE2 altered.

SQL> alter table bowie2 move online;

Table BOWIE2 altered.

If we now look at the Clustering Factor of this important index:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE2', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE2';

   TABLE_NAME    NUM_ROWS    BLOCKS
_____________ ___________ _________
BOWIE2             200000      4936

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE2';

    INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
______________ _________ ______________ ____________________
BOWIE2_ID_I            1            473                 4850

The Clustering Factor has been reduced down to the almost perfect 4850, down from the previous 109061.

If we now re-run the query:

SQL> select * from bowie2 where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

-------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |   4200 |00:00:00.01 |     102 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   4200 |00:00:00.01 |     102 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      11 |
-------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)

Statistics
-----------------------------------------------------------
          1 CPU used by this session
          1 CPU used when call started
         90 Cached Commit SCN referenced
      11345 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
         93 buffer is not pinned count
       8308 buffer is pinned count
        325 bytes received via SQL*Net from client
     462117 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
        102 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
        102 consistent gets from cache
        101 consistent gets pin
        101 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
     835584 logical read bytes from cache
        101 no work - consistent read gets
         72 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          2 process last non-idle time
          1 session cursor cache count
          1 session cursor cache hits
        102 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4200 table fetch by rowid
          3 user calls

 

We can see the query now automatically uses the index and only requires just 102 consistent gets, down from 4572 when it performed the FTS.

If we look at the cost of this new plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'25qktyn35b662',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0

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

select * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |       |   113 (100)|   4200 |00:00:00.01 |     102 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   684K|     113 (0)|   4200 |00:00:00.01 |     102 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |       |      11 (0)|   4200 |00:00:00.01 |      11 |
----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=4200)

 

We can see the plan has a cost of just 113, which is both much more accurate and close to the 102 consistent gets and much less than the previous cost of 1340 for the FTS plan.

So in specific examples where migrated rows significantly impact the Clustering Factor of indexes important to our applications, including when ROWIDs are updated on the fly in Oracle Autonomous Databases, we may need to appropriately reorg such tables to repair the Clustering Factor of impacted indexes.

I’ve mentioned a number of times in this series how tables in Oracle Autonomous Databases with ENABLE ROW MOVEMENT have their ROWIDs updated on the fly when a row migrates. In my next post, I’ll discuss how even tables that don’t have the ENABLE ROW MOVEMENT clause set can still have their ROWIDs updated on the fly when a row migrates…

Possible Impact To Clustering Factor Now ROWIDs Are Updated When Rows Migrate Part II (“Dancing Out In Space”) March 7, 2023

Posted by Richard Foote in 19c, 19c New Features, Attribute Clustering, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, CBO, Changing ROWID, Clustering Factor, Data Clustering, David Bowie, Full Table Scans, Index Access Path, Index Internals, Index Rebuild, Index statistics, Leaf Blocks, Migrated Rows, Oracle, Oracle 21c, Oracle Blog, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle Statistics, Oracle19c, Performance Tuning, Richard's Musings, ROWID.
1 comment so far

In my previous post, I discussed how the clustering of data can be impacted if rows migrate and how this in turn can have a detrimental impact on the efficiency of associated indexes.

In this post, I’ll discuss what you can do (and not do) to remedy things in the relatively unlikely event that you hit this issue with migrated rows.

I’ll just discuss initially the example where the table is defined without ENABLE ROW MOVEMENT enabled in the Transaction Processing Autonomous Database (and so does NOT update ROWIDs on the fly when a row migrates).

I’ll start by again creating and populating a tightly packed table, with the data inserted in ID column order:

SQL> create table bowie(id number, code1 number, code2 number, code3 number, code4 number, code5 number, code6 number, code7 number, code8 number, code9 number, code10 number, code11 number, code12 number, code13 number, code14 number, code15 number, code16 number, code17 number, code18 number, code19 number, code20 number, name varchar2(142)) PCTFREE 0;

Table BOWIE created.

SQL> insert into bowie SELECT rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, 'BOWIE' FROM dual CONNECT BY LEVEL <= 200000;

200,000 rows inserted.

SQL> commit;

Commit complete.

I’ll now create an index on this well ordered/clustered ID column:

SQL> create index bowie_id_i on bowie(id);

Index BOWIE_ID_I created.

Next, I’ll update the table, increasing the size of the rows such that I generate a bunch of migrated rows:

SQL> update bowie set name='THE RISE AND FALL OF BOWIE STARDUST AND THE SPIDERS FROM MARS';

200,000 rows updated.

SQL> commit;

Commit complete.

 

If we check the number of migrated rows:

SQL> analyze table bowie compute statistics;

Table BOWIE analyzed.

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt from user_tables

where table_name='BOWIE';

   TABLE_NAME    NUM_ROWS    BLOCKS    EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
BOWIE              200000      4906              86          414            170        56186

 

We notice there are indeed 56186 migrated rows.

If we check the current Clustering Factor of the index:

SQL> execute dbms_stats.delete_table_stats(ownname=>null, tabname=>'BOWIE');

PL/SQL procedure successfully completed.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE';

   TABLE_NAME    NUM_ROWS    BLOCKS
_____________ ___________ _________
BOWIE              200000      4906

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE';

   INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
_____________ _________ ______________ ____________________
BOWIE_ID_I            1            473                 3250

 

We notice the index still has an excellent Clustering Factor of just 3250. As the ROWIDs are NOT updated in this example when rows migrate, the index retains the same Clustering Factor as before the Update statement.

If we run the following query that returns 4200 rows (as per my previous post):

SQL> select * from bowie where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0
-------------------------------------
select * from bowie where id between 1 and 4200

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |   4200 |00:00:00.01 |    2771 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   4200 |00:00:00.01 |    2771 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      11 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)


Statistics
-----------------------------------------------------------
          2 CPU used by this session
          2 CPU used when call started
          3 DB time
      24901 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
       2762 buffer is not pinned count
       7005 buffer is pinned count
        324 bytes received via SQL*Net from client
     461909 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
       2771 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
       2771 consistent gets from cache
       2770 consistent gets pin
       2770 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
   22700032 logical read bytes from cache
       2770 no work - consistent read gets
         73 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          1 session cursor cache count
          1 session cursor cache hits
       2771 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4200 table fetch by rowid
       1366 table fetch continued row
          3 user calls

We can see the query currently uses 2771 consistent gets, which is significantly higher than it could be, as Oracle has to visit the original table block and then follow the pointer to the new location for any migrated row that needs to be retrieved.

However, if we look at the cost of the current plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'c376kdhy5b0x9',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
____________________________________________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0

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

select * from bowie where id between 1 and 4200

Plan hash value: 1405654398

---------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
---------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |       |    80 (100)|   4200 |00:00:00.01 |    2771 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   684K|      80 (0)|   4200 |00:00:00.01 |    2771 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |       |      11 (0)|   4200 |00:00:00.01 |      11 |
---------------------------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)

 

We can see it only has a cost of 80, as Oracle does not consider the additional accesses required now for these migrated rows. With such a perfect Clustering Factor, this cost is not particularly accurate and does not represent the true cost of the 2771 consistent gets now required.

Now there are various ways we can look at fixing this issue with all these migrated rows requiring additional consistent gets to access.

One method is to capture all the ROWIDs of the migrated rows, copy these rows to a temporary holding table, delete these rows and then re-insert them all back into the table from the temporary table.

We can identify the migrated rows by creating the CHAIN_ROWS table as per the Oracle supplied UTLCHAIN.SQL script and then use the ANALYZE command to store their ROWIDs in this CHAIN_ROWS table:

SQL> create table CHAINED_ROWS (
2 owner_name varchar2(128),
3 table_name varchar2(128),
4 cluster_name varchar2(128),
5 partition_name varchar2(128),
6 subpartition_name varchar2(128),
7 head_rowid rowid,
8 analyze_timestamp date
9* );

Table CHAINED_ROWS created.

SQL> analyze table bowie list chained rows;

Table BOWIE analyzed.

SQL> select table_name, head_rowid from chained_rows where table_name='BOWIE' and rownum<=10;

   TABLE_NAME            HEAD_ROWID
_____________ _____________________
BOWIE         AAAqFjAAAAAE6CzAAP
BOWIE         AAAqFjAAAAAE6CzAAR
BOWIE         AAAqFjAAAAAE6CzAAU
BOWIE         AAAqFjAAAAAE6CzAAW
BOWIE         AAAqFjAAAAAE6CzAAZ
BOWIE         AAAqFjAAAAAE6CzAAb
BOWIE         AAAqFjAAAAAE6CzAAe
BOWIE         AAAqFjAAAAAE6CzAAg
BOWIE         AAAqFjAAAAAE6CzAAj
BOWIE         AAAqFjAAAAAE6CzAAl

 

Another method we can now utilise is to simply MOVE ONLINE the table:

SQL> alter table bowie move online;

Table BOWIE altered.

 

If we now look at the number of migrated rows after the table reorg:

SQL> analyze table bowie compute statistics;

Table BOWIE analyzed.

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt from user_tables where table_name='BOWIE';

   TABLE_NAME    NUM_ROWS    BLOCKS    EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
BOWIE              200000      4936              56          838            169            0

 

We can see we no longer have any migrated rows.

BUT, if we now look at the Clustering Factor of this index:

SQL> execute dbms_stats.delete_table_stats(ownname=>null, tabname=>'BOWIE');

PL/SQL procedure successfully completed.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE';

   TABLE_NAME    NUM_ROWS    BLOCKS
_____________ ___________ _________
BOWIE              200000      4936

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE';

   INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
_____________ _________ ______________ ____________________
BOWIE_ID_I            1            473               114560

 

We can see it has now significantly increased to 114560 (previously it was just 3250).

The problem of course is that if the ROWIDs now represent the correct new physical location of the migrated rows, the previously perfect clustering/ordering of the ID column has been impacted.

If we now re-run the query returning the 4200 rows:

SQL> select * from bowie where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0
-------------------------------------
select * from bowie where id between 1 and 4200

Plan hash value: 1845943507

---------------------------------------------------------------------------------------------
| Id | Operation                  | Name  | Starts | E-Rows | A-Rows | A-Time     | Buffers |
---------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |       |      1 |        |   4200 |00:00:00.02 |    4857 |
|* 1 |  TABLE ACCESS STORAGE FULL | BOWIE |      1 |   4200 |   4200 |00:00:00.02 |    4857 |
---------------------------------------------------------------------------------------------

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

   1 - storage(("ID"<=4200 AND "ID">=1))
       filter(("ID"<=4200 AND "ID">=1))

Statistics
-----------------------------------------------------------
          3 CPU used by this session
          3 CPU used when call started
       4849 Cached Commit SCN referenced
          2 DB time
      25870 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
          2 buffer is not pinned count
        324 bytes received via SQL*Net from client
     461962 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          9 calls to kcmgcs
       4857 consistent gets
       4857 consistent gets from cache
       4857 consistent gets pin
       4857 consistent gets pin (fastpath)
          2 execute count
   39788544 logical read bytes from cache
       4850 no work - consistent read gets
         72 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          2 process last non-idle time
          1 session cursor cache count
       4857 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4850 table scan blocks gotten
     200000 table scan disk non-IMC rows gotten
     200000 table scan rows gotten
          1 table scans (short tables)
          3 user calls

 

Oracle is now performing a Full Table Scan (FTS). The number of consistent gets now at 4857 is actually worse than when we had the migrated rows (previously at 2771)

The Clustering Factor of the ID column is now so bad, that returning 4200 rows via such an index is just too expensive. The FTS is now deemed the cheaper option by the CBO.

If we look at the CBO cost of using this FTS plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'c376kdhy5b0x9',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0
-------------------------------------
select * from bowie where id between 1 and 4200

Plan hash value: 1845943507

------------------------------------------------------------------------------------------------------------------
| Id | Operation                  | Name  | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |       |      1 |        |       |  1340 (100)|   4200 |00:00:00.02 |    4857 |
|* 1 |  TABLE ACCESS STORAGE FULL | BOWIE |      1 |   4200 |   684K|    1340 (1)|   4200 |00:00:00.02 |    4857 |
------------------------------------------------------------------------------------------------------------------

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

   1 - storage(("ID"<=4200 AND "ID">=1))
       filter(("ID"<=4200 AND "ID">=1))

 

We can see the cost of this plan is 1340.

If we compare this with the cost of using the (now deemed) inefficient index:

SQL> select /*+ index (bowie) */ * from bowie where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID 9215hkzd3v1up, child number 0
-------------------------------------
select /*+ index (bowie) */ * from bowie where id between 1 and 4200

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |   4200 |00:00:00.01 |    2784 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   4200 |00:00:00.01 |    2784 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      11 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)


Statistics
-----------------------------------------------------------
          2 CPU used by this session
          2 CPU used when call started
       2741 Cached Commit SCN referenced
          2 DB time
      12633 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
       2775 buffer is not pinned count
       5626 buffer is pinned count
        345 bytes received via SQL*Net from client
     462170 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
       2784 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
       2784 consistent gets from cache
       2783 consistent gets pin
       2783 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
   22806528 logical read bytes from cache
       2783 no work - consistent read gets
         72 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          4 process last non-idle time
          1 session cursor cache count
          1 session cursor cache hits
       2784 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4200 table fetch by rowid
          3 user calls

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'9215hkzd3v1up',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
____________________________________________________________________________________________________________________________________
SQL_ID 9215hkzd3v1up, child number 0

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

select /*+ index (bowie) */ * from bowie where id between 1 and 4200

Plan hash value: 1405654398

---------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
---------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |       |  2418 (100)|   4200 |00:00:00.01 |    2784 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   684K|    2418 (1)|   4200 |00:00:00.01 |    2784 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |       |      11 (0)|   4200 |00:00:00.01 |      11 |
---------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=4200)

 

We can see the CBO cost of the index is now 2418, more than the 1340 cost of using the FTS.

So in the scenario where by migrating a significant number of rows, we impact the Clustering Factor and so the efficiency of vital indexes in our applications, we need to eliminate the migrated rows in a more thoughtful manner.

An option we have available is to first add an appropriate Clustering Attribute before we perform the table reorg:

SQL> alter table bowie add clustering by linear order (id);

Table BOWIE altered.

SQL> alter table bowie move online;

Table BOWIE altered.

 

If we now look at the Clustering Factor of this important index:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE';

   TABLE_NAME    NUM_ROWS    BLOCKS
_____________ ___________ _________
BOWIE              200000      4936

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE';

   INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
_____________ _________ ______________ ____________________
BOWIE_ID_I            1            473                 4850

 

The Clustering Factor has been reduced down to the almost perfect 4850, down from the previous 114560.

If we now re-run the query:

SQL> select * from bowie where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0
-------------------------------------
select * from bowie where id between 1 and 4200

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |   4200 |00:00:00.01 |     102 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   4200 |00:00:00.01 |     102 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      11 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)


Statistics
-----------------------------------------------------------
          1 CPU used by this session
          1 CPU used when call started
         89 Cached Commit SCN referenced
          1 DB time
      11249 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
         93 buffer is not pinned count
       8308 buffer is pinned count
        324 bytes received via SQL*Net from client
     462165 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
        102 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
        102 consistent gets from cache
        101 consistent gets pin
        101 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
     835584 logical read bytes from cache
        101 no work - consistent read gets
         72 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          1 session cursor cache count
          1 session cursor cache hits
        102 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4200 table fetch by rowid
          3 user calls

We can see the query now automatically uses the index and only requires just 102 consistent gets (down from 4857 when it performed the FTS and down from 2771 when we had the migrated rows).

If we look at the cost of this new plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'c376kdhy5b0x9',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
____________________________________________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0

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

select * from bowie where id between 1 and 4200

Plan hash value: 1405654398

---------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
---------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |       |   113 (100)|   4200 |00:00:00.01 |     102 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   684K|     113 (0)|   4200 |00:00:00.01 |     102 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |       |      11 (0)|   4200 |00:00:00.01 |      11 |
---------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=4200)

 

We can see the plan has a cost of just 113, which is both much more accurate and close to the 102 consistent gets and much less than the previous cost of 1340 for the FTS plan.

So in specific scenarios where by having migrated rows we significantly impact the Clustering Factor of indexes important to our applications, we have to be a little cleverer in how we address the migrated rows.

This can also the case in the new scenario where Oracle automatically updates the ROWIDs of migrated rows, as I’ll discuss in my next post…

Possible Impact To Clustering Factor Now ROWIDs Are Updated When Rows Migrate Part I (“Growin’ Up”) March 1, 2023

Posted by Richard Foote in 19c, 19c New Features, Attribute Clustering, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, BLEVEL, CBO, Changing ROWID, Clustering Factor, Data Clustering, Hints, Index Access Path, Index Block Splits, Index Delete Operations, Index Height, Index Internals, Index Rebuild, Index statistics, Leaf Blocks, Migrated Rows, Oracle, Oracle Blog, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle Indexing Internals Webinar, Oracle Statistics, Oracle19c, Performance Tuning, Richard Foote Training, Richard's Blog, ROWID.
2 comments

In my previous post I discussed how an index can potentially be somewhat inflated in size after ROWIDs are updated on the fly after a substantial number of rows are migrated.

However, there’s another key “factor” of an index that in some scenarios can be impacted by this new ROWID behaviour with regard migrated rows.

To highlight this scenario, I’ll again start by creating and populating a table with ENABLE ROW MOVEMENT disabled:

SQL> create table bowie(id number, code1 number, code2 number, code3 number, code4 number, code5 number, code6 number, code7 number, code8 number, code9 number, code10 number, code11 number, code12 number, code13 number, code14 number, code15 number, code16 number, code17 number, code18 number, code19 number, code20 number, name varchar2(142)) PCTFREE 0;

Table BOWIE created.

SQL> insert into bowie SELECT rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, 'BOWIE' FROM dual CONNECT BY LEVEL <= 200000;

200,000 rows inserted.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

I’ll next create an index on the ID column. The important aspect with the ID column is that the data is entered monotonically in ID column order, so the associated index will have an excellent (very low) Clustering Factor:

SQL> create index bowie_id_i on bowie(id);

Index BOWIE_ID_I created.

If we look at some key statistics of the table and index:

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE';

   TABLE_NAME    NUM_ROWS    BLOCKS
_____________ ___________ _________
BOWIE              200000      3268

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE';

   INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
_____________ _________ ______________ ____________________
BOWIE_ID_I            1            473                 3250

We can see that the number of table blocks is 3268, the number of index leaf blocks is 473 and we indeed have a near perfect Clustering Factor of 3250.

If we run a couple of queries:

SQL> select * from bowie where id between 1 and 1000;

1,000 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID gz5u92hmjwz1h, child number 0
-------------------------------------
select * from bowie where id between 1 and 1000

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |   1000 |00:00:00.01 |      18 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   1000 |   1000 |00:00:00.01 |      18 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   1000 |   1000 |00:00:00.01 |       4 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=1000)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          1 CPU used by this session
          1 CPU used when call started
       7353 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
         16 buffer is not pinned count
       1985 buffer is pinned count
        324 bytes received via SQL*Net from client
     171305 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
         18 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
         18 consistent gets from cache
         17 consistent gets pin
         17 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
     147456 logical read bytes from cache
         17 no work - consistent read gets
         38 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          2 session cursor cache count
         18 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       1000 table fetch by rowid
          3 user calls

We can see for this first query that returns 1000 rows, it requires just 18 consistent gets, thanks primarily due to the efficient index with the perfect Clustering Factor.

If we look at the cost of this plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'gz5u92hmjwz1h',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
____________________________________________________________________________________________________________________________________
SQL_ID gz5u92hmjwz1h, child number 0

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

select * from bowie where id between 1 and 1000

Plan hash value: 1405654398

---------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
---------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |       |    21 (100)|   1000 |00:00:00.01 |      18 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   1000 |   108K|      21 (0)|   1000 |00:00:00.01 |      18 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   1000 |       |       4 (0)|   1000 |00:00:00.01 |       4 |
---------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=1000)

We can see the plan has an accurate cost of just 21.

If we now run a similar query that returns a few more rows:

SQL> select * from bowie where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0
-------------------------------------
select * from bowie where id between 1 and 4200

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |   4200 |00:00:00.01 |      68 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   4200 |00:00:00.01 |      68 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      11 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          1 CPU used by this session
          1 CPU used when call started
          1 DB time
      11353 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
         59 buffer is not pinned count
       8342 buffer is pinned count
        324 bytes received via SQL*Net from client
     461834 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
         68 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
         68 consistent gets from cache
         67 consistent gets pin
         67 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
     557056 logical read bytes from cache
         67 no work - consistent read gets
         73 non-idle wait count
         2 opened cursors cumulative
         1 opened cursors current
         2 parse count (total)
         1 process last non-idle time
         2 session cursor cache count
        68 session logical reads
         1 sorts (memory)
      2024 sorts (rows)
      4200 table fetch by rowid
         3 user calls

We can see that it only required just 68 consistent gets to return 4200 rows, thanks to the excellent data clustering and associated very low Clustering Factor.

If we look at the cost of this plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'c376kdhy5b0x9',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
____________________________________________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0

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

select * from bowie where id between 1 and 4200

Plan hash value: 1405654398

---------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
---------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |       |    80 (100)|   4200 |00:00:00.01 |      68 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   455K|      80 (0)|   4200 |00:00:00.01 |      68 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |       |      11 (0)|   4200 |00:00:00.01 |      11 |
---------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=4200)

 

We can see the cost of the plan is currently a relatively accurate 80.

OK, let’s now perform an update on this table that generates a bunch of migrated rows:

SQL> update bowie set name='THE RISE AND FALL OF BOWIE STARDUST AND THE SPIDERS FROM MARS';

200,000 rows updated.

SQL> commit;

Commit complete.

If we now look at the table and index statistics:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE';

   TABLE_NAME    NUM_ROWS    BLOCKS
_____________ ___________ _________
BOWIE              200000      4906

We can see that the table blocks value has increased to 4906 (previously 3268). This as explained previously is to due in large part to the increased NAME column values and also due to the pointers in the original table blocks that point to the new locations of the migrated rows.

This relates to approximately a 50% increase in table blocks.

If we look at the current index statistics:

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE';

   INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
_____________ _________ ______________ ____________________
BOWIE_ID_I            1            473                 3250

We can see that these values are all unchanged, as the ROWIDs in indexes remain unchanged when a row migrates, when ENABLE ROW MOVEMENT is not set.

Therefore, when we re-run these same queries:

SQL> select * from bowie where id between 1 and 1000;

1,000 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID gz5u92hmjwz1h, child number 0
-------------------------------------
select * from bowie where id between 1 and 1000

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |   1000 |00:00:00.01 |     666 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   1000 |   1000 |00:00:00.01 |     666 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   1000 |   1000 |00:00:00.01 |       4 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=1000)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          1 DB time
       7967 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
        664 buffer is not pinned count
       1664 buffer is pinned count
        324 bytes received via SQL*Net from client
     171419 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
        666 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
        666 consistent gets from cache
        665 consistent gets pin
        665 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
    5455872 logical read bytes from cache
        665 no work - consistent read gets
         37 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          2 session cursor cache count
        666 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       1000 table fetch by rowid
        327 table fetch continued row
          3 user calls

The number of consistent gets has increased significantly to 666 (previously it was just 18).

Now we can attributed an increase of approximately 50% of the previous consistent gets (18 x 0.50 = 9) due to the 50% increase in table blocks required now to store the rows due to the increased row size.

We can also attribute an additional 327 consistent gets for the table fetch continued row value listed in the statistics, representing the extra consistent gets required to access the migrated rows from their new physical location.

But 18 + 9 + 327 = 354 still leaves us short of the new 666 consistent gets value.

The problem with having to visit another table block to get a row from its new location is that it means Oracle has to re-access again the original table block to get the next row (rather than reading multiple rows with the same consistent get).

So it’s actually approximately 2 x table fetch continued row, by which the number of consistent gets is going to increase when accessing migrated rows (noting that the last migrated row in a block will only incur a additional consistent get as the next table block accessed will differ regardless).

If we look at the new CBO cost for this plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'gz5u92hmjwz1h',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
____________________________________________________________________________________________________________________________________
SQL_ID gz5u92hmjwz1h, child number 0

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

select * from bowie where id between 1 and 1000

Plan hash value: 1405654398

---------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
---------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |       |    21 (100)|   1000 |00:00:00.01 |     666 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   1000 |   163K|      21 (0)|   1000 |00:00:00.01 |     666 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   1000 |       |       4 (0)|   1000 |00:00:00.01 |       4 |
---------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=1000)

 

We notice the CBO cost for this plan remains unchanged at 21.

This is totally to be expected, as the index statistics by which the cost of an index scan is calculated are unchanged.

Considering the rough “rule of thumb” is that the CBO cost of an index scan should be in the ball-park of the number of possible IOs, the fact the plan now uses 666 consistent gets highlights this cost of just 21 is no longer as accurate…

If we look at the second SQL that returns 4200 rows:

SQL> select * from bowie where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID c376kdhy5b0x9, child number 0
-------------------------------------
select * from bowie where id between 1 and 4200

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |   4200 |00:00:00.01 |    2771 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   4200 |00:00:00.01 |    2771 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      11 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          2 CPU used by this session
          2 CPU used when call started
          2 DB time
      14103 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
       2762 buffer is not pinned count
       7005 buffer is pinned count
        324 bytes received via SQL*Net from client
      461947 bytes sent via SQL*Net to client
           2 calls to get snapshot scn: kcmgss
           2 calls to kcmgcs
        2771 consistent gets
           1 consistent gets examination
           1 consistent gets examination (fastpath)
        2771 consistent gets from cache
        2770 consistent gets pin
        2770 consistent gets pin (fastpath)
           2 execute count
           1 index range scans
    22700032 logical read bytes from cache
        2770 no work - consistent read gets
          72 non-idle wait count
           2 opened cursors cumulative
           1 opened cursors current
           2 parse count (total)
           1 process last non-idle time
           2 session cursor cache count
        2771 session logical reads
           1 sorts (memory)
        2024 sorts (rows)
        4200 table fetch by rowid
        1366 table fetch continued row
           3 user calls

We again notice consistent gets has increased significantly to 2771 (previously it was just 68). Again, these additional consistent gets can not be attributed to the extra size of the table and the additional approximate 2 x 1366 table fetch continued row gets.

If we now look at the cost of this plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'c376kdhy5b0x9',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________________
____________

SQL_ID c376kdhy5b0x9, child number 0

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

select * from bowie where id between 1 and 4200

Plan hash value: 1405654398

---------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name       | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
---------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |            |      1 |        |       |    80 (100)|   4200 |00:00:00.01 |    2771 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE      |      1 |   4200 |   684K|      80 (0)|   4200 |00:00:00.01 |    2771 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE_ID_I |      1 |   4200 |       |      11 (0)|   4200 |00:00:00.01 |      11 |
---------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=4200)

 

We again notice the CBO cost for this plan remains unchanged at 80, again totally expected as the underlying index statistics have remain unchanged after the update statement.

But again, not necessary as accurate a cost as it was previously…

 

If we repeat this demo, but this time on a table with ENABLE ROW MOVEMENT enabled:

SQL> create table bowie2(id number, code1 number, code2 number, code3 number, code4 number, code5 number, code6 number, code7 number, code8 number, code9 number, code10 number, code11 number, code12 number, code13 number, code14 number, code15 number, code16 number, code17 number, code18 number, code19 number, code20 number, name varchar2(142)) PCTFREE 0 ENABLE ROW MOVEMENT;

Table BOWIE2 created.

SQL> insert into bowie2 SELECT rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, 'BOWIE' FROM dual CONNECT BY LEVEL <= 200000;

200,000 rows inserted.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE2', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

SQL> create index bowie2_id_i on bowie2(id);

Index BOWIE2_ID_I created.

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE2';

   TABLE_NAME    NUM_ROWS    BLOCKS
_____________ ___________ _________
BOWIE2             200000      3268

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE2';

        INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
__________________ _________ ______________ ____________________
BOWIE2_ID_I                1            473                 3250

 

The table and index statistics are currently identical to the previous demo.

If we run the same two equivalent queries:

 

SQL> select * from bowie2 where id between 1 and 1000;

1,000 rows selected.

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________
SQL_ID gtkw2704bxj7q, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 1000

Plan hash value: 3243780227

-------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |   1000 |00:00:00.01 |      18 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   1000 |   1000 |00:00:00.01 |      18 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   1000 |   1000 |00:00:00.01 |       4 |
-------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=1000)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          1 CPU used by this session
          1 CPU used when call started
       7909 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
         16 buffer is not pinned count
       1985 buffer is pinned count
        325 bytes received via SQL*Net from client
     171306 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
         18 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
         18 consistent gets from cache
         17 consistent gets pin
         17 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
     147456 logical read bytes from cache
         17 no work - consistent read gets
         37 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          2 session cursor cache count
         18 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       1000 table fetch by rowid
     3 user calls

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'gtkw2704bxj7q',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________________________________________
SQL_ID gtkw2704bxj7q, child number 0

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

select * from bowie2 where id between 1 and 1000

Plan hash value: 3243780227

----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |       |    21 (100)|   1000 |00:00:00.01 |      18 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   1000 |   108K|      21 (0)|   1000 |00:00:00.01 |      18 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   1000 |       |       4 (0)|   1000 |00:00:00.01 |       4 |
----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=1000)



SQL> select * from bowie2 where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

-------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |   4200 |00:00:00.01 |      68 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   4200 |00:00:00.01 |      68 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      11 |
-------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          1 CPU used by this session
          1 CPU used when call started
          2 DB time
      13157 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
         59 buffer is not pinned count
       8342 buffer is pinned count
        325 bytes received via SQL*Net from client
     461838 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
         68 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
         68 consistent gets from cache
         67 consistent gets pin
         67 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
     557056 logical read bytes from cache
         67 no work - consistent read gets
         73 non-idle wait count
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          2 session cursor cache count
         68 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       4200 table fetch by rowid
          3 user calls

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'25qktyn35b662',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0

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

select * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |       |    80 (100)|   4200 |00:00:00.01 |      68 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   455K|      80 (0)|   4200 |00:00:00.01 |      68 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |       |      11 (0)|   4200 |00:00:00.01 |      11 |
----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=4200)

 

With identical table/index statistics, we notice as expected that both SQLs have the same consistent gets and CBO costs as with the previous demo.

If we now repeat the equivalent Update statement:

SQL> update bowie2 set name='THE RISE AND FALL OF BOWIE STARDUST AND THE SPIDERS FROM MARS';

200,000 rows updated.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE2', estimate_percent=> null, no_invalidate=>false);

PL/SQL procedure successfully completed.

 

If we look at the table statistics:

SQL> select table_name, num_rows, blocks from user_tables where table_name='BOWIE2';

   TABLE_NAME   NUM_ROWS     BLOCKS
_____________ ___________ _________
BOWIE2             200000      4654

 

We notice the number of table blocks has increased to 4654 due to the increased row lengths, but not as much as with the previous demo (where table blocks increased to 4906) as in this scenario, Oracle does not have to store the row location pointers in the original blocks for the migrated rows.

If we look at the index statistics:

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE2';

    INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
______________ _________ ______________ ____________________
BOWIE2_ID_I            2            945               109061

We notice that these are substantially different from the first demo, where ROWIDs for migrated rows are not updated on the fly.

By now updating the ROWIDs, the indexes can possibly increase in size as they have to store both the previous and new ROWIDs in separate index entries and hence Oracle is more likely to perform additional index block splits (as I discussed in my previous post).

The LEAF_BLOCKS are now 945 (previously 473) and even the BLEVEL has increased from 1 to 2.

Additionally, and perhaps importantly for specific key indexes, the Clustering Factor value of indexes can also be impacted. By migrating rows and physically storing them in different locations, this can potentially detrimentally impact the tight clustering of rows based on specific column values.

The Clustering Factor for the index on the monotonically increased ID column has now increased significantly to 109061, up from the previously perfect 3250.

So columns that have naturally good clustering (e.g.: monotonically increasing values such as IDs and dates) or have been manually well clustered for performance purposes, can have the Clustering Factor of associated indexes detrimentally impacted by migrated rows.

If we re-run the first query:

SQL> select * from bowie2 where id between 1 and 1000;

1,000 rows selected.

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________
SQL_ID gtkw2704bxj7q, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 1000

Plan hash value: 3243780227

-------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |   1000 |00:00:00.01 |     639 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   1000 |   1000 |00:00:00.01 |     639 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   1000 |   1000 |00:00:00.01 |       7 |
-------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=1000)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          1 CPU used by this session
          1 CPU used when call started
          1 DB time
      15262 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
        634 buffer is not pinned count
       1367 buffer is pinned count
        325 bytes received via SQL*Net from client
     171421 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
        639 consistent gets
          2 consistent gets examination
          2 consistent gets examination (fastpath)
        639 consistent gets from cache
        637 consistent gets pin
        637 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
    5234688 logical read bytes from cache
        637 no work - consistent read gets
         38 non-idle wait count
          1 non-idle wait time
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 process last non-idle time
          2 session cursor cache count
        639 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
       1000 table fetch by rowid
          3 user calls

I discussed in a previous post how by updating the ROWIDs of migrated rows we can improve performance, as Oracle can go directly to the correct new physical location of a migrated row.

But for some specific indexes, where data clustering is crucial, and we have a significant number migrated rows, this might not necessarily be the case.

We can see consistent gets here has increased to 639 (previously is was just 21), and so not hugely different from the 666 consistent gets required to fetch the migrated rows when the ROWIDs were not updated in the first demo.

If we look at the CBO costings:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'gtkw2704bxj7q',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________________________________________
SQL_ID gtkw2704bxj7q, child number 0

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

select * from bowie2 where id between 1 and 1000

Plan hash value: 3243780227

----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |       |   553 (100)|   1000 |00:00:00.01 |     639 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   1000 |   163K|     553 (0)|   1000 |00:00:00.01 |     639 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   1000 |       |       7 (0)|   1000 |00:00:00.01 |       7 |
----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

   2 - access("ID">=1 AND "ID"<=1000)

 

We can see the CBO cost has increased significantly to 553 (previously it was just 21).

With a much increased Clustering Factor, this will obviously impact the CBO costs of associated index scans.

In very extreme cases, these possible changes in the Clustering Factor can even impact the viability of using the index.

If we re-run the second query returning the 4200 rows:

SQL> select * from bowie2 where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 4200

Plan hash value: 1495904576

----------------------------------------------------------------------------------------------
| Id | Operation                  | Name   | Starts | E-Rows | A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |        |      1 |        |   4200 |00:00:00.02 |    4572 |
|* 1 |  TABLE ACCESS STORAGE FULL | BOWIE2 |      1 |   4200 |   4200 |00:00:00.02 |    4572 |
----------------------------------------------------------------------------------------------

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

   1 - storage(("ID"<=4200 AND "ID">=1))
       filter(("ID"<=4200 AND "ID">=1))

We can see that the CBO has now chosen to perform a Full Table Scan (FTS), rather than use the now less efficient index to return this number of rows.

If we look at the CBO costings of this FTS plan:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'25qktyn35b662',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
______________________________________________________________________________________________________________________
SQL_ID 25qktyn35b662, child number 0
-------------------------------------
select * from bowie2 where id between 1 and 4200

Plan hash value: 1495904576

-------------------------------------------------------------------------------------------------------------------
| Id | Operation                  | Name   | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |        |      1 |        |       |  1264 (100)|   4200 |00:00:00.02 |    4572 |
|* 1 |  TABLE ACCESS STORAGE FULL | BOWIE2 |      1 |   4200 |   684K|    1264 (1)|   4200 |00:00:00.02 |    4572 |
-------------------------------------------------------------------------------------------------------------------

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

   1 - storage(("ID"<=4200 AND "ID">=1))
       filter(("ID"<=4200 AND "ID">=1))

 

The cost of the FTS plan is 1264.

If we compare this is a plan that used the index:

SQL> select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200;

4,200 rows selected.

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________
SQL_ID bzm2vhchqpq7w, child number 0
-------------------------------------
select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

-------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |   4200 |00:00:00.01 |    2665 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   4200 |00:00:00.01 |    2665 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |   4200 |00:00:00.01 |      21 |
-------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=4200)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          2 CPU used by this session
          2 CPU used when call started
          2 DB time
      14531 RM usage
          3 Requests to/from client
          2 SQL*Net roundtrips to/from client
       2646 buffer is not pinned count
       5755 buffer is pinned count
        348 bytes received via SQL*Net from client
     462143 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
       2665 consistent gets
         2 consistent gets examination
         2 consistent gets examination (fastpath)
      2665 consistent gets from cache
      2663 consistent gets pin
      2663 consistent gets pin (fastpath)
         2 execute count
         1 index range scans
  21831680 logical read bytes from cache
      2663 no work - consistent read gets
        73 non-idle wait count
         2 opened cursors cumulative
         1 opened cursors current
         2 parse count (total)
         3 process last non-idle time
         2 session cursor cache count
      2665 session logical reads
         1 sorts (memory)
      2024 sorts (rows)
      4200 table fetch by rowid
         3 user calls

SQL> SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(sql_id=>'bzm2vhchqpq7w',format=>'ALLSTATS LAST +cost +bytes'));

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________________________________________
SQL_ID bzm2vhchqpq7w, child number 0

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

select /*+ index (bowie2) */ * from bowie2 where id between 1 and 4200

Plan hash value: 3243780227

----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows |E-Bytes| Cost (%CPU)| A-Rows | A-Time     | Buffers |
----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |       |  2314 (100)|   4200 |00:00:00.01 |    2665 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE2      |      1 |   4200 |   684K|    2314 (1)|   4200 |00:00:00.01 |    2665 |
|* 2 |   INDEX RANGE SCAN                   | BOWIE2_ID_I |      1 |   4200 |       |      22 (0)|   4200 |00:00:00.01 |      21 |
----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

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

2 - access("ID">=1 AND "ID"<=4200)

 

The cost of using the index to retrieve the 4200 rows is 2310, more than the 1264 of the FTS.

 

For the vast majority of indexes, updating the ROWIDs for migrated rows will result in better performance, as such indexes will be able to directly access the correct new physical location of migrated rows, rather than having to visit the original table block and then follow the stored pointer to the new table block.

But for some very specific indexes, where data clustering is crucial, AND we have a significant number migrated rows, this might not necessarily be the case. The performance benefit might be minimal at best.

That’s more than enough for one post 🙂

In my next post, I’ll discuss how to potentially remedy these performance implications, both for tables with or without ENABLE TABLE MOVEMENT enabled…

Some Things To Consider Now ROWIDs Are Updated When Rows Migrate Part II (“Look Back In Anger”) February 24, 2023

Posted by Richard Foote in 19c, Autonomous Database, Autonomous Transaction Processing, BLEVEL, Changing ROWID, Index Internals, Leaf Blocks, Migrated Rows, Oracle, Oracle Blog, Oracle Cloud, Oracle General, Oracle Indexes, Oracle19c, Richard's Blog, ROWID.
4 comments

Some weekend reading…

In my previous post, I discussed a couple of potential areas of concern with the ROWIDs of migrated rows now being updated on the fly in Oracle Autonomous Databases, namely that it can cause issues with applications that reply on stored ROWIDs not changing and that there are additional resources required to maintain such ROWIDs in corresponding indexes, especially if there are many indexes on a table.

In this post, I’ll discuss another issue to just bear in mind with this change in behaviour.

To illustrate, I’ll run a demo similar to the previous post, first creating and populating a table with no ENABLE ROW MOVEMENT set:

SQL> create table ziggy(id number, code1 number, code2 number, code3 number, code4 number, code5 number, code6 number, code7 number, code8 number, code9 number, code10 number, code11 number, code12 number, code13 number, code14 number, code15 number, code16 number, code17 number, code18 number, code19 number, code20 number, name varchar2(142)) PCTFREE 0;

Table ZIGGY created.

SQL> insert into ziggy SELECT rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, 'BOWIE' FROM dual CONNECT BY LEVEL <= 200000;

200,000 rows inserted.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'ZIGGY');

PL/SQL procedure successfully completed.

SQL> analyze table ziggy compute statistics;

Table ZIGGY analyzed.

I’ll next create a bunch of indexes on the table:

SQL> create index ziggy_id_i on ziggy(id);

Index ZIGGY_ID_I created.

SQL> create index ziggy_code1_i on ziggy(code1);

Index ZIGGY_CODE1_I created.

SQL> create index ziggy_code2_i on ziggy(code2);

Index ZIGGY_CODE2_I created.

SQL> create index ziggy_code3_i on ziggy(code3);

Index ZIGGY_CODE3_I created.

SQL> create index ziggy_code4_i on ziggy(code4);

Index ZIGGY_CODE4_I created.

SQL> create index ziggy_code5_i on ziggy(code5);

Index ZIGGY_CODE5_I created.

SQL> create index ziggy_code6_i on ziggy(code6);

Index ZIGGY_CODE6_I created.

SQL> create index ziggy_code7_i on ziggy(code7);

Index ZIGGY_CODE7_I created.

SQL> create index ziggy_code8_i on ziggy(code8);

Index ZIGGY_CODE8_I created.

SQL> create index ziggy_code9_i on ziggy(code9);

Index ZIGGY_CODE9_I created.

SQL> create index ziggy_code10_i on ziggy(code10);

Index ZIGGY_CODE10_I created.

SQL> create index ziggy_code11_i on ziggy(code11);

Index ZIGGY_CODE11_I created.

SQL> create index ziggy_code12_i on ziggy(code12);

Index ZIGGY_CODE12_I created.

SQL> create index ziggy_code13_i on ziggy(code13);

Index ZIGGY_CODE13_I created.

SQL> create index ziggy_code14_i on ziggy(code14);

Index ZIGGY_CODE14_I created.

SQL> create index ziggy_code15_i on ziggy(code15);

Index ZIGGY_CODE15_I created.

SQL> create index ziggy_code16_i on ziggy(code16);

Index ZIGGY_CODE16_I created.

SQL> create index ziggy_code17_i on ziggy(code17);

Index ZIGGY_CODE17_I created.

SQL> create index ziggy_code18_i on ziggy(code18);

Index ZIGGY_CODE18_I created.

SQL> create index ziggy_code19_i on ziggy(code19);

Index ZIGGY_CODE19_I created.

SQL> create index ziggy_code20_i on ziggy(code20);

Index ZIGGY_CODE20_I created.

Let’s take note of the size of the table and its associated indexes:

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt
from user_tables where table_name='ZIGGY';

   TABLE_NAME    NUM_ROWS    BLOCKS    EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
ZIGGY              200000      3268              60          857            113            0

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes 
where table_name='ZIGGY';

       INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
_________________ _________ ______________ ____________________
ZIGGY_CODE20_I            1            473                 3250
ZIGGY_ID_I                1            473                 3250
ZIGGY_CODE1_I             1            473                 3250
ZIGGY_CODE2_I             1            473                 3250
ZIGGY_CODE3_I             1            473                 3250
ZIGGY_CODE4_I             1            473                 3250
ZIGGY_CODE5_I             1            473                 3250
ZIGGY_CODE6_I             1            473                 3250
ZIGGY_CODE7_I             1            473                 3250
ZIGGY_CODE8_I             1            473                 3250
ZIGGY_CODE9_I             1            473                 3250
ZIGGY_CODE10_I            1            473                 3250
ZIGGY_CODE11_I            1            473                 3250
ZIGGY_CODE12_I            1            473                 3250
ZIGGY_CODE13_I            1            473                 3250
ZIGGY_CODE14_I            1            473                 3250
ZIGGY_CODE15_I            1            473                 3250
ZIGGY_CODE16_I            1            473                 3250
ZIGGY_CODE17_I            1            473                 3250
ZIGGY_CODE18_I            1            473                 3250
ZIGGY_CODE19_I            1            473                 3250

We next perform an update on the table that will increase the row size sufficiently to result in a bunch of migrated rows:

SQL> update ziggy set name='THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS';

200,000 rows updated.
Elapsed: 00:00:07.716

I’ll then perform a COMMIT and look at differences in the table statistics:

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'ZIGGY');

PL/SQL procedure successfully completed.

SQL> analyze table ziggy compute statistics;

Table ZIGGY analyzed.

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt 
from user_tables where table_name='ZIGGY';

   TABLE_NAME   NUM_ROWS     BLOCKS    EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
ZIGGY              200000      4906              86          415            170        56186

 

We can see that there are indeed a bunch of migrated/chained rows, some 56186 of them.

We also notice that the size of the table has increased to 4906 blocks (previously is was 3268). This increase is in large part due to the increased size of the NAME column value, but also partly due to the storage allocated to pointers that are stored in the original block to denote the new location of the migrated rows (as discussed previously here).

If we look at the current state of the indexes:

SQL> select index_name, blevel, leaf_blocks, clustering_factor 
from user_indexes where table_name='ZIGGY';

       INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
_________________ _________ ______________ ____________________
ZIGGY_CODE7_I             1            473                 3250
ZIGGY_CODE8_I             1            473                 3250
ZIGGY_CODE9_I             1            473                 3250
ZIGGY_CODE10_I            1            473                 3250
ZIGGY_CODE11_I            1            473                 3250
ZIGGY_CODE12_I            1            473                 3250
ZIGGY_CODE13_I            1            473                 3250
ZIGGY_CODE14_I            1            473                 3250
ZIGGY_CODE15_I            1            473                 3250
ZIGGY_CODE16_I            1            473                 3250
ZIGGY_CODE17_I            1            473                 3250
ZIGGY_CODE18_I            1            473                 3250
ZIGGY_CODE19_I            1            473                 3250
ZIGGY_CODE20_I            1            473                 3250
ZIGGY_ID_I                1            473                 3250
ZIGGY_CODE1_I             1            473                 3250
ZIGGY_CODE2_I             1            473                 3250
ZIGGY_CODE3_I             1            473                 3250
ZIGGY_CODE4_I             1            473                 3250
ZIGGY_CODE5_I             1            473                 3250
ZIGGY_CODE6_I             1            473                 3250

We notice that the indexes remain unchanged. As the table does NOT have ENABLE ROW MOVEMENT set, the indexes are NOT updated at all as part of the migrated row process (thus the same behaviour as non-autonomous database environments).

However, if I perform the same demo but instead perform a ROLLBACK of the transaction rather than the commit:

SQL> rollback;

Rollback complete.

Elapsed: 00:00:06.919

Note that the rollback takes 00:00:06.919 to complete.

If we now look at the size of the table and corresponding indexes:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'ZIGGY');

PL/SQL procedure successfully completed.

SQL> analyze table ziggy compute statistics;

Table ZIGGY analyzed.

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt 
from user_tables where table_name='ZIGGY';

   TABLE_NAME    NUM_ROWS    BLOCKS    EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
ZIGGY              200000      4906              86         2899            113            0

We notice that the size of the table has increased to be the same 4906 blocks as when we performed the commit. The extra storage remains allocated even after the rollback operation.

That’s because Oracle does NOT deallocate any additional storage that might have been consumed during the original transaction. We notice that the AVG_SPACE has increased substantially as a result (now 2899, previously it was just 857).

If we look at the current state of the indexes after the rollback:

SQL> select index_name, blevel, leaf_blocks, clustering_factor 
from user_indexes where table_name='ZIGGY';

       INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
_________________ _________ ______________ ____________________
ZIGGY_CODE20_I            1            473                 3250
ZIGGY_ID_I                1            473                 3250
ZIGGY_CODE1_I             1            473                 3250
ZIGGY_CODE2_I             1            473                 3250
ZIGGY_CODE3_I             1            473                 3250
ZIGGY_CODE4_I             1            473                 3250
ZIGGY_CODE5_I             1            473                 3250
ZIGGY_CODE6_I             1            473                 3250
ZIGGY_CODE7_I             1            473                 3250
ZIGGY_CODE8_I             1            473                 3250
ZIGGY_CODE9_I             1            473                 3250
ZIGGY_CODE10_I            1            473                 3250
ZIGGY_CODE11_I            1            473                 3250
ZIGGY_CODE12_I            1            473                 3250
ZIGGY_CODE13_I            1            473                 3250
ZIGGY_CODE14_I            1            473                 3250
ZIGGY_CODE15_I            1            473                 3250
ZIGGY_CODE16_I            1            473                 3250
ZIGGY_CODE17_I            1            473                 3250
ZIGGY_CODE18_I            1            473                 3250
ZIGGY_CODE19_I            1            473                 3250

We notice that all the indexes again remain unchanged. As the indexes are not updated during the transaction, this is of course to be expected.

 

Let’s now repeat the same demo, but this time on a table with ENABLE ROW MOVEMENT set:

SQL> create table ziggy2(id number, code1 number, code2 number, code3 number, code4 number, code5 number, code6 number, code7 number, code8 number, code9 number, code10 number, code11 number, code12 number, code13 number, code14 number, code15 number, code16 number, code17 number, code18 number, code19 number, code20 number, name varchar2(142)) PCTFREE 0 ENABLE ROW MOVEMENT;

Table ZIGGY2 created.

SQL> insert into ziggy2 SELECT rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, 'BOWIE' FROM dual CONNECT BY LEVEL <= 200000;

200,000 rows inserted.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'ZIGGY2');

PL/SQL procedure successfully completed.

SQL> analyze table ziggy2 compute statistics;

Table ZIGGY2 analyzed.

SQL> create index ziggy2_id_i on ziggy2(id);

Index ZIGGY2_ID_I created.

SQL> create index ziggy2_code1_i on ziggy2(code1);

Index ZIGGY2_CODE1_I created.

SQL> create index ziggy2_code2_i on ziggy2(code2);

Index ZIGGY2_CODE2_I created.

SQL> create index ziggy2_code3_i on ziggy2(code3);

Index ZIGGY2_CODE3_I created.

SQL> create index ziggy2_code4_i on ziggy2(code4);

Index ZIGGY2_CODE4_I created.

SQL> create index ziggy2_code5_i on ziggy2(code5);

Index ZIGGY2_CODE5_I created.

SQL> create index ziggy2_code6_i on ziggy2(code6);

Index ZIGGY2_CODE6_I created.

SQL> create index ziggy2_code7_i on ziggy2(code7);

Index ZIGGY2_CODE7_I created.

SQL> create index ziggy2_code8_i on ziggy2(code8);

Index ZIGGY2_CODE8_I created.

SQL> create index ziggy2_code9_i on ziggy2(code9);

Index ZIGGY2_CODE9_I created.

SQL> create index ziggy2_code10_i on ziggy2(code10);

Index ZIGGY2_CODE10_I created.

SQL> create index ziggy2_code11_i on ziggy2(code11);

Index ZIGGY2_CODE11_I created.

SQL> create index ziggy2_code12_i on ziggy2(code12);

Index ZIGGY2_CODE12_I created.

SQL> create index ziggy2_code13_i on ziggy2(code13);

Index ZIGGY2_CODE13_I created.

SQL> create index ziggy2_code14_i on ziggy2(code14);

Index ZIGGY2_CODE14_I created.

SQL> create index ziggy2_code15_i on ziggy2(code15);

Index ZIGGY2_CODE15_I created.

SQL> create index ziggy2_code16_i on ziggy2(code16);

Index ZIGGY2_CODE16_I created.

SQL> create index ziggy2_code17_i on ziggy2(code17);

Index ZIGGY2_CODE17_I created.

SQL> create index ziggy2_code18_i on ziggy2(code18);

Index ZIGGY2_CODE18_I created.

SQL> create index ziggy2_code19_i on ziggy2(code19);

Index ZIGGY2_CODE19_I created.

SQL> create index ziggy2_code20_i on ziggy2(code20);

Index ZIGGY2_CODE20_I created.

 

The table and indexes all have the same initial size as the previous example:

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt
from user_tables where table_name='ZIGGY2';

   TABLE_NAME    NUM_ROWS    BLOCKS    EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
ZIGGY2             200000      3268              60          857            113            0

SQL> select index_name, blevel, leaf_blocks, clustering_factor from user_indexes 
where table_name='ZIGGY2';

        INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
__________________ _________ ______________ ____________________
ZIGGY2_ID_I                1            473                 3250
ZIGGY2_CODE1_I             1            473                 3250
ZIGGY2_CODE2_I             1            473                 3250
ZIGGY2_CODE3_I             1            473                 3250
ZIGGY2_CODE4_I             1            473                 3250
ZIGGY2_CODE5_I             1            473                 3250
ZIGGY2_CODE6_I             1            473                 3250
ZIGGY2_CODE7_I             1            473                 3250
ZIGGY2_CODE8_I             1            473                 3250
ZIGGY2_CODE9_I             1            473                 3250
ZIGGY2_CODE10_I            1            473                 3250
ZIGGY2_CODE11_I            1            473                 3250
ZIGGY2_CODE12_I            1            473                 3250
ZIGGY2_CODE13_I            1            473                 3250
ZIGGY2_CODE14_I            1            473                 3250
ZIGGY2_CODE15_I            1            473                 3250
ZIGGY2_CODE16_I            1            473                 3250
ZIGGY2_CODE17_I            1            473                 3250
ZIGGY2_CODE18_I            1            473                 3250
ZIGGY2_CODE19_I            1            473                 3250
ZIGGY2_CODE20_I            1            473                 3250

 

If we now perform the same Update followed by the commit:

SQL> update ziggy2 set name='THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS';

200,000 rows updated.
Elapsed: 00:00:33.390

SQL> commit;

Commit complete.

If we look at the current size of the table after the update:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'ZIGGY2');

PL/SQL procedure successfully completed.

SQL> analyze table ziggy2 compute statistics;

Table ZIGGY2 analyzed.

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt 
from user_tables where table_name='ZIGGY2';

   TABLE_NAME    NUM_ROWS   BLOCKS     EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
ZIGGY2             200000      4654              82          367            169            0

We notice it has increased to 4654 blocks (previously 3268). But the table is not quite as large in size as in the first demo, where the table grew to 4906 blocks (so 252 fewer blocks).

The increase in table size is now due entirely as a result in the increased NAME column values, as Oracle has not had to consume any storage for pointers in the original table blocks to denote a new location of the rows, as the ROWIDs are now updated in the indexes on the fly.

So this is a positive, a DECREASE in the comparative size of table after such updates that migrate rows.

And of course, there are no migrated/chained rows.

But if we look at the index statistics after the commit:

SQL> select index_name, blevel, leaf_blocks, clustering_factor 
from user_indexes where table_name='ZIGGY2';

        INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
__________________ _________ ______________ ____________________
ZIGGY2_ID_I                2            945               109061
ZIGGY2_CODE1_I             2            945               109061
ZIGGY2_CODE2_I             2            945               109061
ZIGGY2_CODE3_I             2            945               109061
ZIGGY2_CODE4_I             2            945               109061
ZIGGY2_CODE5_I             2            945               109061
ZIGGY2_CODE6_I             2            945               109061
ZIGGY2_CODE7_I             2            945               109061
ZIGGY2_CODE8_I             2            945               109061
ZIGGY2_CODE9_I             2            945               109061
ZIGGY2_CODE10_I            2            945               109061
ZIGGY2_CODE11_I            2            945               109061
ZIGGY2_CODE12_I            2            945               109061
ZIGGY2_CODE13_I            2            945               109061
ZIGGY2_CODE14_I            2            945               109061
ZIGGY2_CODE15_I            2            945               109061
ZIGGY2_CODE16_I            2            945               109061
ZIGGY2_CODE17_I            2            945               109061
ZIGGY2_CODE18_I            2            945               109061
ZIGGY2_CODE19_I            2            945               109061
ZIGGY2_CODE20_I            2            945               109061

 

We notice that ALL the indexes have significantly increased in size and now have 945 leaf blocks (previously it was just 473 leaf blocks). Additionally as a result of this increase in index size, the BLEVEL of the indexes has also increased and are now 2 (previously it was 1).

Here’s the thing. As I’ve discussed many times before, when Oracle performs an “Update” of an index entry, this is actually implemented as a Delete/Insert operation. By changing the ROWID of an index entry, Oracle first deletes the original index entry and inserts a new index entry with the new ROWID. So the previous index entry remains (with the space likely eventually reused by another new index entry in the future).

So these “on the fly” updates of the indexes to keep the ROWIDs current due to row migrations increases the likelihood of index block splits and the subsequent increase in index storage allocations.

In very rare, extreme cases (and this demo is indeed an extreme case as I’m updating all rows in my table), this extra index storage could potentially result in an increase in the index BLEVEL.

However, in most scenarios, this increase in index storage is likely to be moderate and result in extra index storage that will eventually be consumed by subsequent new rows anyways.

If instead of the commit operation, we instead performed a rollback:

SQL> rollback;

Rollback complete.

Elapsed: 00:00:36.639

We notice that the rollback takes considerably longer at 00:00:36.639 (previously in the first demo, it was just 00:00:06.919).

As the ROWIDs are now all updated on the fly on all the corresponding indexes, there’s that much more data that needs to be rolled back within these indexes.

If we look at the current size of the table after the rollback:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'ZIGGY2');

PL/SQL procedure successfully completed.

SQL> analyze table ziggy2 compute statistics;

Table ZIGGY2 analyzed.

SQL> select table_name, num_rows, blocks, empty_blocks, avg_space, avg_row_len, chain_cnt
from user_tables where table_name='ZIGGY2';

   TABLE_NAME    NUM_ROWS    BLOCKS    EMPTY_BLOCKS    AVG_SPACE    AVG_ROW_LEN    CHAIN_CNT
_____________ ___________ _________ _______________ ____________ ______________ ____________
ZIGGY2             200000      4654              82         2823            113            0

We notice it has increased to the same 4654 blocks as with the commit. The extra storage remains allocated even after the rollback operation.

That’s again because Oracle does NOT deallocate any additional storage that might have been consumed during the original transaction. We notice that the AVG_SPACE has again increased substantially as a result (now 2823, previously it was just 857).

If we look at the current state of the indexes after the rollback:

 

SQL> select index_name, blevel, leaf_blocks, clustering_factor 
from user_indexes where table_name='ZIGGY2';

        INDEX_NAME    BLEVEL    LEAF_BLOCKS    CLUSTERING_FACTOR
__________________ _________ ______________ ____________________
ZIGGY2_ID_I                2            945                 3250
ZIGGY2_CODE1_I             2            945                 3250
ZIGGY2_CODE2_I             2            945                 3250
ZIGGY2_CODE3_I             2            945                 3250
ZIGGY2_CODE4_I             2            945                 3250
ZIGGY2_CODE5_I             2            945                 3250
ZIGGY2_CODE6_I             2            945                 3250
ZIGGY2_CODE7_I             2            945                 3250
ZIGGY2_CODE8_I             2            945                 3250
ZIGGY2_CODE9_I             2            945                 3250
ZIGGY2_CODE10_I            2            945                 3250
ZIGGY2_CODE11_I            2            945                 3250
ZIGGY2_CODE12_I            2            945                 3250
ZIGGY2_CODE13_I            2            945                 3250
ZIGGY2_CODE14_I            2            945                 3250
ZIGGY2_CODE15_I            2            945                 3250
ZIGGY2_CODE16_I            2            945                 3250
ZIGGY2_CODE17_I            2            945                 3250
ZIGGY2_CODE18_I            2            945                 3250
ZIGGY2_CODE19_I            2            945                 3250
ZIGGY2_CODE20_I            2            945                 3250

We notice that they all remain at their increased size of 945 leaf blocks and with the Blevel of 2.

Again, when Oracle performs the rollback, Oracle does NOT undo all the index block splits and leaves all the additional storage allocated to the indexes.

So, just a word of caution.

Updating all the ROWIDs on the fly when a row migrates does not come for free. There’s additional resources that need to be consumed during these updates AND there is a potential issue with indexes having to perform additional index block splits and consume additional storage (at least immediately) after such operations.

If you have applications that makes bulk changes to data that can result in rollbacks, again, it’s just worth noting the extra storage that may be consumed as a result (until the additional data is finally added to the table).

Yes, index rebuilds can be performed to reduce the subsequent size of these inflated indexes.

BUT, for those of you with sharp eyes, you might also have noted another potential issue with this new behaviour, which I’ll discuss in my next post… 🙂

Some Things To Consider Now ROWIDs Are Updated When Rows Migrate Part I (“More”) February 22, 2023

Posted by Richard Foote in 19c, Autonomous Database, Autonomous Transaction Processing, Changing ROWID, Migrated Rows, Oracle, Oracle 21c, Oracle Blog, Oracle Cloud, Oracle General, Oracle Indexes, Oracle19c, Pink Floyd, Richard's Blog.
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In my previous post, I discussed the obvious advantage of ROWIDs now being updated when rows migrate in an Oracle Autonomous Database, that being subsequent accesses to these rows via an index being more efficient.

However, there were likely reasons why Oracle has not historically updated ROWIDs on the fly in the past, so it’s worth exploring some of the possible side-effects of this new behaviour.

The most obvious issue will be for those applications that explicitly currently store ROWIDs, to enable the direct and very fast retrieval of such rows without having to read and access additional index blocks. If the ROWID can now suddenly change when a row is simply migrated, then of course these applications will no longer be guaranteed to be able to access these rows via the stored ROWIDs. Worse, it may now be possible for such applications to unknowingly fetch the wrong row, with the ROWID value now potentially associated with an entirely different row.

If this is a legitimate concern, then the remedy is simply to just NOT assign such tables the ENABLE ROW MOVEMENT attribute (which is disabled by default on a table) and the behaviour of migrated rows in association with ROWIDs will remain unchanged in Oracle Autonomous Databases. The risks here can be clearly and easily limited.

The other obvious disadvantage with ROWIDs being updated on the fly when a row migrates is in the additional costs associated with such Update statements in maintaining all the corresponding indexes.

As I discussed previously, these additional costs can be significant, especially if we have many indexes on a table.

To illustrate these extra costs, a simple example.

I’ll first start by creating and populating a table called BIG_ZIGGY (which at 100,000 rows is actually quite tiny, but it does have a number of columns) that does NOT have ENABLE ROW MOVEMENT set. The PCTFREE is set to 0 is ensure the rows are nicely packed in each block:

SQL> CREATE TABLE big_ziggy(id number, code1 number, code2 number, code3 number, code4 number, code5 number, code6 number, code7 number, code8 number, code9 number, code10 number, code11 number, code12 number, code13 number, code14 number, code15 number, code16 number, code17 number, code18 number, code19 number, code20 number, name varchar2(142)) PCTFREE 0;

Table BIG_ZIGGY created.

SQL> INSERT INTO big_ziggy SELECT rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, 'BOWIE' FROM dual CONNECT BY LEVEL <= 100000;

100,000 rows inserted.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=> null, tabname=> 'BIG_ZIGGY');

PL/SQL procedure successfully completed.

 

As we’ve only inserted rows, there are currently no migrated rows:

SQL> analyze table big_ziggy compute statistics;

Table BIG_ZIGGY analyzed.

SQL> select table_name, num_rows, chain_cnt from user_tables where table_name='BIG_ZIGGY';

   TABLE_NAME    NUM_ROWS    CHAIN_CNT
_____________ ___________ ____________
BIG_ZIGGY          100000            0

 

I’ll next create a whole bunch of indexes on many of these columns:

SQL> create index big_ziggy_id_i on big_ziggy(id);

Index BIG_ZIGGY_ID_I created.

SQL> create index big_ziggy_code1_i on big_ziggy(code1);

Index BIG_ZIGGY_CODE1_I created.

SQL> create index big_ziggy_code2_i on big_ziggy(code2);

Index BIG_ZIGGY_CODE2_I created.

SQL> create index big_ziggy_code3_i on big_ziggy(code3);

Index BIG_ZIGGY_CODE3_I created.

SQL> create index big_ziggy_code4_i on big_ziggy(code4);

Index BIG_ZIGGY_CODE4_I created.

SQL> create index big_ziggy_code5_i on big_ziggy(code5);

Index BIG_ZIGGY_CODE5_I created.

SQL> create index big_ziggy_code6_i on big_ziggy(code6);

Index BIG_ZIGGY_CODE6_I created.

SQL> create index big_ziggy_code7_i on big_ziggy(code7);

Index BIG_ZIGGY_CODE7_I created.

SQL> create index big_ziggy_code8_i on big_ziggy(code8);

Index BIG_ZIGGY_CODE8_I created.

SQL> create index big_ziggy_code9_i on big_ziggy(code9);

Index BIG_ZIGGY_CODE9_I created.

SQL> create index big_ziggy_code10_i on big_ziggy(code10);

Index BIG_ZIGGY_CODE10_I created.

SQL> create index big_ziggy_code11_i on big_ziggy(code11);

Index BIG_ZIGGY_CODE11_I created.

SQL> create index big_ziggy_code12_i on big_ziggy(code12);

Index BIG_ZIGGY_CODE12_I created.

SQL> create index big_ziggy_code13_i on big_ziggy(code13);

Index BIG_ZIGGY_CODE13_I created.

SQL> create index big_ziggy_code14_i on big_ziggy(code14);

Index BIG_ZIGGY_CODE14_I created.

SQL> create index big_ziggy_code15_i on big_ziggy(code15);

Index BIG_ZIGGY_CODE15_I created.

SQL> create index big_ziggy_code16_i on big_ziggy(code16);

Index BIG_ZIGGY_CODE16_I created.

SQL> create index big_ziggy_code17_i on big_ziggy(code17);

Index BIG_ZIGGY_CODE17_I created.

SQL> create index big_ziggy_code18_i on big_ziggy(code18);

Index BIG_ZIGGY_CODE18_I created.

SQL> create index big_ziggy_code19_i on big_ziggy(code19);

Index BIG_ZIGGY_CODE19_I created.

SQL> create index big_ziggy_code20_i on big_ziggy(code20);

Index BIG_ZIGGY_CODE20_I created.

I’ll now run an UPDATE statement, that will increase the row size and result in a number of row migrations:

SQL> update big_ziggy set name='THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS';

100,000 rows updated.

PLAN_TABLE_OUTPUT
____________________________________________________________________________________
SQL_ID 53xtnn8mmtwj5, child number 0
-------------------------------------
update big_ziggy set name='THE RISE AND FALL OF ZIGGY STARDUST AND THE
SPIDERS FROM MARS'

Plan hash value: 1689330390

---------------------------------------------------------
| Id | Operation                  | Name      | E-Rows  |
---------------------------------------------------------
|  0 | UPDATE STATEMENT           |           |         |
|  1 |  UPDATE                    | BIG_ZIGGY |         |
|  2 |   TABLE ACCESS STORAGE FULL| BIG_ZIGGY |    100K |
---------------------------------------------------------

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1
   - PDML disabled because object is not decorated with parallel clause
   - Warning: basic plan statistics not available. These are only collected when:
       * hint 'gather_plan_statistics' is used for the statement or
       * parameter 'statistics_level' is set to 'ALL', at session or system level

Statistics
-----------------------------------------------------------
        345 CPU used by this session
        347 CPU used when call started
        399 DB time
    3442830 RM usage
          5 Requests to/from client
        491 Session total flash IO requests
   25403392 cell physical IO interconnect bytes
      48814 consistent gets
      10111 consistent gets examination
      10111 consistent gets examination (fastpath)
      48814 consistent gets from cache
      38703 consistent gets pin
      38702 consistent gets pin (fastpath)
     544587 db block gets
     544587 db block gets from cache
     538582 db block gets from cache (fastpath)
        127 enqueue releases
        129 enqueue requests
       3086 gcs affinity lock grants
        803 gcs data block access records
          3 ges messages sent
      33574 global enqueue gets sync
      33573 global enqueue releases
         43 messages sent
        483 non-idle wait count
         44 non-idle wait time
          8 opened cursors cumulative
          1 opened cursors current
         71 physical read requests optimized
        420 physical read total IO requests
   25403392 physical read total bytes
    3219456 physical read total bytes optimized
          1 pinned cursors current
          4 process last non-idle time
         55 recursive calls
          1 recursive cpu usage
     593401 session logical reads
         42 user I/O wait time
          6 user calls
Elapsed: 00:00:04.532

SQL> commit

Commit complete.

Note that the CPU used by session is 335, the number of db block gets is 544587 and that the raw elapsed time is 00:00:04.532. We’ll shortly compare these values with those of the same demo, but on a table with ENABLE ROW MOVEMENT set.

If we now check for migrated (chained) rows:

SQL> analyze table big_ziggy compute statistics;

Table BIG_ZIGGY analyzed.

SQL> select table_name, num_rows, chain_cnt from user_tables where table_name='BIG_ZIGGY';

  TABLE_NAME     NUM_ROWS    CHAIN_CNT
_____________ ___________ ____________
BIG_ZIGGY          100000        28323

 

We notice we indeed have 28323 migrated rows.

We’ll now repeat the exactly same demo, but this time on the BIG_ZIGGY2 table that has ENABLE ROW MOVEMENT set:

SQL> CREATE TABLE big_ziggy2(id number, code1 number, code2 number, code3 number, code4 number, code5 number, code6 number, code7 number, code8 number, code9 number, code10 number, code11 number, code12 number, code13 number, code14 number, code15 number, code16 number, code17 number, code18 number, code19 number, code20 number, name varchar2(142)) PCTFREE 0 ENABLE ROW MOVEMENT;

Table BIG_ZIGGY2 created.

SQL> INSERT INTO big_ziggy2 SELECT rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, rownum, 'BOWIE' FROM dual CONNECT BY LEVEL <= 100000;

100,000 rows inserted.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=> null, tabname=> 'BIG_ZIGGY2');

PL/SQL procedure successfully completed.

SQL> analyze table big_ziggy2 compute statistics;

Table BIG_ZIGGY2 analyzed.

SQL> select table_name, num_rows, chain_cnt from user_tables where table_name='BIG_ZIGGY2';

   TABLE_NAME    NUM_ROWS    CHAIN_CNT
_____________ ___________ ____________
BIG_ZIGGY2         100000            0

SQL> create index big_ziggy2_id_i on big_ziggy2(id);

Index BIG_ZIGGY2_ID_I created.

SQL> create index big_ziggy2_code1_i on big_ziggy2(code1);

Index BIG_ZIGGY2_CODE1_I created.

SQL> create index big_ziggy2_code2_i on big_ziggy2(code2);

Index BIG_ZIGGY2_CODE2_I created.

SQL> create index big_ziggy2_code3_i on big_ziggy2(code3);

Index BIG_ZIGGY2_CODE3_I created.

SQL> create index big_ziggy2_code4_i on big_ziggy2(code4);

Index BIG_ZIGGY2_CODE4_I created.

SQL> create index big_ziggy2_code5_i on big_ziggy2(code5);

Index BIG_ZIGGY2_CODE5_I created.

SQL> create index big_ziggy2_code6_i on big_ziggy2(code6);

Index BIG_ZIGGY2_CODE6_I created.

SQL> create index big_ziggy2_code7_i on big_ziggy2(code7);

Index BIG_ZIGGY2_CODE7_I created.

SQL> create index big_ziggy2_code8_i on big_ziggy2(code8);

Index BIG_ZIGGY2_CODE8_I created.

SQL> create index big_ziggy2_code9_i on big_ziggy2(code9);

Index BIG_ZIGGY2_CODE9_I created.

SQL> create index big_ziggy2_code10_i on big_ziggy2(code10);

Index BIG_ZIGGY2_CODE10_I created.

SQL> create index big_ziggy2_code11_i on big_ziggy2(code11);

Index BIG_ZIGGY2_CODE11_I created.

SQL> create index big_ziggy2_code12_i on big_ziggy2(code12);

Index BIG_ZIGGY2_CODE12_I created.

SQL> create index big_ziggy2_code13_i on big_ziggy2(code13);

Index BIG_ZIGGY2_CODE13_I created.

SQL> create index big_ziggy2_code14_i on big_ziggy2(code14);

Index BIG_ZIGGY2_CODE14_I created.

SQL> create index big_ziggy2_code15_i on big_ziggy2(code15);

Index BIG_ZIGGY2_CODE15_I created.

SQL> create index big_ziggy2_code16_i on big_ziggy2(code16);

Index BIG_ZIGGY2_CODE16_I created.

SQL> create index big_ziggy2_code17_i on big_ziggy2(code17);

Index BIG_ZIGGY2_CODE17_I created.

SQL> create index big_ziggy2_code18_i on big_ziggy2(code18);

Index BIG_ZIGGY2_CODE18_I created.

SQL> create index big_ziggy2_code19_i on big_ziggy2(code19);

Index BIG_ZIGGY2_CODE19_I created.

SQL> create index big_ziggy2_code20_i on big_ziggy2(code20);

Index BIG_ZIGGY2_CODE20_I created.

If we now repeat the same UPDATE statement:

SQL> update big_ziggy2 set name='THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS';

100,000 rows updated.

PLAN_TABLE_OUTPUT
____________________________________________________________________________________
SQL_ID gupa6k30c341n, child number 0
-------------------------------------
update big_ziggy2 set name='THE RISE AND FALL OF ZIGGY STARDUST AND THE
SPIDERS FROM MARS'

Plan hash value: 3856369697

----------------------------------------------------------
| Id | Operation                  | Name       | E-Rows  |
----------------------------------------------------------
|  0 | UPDATE STATEMENT           |            |         |
|  1 |  UPDATE                    | BIG_ZIGGY2 |         |
|  2 |   TABLE ACCESS STORAGE FULL| BIG_ZIGGY2 |    100K |
----------------------------------------------------------

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1
   - PDML disabled because object is not decorated with parallel clause
   - Warning: basic plan statistics not available. These are only collected when:
       * hint 'gather_plan_statistics' is used for the statement or
       * parameter 'statistics_level' is set to 'ALL', at session or system level

Statistics
-----------------------------------------------------------
       1310 CPU used by this session
       1310 CPU used when call started
       1732 DB time
   13104856 RM usage
          5 Requests to/from client
         12 Session IORM flash wait time
      13343 Session total flash IO requests
  235888640 cell physical IO interconnect bytes
      36437 consistent gets
        994 consistent gets examination
        994 consistent gets examination (fastpath)
      36437 consistent gets from cache
      35443 consistent gets pin
      35275 consistent gets pin (fastpath)
    2574278 db block gets
    2574278 db block gets from cache
    1418826 db block gets from cache (fastpath)
       5729 enqueue releases
       5731 enqueue requests
      23745 gcs affinity lock grants
      11119 gcs data block access records
         25 ges messages sent
       1165 global enqueue gets sync
       1164 global enqueue releases
        215 messages sent
       8254 non-idle wait count
        476 non-idle wait time
         31 opened cursors cumulative
       5324 physical read requests optimized
       8019 physical read total IO requests
  235888640 physical read total bytes
   63856640 physical read total bytes optimized
         17 process last non-idle time
        160 recursive calls
          7 recursive cpu usage
    2610715 session logical reads
        475 user I/O wait time
          6 user calls
Elapsed: 00:00:17.600

SQL> commit

Commit complete.

We notice that this update consumed more resources than the previous example.

Note that the CPU used by session is now 1310 (previously 335), the number of db block gets is now 2574278 (previously 544587) and that the raw elapsed time has increased to 00:00:17.600 (previously it was 00:00:04.532).

SQLcl doesn’t automatically display redo statistics which is a shame and something I’ve only just noticed, but it will have increased significantly as I discussed previously.

However, if we look at the number of migrated rows on the BIG_ZIGGY2 table:

SQL> analyze table big_ziggy2 compute statistics;

Table BIG_ZIGGY2 analyzed.

SQL> select table_name, num_rows, chain_cnt from user_tables where table_name='BIG_ZIGGY2';

   TABLE_NAME    NUM_ROWS    CHAIN_CNT
_____________ ___________ ____________
BIG_ZIGGY2         100000            0

 

We notice there are no rows considered chained (migrated), as in this scenario on Oracle Autonomous Databases, all rows that moved to a different block had their associated ROWIDs updated on the fly in all the corresponding indexes and as such there was no need to have the pointer in the original block to denote the row’s new location.

So the choice is entirely yours.

If you have applications that rely on stored ROWIDs not changing in the background when a row happens to migrate OR you have applications in which the performance of the UPDATE DML is absolutely paramount and you wish to avoid the overheads associated with updating ROWIDs on the fly (which in an Exadata environment is less likely to be an issue), then do NOT set ENABLE ROW MOVEMENT on the table.

Generally, the improvements associated with more efficient indexed-based accesses overrides the overheads associated with (usually) one-off and uncommon row migrations (which might be mitigated with more appropriate settings of PCTFREE).

That said, I’ll discuss a few other areas of potential concern associated with this change of behaviour in my next post…

Advantages To Updating ROWID When Rows Migrate (“Fantastic Voyage”) February 13, 2023

Posted by Richard Foote in 19c, Autonomous Database, Autonomous Transaction Processing, Changing ROWID, Migrated Rows, Oracle, Oracle Blog, Oracle Cloud, Oracle General, Oracle Statistics, Performance Tuning, ROWID.
1 comment so far

In my last post, I discussed how with Oracle Autonomous Databases, when a row migrates and the ENABLE ROW MOVEMENT clause is specified for a table (be it Partitioned or Non-Partitioned), the ROWID of such rows are now updated on the fly. In non-autonomous database environments, such ROWIDs would NOT be updated, with a pointer in the previous table blocks pointing to the new physical location of the migrated row (as I previously discussed here).

So what’s the advantage of this new behaviour? Why might Oracle have made this change?

Well, the obvious benefit is that subsequent index scans that need to access migrated rows will have ROWIDs that directly point to the new, correct physical location of the row. Previously, indexes still had ROWIDs that reference the original row location and an additional table block access was required to access the row in its new physical location.

To illustrate this reduction in table block accesses, I’ll run a simple SQL that reads all 10,000 rows via an index from the BOWIE table that did not have the ENABLE ROW MOVEMENT clause when most rows were updated with significantly increased row sizes (as created in my previous post):

SQL> select /*+ index (bowie) */ * from bowie where id between 1 and 10000;

10,000 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID 5gum0cs9pb3zf, child number 0
-------------------------------------
select /*+ index (bowie) */ * from bowie where id between 1 and 10000

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                           | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers  |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                    |            |      1 |        |  10000 |00:00:00.03 |    18866 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED| BOWIE      |      1 |  10000 |  10000 |00:00:00.03 |    18866 |
|* 2 |   INDEX RANGE SCAN                  | BOWIE_ID_I |      1 |  10000 |  10000 |00:00:00.01 |      688 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=10000)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          9 CPU used by this session
          9 CPU used when call started
         13 DB time
     136499 RM usage
        707 Requests to/from client
        706 SQL*Net roundtrips to/from client
      19508 buffer is not pinned count
      10216 buffer is pinned count
       5273 bytes received via SQL*Net from client
     201460 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
      18866 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
      18866 consistent gets from cache
      18865 consistent gets pin
      18865 consistent gets pin (fastpath)
          1 cursor authentications
          2 execute count
          2 global enqueue gets sync
          2 global enqueue releases
          1 index range scans
  154550272 logical read bytes from cache
      18865 no work - consistent read gets
        721 non-idle wait count
          1 non-idle wait time
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
          1 parse time cpu
         20 process last non-idle time
      18866 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
      10000 table fetch by rowid
       9059 table fetch continued row
        707 user calls

I’m using a SQLcl connection to my autonomous database here to more easily list a bunch of useful statistics.

The 2 statistics I just want to highlight are the number of consistent gets (18866) and the number of table fetch continued rows (9059).

If we compare this with the exactly same SQL on the exact same data, but this time on the BOWIE2 table that did have ENABLE ROW MOVEMENT enabled and thus had the ROWIDs updated on the fly when most of its rows migrated:

SQL> select /*+ index (bowie2) */ * from bowie2 where id between 1 and 10000;

10,000 rows selected.

PLAN_TABLE_OUTPUT
________________________________________________________________________________________________________________
SQL_ID c346wwr8f4hfu, child number 0
-------------------------------------
select /*+ index (bowie2) */ * from bowie2 where id between 1 and 10000

Plan hash value: 3243780227

-------------------------------------------------------------------------------------------------------------
| Id | Operation                           | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers  |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                    |             |      1 |        |  10000 |00:00:00.02 |     4443 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED| BOWIE2      |      1 |  10000 |  10000 |00:00:00.02 |     4443 |
|* 2 |   INDEX RANGE SCAN                  | BOWIE2_ID_I |      1 |  10000 |  10000 |00:00:00.01 |      710 |
-------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=10000)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          8 CPU used by this session
          8 CPU used when call started
         13 DB time
        340 OS Involuntary context switches
       2532 OS Page reclaims
         14 OS System time used
         45 OS User time used
       2425 OS Voluntary context switches
     188462 RM usage
        707 Requests to/from client
        706 SQL*Net roundtrips to/from client
       2244 Server Data Segments In
       2244 Server Data Segments Out
      62270 Server Elapsed Time (msec) Last Data Sent
   35307000 Server Time (usec) Busy Sending Data
       2596 Server Time (usec) Round Trip Time
         72 Server Time (usec) Round Trip Time Variance
     869824 Server Total Bytes Acked
      40188 Server Total Bytes Received
       5063 buffer is not pinned count
      15602 buffer is pinned count
       5274 bytes received via SQL*Net from client
     201450 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
       4443 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
       4443 consistent gets from cache
       4442 consistent gets pin
       4442 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
   36397056 logical read bytes from cache
       4442 no work - consistent read gets
        720 non-idle wait count
          5 non-idle wait time
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
        173 process last non-idle time
         14 session cursor cache count
          1 session cursor cache hits
       4443 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
      10000 table fetch by rowid
        707 user calls

In this case, the number of consistent gets (4443) is much less than the previous 18866 and there are no table fetch continued row listed.

Now just a couple of points to make here.

Firstly, this is a tiny table and so the actual overall benefits here are relatively trivial, especially considering this all sits on an Exadata platform, where much of this data is effectively cached.

But as the saying goes, data may be updated once but accessed 10s of 1000s of times and so tiny savings can be considerable if SQLs are executed very frequently and/or tables are much larger and so less well cached within the database or the Exadata storage cells as a result.

You can determine if there’s potentially a migrated row problem by checking out CHAIN_CNT after analyzing a table:

SQL> analyze table bowie compute statistics;

Table BOWIE analyzed.

SQL> analyze table bowie2 compute statistics;

Table BOWIE2 analyzed.

SQL> select table_name, chain_cnt from user_tables where table_name in ('BOWIE', 'BOWIE2');

   TABLE_NAME    CHAIN_CNT
_____________ ____________
BOWIE                 9059
BOWIE2                   0

 

Note that CHAIN_CNT can also be a result of large rows that simply can’t fit within a data block, so you need to know your data to fully appreciate this figure. In this scenario, all 9059 chained rows are indeed associated with the migration of rows when the row length was substantially increased by an UPDATE statement.

A method of addressing ROWIDs that still point to the original table block following a row migration, is to reorganise the table (which can now be performed ONLINE):

SQL> alter table bowie move online;

Table BOWIE altered.

SQL> analyze table bowie compute statistics;

Table BOWIE analyzed.

SQL> select table_name, chain_cnt from user_tables where table_name in ('BOWIE', 'BOWIE2');

   TABLE_NAME    CHAIN_CNT
_____________ ____________
BOWIE2                   0
BOWIE                    0

As we can see, there are no longer any Chained Rows associated with the previously migrated rows.

This will now reduce the consistent gets and the overall overheads associated with accessing these previously migrated (chained) rows via an index, as we can now directly access their current table blocks via the correct ROWIDs.

If we now re-run the first SQL:

SQL> select /*+ index (bowie) */ * from bowie where id between 1 and 10000;

10,000 rows selected.

PLAN_TABLE_OUTPUT
_______________________________________________________________________________________________________________
SQL_ID 5gum0cs9pb3zf, child number 0
-------------------------------------
select /*+ index (bowie) */ * from bowie where id between 1 and 10000

Plan hash value: 1405654398

------------------------------------------------------------------------------------------------------------
| Id | Operation                           | Name       | Starts | E-Rows | A-Rows | A-Time     | Buffers  |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                    |            |      1 |        |  10000 |00:00:00.02 |     2677 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED| BOWIE      |      1 |  10000 |  10000 |00:00:00.02 |     2677 |
|* 2 |   INDEX RANGE SCAN                  | BOWIE_ID_I |      1 |  10000 |  10000 |00:00:00.01 |      688 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("ID">=1 AND "ID"<=10000)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

Statistics
-----------------------------------------------------------
          4 CPU used by this session
          4 CPU used when call started
       1850 Cached Commit SCN referenced
          5 DB time
     150963 RM usage
        707 Requests to/from client
        706 SQL*Net roundtrips to/from client
       3319 buffer is not pinned count
      17346 buffer is pinned count
       5273 bytes received via SQL*Net from client
     201475 bytes sent via SQL*Net to client
          2 calls to get snapshot scn: kcmgss
          2 calls to kcmgcs
       2677 consistent gets
          1 consistent gets examination
          1 consistent gets examination (fastpath)
       2677 consistent gets from cache
       2676 consistent gets pin
       2676 consistent gets pin (fastpath)
          2 execute count
          1 index range scans
   21929984 logical read bytes from cache
       2676 no work - consistent read gets
        720 non-idle wait count
          2 non-idle wait time
          2 opened cursors cumulative
          1 opened cursors current
          2 parse count (total)
         15 process last non-idle time
          1 session cursor cache count
          1 session cursor cache hits
       2677 session logical reads
          1 sorts (memory)
       2024 sorts (rows)
      10000 table fetch by rowid
        707 user calls

The consistent gets has gone way down to just 2677, down from the previous 18866…

In my next post, I’ll highlight some of the disadvantages with this new approached on how autonomous databases handle migrated rows in relation to now maintaining ROWIDs on the fly (and the discerning reader might even find a clue or two within this very post)… 🙂

When Does A ROWID Change? Part V (“The Wedding”) February 7, 2023

Posted by Richard Foote in 19c, 19c New Features, Autonomous Database, Autonomous Transaction Processing, Changing ROWID, Index Internals, Oracle, Oracle Cloud, Oracle General, Oracle Indexes, Richard's Blog, ROWID.
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It’s been a busy period. First Christmas, then the wedding of my beautiful daughter, then a nice get-a-way to get over the wedding of my beautiful daughter, and then a busy period with work.

But now I’m back 🙂

In this series on when does a ROWID change, I previously discussed how a row is generally “migrated”, but the ROWID remains unchanged, when a row is updated such that it can no longer fit within its current block. Hence the general rule has always been that the ROWID of a row does not change (although I also previously discussed various exceptions to this general rule).

However, things change in an Oracle Autonomous Database, when looking at the behaviour of the ROWID after a row migrates…

To illustrate, I’m going to run a similar demo as previously, but this time within (one of my free) Transaction Processing Autonomous Databases. I start by creating and populating a basic table, with the PCTFREE set to 0 to ensure my data blocks are initially nicely filled:

SQL> create table bowie (id number, name varchar2(142)) pctfree 0;

Table BOWIE created.

SQL> insert into bowie select rownum, 'BOWIE' from dual connect by level <=10000;

10,000 rows inserted.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE');

PL/SQL procedure successfully completed.

SQL> create index bowie_id_i on bowie(id);

Index BOWIE_ID_I created.

 

Let’s just take note of a few random ROWID values:

SQL> select id, rowid from bowie where id in (42, 424, 4242) order by id;

     ID                 ROWID
_______ _____________________
     42 AAApUqAAAAACKD/AAp
    424 AAApUqAAAAACKD/AGn
   4242 AAApUqAAAAACKGEAHF

 

I’ll next update the rows with the NAME column value that is significantly larger than previously, to force the migration of many of my existing rows:

SQL> update bowie set name='THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS';

10,000 rows updated.

SQL> commit;

Commit complete.

 

If we now look at the ROWID of these same rows:

SQL> select id, rowid from bowie where id in (42, 424, 4242) order by id;

     ID                 ROWID
_______ _____________________
     42 AAApUqAAAAACKD/AAp
    424 AAApUqAAAAACKD/AGn
   4242 AAApUqAAAAACKGEAHF

 

We notice that they have NOT changed.

So the default behaviour in an Autonomous Database is as it has always been, that even though rows are migrated, it does NOT change the resultant ROWIDs.

This is an important point if you do NOT want your ROWIDs to change when a row is migrated (in the example perhaps that you have applications that explicitly use stored ROWIDs and are dependant on them not changing).

I’ll next run the same demo again, but with one key difference. This time, I’m explicitly setting ENABLE ROW MOVEMENT in the creation of my non-partitioned table:

 

SQL> create table bowie2 (id number, name varchar2(142)) pctfree 0 enable row movement;

Table BOWIE2 created.

SQL> insert into bowie2 select rownum, 'BOWIE' from dual connect by level <=10000;

10,000 rows inserted.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE2');

PL/SQL procedure successfully completed.

SQL> create index bowie2_id_i on bowie2(id);

Index BOWIE2_ID_I created.

 

Let’s again have a look at the current ROWID of a few random rows:

SQL> select id, rowid from bowie2 where id in (42, 424, 4242) order by id;

     ID                 ROWID
_______ _____________________
     42 AAApUxAAAAACKIfAAp
    424 AAApUxAAAAACKIfAGn
   4242 AAApUxAAAAACKIkAHF

 

Let’s now perform the same update as before, forcing the migration of rows in the table:

SQL> update bowie2 set name='THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS';

10,000 rows updated.

SQL> commit;

Commit complete.

 

Now, as I discussed previously, in a non-autonomous environment, on a non-partitioned table, ENABLE ROW MOVEMENT would have no impact in this scenario and the ROWIDs would NOT have changed for any of these migrated rows.

But if we look at the ROWIDs in this autonomous database environment:

SQL> select id, rowid from bowie2 where id in (42, 424, 4242) order by id;

     ID                 ROWID
_______ _____________________
     42 AAApUxAAAAACKJqABX
    424 AAApUxAAAAACKJuAAJ
   4242 AAApUxAAAAACKMJABN

We can see that they have all indeed changed.

When a row migrates in an autonomous database environment AND we set the ENABLE ROW MOVEMENT on a non-partitioned table, the ROWIDs are indeed updated on the fly.

If we had an application that relied on these ROWIDs not changing:

SQL> select id from bowie2 where rowid in ('AAApUxAAAAACKIfAAp', 'AAApUxAAAAACKIfAGn', 'AAApUxAAAAACKIkAHF');

no rows selected

Well, the results would be “disappointing” (or downright disastrous if they then happen to select completed different rows)…

However, if we use an indexed key to fetched the required rows:

SQL> select * from bowie2 where id in (42, 424, 4242);

     ID                                                             NAME
_______ ________________________________________________________________
     42 THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS
    424 THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS
   4242 THE RISE AND FALL OF ZIGGY STARDUST AND THE SPIDERS FROM MARS

PLAN_TABLE_OUTPUT
_________________________________________________________________________________________________________________
SQL_ID atz1zbtyptu6n, child number 0
-------------------------------------
select * from bowie2 where id in (42, 424, 4242)

Plan hash value: 1734578469

--------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name        | Starts | E-Rows | A-Rows | A-Time     | Buffers  |
--------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |             |      1 |        |      3 |00:00:00.01 |        8 |
|  1 |  INLIST ITERATOR                     |             |      1 |        |      3 |00:00:00.01 |        8 |
|  2 |   TABLE ACCESS BY INDEX ROWID BATCHED| BOWIE2      |      3 |      3 |      3 |00:00:00.01 |        8 |
|* 3 |    INDEX RANGE SCAN                  | BOWIE2_ID_I |      3 |      3 |      3 |00:00:00.01 |        5 |
--------------------------------------------------------------------------------------------------------------

PLAN_TABLE_OUTPUT
_____________________________________________________________________________________________________

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

   3 - access(("ID"=42 OR "ID"=424 OR "ID"=4242))

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation

 

They thankfully have indeed been correctly updated within the index and can successfully access the required rows.

So the decision is entirely yours. If you want to keep to the existing behaviour in relation to the non-changing of ROWIDs of migrated rows, do NOT set ENABLE ROW MOVEMENT on the tables in the autonomous database environments.

If you do want to adopt this new behaviour, then simply set ENABLE ROW MOVEMENT.

I’ll discuss the advantages and disadvantages of this new behaviour in future posts…

When Does A ROWID Change? Part IV (“Mass Production”) December 21, 2022

Posted by Richard Foote in Attribute Clustering, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, Changing ROWID, Clustering Factor, Data Clustering, Flashback, Move Partitions, Oracle, Oracle Blog, Oracle Cloud, Oracle General, Oracle Indexes, Partitioning, Richard's Blog, ROWID.
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In Part II in this series, I discussed how the update of the partitioned key column of a row that results in the row being moved to a different partition, will result in the ROWID of such rows changing.

However, there a quite a number of other user initiated actions in which ROWIDs can easily change (as indeed discussed in Connor McDonald’s video on this subject).

Some of these include:

  • Moving a table or partition, as this results in the segment being reorganised, with all associated rows being physically relocated and their associated ROWIDs changing
  • Altering a non-partitioned table such that it be now be partitioned, which again results in the physical relocation of all rows and their ROWIDs changing (which BTW, can potentially occur on a Autonomous Database without any user intervention)
  • Altering the partitioning strategy of a partitioned table, again changes the physical location of all rows
  • Hybrid Columnar Compression (HCC), which by packing rows more tightly, can more likely result in the physical relocation of a row during subsequent DML statements
  • Altering a table to Shrink Space, which attempts to move rows between table blocks to pack rows more tightly, again potentially resulting in rows physically moving and the changing of their associated ROWIDs
  • Flashback of a table, which results in rows being deleted and inserted and hence the change of their associated ROWIDs

I’ll illustrate an example of all this, with one of the key reasons why you may want to re-organise a table (and implicitly change all the ROWIDs of a table).

I’ll start by creating and populating a simple little table, with a CODE column that has very poorly clustered data:

SQL> create table bowie (id number, code number, name varchar2(42));

Table created.

SQL> insert into bowie select rownum, mod(rownum, 500), 'DAVID BOWIE' from dual connect by level <=1000000;

1000000 rows created.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE');

PL/SQL procedure successfully completed.

Let’s now create an index on this CODE column:

SQL> create index bowie_code_i on bowie(code);

Index created.

We take note of the ROWIDs of a few random rows:

SQL> select id, rowid from bowie where id in (42, 4242, 424242) order by id;

        ID ROWID
---------- ------------------
        42 AAASn1AAMAAAgB2AAp
      4242 AAASn1AAMAAAgCHACL
    424242 AAASn1AAMAAAgbtAAJ

If we run a simple query with a predicate based on the CODE column:

SQL> select * from bowie where code=42;

2000 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1845943507

---------------------------------------------------------------------------
| Id  | Operation          | Name  | Rows | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |       | 2000 | 42000 |    1004 (2)| 00:00:01 |
| * 1 |  TABLE ACCESS FULL | BOWIE | 2000 | 42000 |    1004 (2)| 00:00:01 |
---------------------------------------------------------------------------

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

   1 - filter("CODE"=42)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
       3596 consistent gets
          0 physical reads
          0 redo size
      20757 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
       2000 rows processed

We notice the CBO has chosen to ignore the index and use a FTS instead, even though only 2000 rows in a 1M row table (just 0.2%) are returned.

Why?

Because the clustering of the CODE data is terrible, with the required values littered throughout the table. If we look at the Clustering Factor of the index:

SQL> select index_name, leaf_blocks, clustering_factor from user_indexes where index_name='BOWIE_CODE_I';

INDEX_NAME   LEAF_BLOCKS CLUSTERING_FACTOR
------------ ----------- -----------------
BOWIE_CODE_I        2063           1000000

We notice the index has the worst possible Clustering Factor value of 1000000.

So to improve the performance of this (say critical) query, we can add a Clustering Attribute to this table based on the CODE column and then reorganise the table:

SQL> alter table bowie add clustering by linear order (code);

Table altered.

SQL> alter table bowie move online;

Table altered.

If we now look at the Clustering Factor of the index:

SQL> select index_name, leaf_blocks, clustering_factor from user_indexes where index_name='BOWIE_CODE_I';

INDEX_NAME   LEAF_BLOCKS CLUSTERING_FACTOR
------------ ----------- -----------------
BOWIE_CODE_I        2063              3568

We can see it has substantially improved, down to just 3568 from the previous 1000000 value, as the data is now perfectly clustered based on the CODE column.

If we now re-run the query:

SQL> select * from bowie where code=42;

2000 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 853003755

----------------------------------------------------------------------------------------------------
| Id  | Operation                            | Name         | Rows | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |              | 2000 | 42000 |      15 (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE        | 2000 | 42000 |      15 (0)| 00:00:01 |
| * 2 |   INDEX RANGE SCAN                   | BOWIE_CODE_I | 2000 |       |       7 (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------

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

   2 - access("CODE"=42) 

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
         17 consistent gets
          0 physical reads
          0 redo size
      50735 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
       2000 rows processed

The CBO now choses to use the index and the query is much more efficient as a result (consistent gets down to just 17 from the previous 3596).

So all is now much better, except for any application that was reliant on using ROWIDs to fetch the data, as all ROWIDs have now changed:

SQL> select id, rowid from bowie where id in (42, 4242, 424242) order by id;

        ID ROWID
---------- ------------------
        42 AAASn6AAMAAAACvAEf
      4242 AAASn6AAMAAAiRaAA4
    424242 AAASn6AAMAAAiRWAEQ

So there are many ways in which the ROWID of a row can potentially change.

And now there’s another key manner in which a ROWID can very easily change in Oracle Autonomous Database environments, as I’ll next discuss…

Automatic Indexing: Non-Equality Predicates Part IV (“Like A Rocket Man”) November 29, 2022

Posted by Richard Foote in 21c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, LIKE Predicates, Non-Equality Predicates, Oracle, Oracle 21c, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, _EXADATA_FEATURE_ON.
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Forgive me, it’s been a while since I last posted, but life has so many distractions these days 🙂

I recently had a question on whether a LIKE predicate can generate an Automatic Index now that non-equality predicates are supported since Oracle Database 21c.

Now the answer I initially provided was “well, why don’t you just test it for yourself“. However, his subsequent responses highlighted to me that not everyone might necessarily know how to potentially play with many of the Exadata features, even if you don’t directly have access to an Exadata environment.

So the purpose of this post is not only to answer this question, but also to just highlight HOW to potentially test things out for yourself when you’re not lucky enough to work directly with Exadata.

One obvious manner in which to play on an Exadata environment is to simply create and connect to an Oracle Autonomous Database environment using Oracle’s Cloud services (which are all Exadata-based environments), where you can easily, FOR FREE, and WITH NO TIME RESTRICTIONS play with many Exadata database features. The “Always Free Cloud Services” is truly a fabulous resource provided by Oracle, where you can have a couple of Autonomous Database environments always at your disposal (and very very easily and quickly just drop an existing database environment and re-create a new one).

Follow the link for all the information you need on how to get started with Oracle’s Always Free Cloud Services: https://www.oracle.com/au/autonomous-database/free-trial/

If the version of Oracle Database you like to play with isn’t currently available on the Oracle Autonomous Database platforms, another option is to simply download the database version you want to play with and just make it think it’s actually on an Exadata platform, by setting the following hidden parameter:

SQL> alter system set "_exadata_feature_on"=true scope=spfile;

System altered.

and restart your database.

You can now at least play and learn about many of the Exadata database features (such as Automatic Indexing), without having an actual Exadata machine on hand.

OK, now that you have an Exadata (or Exadata-like) environment on hand, you can go about answering for yourself these types of questions…

So, does Automatic Indexing now work in the case of a LIKE predicate?

First, make sure Automatic Indexing is enabled:

SQL> EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_MODE','IMPLEMENT');

PL/SQL procedure successfully completed.

Begin by creating and populating a basic table structure to test. The following table just has a few basic columns, with the MIXED_STUFF column simply populated with the rownum concatenated with a constant string:

SQL> create table aladdin_sane (id number, code1 number, grade number, mixed_stuff varchar2(42), name varchar2(42));

Table created.

SQL> insert into aladdin_sane select rownum, mod(rownum,100000), mod(rownum,100), rownum || ' David Bowie ' || rownum, 'ZIGGY STARDUST' from dual connect by level <=10000000;

10000000 rows created.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'ALADDIN_SANE');

PL/SQL procedure successfully completed.

 

I then run the following query several times with a LIKE predicate that returns just the one row from my 10M row table:

SQL> select * from aladdin_sane where mixed_stuff like '4242 %';

Execution Plan
----------------------------------------------------------------------------------
| Id  | Operation          | Name         | Rows | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |              |    1 |    57 |   14805 (2)| 00:00:01 |
| * 1 |  TABLE ACCESS FULL | ALADDIN_SANE |    1 |    57 |   14805 (2)| 00:00:01 |
----------------------------------------------------------------------------------

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

1 - storage("MIXED_STUFF" LIKE '4242 %')
    filter("MIXED_STUFF" LIKE '4242 %')

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
     169813 consistent gets
      84940 physical reads
          0 redo size
        912 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          1 rows processed

 

Without any indexes currently in place, the CBO has no choice but to use a FTS. But, with only 1 row returned from this 10M table, an appropriate index would almost certainly be beneficial. So what does Automatic Index do in this scenario?

Once we wait for the next running of the Automatic Indexing jobs to complete, we can check:

SQL> select dbms_auto_index.report_last_activity() report from dual;

REPORT
--------------------------------------------------------------------------------
GENERAL INFORMATION
-------------------------------------------------------------------------------
Activity start              : 29-NOV-2022 12:52:30
Activity end                : 29-NOV-2022 12:53:50
Executions completed        : 1
Executions interrupted      : 0
Executions with fatal error : 0
-------------------------------------------------------------------------------

SUMMARY (AUTO INDEXES)
-------------------------------------------------------------------------------
Index candidates                             : 1
Indexes created (visible / invisible)        : 1 (1 / 0)
Space used (visible / invisible)             : 452.98 MB (452.98 MB / 0 B)
Indexes dropped                              : 0
SQL statements verified                      : 6
SQL statements improved (improvement factor) : 1 (169815.2x)
SQL plan baselines created (SQL statements)  : 1 (1)
Overall improvement factor                   : 2x
-------------------------------------------------------------------------------

SUMMARY (MANUAL INDEXES)
-------------------------------------------------------------------------------
Unused indexes   : 0
Space used       : 0 B
Unusable indexes : 0
-------------------------------------------------------------------------------

INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
-------------------------------------------------------------------------------
-----------------------------------------------------------------------------------
| Owner | Table        | Index                | Key         | Type   | Properties |
-----------------------------------------------------------------------------------
| BOWIE | ALADDIN_SANE | SYS_AI_dzhahcw1cf0mw | MIXED_STUFF | B-TREE | NONE       |
-----------------------------------------------------------------------------------
-------------------------------------------------------------------------------

VERIFICATION DETAILS
-------------------------------------------------------------------------------
The performance of the following statements improved:
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE

SQL ID              : fgvdbfsfwb9jv

SQL Text            : select * from aladdin_sane where mixed_stuff like '4242%'

Improvement Factor  : 169815.2x

Execution Statistics:
-----------------------------
                  Original Plan                Auto Index Plan
                  ---------------------------- ----------------------------
Elapsed Time (s): 10869872                     433
CPU Time (s):     9778626                      433
Buffer Gets:      2377413                      4
Optimizer Cost:   14805                        4
Disk Reads:       1189160                      2
Direct Writes:    0                            0
Rows Processed:   14                           1
Executions:       14                           1

 

So, it appears that Automatic Indexing has indeed created a new index. We can now check out the new index details:

 

select index_name, auto, constraint_index, visibility, compression, status 
from user_indexes where table_name='ALADDIN_SANE';

INDEX_NAME           AUT CON VISIBILIT COMPRESSION   STATUS
-------------------- --- --- --------- ------------- --------
SYS_AI_bnyacywycxx8b YES NO  VISIBLE   DISABLED      VALID

SQL> select index_name, column_name, column_position 
from user_ind_columns where table_name='ALADDIN_SANE' order by index_name, column_position;

INDEX_NAME                     COLUMN_NAME                    COLUMN_POSITION
------------------------------ ------------------------------ ---------------
SYS_AI_dzhahcw1cf0mw           MIXED_STUFF                                  1

 

Automatic Indexing has indeed created a VALID/VISIBLE index on the MIXED_STUFF column.

If we now re-run the query:

SQL> select * from aladdin_sane where mixed_stuff like '4242 %';

Execution Plan
------------------------------------------------------------------------------------------------------------
| Id  | Operation                            | Name                 | Rows | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |                      |    1 |    57 |       4 (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED | ALADDIN_SANE         |    1 |    57 |       4 (0)| 00:00:01 |
| * 2 |   INDEX RANGE SCAN                   | SYS_AI_dzhahcw1cf0mw |    1 |       |       3 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------------

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

2 - access("MIXED_STUFF" LIKE '4242 %')
    filter("MIXED_STUFF" LIKE '4242 %')

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
     116204 consistent gets
      84940 physical reads
          0 redo size
        912 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          1 rows processed

 

We can see the newly generated plan now uses the new Automatic Index.

But due to Deferred Invalidations (which I’ve discussed previously), which in Oracle 21c delay the invalidation of SQL cursors due to new indexes, we may need to (for example) flush the shared_pool for the new plan to actually be used (a safe enough option in our play/test environment):

SQL> alter system flush shared_pool;

System altered.

SQL> select * from aladdin_sane where mixed_stuff like '4242 %';

Execution Plan

------------------------------------------------------------------------------------------------------------
| Id  | Operation                            | Name                 | Rows | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |                      |    1 |    57 |       4 (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED | ALADDIN_SANE         |    1 |    57 |       4 (0)| 00:00:01 |
| * 2 |   INDEX RANGE SCAN                   | SYS_AI_dzhahcw1cf0mw |    1 |       |       3 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------------

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

2 - access("MIXED_STUFF" LIKE '4242 %')
    filter("MIXED_STUFF" LIKE '4242 %')

Statistics
----------------------------------------------------------
        263 recursive calls
          0 db block gets
        508 consistent gets
          0 physical reads
          0 redo size
        916 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
         70 sorts (memory)
          0 sorts (disk)
          1 rows processed

SQL> select * from aladdin_sane where mixed_stuff like '4242 %';

Execution Plan

------------------------------------------------------------------------------------------------------------
| Id  | Operation                            | Name                 | Rows | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |                      |    1 |    57 |       4 (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED | ALADDIN_SANE         |    1 |    57 |       4 (0)| 00:00:01 |
| * 2 |   INDEX RANGE SCAN                   | SYS_AI_dzhahcw1cf0mw |    1 |       |       3 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------------

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

2 - access("MIXED_STUFF" LIKE '4242 %')
    filter("MIXED_STUFF" LIKE '4242 %')

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
          5 consistent gets
          0 physical reads
          0 redo size
        916 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          1 rows processed

 

We can now see that the new plan has indeed been adopted with the substantial decrease in consistent gets, down to just 5 from the previous 169813 with the FTS.

So yes, Automatic Indexing does indeed now work with LIKE predicates, but most importantly, it’s very easy for you to test and see these things for yourself.

In which case, you won’t need blogs such as this in the future to show you the way… 🙂

Automatic Indexing 21c: Non-Equality Predicate Anomaly (“Strangers When We Meet”) July 14, 2022

Posted by Richard Foote in 21c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Exadata X8, Full Table Scans, Index Column Order, Invisible Indexes, Non-Equality Predicates, Oracle, Oracle 21c, Oracle Blog, Oracle Cloud, Oracle Cost Based Optimizer, Oracle Indexes, Performance Tuning, Richard Foote Training, Richard's Blog.
3 comments

I’m currently putting together some Exadata related training for a couple of customers and came across a rather strange anomaly with regard the status of Automatic Indexes, when created in part on unselective, non-equality predicates.

As discussed previously, Oracle Database 21c now allows the creation of Automatic Indexes based on non-equality predicates (previously, Automatic Indexes were only created on equality-based predicates).

But one appears to get rather odd resultant Automatic Indexes in the scenario where the non-equality predicate is not particularly selective but other predicates are highly selective.

To illustrate, I’ll create a basic table that has two columns (ID and CODE) that are both highly selective:

SQL> create table ziggy_new (id number, code number, name varchar2(42));

Table created.

SQL> insert into ziggy_new select rownum, mod(rownum, 1000000)+1, 'David Bowie' from dual connect by level <= 10000000;

10000000 rows created.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'ZIGGY_NEW');

PL/SQL procedure successfully completed.

So there are currently no indexes on this table.

I’ll next run the following SQL (and others similar) a number of times:

SQL> select * from ziggy_new where code=42 and id between 1 and 100000;

Execution Plan
----------------------------------------------------------
Plan hash value: 3165184525

----------------------------------------------------------------------------------------
| Id  | Operation                  | Name      | Rows | Bytes | Cost (%CPU) | Time     |
----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |           |    1 |    23 |    6738 (2) | 00:00:01 |
| * 1 |  TABLE ACCESS STORAGE FULL | ZIGGY_NEW |    1 |    23 |    6738 (2) | 00:00:01 |
----------------------------------------------------------------------------------------

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

   1 - storage("CODE"=42 AND "ID"<=100000 AND "ID">=1)
       filter("CODE"=42 AND "ID"<=100000 AND "ID">=1)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
      38605 consistent gets
      38600 physical reads
          0 redo size
        725 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          1 rows processed

Without any indexes, the CBO currently has no choice but to use a Full Table Scan.

But only 1 row is returned. The first equality predicate on the CODE column is highly selective and on its own would only return 10 rows out of the 10M row table. The second, non-equality range-based predicate on the ID column is nowhere near as selective and offers limited additional filtering.

The CBO stops calculating index related costs after a non-equality predicate column (as subsequent column values could exist anywhere within the preceding range), and so the more effective index here is one based on (CODE, ID) with the non-equality predicate column second,  or potentially just on the CODE column only, as the ID range offers minimal filtering benefits.

So what does Automatic Indexing make of things?

If we look at the subsequent Automatic Indexing report:

SUMMARY (AUTO INDEXES)
-------------------------------------------------------------------------------
Index candidates                             : 3
Indexes created (visible / invisible)        : 1 (0 / 1)
Space used (visible / invisible)             : 209.72 MB (0 B / 209.72 MB)
Indexes dropped                              : 0
SQL statements verified                      : 44
SQL statements improved (improvement factor) : 12 (64.7x)
SQL plan baselines created                   : 0
Overall improvement factor                   : 1.6x
-------------------------------------------------------------------------------

SUMMARY (MANUAL INDEXES)
-------------------------------------------------------------------------------
Unused indexes   : 0
Space used       : 0 B
Unusable indexes : 0
-------------------------------------------------------------------------------

INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
-------------------------------------------------------------------------------
----------------------------------------------------------------------------
| Owner | Table     | Index                | Key     | Type   | Properties |
----------------------------------------------------------------------------
| BOWIE | ZIGGY_NEW | SYS_AI_75j16xff1ag3j | CODE,ID | B-TREE | NONE       |
----------------------------------------------------------------------------

So Automatic Indexing has indeed created an index based on CODE,ID (a common Automatic Indexing trait appears to be to create an index based on all available predicates).

BUT the index is created as an INVISIBLE Index and so can not generally be used by database sessions.

SQL> select index_name, auto, visibility, status, num_rows, leaf_blocks, clustering_factor
from user_indexes where table_name='ZIGGY_NEW';

INDEX_NAME                     AUT VISIBILIT STATUS     NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR
------------------------------ --- --------- -------- ---------- ----------- -----------------
SYS_AI_75j16xff1ag3j           YES INVISIBLE VALID      10000000       25123          10000000

SQL> select index_name, column_name, column_position
     from user_ind_columns where table_name='ZIGGY_NEW';

INDEX_NAME                     COLUMN_NAME  COLUMN_POSITION
------------------------------ ------------ ---------------
SYS_AI_75j16xff1ag3j           CODE                       1
SYS_AI_75j16xff1ag3j           ID                         2

 

So re-running the previous SQL statements continues to use a Full Table Scan:

SQL> select * from ziggy_new where code=42 and id between 1 and 100000;

Execution Plan
----------------------------------------------------------
Plan hash value: 3165184525

----------------------------------------------------------------------------------------
|  Id | Operation                  | Name      | Rows | Bytes | Cost (%CPU) | Time     |
----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |           |    1 |    23 |    6738 (2) | 00:00:01 |
| * 1 |  TABLE ACCESS STORAGE FULL | ZIGGY_NEW |    1 |    23 |    6738 (2) | 00:00:01 |
----------------------------------------------------------------------------------------

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

   1 - storage("CODE"=42 AND "ID"<=100000 AND "ID">=1)
       filter("CODE"=42 AND "ID"<=100000 AND "ID">=1)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
      38605 consistent gets
      38600 physical reads
          0 redo size
        725 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          1 rows processed

 

Automatic Indexing appears to only create Invisible indexes when there is an inefficient non-equality predicate present. It won’t create the index as a Visible index, even though it would significantly benefit these SQL statements that caused its creation. And Automatic Indexing won’t create an index on just the highly selective CODE equality predicate, which would also be of much benefit to these SQL statements.

If we now run similar queries, but with much more selective non-equality predicates, such as:

SQL> select * from ziggy_new where code=1 and id between 1 and 10;

no rows selected

Execution Plan
----------------------------------------------------------
Plan hash value: 3165184525

----------------------------------------------------------------------------------------
|  Id | Operation                  | Name      | Rows | Bytes | Cost (%CPU) | Time     |
----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |           |    1 |    23 |    6738 (2) | 00:00:01 |
| * 1 |  TABLE ACCESS STORAGE FULL | ZIGGY_NEW |    1 |    23 |    6738 (2) | 00:00:01 |
----------------------------------------------------------------------------------------

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

   1 - storage("CODE"=1 AND "ID"<=10 AND "ID">=1)
       filter("CODE"=1 AND "ID"<=10 AND "ID">=1)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
      38604 consistent gets
      38600 physical reads
          0 redo size
        503 bytes sent via SQL*Net to client
         41 bytes received via SQL*Net from client
          1 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          0 rows processed

Again, with no (Visible) index present, the CBO currently has no choice but to use the Full Table Scan.

But during the next cycle, after Automatic Indexing kicks in again:

SUMMARY (AUTO INDEXES)
-------------------------------------------------------------------------------
Index candidates                             : 5
Indexes created (visible / invisible)        : 1 (1 / 0)
Space used (visible / invisible)             : 209.72 MB (209.72 MB / 0 B)
Indexes dropped                              : 0
SQL statements verified                      : 89
SQL statements improved (improvement factor) : 31 (71.9x)
SQL plan baselines created                   : 0
Overall improvement factor                   : 1.7x
-------------------------------------------------------------------------------

SUMMARY (MANUAL INDEXES)
-------------------------------------------------------------------------------
Unused indexes   : 0
Space used       : 0 B
Unusable indexes : 0
-------------------------------------------------------------------------------

INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
-------------------------------------------------------------------------------
----------------------------------------------------------------------------
| Owner | Table     | Index                | Key     | Type   | Properties |
----------------------------------------------------------------------------
| BOWIE | ZIGGY_NEW | SYS_AI_75j16xff1ag3j | CODE,ID | B-TREE | NONE       |
----------------------------------------------------------------------------
-------------------------------------------------------------------------------

VERIFICATION DETAILS
-------------------------------------------------------------------------------
The performance of the following statements improved:
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL ID              : d4znwcu4h52ca
SQL Text            : select * from ziggy_new where code=42 and id between 1 and 10
Improvement Factor  : 38604x

Execution Statistics:
-----------------------------
                    Original Plan                Auto Index Plan
                    ---------------------------- ----------------------------
Elapsed Time (s):   3398605                      68
CPU Time (s):       3166824                      68
Buffer Gets:        463250                       3
Optimizer Cost:     6738                         4
Disk Reads:         463200                       0
Direct Writes:      0                            0
Rows Processed:     0                            0
Executions:         12                           1

PLANS SECTION
--------------------------------------------------------------------------------
-------------

- Original
-----------------------------
Plan Hash Value : 3165184525

--------------------------------------------------------------------------------
| Id | Operation                  | Name      | Rows | Bytes | Cost | Time     |
--------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |           |      |       | 6738 |          |
|  1 |  TABLE ACCESS STORAGE FULL | ZIGGY_NEW |    1 |    23 | 6738 | 00:00:01 |
--------------------------------------------------------------------------------

- With Auto Indexes
-----------------------------
Plan Hash Value : 1514586396

-------------------------------------------------------------------------------------------------------
|  Id | Operation                            | Name                 | Rows | Bytes | Cost | Time     |
-------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |                      |    1 |    23 |    4 | 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED | ZIGGY_NEW            |    1 |    23 |    4 | 00:00:01 |
| * 2 |   INDEX RANGE SCAN                   | SYS_AI_75j16xff1ag3j |    1 |       |    3 | 00:00:01 |
-------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
------------------------------------------
* 2 - access("CODE"=42 AND "ID">=1 AND "ID"<=10)

Notes
-----
- Dynamic sampling used for this statement ( level = 11 )

 

But this time, the index on the CODE,ID columns is created as a Visible index.

INDEX_NAME                     AUT VISIBILIT STATUS     NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR
------------------------------ --- --------- -------- ---------- ----------- -----------------
SYS_AI_75j16xff1ag3j           YES VISIBLE   VALID      10000000       25123          10000000

SQL> select index_name, column_name, column_position from user_ind_columns where table_name='ZIGGY_NEW';

INDEX_NAME                     COLUMN_NAME  COLUMN_POSITION
------------------------------ ------------ ---------------
SYS_AI_75j16xff1ag3j           CODE                       1
SYS_AI_75j16xff1ag3j           ID                         2

So this index can be generally used, both by the newer SQLs that generated the now Visible index:

SQL> select * from ziggy_new where code=42 and id between 1 and 10;

no rows selected

Execution Plan
----------------------------------------------------------
Plan hash value: 1514586396

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name                 | Rows | Bytes | Cost (%CPU) | Time     |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |                      |    1 |    23 |       4 (0) | 00:00:01 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | ZIGGY_NEW            |    1 |    23 |       4 (0) | 00:00:01 |
|* 2 |   INDEX RANGE SCAN                   | SYS_AI_75j16xff1ag3j |    1 |       |       3 (0) | 00:00:01 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("CODE"=42 AND "ID">=1 AND "ID"<=10)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
          3 consistent gets
          0 physical reads
          0 redo size
        503 bytes sent via SQL*Net to client
         41 bytes received via SQL*Net from client
          1 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          0 rows processed

And also used by the SQLs with the unselective non-equality predicates, that Automatic Indexing would only create as Invisible indexes:

SQL> select * from ziggy_new where code=42 and id between 1 and 100000;

Execution Plan
----------------------------------------------------------
Plan hash value: 1514586396

------------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name                 | Rows | Bytes | Cost (%CPU) | Time     |
------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                     |                      |    1 |    23 |       4 (0) | 00:00:01 |
|  1 |  TABLE ACCESS BY INDEX ROWID BATCHED | ZIGGY_NEW            |    1 |    23 |       4 (0) | 00:00:01 |
|* 2 |   INDEX RANGE SCAN                   | SYS_AI_75j16xff1ag3j |    1 |       |       3 (0) | 00:00:01 |
------------------------------------------------------------------------------------------------------------

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

   2 - access("CODE"=42 AND "ID">=1 AND "ID"<=100000)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
          5 consistent gets
          0 physical reads
          0 redo size
        729 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          1 rows processed

 

Automatic Indexing appears to currently not quite do the right thing with SQL statements that have unselective non-equality predicates, by creating such indexes as only Invisible Indexes, inclusive of the unselective columns.

Although an edge case, I would recommend looking through the list of created Automatic Indexes to see if any such Invisible/Valid indexes exists, as it can suggest there are current inefficient SQL statements that could benefit from such indexes being Visible.

Automatic Indexes: Automatically Rebuild Unusable Indexes Part IV (“Nothing Has Changed”) May 31, 2022

Posted by Richard Foote in 19c, 19c New Features, 21c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Full Table Scans, Index Column Order, Index Internals, Local Indexes, Mixing Auto and Manual Indexes, Oracle, Oracle 21c, Oracle Blog, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle Indexing Internals Webinar, Oracle19c, Unusable Indexes.
1 comment so far

In a previous post, I discussed how Automatic Indexing (AI) does not automatically rebuild a manually built index that is in an Unusable state (but will rebuild an Unusable automatically created index).

The demo I used was a simple one, based on manually created indexes referencing a non-partitioned table.

In this post, I’m going to use a demo based on manually created indexes referencing a partitioned table.

I’ll start by creating a rather basic range-based partitioned table, using the RELEASE_DATE column to partition the data by year:

SQL> CREATE TABLE big_bowie (id number, album_id number, country_id number, release_date date,
total_sales number) PARTITION BY RANGE (release_date)
(PARTITION ALBUMS_2014 VALUES LESS THAN (TO_DATE('01-JAN-2015', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2015 VALUES LESS THAN (TO_DATE('01-JAN-2016', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2016 VALUES LESS THAN (TO_DATE('01-JAN-2017', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2017 VALUES LESS THAN (TO_DATE('01-JAN-2018', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2018 VALUES LESS THAN (TO_DATE('01-JAN-2019', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2019 VALUES LESS THAN (TO_DATE('01-JAN-2020', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2020 VALUES LESS THAN (TO_DATE('01-JAN-2021', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2021 VALUES LESS THAN (MAXVALUE));

Table created.

SQL> INSERT INTO big_bowie SELECT rownum, mod(rownum,5000)+1, mod(rownum,100)+1, sysdate-mod(rownum,2800),
ceil(dbms_random.value(1,500000)) FROM dual CONNECT BY LEVEL <= 10000000;

10000000 rows created.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=> null, tabname=> 'BIG_BOWIE');

PL/SQL procedure successfully completed.

I’ll next manually create a couple indexes; a non-partitioned index based on just the ALBUM_ID column and a prefixed locally partitioned index, based on the columns RELEASE_DATE, TOTAL_SALES:

 

SQL> create index album_id_i on big_bowie(album_id);

Index created.

SQL> create index release_date_total_sales_i on big_bowie(release_date, total_sales) local;

Index created.

 

If we now re-organise just partition ALBUMS_2017 (without using the ONLINE clause):

SQL> alter table big_bowie move partition albums_2017;

Table altered.

This results in the non-partitioned index and the ALBUMS_2017 local index partition becoming Unusable:

SQL> select index_name, status from user_indexes where table_name='BIG_BOWIE';

INDEX_NAME                     STATUS
------------------------------ --------
ALBUM_ID_I                     UNUSABLE
RELEASE_DATE_TOTAL_SALES_I     N/A

SQL> select index_name, partition_name, status from user_ind_partitions
     where index_name='RELEASE_DATE_TOTAL_SALES_I';

INDEX_NAME                     PARTITION_NAME       STATUS
------------------------------ -------------------- --------
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2014          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2015          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2016          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2017          UNUSABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2018          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2019          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2020          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2021          USABLE

Let’s now run a number of queries a number of times. The first series is based on a predicate on just the ALBUM_ID column, such as:

SQL> select * from big_bowie where album_id=42;

2000 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1510748290

-------------------------------------------------------------------------------------------------
| Id  | Operation           | Name      | Rows | Bytes | Cost (%CPU) | Time     | Pstart| Pstop |
-------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |           | 2000 | 52000 |    7959 (2) | 00:00:01 |       |       |
|   1 | PARTITION RANGE ALL |           | 2000 | 52000 |    7959 (2) | 00:00:01 |     1 |     8 |
| * 2 |  TABLE ACCESS FULL  | BIG_BOWIE | 2000 | 52000 |    7959 (2) | 00:00:01 |     1 |     8 |
-------------------------------------------------------------------------------------------------

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

2 - storage("ALBUM_ID"=42)
  - filter("ALBUM_ID"=42)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
      48593 consistent gets
      42881 physical reads
          0 redo size
      44289 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
       2000 rows processed

We’ll also run a series of queries based on both the RELEASE_DATE column using dates from the unusable index partition and the TOTAL_SALES column, such as:

SQL> select * from big_bowie where release_date='01-JUN-2017' and total_sales=42;

no rows selected

Execution Plan
----------------------------------------------------------
Plan hash value: 3245457041

----------------------------------------------------------------------------------------------------
| Id  | Operation              | Name      | Rows | Bytes | Cost (%CPU) | Time     | Pstart| Pstop |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |           |    1 |    26 |     986 (2) | 00:00:01 |       |       |
|   1 | PARTITION RANGE SINGLE |           |    1 |    26 |     986 (2) | 00:00:01 |     4 |     4 |
| * 2 |  TABLE ACCESS FULL     | BIG_BOWIE |    1 |    26 |     986 (2) | 00:00:01 |     4 |     4 |
----------------------------------------------------------------------------------------------------

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

2 - storage("TOTAL_SALES"=42 AND "RELEASE_DATE"=TO_DATE(' 2017-06-01 00:00:00',
'syyyy-mm-dd hh24:mi:ss'))
   - filter("TOTAL_SALES"=42 AND "RELEASE_DATE"=TO_DATE(' 2017-06-01 00:00:00',
'syyyy-mm-dd hh24:mi:ss'))

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
       5573 consistent gets
          0 physical reads
          0 redo size
        676 bytes sent via SQL*Net to client
         41 bytes received via SQL*Net from client
          1 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          0 rows processed

Without a valid/usable index, the CBO currently has no choice but to use a Full Table Scan on the first query, and a Full Partition Scan on the partition with the unusable local index.

So what does AI make of things? Does it rebuild the unusable manually created indexes so the associated indexes can be used to improve these queries?

If we wait until the next AI task completes and check out the indexes on the table:

SQL> select index_name, status, partitioned from user_indexes where table_name='BIG_BOWIE';

INDEX_NAME                     STATUS   PAR
------------------------------ -------- ---
RELEASE_DATE_TOTAL_SALES_I     N/A      YES
ALBUM_ID_I                     UNUSABLE NO
SYS_AI_aw2825ffpus5s           VALID    NO
SYS_AI_2hf33fpvnqztw           VALID    NO

SQL> select index_name, partition_name, status from user_ind_partitions
     where index_name='RELEASE_DATE_TOTAL_SALES_I';

INDEX_NAME                     PARTITION_NAME       STATUS
------------------------------ -------------------- --------
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2014          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2015          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2016          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2017          UNUSABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2018          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2019          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2020          USABLE
RELEASE_DATE_TOTAL_SALES_I     ALBUMS_2021          USABLE

We notice that AI has created two new non-partitioned automatic indexes, while both the manually created indexes remain in the same unusable state. If we look at the columns associated with these new automatic indexes:

SQL> select index_name, column_name, column_position
from user_ind_columns where table_name='BIG_BOWIE';

INDEX_NAME                     COLUMN_NAME          COLUMN_POSITION
------------------------------ -------------------- ---------------
ALBUM_ID_I                     ALBUM_ID                           1
RELEASE_DATE_TOTAL_SALES_I     RELEASE_DATE                       1
RELEASE_DATE_TOTAL_SALES_I     TOTAL_SALES                        2
SYS_AI_aw2825ffpus5s           ALBUM_ID                           1
SYS_AI_aw2825ffpus5s           RELEASE_DATE                       2
SYS_AI_2hf33fpvnqztw           TOTAL_SALES                        1
SYS_AI_2hf33fpvnqztw           RELEASE_DATE                       2

As we can see, AI has logically replaced both unusable indexes.

The manual index based on ALBUM_ID has been replaced with an inferior index based on the ALBUM_ID, RELEASE_DATE columns. Inferior in that the automatic index is both redundant (if only the manual index on ALBUM_ID were rebuilt) and in that it has the logically unnecessary RELEASE_DATE column to inflate the size of the index.

The manual index based on the RELEASE_DATE, TOTAL_SALES columns has been replaced with a redundant automatic index based on the reversed TOTAL_SALES, RELEASE_DATE columns.

Now, AI has indeed automatically addressed the current FTS performance issues associated with these queries by creating these indexes, but a better remedy would have been to rebuild the unusable manual indexes and hence negate the need for these redundant automatic indexes.

But currently (including with version 21.3), AI will NOT rebuild unusable manually created indexes, no matter the scenario, and will instead create additional automatic indexes if it’s viable for it to do so.

A reason why Oracle at times recommends dropping all current manually created secondary indexes before implementing AI (although of course this comes with a range of obvious issues and concerns).

If these manually created indexes didn’t exist, I’ll leave it as an exercise to the discernable reader on what automatic indexes would have been created…

As always, this restriction may change in future releases…

Automatic Indexes: Automatically Rebuild Unusable Indexes Part III (“Waiting For The Man”) May 17, 2022

Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, Exadata, Full Table Scans, Manual Indexes, Mixing Auto and Manual Indexes, Oracle, Oracle Blog, Oracle Cloud, Oracle General, Oracle Indexes, Oracle19c, Unusable Indexes.
1 comment so far

I’ve previously discussed how Automatic Indexing (AI) will not only create missing indexes, but will also rebuild unusable indexes, be it a Global or Local index.

However, all my previous examples have been with Automatic Indexes. How does AI handle unusable indexes in which the indexes were manually created?

In my first demo, I’ll start by creating a basic non-partitioned table:

SQL> create table bowie_stuff (id number, album_id number, country_id number, release_date date, total_sales number);

Table created.

SQL> insert into bowie_stuff select rownum, mod(rownum,5000)+1, mod(rownum,100)+1, sysdate-mod(rownum,2800),
ceil(dbms_random.value(1,500000)) FROM dual CONNECT BY LEVEL <= 10000000;

10000000 rows created.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=> null, tabname=> 'BOWIE_STUFF');

PL/SQL procedure successfully completed.

We next manually create an index on the highly selective TOTAL_SALES column:

SQL> create index bowie_stuff_total_sales_i on bowie_stuff(total_sales);

Index created.

Let’s now invalidate the index by re-organising the table without the online clause:

SQL> alter table bowie_stuff move;

Table altered.

SQL> select index_name, status from user_indexes where table_name='BOWIE_STUFF';

INDEX_NAME                     STATUS
------------------------------ --------
BOWIE_STUFF_TOTAL_SALES_I      UNUSABLE

So the index is now in an UNUSABLE state.

To perk up the interest of AI, I’ll run a number of queries such as the following with a predicate condition on TOTAL_SALES:

select * from bowie_stuff where total_sales=42;

18 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 910563088

---------------------------------------------------------------------------------
| Id | Operation          | Name        | Rows | Bytes | Cost (%CPU) | Time     |
---------------------------------------------------------------------------------
|  0 | SELECT STATEMENT   |             |   20 |   520 |    7427 (2) | 00:00:01 |
|* 1 |  TABLE ACCESS FULL | BOWIE_STUFF |   20 |   520 |    7427 (2) | 00:00:01 |
---------------------------------------------------------------------------------

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

1 - storage("TOTAL_SALES"=42)
    filter("TOTAL_SALES"=42)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
      42746 consistent gets
      42741 physical reads
          0 redo size
       1392 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
         18 rows processed

Without a valid index, the CBO has no choice but to perform an expensive full table scan.

However, it doesn’t matter how long I wait or how many different queries I run similar to the above, AI currently will never rebuild an unusable index if the index was manually created.

AI will only rebuild unusable automatically created indexes.

I’ve discussed previously how automatic and manually created indexes often don’t gel well together and is one of the key reasons why Oracle recommends dropping all manually created secondary indexes if you wish to implement AI (using the DBMS_AUTO_INDEX.DROP_SECONDARY_INDEXES procedure, which I’ll discuss in a future post).

Things can get a little interesting with AI, if the underlining table is partitioned and you have manually created unusable indexes.

As I’ll discuss in my next post…

Automatic Indexes: Automatically Rebuild Unusable Indexes Part II (“I Wish You Would”) May 11, 2022

Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Full Table Scans, Local Indexes, Oracle, Oracle Blog, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle19c, Partitioned Indexes, Partitioning, Performance Tuning, Rebuild Unusable Indexes.
1 comment so far

Within a few hours of publishing my last blog piece on how Automatic Indexing (AI) can automatically rebuild indexes that have been placed in an UNUSABLE state, I was asked by a couple of readers a similar question: “Does this also work if just a single partition of an partitioned index becomes unusable”?

My answer to them both is that I’ve provided them the basic framework in the demo to check out the answer to that question for themselves (Note: a fantastic aspect of working with the Oracle Database is that it’s available for free to play around with, including the Autonomous Database environments).

But based on the principle that for every time someone asks a question, there’s probably a 100 others who potentially might be wondering the same thing, thought I’ll quickly whip up a demo to answer this for all.

I’ll begin with the same table format and data as my previous blog:

SQL> CREATE TABLE big_ziggy(id number, album_id number, country_id number, release_date date,
total_sales number) PARTITION BY RANGE (release_date)
(PARTITION ALBUMS_2015 VALUES LESS THAN (TO_DATE('01-JAN-2016', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2016 VALUES LESS THAN (TO_DATE('01-JAN-2017', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2017 VALUES LESS THAN (TO_DATE('01-JAN-2018', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2018 VALUES LESS THAN (TO_DATE('01-JAN-2019', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2019 VALUES LESS THAN (TO_DATE('01-JAN-2020', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2020 VALUES LESS THAN (TO_DATE('01-JAN-2021', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2021 VALUES LESS THAN (TO_DATE('01-JAN-2022', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2022 VALUES LESS THAN (MAXVALUE));

Table created.

SQL> INSERT INTO big_ziggy SELECT rownum, mod(rownum,5000)+1, mod(rownum,100)+1, sysdate-mod(rownum,2800),
ceil(dbms_random.value(1,500000)) FROM dual CONNECT BY LEVEL <= 10000000;

10000000 rows created.

SQL> COMMIT;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=> null, tabname=> 'BIG_ZIGGY');

PL/SQL procedure successfully completed.

 

But this time, I’ll run a number of queries similar to the following, that also has a predicate based on the partitioned key (RELEASE_DATE) of the table:

SQL> select * FROM big_ziggy where release_date = '01-JUN-2017' and total_sales = 123456;

no rows selected

Execution Plan
----------------------------------------------------------
Plan hash value: 3599046327

----------------------------------------------------------------------------------------------------
| Id | Operation              | Name      | Rows | Bytes | Cost (%CPU) | Time     | Pstart | Pstop |
----------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT       |           |    1 |    26 |    1051 (2) | 00:00:01 |        |       |
|  1 | PARTITION RANGE SINGLE |           |    1 |    26 |    1051 (2) | 00:00:01 |      3 |     3 |
|* 2 |  TABLE ACCESS FULL     | BIG_ZIGGY |    1 |    26 |    1051 (2) | 00:00:01 |      3 |     3 |
----------------------------------------------------------------------------------------------------

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

2 - storage(("TOTAL_SALES"=123456 AND "RELEASE_DATE"=TO_DATE('2017-06-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss')))
    filter(("TOTAL_SALES"=123456 AND "RELEASE_DATE"=TO_DATE('2017-06-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss')))

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
       5618 consistent gets
          0 physical reads
          0 redo size
        676 bytes sent via SQL*Net to client
         41 bytes received via SQL*Net from client
          1 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          0 rows processed

 

If we wait for the next AI task to kick in:

DBMS_AUTO_INDEX.REPORT_LAST_ACTIVITY()
--------------------------------------------------------------------------------
GENERAL INFORMATION
-------------------------------------------------------------------------------
Activity start              : 11-MAY-2022 10:55:43
Activity end                : 11-MAY-2022 10:56:27
Executions completed        : 1
Executions interrupted      : 0
Executions with fatal error : 0
-------------------------------------------------------------------------------

SUMMARY (AUTO INDEXES)
-------------------------------------------------------------------------------
Index candidates                             : 0
Indexes created (visible / invisible)        : 1 (1 / 0)
Space used (visible / invisible)             : 192.94 MB (192.94 MB / 0 B)
Indexes dropped                              : 0
SQL statements verified                      : 6
SQL statements improved (improvement factor) : 3 (6670.1x)
SQL plan baselines created                   : 0
Overall improvement factor                   : 2x
-------------------------------------------------------------------------------

SUMMARY (MANUAL INDEXES)
-------------------------------------------------------------------------------
Unused indexes   : 0
Space used       : 0 B
Unusable indexes : 0
-------------------------------------------------------------------------------

INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
-------------------------------------------------------------------------------
--------------------------------------------------------------------------------
-------------
| Owner | Table     | Index                | Key                      | Type   | Properties |
---------------------------------------------------------------------------------------------
| BOWIE | BIG_ZIGGY | SYS_AI_6wv99zdbsy8ar | RELEASE_DATE,TOTAL_SALES | B-TREE | LOCAL      |
---------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------

 

We can see that AI has indeed automatically created a LOCAL, partitioned index (on columns RELEASE_DATE, TOTAL_SALES) in this scenario, as we have an equality predicate based on the partitioned key (RELEASE_DATE).

Currently, all is well with the index, with all partitions in a USABLE state:

SQL> SELECT index_name, partitioned, auto, visibility, status FROM user_indexes WHERE table_name = 'BIG_ZIGGY';

INDEX_NAME                     PAR AUT VISIBILIT STATUS
------------------------------ --- --- --------- --------
SYS_AI_6wv99zdbsy8ar           YES YES VISIBLE   N/A

SQL> select index_name, partition_name, status from user_ind_partitions where index_name='SYS_AI_6wv99zdbsy8ar';

INDEX_NAME                     PARTITION_NAME       STATUS
------------------------------ -------------------- --------
SYS_AI_6wv99zdbsy8ar           ALBUMS_2015          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2016          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2017          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2018          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2019          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2020          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2021          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2022          USABLE

SQL> select index_name, column_name, column_position from user_ind_columns 
     where index_name='SYS_AI_6wv99zdbsy8ar';

INDEX_NAME                     COLUMN_NAME     COLUMN_POSITION
------------------------------ --------------- ---------------
SYS_AI_6wv99zdbsy8ar           RELEASE_DATE                  1
SYS_AI_6wv99zdbsy8ar           TOTAL_SALES                   2

 

But if we now do an offline reorg of a specific table partition:

SQL> alter table big_ziggy move partition albums_2017;

Table altered.

SQL> select index_name, partition_name, status from user_ind_partitions where index_name='SYS_AI_6wv99zdbsy8ar';

INDEX_NAME                     PARTITION_NAME       STATUS
------------------------------ -------------------- --------
SYS_AI_6wv99zdbsy8ar           ALBUMS_2015          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2016          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2017          UNUSABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2018          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2019          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2020          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2021          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2022          USABLE

 

We can see we’ve now made the associated Local Index partition UNUSABLE.

If we run the following query:

SQL> select * FROM big_ziggy where release_date = '01-JUN-2017' and total_sales = 123456;

no rows selected

Execution Plan
----------------------------------------------------------
Plan hash value: 3599046327

----------------------------------------------------------------------------------------------------
| Id | Operation              | Name      | Rows | Bytes | Cost (%CPU) | Time     | Pstart | Pstop |
----------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT       |           |    1 |    26 |     986 (2) | 00:00:01 |        |       |
|  1 | PARTITION RANGE SINGLE |           |    1 |    26 |     986 (2) | 00:00:01 |      3 |     3 |
|* 2 |  TABLE ACCESS FULL     | BIG_ZIGGY |    1 |    26 |     986 (2) | 00:00:01 |      3 |     3 |
----------------------------------------------------------------------------------------------------

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

2 - storage(("TOTAL_SALES"=123456 AND "RELEASE_DATE"=TO_DATE('2017-06-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss')))
    filter(("TOTAL_SALES"=123456 AND "RELEASE_DATE"=TO_DATE('2017-06-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss')))

Statistics
----------------------------------------------------------
          3 recursive calls
          4 db block gets
       5578 consistent gets
       5571 physical reads
        924 redo size
        676 bytes sent via SQL*Net to client
         41 bytes received via SQL*Net from client
          1 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          0 rows processed

The CBO has no choice here but to do a full partition table scan.

If now wait again for the next AI task to strut its stuff:

SQL> select dbms_auto_index.report_last_activity() from dual;

DBMS_AUTO_INDEX.REPORT_LAST_ACTIVITY()
--------------------------------------------------------------------------------
GENERAL INFORMATION
-------------------------------------------------------------------------------
Activity start              : 11-MAY-2022 11:42:42
Activity end                : 11-MAY-2022 11:43:13
Executions completed        : 1
Executions interrupted      : 0
Executions with fatal error : 0
-------------------------------------------------------------------------------

SUMMARY (AUTO INDEXES)
-------------------------------------------------------------------------------
Index candidates                             : 0
Indexes created (visible / invisible)        : 1 (1 / 0)
Space used (visible / invisible)             : 192.94 MB (192.94 MB / 0 B)
Indexes dropped                              : 0
SQL statements verified                      : 4
SQL statements improved (improvement factor) : 1 (5573x)
SQL plan baselines created                   : 0
Overall improvement factor                   : 1.1x
-------------------------------------------------------------------------------

SUMMARY (MANUAL INDEXES)
-------------------------------------------------------------------------------
Unused indexes   : 0
Space used       : 0 B
Unusable indexes : 0
-------------------------------------------------------------------------------

INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
-------------------------------------------------------------------------------
--------------------------------------------------------------------------------
-------------
| Owner | Table     | Index                | Key                      | Type   | Properties |
---------------------------------------------------------------------------------------------
| BOWIE | BIG_ZIGGY | SYS_AI_6wv99zdbsy8ar | RELEASE_DATE,TOTAL_SALES | B-TREE | LOCAL      |
---------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------


SQL> select index_name, partition_name, status from user_ind_partitions where index_name='SYS_AI_6wv99zdbsy8ar';

INDEX_NAME                     PARTITION_NAME       STATUS
------------------------------ -------------------- --------
SYS_AI_6wv99zdbsy8ar           ALBUMS_2015          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2016          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2017          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2018          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2019          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2020          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2021          USABLE
SYS_AI_6wv99zdbsy8ar           ALBUMS_2022          USABLE

The index partition is now automatically in a USABLE state again.

If we look at the index object data:

SQL> select object_name, subobject_name, to_char(created, 'dd-Mon-yy hh24:mi:ss') created, to_char(last_ddl_time, 'dd-Mon-yy hh24:mi:ss’)
last_ddl_time from dba_objects where object_name='SYS_AI_6wv99zdbsy8ar';

OBJECT_NAME                    SUBOBJECT_NAME       CREATED                     LAST_DDL_TIME
------------------------------ -------------------- --------------------------- ---------------------------
SYS_AI_6wv99zdbsy8ar           ALBUMS_2015          11-May-22 10:41:33          11-May-22 10:56:14
SYS_AI_6wv99zdbsy8ar           ALBUMS_2016          11-May-22 10:41:33          11-May-22 10:56:15
SYS_AI_6wv99zdbsy8ar           ALBUMS_2017          11-May-22 10:41:33          11-May-22 11:42:42
SYS_AI_6wv99zdbsy8ar           ALBUMS_2018          11-May-22 10:41:33          11-May-22 10:56:18
SYS_AI_6wv99zdbsy8ar           ALBUMS_2019          11-May-22 10:41:33          11-May-22 10:56:19
SYS_AI_6wv99zdbsy8ar           ALBUMS_2020          11-May-22 10:41:33          11-May-22 10:56:20
SYS_AI_6wv99zdbsy8ar           ALBUMS_2021          11-May-22 10:41:33          11-May-22 10:56:22
SYS_AI_6wv99zdbsy8ar           ALBUMS_2022          11-May-22 10:41:33          11-May-22 10:56:22
SYS_AI_6wv99zdbsy8ar                                11-May-22 10:41:33          11-May-22 11:43:13

 

We can see that just the impacted index partition has been rebuilt.

The CBO can now successfully use the index to avoid the full partition table scan:

SQL> select * FROM big_ziggy where release_date = '01-JUN-2017' and total_sales = 123456;

no rows selected

Execution Plan
----------------------------------------------------------
Plan hash value: 3640710173

-----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                                  | Name                 | Rows | Bytes | Cost (%CPU)| Time     | Pstart | Pstop |
-----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                           |                      |    1 |    26 |      4 (0) | 00:00:01 |        |       |
|  1 | PARTITION RANGE SINGLE                     |                      |    1 |    26 |      4 (0) | 00:00:01 |      3 |     3 |
|  2 |  TABLE ACCESS BY LOCAL INDEX ROWID BATCHED | BIG_ZIGGY            |    1 |    26 |      4 (0) | 00:00:01 |      3 |     3 |
|* 3 |   INDEX RANGE SCAN                         | SYS_AI_6wv99zdbsy8ar |    1 |       |      3 (0) | 00:00:01 |      3 |     3 |
-----------------------------------------------------------------------------------------------------------------------------------

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

3 - access("RELEASE_DATE"=TO_DATE(' 2017-06-01 00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND "TOTAL_SALES"=123456)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
          3 consistent gets
          0 physical reads
          0 redo size
        676 bytes sent via SQL*Net to client
         41 bytes received via SQL*Net from client
          1 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
          0 rows processed

 

I’ll leave it to the discernible reader to determine if this also works in the scenario where the partitioned index were to be global… 🙂

Automatic Indexes: Automatically Rebuild Unusable Indexes Part I (“Andy Warhol”) May 10, 2022

Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Oracle, Oracle Cloud, Oracle General, Oracle Indexes, Oracle19c, Rebuild Unusable Indexes.
2 comments

Obviously, the main feature of Automatic Indexing (AI) is for Oracle to automatically create indexes, that have been proven to improve performance, in a relatively safe and timely manner.

However, another nice and useful capability is for AI to automatically rebuild indexes that are placed in an “Unusable” state.

The documentation states that:

Automatic indexing provides the following functionality:

Rebuilds the indexes that are marked unusable due to table partitioning maintenance operations, such as ALTER TABLE MOVE.

Now, when AI was initially released, I was unable to get this rebuild capability to work as advertised. I don’t know whether this was because the capability had not yet been successfully implemented or because of some failings in my testing.

However, with both the current versions of Oracle Database 19c (19.15.0.1.0 as now implemented in Autonomous Databases) and Oracle Database 21c, the following demo now works successfully.

Let’s begin by creating a simple partitioned table:

SQL> CREATE TABLE big_bowie(id number, album_id number, country_id number, release_date date,
total_sales number) PARTITION BY RANGE (release_date)
(PARTITION ALBUMS_2015 VALUES LESS THAN (TO_DATE('01-JAN-2016', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2016 VALUES LESS THAN (TO_DATE('01-JAN-2017', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2017 VALUES LESS THAN (TO_DATE('01-JAN-2018', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2018 VALUES LESS THAN (TO_DATE('01-JAN-2019', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2019 VALUES LESS THAN (TO_DATE('01-JAN-2020', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2020 VALUES LESS THAN (TO_DATE('01-JAN-2021', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2021 VALUES LESS THAN (TO_DATE('01-JAN-2022', 'DD-MON-YYYY')),
 PARTITION ALBUMS_2022 VALUES LESS THAN (MAXVALUE));

Table created.

SQL> INSERT INTO big_bowie SELECT rownum, mod(rownum,5000)+1, mod(rownum,100)+1, sysdate-mod(rownum,2800),
ceil(dbms_random.value(1,500000)) FROM dual CONNECT BY LEVEL <= 10000000;

10000000 rows created.

SQL> COMMIT;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=> null, tabname=> 'BIG_BOWIE');

PL/SQL procedure successfully completed.

We next run a number of SQL statements such as the following:

SQL> SELECT * FROM big_bowie WHERE total_sales = 123456;

19 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1510748290

-------------------------------------------------------------------------------------------------
| Id  | Operation            | Name      | Rows | Bytes | Cost (%CPU) | Time     | Pstart| Pstop|
-------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |           |   20 |   520 |    7958 (2) | 00:00:01 |       |      |
|   1 |  PARTITION RANGE ALL |           |   20 |   520 |    7958 (2) | 00:00:01 |     1 |    8 |
| * 2 |   TABLE ACCESS FULL  | BIG_BOWIE |   20 |   520 |    7958 (2) | 00:00:01 |     1 |    8 |
-------------------------------------------------------------------------------------------------

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

2 - storage("TOTAL_SALES"=123456)
    filter("TOTAL_SALES"=123456)

Statistics
----------------------------------------------------------
          1 recursive calls
          0 db block gets
      49573 consistent gets
      42778 physical reads
          0 redo size
       1423 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
         19 rows processed

If we wait for the AI task to kick in, we notice is has successfully created an associated automatic index:

SQL> SELECT index_name, partitioned, auto, visibility, status FROM user_indexes WHERE table_name = 'BIG_BOWIE';

INDEX_NAME                     PAR AUT VISIBILIT STATUS
------------------------------ --- --- --------- --------
SYS_AI_17cd4101fvrk1           NO  YES VISIBLE   VALID

SQL> select index_name, column_name, column_position from user_ind_columns where table_name='BIG_BOWIE';

INDEX_NAME                     COLUMN_NAME     COLUMN_POSITION
------------------------------ --------------- ---------------
SYS_AI_17cd4101fvrk1           TOTAL_SALES                   1

As discussed previously, AI can now create a non-partitioned, Global index if deemed more efficient than a corresponding Local index.

Note that the newly created automatic index is currently VALID.

However, if we re-organise a partition within the table without using the Online clause:

SQL> alter table big_bowie move partition albums_2017;

Table altered.

SQL> select index_name, partitioned, auto, visibility, status from user_indexes where table_name = 'BIG_BOWIE';

INDEX_NAME                     PAR AUT VISIBILIT STATUS
------------------------------ --- --- --------- --------
SYS_AI_17cd4101fvrk1           NO  YES VISIBLE   UNUSABLE

The index as a result goes into an UNUSABLE state.

Running similar queries from this point will result in a FTS again:

SQL> select * from big_bowie where total_sales=42;

22 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1510748290

-------------------------------------------------------------------------------------------------
| Id | Operation            | Name      | Rows | Bytes | Cost (%CPU) | Time     | Pstart| Pstop |
-------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT     |           |   20 |   520 |    7937 (2) | 00:00:01 |       |       |
|  1 |  PARTITION RANGE ALL |           |   20 |   520 |    7937 (2) | 00:00:01 |     1 |     8 |
|* 2 |   TABLE ACCESS FULL  | BIG_BOWIE |   20 |   520 |    7937 (2) | 00:00:01 |     1 |     8 |
-------------------------------------------------------------------------------------------------

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

2 - storage("TOTAL_SALES"=123456)
    filter("TOTAL_SALES"=123456)

Statistics
----------------------------------------------------------
          126 recursive calls
            0 db block gets
        48962 consistent gets
        42799 physical reads
            0 redo size
         1497 bytes sent via SQL*Net to client
           52 bytes received via SQL*Net from client
            2 SQL*Net roundtrips to/from client
           17 sorts (memory)
            0 sorts (disk)
           22 rows processed

If we now wait until the next AI task period and check out the index:

SQL> SELECT index_name, partitioned, auto, visibility, status FROM user_indexes WHERE table_name = 'BIG_BOWIE';

INDEX_NAME                     PAR AUT VISIBILIT STATUS
------------------------------ --- --- --------- --------
SYS_AI_17cd4101fvrk1           NO  YES VISIBLE   VALID

We notice the index is now back in a VALID state again.

 

Checking out the date attributes of the index confirms the index has indeed been rebuilt:

SQL> select object_name, to_char(created, 'dd-Mon-yy hh24:mi:ss') created, to_char(last_ddl_time, 'dd-Mon-yyhh24:mi:ss’)
last_ddl_time from dba_objects where object_name='SYS_AI_17cd4101fvrk1';

OBJECT_NAME                    CREATED                     LAST_DDL_TIME
------------------------------ --------------------------- ---------------------------
SYS_AI_17cd4101fvrk1           18-Apr-22 11:59:36          18-Apr-22 18:37:42

Being in a VALID state again, the CBO can now use the automatic index:

SQL> select * from big_bowie where total_sales=42;

22 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 920768077

-----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation                                   | Name                 | Rows | Bytes | Cost (%CPU) | Time     | Pstart| Pstop |
-----------------------------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                            |                      |   20 |   520 |      23 (0) | 00:00:01 |       |       |
|  1 |  TABLE ACCESS BY GLOBAL INDEX ROWID BATCHED | BIG_BOWIE            |   20 |   520 |      23 (0) | 00:00:01 | ROWID | ROWID |
|* 2 |   INDEX RANGE SCAN                          | SYS_AI_17cd4101fvrk1 |   20 |       |       3 (0) | 00:00:01 |       |       |
-----------------------------------------------------------------------------------------------------------------------------------

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

2 - access("TOTAL_SALES"=42)

Statistics
----------------------------------------------------------
          0 recursive calls
          0 db block gets
      48711 consistent gets
      42799 physical reads
          0 redo size
       1497 bytes sent via SQL*Net to client
         52 bytes received via SQL*Net from client
          2 SQL*Net roundtrips to/from client
          0 sorts (memory)
          0 sorts (disk)
         22 rows processed

Note: This scenario works the same if the table is Non-Partitioned.

In my next post, I’ll discuss a scenario where the automatic rebuild of an Unusable index will currently NOT work…