Oracle 19c Automatic Indexing: Function-Based Indexes? Part II (If You Can See Me) February 5, 2021
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Function Based Indexes, Oracle, Oracle Blog, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle19c, Virtual Columns.add a comment
In my previous post, I discussed how Automatic Indexing does not currently support creating an index based on a function or expression predicate, even if it’s an equality predicate. You must manually create the associated function-based index.
However, if you have access to the application, there’s a better strategy when frequently searching on a function-based predicate. That’s to create a Virtual Column and use this column in your searching criteria (as mentioned by Connor McDonald in this comment).
To illustrate, I’m going to drop the previously manually created function-based index and hence the associated hidden virtual column, as Oracle quite rightly doesn’t allow you to have two virtual columns based on the same expression in the same table.
SQL> drop index david_upper_name_i; Index dropped.
Since Oracle 11g, Oracle has supported the use of Visible Virtual Columns, a column that doesn’t physically exist, but defines a function/expression that can be easily accessed and populated when queried.
I’ll next create a Virtual Column called UPPER_NAME that is defined not based on a Data Type, but on the result on the UPPER function on the previously defined NAME column:
SQL> alter table david add (upper_name as (upper(name))); Table altered.
Regardless of size of table, this column is added virtually instantly (pun fully intended), as no data is physically stored in the table itself. I view it (yep, another pun) as a “mini-view”, that can be used to hide complexity from the developer, with the actual data derived at run-time when the column is accessed in an SQL.
After I generate fresh statistics:
SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'DAVID', estimate_percent=>null); PL/SQL procedure successfully completed. SQL> select column_name, hidden_column, virtual_column, num_distinct, density, histogram from dba_tab_cols where table_name='DAVID'; COLUMN_NAME HID VIR NUM_DISTINCT DENSITY HISTOGRAM -------------------- --- --- ------------ ---------- --------------- NAME NO NO 10000000 0 HYBRID MORE_STUFF9 NO NO 1 .00000005 FREQUENCY MORE_STUFF8 NO NO 1 .00000005 FREQUENCY MORE_STUFF7 NO NO 1 .00000005 FREQUENCY MORE_STUFF6 NO NO 1 .00000005 FREQUENCY MORE_STUFF5 NO NO 1 .00000005 FREQUENCY MORE_STUFF4 NO NO 1 .00000005 FREQUENCY MORE_STUFF3 NO NO 1 .00000005 FREQUENCY MORE_STUFF2 NO NO 1 .00000005 FREQUENCY MORE_STUFF10 NO NO 1 .00000005 FREQUENCY MORE_STUFF1 NO NO 1 .00000005 FREQUENCY ID NO NO 10000000 0 HYBRID CODE NO NO 10000 .0001 HYBRID UPPER_NAME NO YES 10000000 0 HYBRID
Note how the UPPER_NAME virtual column is NOT hidden and now has up to date statistics.
We can now run this simplified query based on the new UPPER_NAME column, which does not need to include the potentially complex function expression:
SQL> select * from david where upper_name='DAVID BOWIE 42';
1 row selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 2426813604
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 200 | 3349 (6) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | DAVID | 1 | 200 | 3349 (6) | 00:00:01 |
-----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("UPPER_NAME"='DAVID BOWIE 42')
filter("UPPER_NAME"='DAVID BOWIE 42')
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
263469 consistent gets
263452 physical reads
0 redo size
1328 bytes sent via SQL*Net to client
375 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed
If we look at portions of the subsequent Automatic Indexing report:
SUMMARY (AUTO INDEXES)
-------------------------------------------------------------------------------
Index candidates : 1
Indexes created (visible / invisible) : 1 (1 / 0)
Space used (visible / invisible) : 360.71 MB (360.71 MB / 0 B)
Indexes dropped : 0
SQL statements verified : 2
SQL statements improved (improvement factor) : 2 (263476.8x)
SQL plan baselines created : 0
Overall improvement factor : 263476.8x
-------------------------------------------------------------------------------
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 | DAVID | SYS_AI_4k4mkgkw049ht | UPPER_NAME | B-TREE | NONE |
---------------------------------------------------------------------------
-------------------------------------------------------------------------------
VERIFICATION DETAILS
-------------------------------------------------------------------------------
The performance of the following statements improved:
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL ID : 7tfqh3pu526mt
SQL Text : select * from david where upper_name='DAVID BOWIE 42'
Improvement Factor : 263484.7x
Execution Statistics:
-----------------------------
Original Plan Auto Index Plan
---------------------------- ----------------------------
Elapsed Time (s): 1471249 1414
CPU Time (s): 300584 986
Buffer Gets: 3161816 4
Optimizer Cost: 3349 4
Disk Reads: 3161432 3
Direct Writes: 0 0
Rows Processed: 12 1
Executions: 12 1
PLANS SECTION
--------------------------------------------------------------------------------
- Original
-----------------------------
Plan Hash Value : 2426813604
-----------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
-----------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | | | 3349 | |
| 1 | TABLE ACCESS STORAGE FULL | DAVID | 1 | 200 | 3349 | 00:00:01 |
-----------------------------------------------------------------------------
Notes
-----
- dop = 1
- px_in_memory_imc = no
- px_in_memory = no
- cardinality_feedback = yes
- With Auto Indexes
-----------------------------
Plan Hash Value : 1447691372
-------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
-------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 200 | 4 | 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED | DAVID | 1 | 200 | 4 | 00:00:01 |
| * 2 | INDEX RANGE SCAN | SYS_AI_4k4mkgkw049ht | 1 | | 3 | 00:00:01 |
-------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
------------------------------------------
* 2 - access("UPPER_NAME"='DAVID BOWIE 42')
Notes
-----
- Dynamic sampling used for this statement ( level = 11 )
We see from the report that Automatic Indexing has now created the associated, implicitly created function-based index (SYS_AI_4k4mkgkw049ht) based on the virtual UPPER_NAME column:
SQL> select index_name, index_type, auto, constraint_index, visibility, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='DAVID'; INDEX_NAME INDEX_TYPE AUT CON VISIBILIT STATUS NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR -------------------- --------------------------- --- --- --------- -------- ---------- ----------- ----------------- SYS_AI_4k4mkgkw049ht FUNCTION-BASED NORMAL YES NO VISIBLE VALID 10000000 43104 2136839 SQL> select index_name, column_name, column_position from user_ind_columns where table_name='DAVID' order by index_name, column_position; INDEX_NAME COLUMN_NAME COLUMN_POSITION -------------------- -------------------- --------------- SYS_AI_4k4mkgkw049ht UPPER_NAME 1
If we now re-run the SQL query:
SQL> select * from david where upper_name='DAVID BOWIE 4242';
1 row selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 1447691372
------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 200 | 4 (0) | 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED | DAVID | 1 | 200 | 4 (0) | 00:00:01 |
|* 2 | INDEX RANGE SCAN | SYS_AI_4k4mkgkw049ht | 1 | | 3 (0) | 00:00:01 |
------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("UPPER_NAME"='DAVID BOWIE 4242')
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
5 consistent gets
0 physical reads
0 redo size
1334 bytes sent via SQL*Net to client
377 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed
The CBO now uses the new Automatic Index to significantly improve the performance of the query.
So not only is using a user defined Virtual Column a cleaner solution with respect to the frequent use of a function-based expressions, but has the added advantage of being supported with Automatic Indexing.
Oracle 19c Automatic Indexing: Function-Based Indexes? (No Plan) February 4, 2021
Posted by Richard Foote in 19c, 19c New Features, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Function Based Indexes, Oracle, Oracle Cloud, Oracle General, Oracle Indexes, Oracle19c, Virtual Columns.3 comments
I previously discussed how Automatic Indexing only currently supports Equality based predicates.
The question I have today is does Automatic Indexing support function-based indexes? Let’s take a look.
The below DAVID table has the key column NAME which is an effectively unique VARCHAR2 column:
SQL> create table david (id number, code number, name varchar2(42), more_stuff1 varchar2(42), more_stuff2 varchar2(42), more_stuff3 varchar2(42), more_stuff4 varchar2(42), more_stuff5 varchar2(42), more_stuff6 varchar2(42), more_stuff7 varchar2(42), more_stuff8 varchar2(42), more_stuff9 varchar2(42), more_stuff10 varchar2(42)); Table created. SQL> insert into david select rownum, mod(rownum, 10000)+1, 'David Bowie '|| rownum, 'Ziggy Stardust', 'Ziggy Stardust', 'Ziggy Stardust', 'Ziggy Stardust', 'Ziggy Stardust', 'Ziggy Stardust', 'Ziggy Stardust', 'Ziggy Stardust', 'Ziggy Stardust', '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=>'DAVID', estimate_percent=>null); PL/SQL procedure successfully completed.
If we look at the current details of the table columns:
SQL> select column_name, num_distinct, density, histogram from dba_tab_cols where table_name='DAVID'; COLUMN_NAME NUM_DISTINCT DENSITY HISTOGRAM -------------------- ------------ ---------- --------------- NAME 10000000 0 HYBRID MORE_STUFF9 1 .00000005 FREQUENCY MORE_STUFF8 1 .00000005 FREQUENCY MORE_STUFF7 1 .00000005 FREQUENCY MORE_STUFF6 1 .00000005 FREQUENCY MORE_STUFF5 1 .00000005 FREQUENCY MORE_STUFF4 1 .00000005 FREQUENCY MORE_STUFF3 1 .00000005 FREQUENCY MORE_STUFF2 1 .00000005 FREQUENCY MORE_STUFF10 1 .00000005 FREQUENCY MORE_STUFF1 1 .00000005 FREQUENCY ID 10000000 0 HYBRID CODE 10000 .0001 HYBRID
We notice the same oddity of my previous post that all columns have histograms…
Let’s run the following query with an UPPER function-based predicate that returns only the one row:
SQL> select * from david where upper(name) = 'DAVID BOWIE 4242';
Execution Plan
----------------------------------------------------------
Plan hash value: 2426813604
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 100K | 17M | 3350 (6) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | DAVID | 100K | 17M | 3350 (6) | 00:00:01 |
-----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage(UPPER("NAME")='DAVID BOWIE 4242')
filter(UPPER("NAME")='DAVID BOWIE 4242')
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
263469 consistent gets
263452 physical reads
0 redo size
1256 bytes sent via SQL*Net to client
381 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed
What does Automatic Indexing make of this scenario?
Basically, it does nothing. Currently, Automatic Indexing does NOT support such function-based indexes, even with equality based predicates (as of at least version 19.5.0.0.0). If we look at the next Automatic Indexing report:
SUMMARY (AUTO INDEXES) ------------------------------------------------------------------------------- Index candidates : 0 Indexes created : 0 Space used : 0 B Indexes dropped : 0 SQL statements verified : 2 SQL statements improved : 0 SQL plan baselines created : 0 Overall improvement factor : 0x ------------------------------------------------------------------------------- SUMMARY (MANUAL INDEXES) ------------------------------------------------------------------------------- Unused indexes : 0 Space used : 0 B Unusable indexes : 0 -------------------------------------------------------------------------------
No such function-based index is ever created by Automatic Indexing:
SQL> select index_name, auto, constraint_index, visibility, compression, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='DAVID'; no rows selected
To improve the performance of this query, one has to manually create the necessary function-based index:
SQL> create index david_upper_name_i on david(upper(name)); Index created.
If we now re-run the query:
SQL> select name from david where upper(name) = 'DAVID BOWIE 4242';
Execution Plan
----------------------------------------------------------
Plan hash value: 2675555529
----------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
----------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 100K | 4199K | 3175 (1) | 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED | DAVID | 100K | 4199K | 3175 (1) | 00:00:01 |
|* 2 | INDEX RANGE SCAN | DAVID_UPPER_NAME_I | 40000 | | 3 (0) | 00:00:01 |
----------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access(UPPER("NAME")='DAVID BOWIE 4242')
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
5 consistent gets
0 physical reads
0 redo size
369 bytes sent via SQL*Net to client
384 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed
The query now uses the function-based index to significantly improve the performance of this query, with just 5 consistent gets.
Note however as with all function-based indexes, by default the estimated cardinality estimate and associated CBO costs are way off (100K rows are estimated, not the 1 row that is actually returned). This is due to the CBO having no real idea of the number and distribution of values coming out of the “black box” function-based predicate.
This is why Oracle automatically creates an hidden virtual column by which to store the necessary statistics associated to the function (in this case the SYS_NC00014$ column):
SQL> select column_name, num_distinct, density, histogram from dba_tab_cols where table_name='DAVID'; COLUMN_NAME NUM_DISTINCT DENSITY HISTOGRAM -------------------- ------------ ---------- --------------- NAME 10000000 0 HYBRID MORE_STUFF9 1 .00000005 FREQUENCY MORE_STUFF8 1 .00000005 FREQUENCY MORE_STUFF7 1 .00000005 FREQUENCY MORE_STUFF6 1 .00000005 FREQUENCY MORE_STUFF5 1 .00000005 FREQUENCY MORE_STUFF4 1 .00000005 FREQUENCY MORE_STUFF3 1 .00000005 FREQUENCY MORE_STUFF2 1 .00000005 FREQUENCY MORE_STUFF10 1 .00000005 FREQUENCY MORE_STUFF1 1 .00000005 FREQUENCY ID 10000000 0 HYBRID CODE 10000 .0001 HYBRID SYS_NC00014$ NONE
But we need to first collect statistics on this hidden virtual column for the statistics to be populated:
SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'DAVID', no_invalidate=> false, method_opt=> 'FOR ALL HIDDEN COLUMNS SIZE 1'); SQL> select column_name, num_distinct, density, histogram from dba_tab_cols where table_name='DAVID'; COLUMN_NAME NUM_DISTINCT DENSITY HISTOGRAM -------------------- ------------ ---------- --------------- NAME 10000000 0 HYBRID MORE_STUFF9 1 .00000005 FREQUENCY MORE_STUFF8 1 .00000005 FREQUENCY MORE_STUFF7 1 .00000005 FREQUENCY MORE_STUFF6 1 .00000005 FREQUENCY MORE_STUFF5 1 .00000005 FREQUENCY MORE_STUFF4 1 .00000005 FREQUENCY MORE_STUFF3 1 .00000005 FREQUENCY MORE_STUFF2 1 .00000005 FREQUENCY MORE_STUFF10 1 .00000005 FREQUENCY MORE_STUFF1 1 .00000005 FREQUENCY ID 10000000 0 HYBRID CODE 10000 .0001 HYBRID SYS_NC00014$ 9947366 0 HYBRID
Now the CBO has the necessary statistics by which to determine a much more accurate cardinality estimate for the function-based predicate and so potentially a more efficient execution plan:
SQL> select * from david where upper(name) = 'DAVID BOWIE 4242';
Execution Plan
----------------------------------------------------------
Plan hash value: 2675555529
----------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
----------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 200 | 4 (0) | 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED | DAVID | 1 | 200 | 4 (0) | 00:00:01 |
|* 2 | INDEX RANGE SCAN | DAVID_UPPER_NAME_I | 1 | | 3 (0) | 00:00:01 |
----------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access(UPPER("NAME")='DAVID BOWIE 4242')
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
1 recursive calls
0 db block gets
5 consistent gets
0 physical reads
0 redo size
1256 bytes sent via SQL*Net to client
381 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed
With the virtual column statistics in place, the CBO now has the cardinality estimate of 1 and associated costs spot on, which is always a good thing.
This requirement to collect the necessary statistics on the associated virtual column created as a result of the function-based index to ensure the index is costed and used effectively is perhaps but one reason why function-based indexes are currently not supported by Automatic Indexing.
As always, this can always change in the future…
Oracle Database 19c Automatic Indexing: Invisible Indexes Oddity (Wild Eyed Boy From Freecloud) February 3, 2021
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Automatic Table Statistics, Autonomous Database, Autonomous Transaction Processing, CBO, Clustering Factor, Exadata, Histograms, Invisible Indexes, Oracle, Oracle Cloud, Oracle General, Oracle Indexes, Oracle Statistics, Oracle19c.1 comment so far
There have been a couple of “oddities” in relation to both Oracle Autonomous Databases and Automatic Indexing behaviour that I’ve seen frequently enough now (on Oracle 19.5.0.0.0) to make it worth a quick blog article.
The following is a simple test case that highlights both these issues. I’ll begin with a basic table, that has the key column CODE with a selectivity that would likely make it too expensive to be accessed via an associated index.
SQL> create table pink_floyd (id number, code number, create_date date, name varchar2(42)); Table created. SQL> insert into pink_floyd select rownum, ceil(dbms_random.value(0, 5000)), sysdate-mod(rownum, 50000)+1, 'Dark Side of the Moon' from dual connect by level <=10000000; 10000000 rows created. SQL> commit; Commit complete.
Importantly, I’ll next collect statistics on this table using all the default attributes, including allowing Oracle to decide the merits of any column histogram:
SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'PINK_FLOYD'); PL/SQL procedure successfully completed.
Note I’ve yet to run a single query against this table. And yet, if we look at the details of each of these columns:
SQL> select column_name, num_distinct, density, histogram from dba_tab_columns where table_name='PINK_FLOYD'; COLUMN_NAME NUM_DISTINCT DENSITY HISTOGRAM -------------------- ------------ ---------- --------------- ID 9705425 0 HYBRID CODE 4835 .00005 HYBRID CREATE_DATE 50357 .00002 HYBRID NAME 1 4.9639E-08 FREQUENCY
All the columns have a histogram !! This despite the columns not meeting either criteria normally required for a histogram, that the column be used in a SQL predicate AND for the column to have an uneven distribution of values.
None of these columns have yet to be used in a filtering predicate and none of these columns have a uneven distribution of values, even the CODE column as highlighted by looking at the minimum and maximum number of occurrences:
SQL> select min(code_count), max(code_count) from (select count(*) code_count from pink_floyd group by code);
MIN(CODE_COUNT) MAX(CODE_COUNT)
--------------- ---------------
1845 2163
So it’s very odd for these histograms to be present.
If we run the following query with a filtering predicate based on the CODE column:
SQL> select * from pink_floyd where code=42;
2012 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 1152280033
----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2068 | 82720 | 844 (11) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | PINK_FLOYD | 2068 | 82720 | 844 (11) | 00:00:01 |
----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE"=42)
filter("CODE"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
63655 consistent gets
63645 physical reads
0 redo size
38575 bytes sent via SQL*Net to client
360 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
2012 rows processed
The CBO currently has no choice but to use a FTS with no index currently present. But what will Automatic Indexing make of things? If we look at the next automatic indexing report:
SUMMARY (AUTO INDEXES) ------------------------------------------------------------------------------- Index candidates : 2 Indexes created (visible / invisible) : 1 (0 / 1) Space used (visible / invisible) : 134.22 MB (0 B / 134.22 MB) Indexes dropped : 0 SQL statements verified : 1 SQL statements improved : 0 SQL plan baselines created : 0 Overall improvement factor : 0x ------------------------------------------------------------------------------- SUMMARY (MANUAL INDEXES) ------------------------------------------------------------------------------- Unused indexes : 0 Space used : 0 B Unusable indexes : 0 ------------------------------------------------------------------------------- INDEX DETAILS ------------------------------------------------------------------------------- The following indexes were created: *: invisible ------------------------------------------------------------------------------- ---------------------------------------------------------------------------- | Owner | Table | Index | Key | Type | Properties | ---------------------------------------------------------------------------- | BOWIE | PINK_FLOYD | * SYS_AI_dp2t0j12zux49 | CODE | B-TREE | NONE | ---------------------------------------------------------------------------- -------------------------------------------------------------------------------
We notice that Oracle has created an Automatic Index, but it’s an INVISIBLE index !!
If we look at the details of this Automatic Index:
SQL> select index_name, auto, constraint_index, visibility, compression, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='PINK_FLOYD'; INDEX_NAME AUT CON VISIBILIT COMPRESSION STATUS NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR ------------------------- --- --- --------- ------------- -------- ---------- ----------- ----------------- SYS_AI_dp2t0j12zux49 YES NO INVISIBLE ADVANCED LOW VALID 10000000 15369 9845256
The index is in an INVISIBLE/VALID state, not the usual INVISIBLE/UNUSABLE state for an index for which Automatic Indexing decides an index is not efficient enough to be implement.
This is NOT expected behaviour.
Usually INVISIBLE/VALID indexes are created when Automatic Indexing is in “REPORT ONLY” mode, although I have come across this scenario when statistics are stale or missing. But in this case, Automatic Indexing is in “IMPLEMENT” mode and the table has recently collected statistics, albeit with odd histograms present (hence why I think these issues to be related).
If we run the same query again:
SQL> select * from pink_floyd where code=42;
2012 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 1152280033
----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2068 | 82720 | 844 (11) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | PINK_FLOYD | 2068 | 82720 | 844 (11) | 00:00:01 |
----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE"=42)
filter("CODE"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
63655 consistent gets
63645 physical reads
0 redo size
38575 bytes sent via SQL*Net to client
360 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
2012 rows processed
The CBO has again no option but to use the FTS as Invisible indexes can not be considered by the CBO. However, it’s important to note that such an index would not be used by the CBO anyways as it would be deemed too expensive to use than the current FTS.
If you’re relying on Automatic Indexing and have it in Implement mode, I would recommend checking for any indexes in this INVISIBLE/VALID state as they’re an indication that something has very likely gone wrong…
Oracle Database 19c Automatic Indexing: Index Compression Update (New Morning) January 27, 2021
Posted by Richard Foote in 19c, 19c New Features, Advanced Index Compression, Autonomous Database, Autonomous Transaction Processing, AUTO_INDEX_COMPRESSION, Exadata, Index Column Order, Index Compression, Oracle, Oracle Blog, Oracle General, Oracle Indexes, Oracle19c.add a comment
I was reminded in a recent comment by Rajeshwaran Jeyabal that I hadn’t updated my post on Automatic Indexing with Advanced Compression that’s in need of a couple of amendments.
Initially when Automatic Indexing was released, the ability to set Advanced Compression was NOT included in the official documentation, although the EXEC DBMS_AUTO_INDEX.CONFIGURE( ‘AUTO_INDEX_COMPRESSION‘ , ‘ON’); option was readily accessible. This has now been fixed and the associated doco on setting Advanced Compression for Automatic Indexes can be found here.
The other significant change is that Advanced Compression Low is now the default behaviour when Automatic Indexes are created in the Oracle ATP Autonomous Database Cloud environment. This makes sense in that if you have access to the Advanced Compression option, setting all indexes to Advanced Compression Low is the no-brainer setting as I’ve discussed previously. So several of my more recent posts show how Automatic Indexes have been created with Advanced Compression Low set.
What hasn’t changed however is how Automatic Indexing does NOT consider the efficiency of an index in relation to Index Compression when deciding how to order the columns within the index.
The default order of columns within an index (when other SQL predicates are not a consideration) is simply the order by which the columns appear within the table. Even though an index could be significantly smaller thanks to Index Compression if columns with more repeated values are ordered first within an index, this is not something Automatic Indexing currently considers.
The demo in my original piece still works exactly the same in the current 19c database versions of the ATP Autonomous Cloud environments. Manually created indexes can be significantly smaller if index columns are reordered or dropped entirely if they don’t provide filtering benefits.
When reading my blog, please do take note of the date of blog piece, especially in relation to Automatic Indexing. Things are only accurate as at time of publication and may change subsequently.
I thank Rajeshwaran for getting me to pull my finger out and update my blog accordingly…
Oracle 19c Automatic Indexing: Non-Equality Predicates Part II (Let’s Spend The Night Together) January 21, 2021
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Full Table Scans, Non-Equality Predicates, Oracle, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle19c, Performance Tuning.1 comment so far
In my previous post in this series, I discussed out Automatic Indexing currently does not consider Non-Equality predicates. Automatic Indexing will index columns based only on Equality predicates.
So how does Oracle handle the scenario when an SQL has a mixture of both Equality and Non-Equality predicates?
I’ll begin by creating two very similar tables, but with the second table having a more selective CODE column:
SQL> create table pink_floyd (id number, code number, create_date date, name varchar2(42)); Table created. SQL> insert into pink_floyd select rownum, ceil(dbms_random.value(0, 5000)), sysdate-mod(rownum, 50000)+1, 'Dark Side of the Moon' from dual connect by level <=10000000; 10000000 rows created. SQL> commit; Commit complete. SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'PINK_FLOYD'); PL/SQL procedure successfully completed. SQL> create table pink_floyd1 (id number, code number, create_date date, name varchar2(42)); Table created. SQL> insert into pink_floyd1 select rownum, ceil(dbms_random.value(0, 25000)), sysdate-mod(rownum, 50000)+1, 'Dark Side of the Moon' from dual connect by level <=10000000; 10000000 rows created. SQL> commit; Commit complete. SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'PINK_FLOYD1'); PL/SQL procedure successfully completed.
So table PINK_FLOYD has 5,000 distinct CODE values, whereas table PINK_FLOYD1 has 25,000 distinct CODE values.
I’ll next run the following identical SQLs, which both use an Equality predicate on the CODE column and a Non-Equality predicate on the CREATE_DATE column. The CODE column provides some filtering (more so with the PINK_FLOYD1 table) but in combination with the CREATE_DATE column, results in the ultimate filtering with no rows returned:
SQL> select * from pink_floyd where code=42 and create_date> '19-JAN-2021';
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 1152280033
----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 40 | 844 (11) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | PINK_FLOYD | 1 | 40 | 844 (11) | 00:00:01 |
----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00', 'syyyy-mm-ddhh24:mi:ss') AND "CODE"=42)
filter("CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00', 'syyyy-mm-ddhh24:mi:ss') AND "CODE"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
63660 consistent gets
63649 physical reads
0 redo size
426 bytes sent via SQL*Net to client
380 bytes received via SQL*Net from client
1 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
0 rows processed
SQL> select * from pink_floyd1 where code=42 and create_date> '19-JAN-2021';
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 564520720
-----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
-----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 41 | 856 (11) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | PINK_FLOYD1 | 1 | 41 | 856 (11) | 00:00:01 |
-----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE"=42 AND "CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00','syyyy-mm-dd hh24:mi:ss'))
filter("CODE"=42 AND "CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00','syyyy-mm-dd hh24:mi:ss'))
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
64424 consistent gets
64413 physical reads
0 redo size
426 bytes sent via SQL*Net to client
381 bytes received via SQL*Net from client
1 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
0 rows processed
So how does Automatic Indexing handle this scenario. If we look at the subsequent Automatic Indexing report (highlights only):
INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
*: invisible
-------------------------------------------------------------------------------
-----------------------------------------------------------------------------
| Owner | Table | Index | Key | Type | Properties |
-----------------------------------------------------------------------------
| BOWIE | PINK_FLOYD1 | SYS_AI_96snkmu4sk44g | CODE | B-TREE | NONE |
-----------------------------------------------------------------------------
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL ID : 7wag3gbk0b3tm
SQL Text : select * from pink_floyd1 where code=42 and create_date> '19-JAN-2021'
Improvement Factor : 64442.3x
Execution Statistics:
-----------------------------
Original Plan Auto Index Plan
---------------------------- ----------------------------
Elapsed Time (s): 568513 2771
CPU Time (s): 275534 1874
Buffer Gets: 1031078 406
Optimizer Cost: 856 405
Disk Reads: 1030609 3
Direct Writes: 0 0
Rows Processed: 0 0
Executions: 16 1
PLANS SECTION
---------------------------------------------------------------------------------------------
- Original
-----------------------------
Plan Hash Value : 564520720
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | | | 856 | |
| 1 | TABLE ACCESS STORAGE FULL | PINK_FLOYD1 | 1 | 41 | 856 | 00:00:01 |
-----------------------------------------------------------------------------------
Notes
-----
- dop = 1
- px_in_memory_imc = no
- px_in_memory = no
- With Auto Indexes
-----------------------------
Plan Hash Value : 2703636439
-------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
-------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 41 | 405 | 00:00:01 |
| * 1 | TABLE ACCESS BY INDEX ROWID BATCHED | PINK_FLOYD1 | 1 | 41 | 405 | 00:00:01 |
| * 2 | INDEX RANGE SCAN | SYS_AI_96snkmu4sk44g | 403 | | 3 | 00:00:01 |
-------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
------------------------------------------
* 1 - filter("CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
* 2 - access("CODE"=42)
Notes
-----
- Dynamic sampling used for this statement ( level = 11 )
If we look at the definitions of all indexes currently on these tables:
SQL> select index_name, auto, visibility, compression, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='PINK_FLOYD'; INDEX_NAME AUT VISIBILIT COMPRESSION STATUS NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR ------------------------------ --- --------- ------------- -------- ---------- ----------- ----------------- SYS_AI_dp2t0j12zux49 YES INVISIBLE ADVANCED LOW UNUSABLE 10000000 21702 4161898 SQL> select index_name, column_name, column_position from user_ind_columns where table_name='PINK_FLOYD'; INDEX_NAME COLUMN_NAME COLUMN_POSITION ------------------------------ --------------- --------------- SYS_AI_dp2t0j12zux49 CODE 1 SQL> select index_name, auto, visibility, compression, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='PINK_FLOYD1'; INDEX_NAME AUT VISIBILIT COMPRESSION STATUS NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR ------------------------------ --- --------- ------------- -------- ---------- ----------- ----------------- SYS_AI_96snkmu4sk44g YES VISIBLE ADVANCED LOW VALID 10000000 15400 9969473 SQL> select index_name, column_name, column_position from user_ind_columns where table_name='PINK_FLOYD1'; INDEX_NAME COLUMN_NAME COLUMN_POSITION ------------------------------ --------------- --------------- SYS_AI_96snkmu4sk44g CODE 1
In both cases, Automatic Indexing only created an index on the CODE column, as it was the only column with an Equality predicate.
However, the Automatic Index on the table PINK_FLOYD remained in an INVISIBLE/UNUSABLE. That’s because an index on only the CODE column was not efficient enough to improve the performance of the SQL, due to the filtering not being sufficient enough and because of the relatively poor Clustering Factor.
The index on the table PINK_FLOYD1 was eventually created as a VISIBLE/VALID index, as its better filtering was sufficient to actually improve the performance of the SQL.
So if we re-run the first query:
SQL> select * from pink_floyd where code=42 and create_date> '19-JAN-2021';
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 1152280033
----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 40 | 844 (11) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | PINK_FLOYD | 1 | 40 | 844 (11) | 00:00:01 |
----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00', 'syyyy-mm-ddhh24:mi:ss') AND "CODE"=42)
filter("CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00', 'syyyy-mm-ddhh24:mi:ss') AND "CODE"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
63660 consistent gets
63649 physical reads
0 redo size
426 bytes sent via SQL*Net to client
380 bytes received via SQL*Net from client
1 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
0 rows processed
It continues to use a Full Table Scan.
If we re-run the second query:
SQL> select * from pink_floyd1 where code=42 and create_date> '19-JAN-2021';
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 2703636439
------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 41 | 415 (0) | 00:00:01 |
|* 1 | TABLE ACCESS BY INDEX ROWID BATCHED | PINK_FLOYD1 | 1 | 41 | 415 (0) | 00:00:01 |
|* 2 | INDEX RANGE SCAN | SYS_AI_96snkmu4sk44g | 412 | | 3 (0) | 00:00:01 |
------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
2 - access("CODE"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
406 consistent gets
0 physical reads
0 redo size
426 bytes sent via SQL*Net to client
381 bytes received via SQL*Net from client
1 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
0 rows processed
If now uses the newly created Automatic Index, with an improved 406 Consistent Gets (down from the previous 64424 Consistent Gets with the FTS).
BUT if we were to manually create an index on BOTH CODE and CREATE_DATE columns:
SQL> create index pink_floyd1_code_create_date_i on pink_floyd1(code, create_date) compress advanced low;
Index created.
SQL> select * from pink_floyd1 where code=42 and create_date> '19-JAN-2021';
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 3366491378
----------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
----------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 41 | 4 (0) | 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED | PINK_FLOYD1 | 1 | 41 | 4 (0) | 00:00:01 |
|* 2 | INDEX RANGE SCAN | PINK_FLOYD1_CODE_CREATE_DATE_I | 1 | | 3 (0) | 00:00:01 |
----------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("CODE"=42 AND "CREATE_DATE">TO_DATE(' 2021-01-19 00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND
"CREATE_DATE" IS NOT NULL)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
3 consistent gets
0 physical reads
0 redo size
426 bytes sent via SQL*Net to client
381 bytes received via SQL*Net from client
1 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
0 rows processed
Performance improves significantly further, by reducing Consistent Gets down to just 3.
So if you have SQL statements with a mixture of both Equality and Non-Equality predicates, you may encounter these 2 scenarios:
A potentially efficient index that is not created at all as the filtering on just the Equality based predicates are not sufficient to create a viable index, or
A potentially suboptimal Automatic Index that doesn’t contain useful filtering columns because they’re used in Non-Equality predicates…
Oracle 19c Automatic Indexing: Non-Equality Predicates Part I (Lucy Can’t Dance) January 14, 2021
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Database, Autonomous Transaction Processing, Exadata, Full Table Scans, Non-Equality Predicates, Oracle, Oracle Blog, Oracle Cloud, Oracle Indexes, Oracle19c.4 comments
I’ve been waiting a while before posting a series on the various limitations associated with Automatic Indexing, in order to see how the feature matures over time.
The following have all been re-tested post 1 January 2021 on the Autonomous ATP Database Cloud service, using Oracle Database version 19.5.0.0.0.
In the Oracle Documentation (including version 21c), the only limitations with regard Automatic Indexing listed are the following:
- Auto indexes are local B-tree indexes.
- Auto indexes can be created for partitioned as well as non-partitioned tables.
- Auto indexes cannot be created for temporary tables.
Well, as I discussed in the previous series on Automatic Indexing on Partitioned tables, we already saw how Oracle can actually also create Non-Partitioned (Global) indexes. So the limitation on Automatic Indexes being “local” indexes is not actually correct, even with 19c.
But are there other limitations that are not officially documented?
If you look at every example I’ve used previously with regard Automatic Indexing, they all feature Equality predicates. In the following examples, I’m going to run a series on Range Scan predicates that heavily filter and would benefit greatly from an index.
I first create a simple table with 10M rows:
SQL> create table ziggy1 (id number, code number, name varchar2(42)); Table created. SQL> insert into ziggy1 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=>'ZIGGY1'); PL/SQL procedure successfully completed.
I then run the following range scan queries several times that each return only a few rows:
SQL> select * from ziggy1 where id between 42 and 50;
9 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 4062853157
------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 8 | 184 | 538 (14) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | ZIGGY1 | 8 | 184 | 538 (14) | 00:00:01 |
------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("ID"<=50 AND "ID">=42)
filter("ID"<=50 AND "ID">=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
39436 consistent gets
39425 physical reads
0 redo size
596 bytes sent via SQL*Net to client
369 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
9 rows processed
SQL> select * from ziggy1 where id < 0;
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 4062853157
------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 23 | 538 (14) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | ZIGGY1 | 1 | 23 | 538 (14) | 00:00:01 |
------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("ID"<0)
filter("ID"<0)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
39436 consistent gets
39425 physical reads
0 redo size
364 bytes sent via SQL*Net to client
344 bytes received via SQL*Net from client
1 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
0 rows processed
SQL> select * from ziggy1 where id > 100000000000;
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 4062853157
------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 23 | 538 (14) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | ZIGGY1 | 1 | 23 | 538 (14) | 00:00:01 |
------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("ID">100000000000)
filter("ID">100000000000)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
39436 consistent gets
39425 physical reads
0 redo size
364 bytes sent via SQL*Net to client
355 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 look at the subsequent Automatic Indexing report:
SQL> select dbms_auto_index.report_last_activity() report from dual; REPORT -------------------------------------------------------------------------------- GENERAL INFORMATION ------------------------------------------------------------------------------- Activity start : 13-JAN-2021 11:55:37 Activity end : 13-JAN-2021 11:56:20 Executions completed : 1 Executions interrupted : 0 Executions with fatal error : 0 ------------------------------------------------------------------------------- SUMMARY (AUTO INDEXES) ------------------------------------------------------------------------------- Index candidates : 0 Indexes created : 0 Space used : 0 B Indexes dropped : 0 SQL statements verified : 3 SQL statements improved : 0 SQL plan baselines created : 0 Overall improvement factor : 0x ------------------------------------------------------------------------------- SUMMARY (MANUAL INDEXES) ------------------------------------------------------------------------------- Unused indexes : 0 Space used : 0 B Unusable indexes : 0 -------------------------------------------------------------------------------
We notice NO Automatic Indexes were created.
We can run these queries endlessly and Automatic Indexing will never create associated Automatic Indexes:
SQL> select index_name, auto, constraint_index, visibility from user_indexes where table_name='ZIGGY1'; no rows selected
These queries are doomed to perform Full Table Scans unless indexes are manually created:
SQL> select * from ziggy1 where id between 42 and 50;
9 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 4062853157
------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU) | Time |
------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 8 | 184 | 538 (14) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL | ZIGGY1 | 8 | 184 | 538 (14) | 00:00:01 |
------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("ID"<=50 AND "ID">=42)
filter("ID"<=50 AND "ID">=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
39436 consistent gets
39425 physical reads
0 redo size
596 bytes sent via SQL*Net to client
369 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
9 rows processed
Currently Automatic Indexes do not support Non-Equality predicates. Automatic Indexes are only created based on Equality-based predicates.
Obviously, Automatic Indexing is a fabulous feature and this might all change in the future. But with Non-Equality predicates so prevalent in SQL, it’s vital to note this current limitation when using and relying on Automatic Indexing…
Oracle 19c Automatic Indexing: Indexing Partitioned Tables Part II (Neighbourhood Threat) January 13, 2021
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Transaction Processing, CBO, Exadata, Local Indexes, Oracle, Oracle Cloud, Oracle General, Oracle Indexes, Oracle19c, Partitioned Indexes, Partitioning.4 comments
In my first post on Automatic Indexing on Partitioned Tables, I discussed how Automatic Indexing (AI) can now create a Non-Partitioned index if deemed the most effective indexing structure (this wasn’t supported when AI was initially released). A Non-Partitioned index is indeed likely the most efficient indexing structure if the underlying table has many partitions and associated SQL equality predicates only reference non-partition key columns. A Non-Partitioned index ensure Oracle only needs to scan the single index structure and not all the partitions of a Local index.
But what if SQLs do reference the column by which the underlying table is partitioned?
The following SQL has an equality filtering predicate on the RELEASE_DATE column, the column by which the BIG_BOWIE1 table is partitioned:
SQL> SELECT * FROM big_bowie1 where release_date = to_date('2013-12-30 22:15:25', 'syyyy-mm-dd hh24:mi:ss');
no rows selected
If we look at the subsequent AI report:
INDEX DETAILS ------------------------------------------------------------------------------- The following indexes were created: *: invisible ------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------ | Owner | Table | Index | Key | Type | Properties | ------------------------------------------------------------------------------------------------ | BOWIE | BIG_BOWIE1 | SYS_AI_14gpurjp8m76s | RELEASE_DATE | B-TREE | LOCAL | ------------------------------------------------------------------------------------------------ -------------------------------------------------------------------------------
We notice that Automatic Indexing has in this instance created a Local Index.
If we look further down the AI report:
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL ID : 4mm3mbkk38pa8
SQL Text : SELECT * FROM big_bowie1 where release_date = to_date('2013-12-30 22:15:25', 'syyyy-mm-dd hh24:mi:ss')
Improvement Factor : 8339x
Execution Statistics:
-----------------------------
Original Plan Auto Index Plan
---------------------------- ----------------------------
Elapsed Time (s): 146957 71
CPU Time (s): 146124 71
Buffer Gets: 16678 3
Optimizer Cost: 162 4
Disk Reads: 0 0
Direct Writes: 0 0
Rows Processed: 0 0
Executions: 2 1
PLANS SECTION
---------------------------------------------------------------------------------------------
- Original
-----------------------------
Plan Hash Value : 4031749531
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | | | 162 | |
| 1 | PARTITION RANGE SINGLE | | 3602 | 93652 | 162 | 00:00:01 |
| 2 | TABLE ACCESS STORAGE FULL | BIG_BOWIE1 | 3602 | 93652 | 162 | 00:00:01 |
-----------------------------------------------------------------------------------
Notes
-----
- dop = 1
- px_in_memory_imc = no
- px_in_memory = no
- With Auto Indexes
-----------------------------
Plan Hash Value : 4049653350
--------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
--------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 3 | 78 | 4 | 00:00:01 |
| 1 | PARTITION RANGE SINGLE | | 3 | 78 | 4 | 00:00:01 |
| 2 | TABLE ACCESS BY LOCAL INDEX ROWID BATCHED | BIG_BOWIE1 | 3 | 78 | 4 | 00:00:01 |
| * 3 | INDEX RANGE SCAN | SYS_AI_14gpurjp8m76s | 1 | | 3 | 00:00:01 |
--------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
------------------------------------------
* 3 - access("RELEASE_DATE"=TO_DATE(' 2013-12-30 22:15:25', 'syyyy-mm-dd hh24:mi:ss'))
Notes
-----
- Dynamic sampling used for this statement ( level = 11 )
We can see Automatic Indexing has created the index because it provides an average Improvement Factor of 8339x. As the necessary indexed column(s) matches the table partitioning key, it makes sense for the associated index be a Local index as Oracle is certain which specific index partition to visit based on the value of the equality predicate.
If we look at the details of this new AI:
SQL> select index_name, auto, constraint_index, visibility, compression, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='BIG_BOWIE1'; INDEX_NAME AUT CON VISIBILIT COMPRESSION STATUS NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR ------------------------------ --- --- --------- ------------- -------- ---------- ----------- ----------------- SYS_AI_14gpurjp8m76s YES NO VISIBLE ADVANCED LOW N/A 20000000 30742 19941449 SYS_AI_8armv0hqq73fa YES NO VISIBLE ADVANCED LOW VALID 20000000 42697 19995451 SQL> select index_name, column_name, column_position from user_ind_columns where table_name='BIG_BOWIE1' order by index_name, column_position; INDEX_NAME COLUMN_NAME COLUMN_POSITION ------------------------------ --------------- --------------- SYS_AI_14gpurjp8m76s RELEASE_DATE 1 SYS_AI_8armv0hqq73fa TOTAL_SALES 1 SQL> SELECT index_name, partitioning_type, partition_count, locality FROM user_part_indexes WHERE table_name = 'BIG_BOWIE1'; INDEX_NAME PARTITION PARTITION_COUNT LOCALI ------------------------------ --------- --------------- ------ SYS_AI_14gpurjp8m76s RANGE 8 LOCAL SQL> select index_name, partition_name, status, compression from user_ind_partitions where index_name in (select index_name from user_indexes where table_name='BIG_BOWIE1') order by partition_position; INDEX_NAME PARTITION_NAME STATUS COMPRESSION -------------------- -------------------- -------- ------------- SYS_AI_14gpurjp8m76s ALBUMS_2013 USABLE ADVANCED LOW SYS_AI_14gpurjp8m76s ALBUMS_2014 USABLE ADVANCED LOW SYS_AI_14gpurjp8m76s ALBUMS_2015 USABLE ADVANCED LOW SYS_AI_14gpurjp8m76s ALBUMS_2016 USABLE ADVANCED LOW SYS_AI_14gpurjp8m76s ALBUMS_2017 USABLE ADVANCED LOW SYS_AI_14gpurjp8m76s ALBUMS_2018 USABLE ADVANCED LOW SYS_AI_14gpurjp8m76s ALBUMS_2019 USABLE ADVANCED LOW SYS_AI_14gpurjp8m76s ALBUMS_2020 USABLE ADVANCED LOW
We can see that indeed, a Visible, Usable, Local index was created by Automatic Indexing.
So depending on the column(s) within the index, Automatic Indexing can potentially create either a Local or Non-Partitioned index when indexing a partitioned table.
Oracle 19c Automatic Indexing: Indexing Partitioned Tables Part I (Conversation Piece) October 14, 2020
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Index Access Path, Local Indexes, Oracle, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle19c, Partitioned Indexes, Partitioning, Performance Tuning.1 comment so far
In this little series, I’m going to discuss how Automatic Indexing works in relation to Partitioning.
I’ve discussed Indexing and Partitioning many times previously and how Oracle has various options when indexing a partitioned table:
- Non-Partitioned Index
- Globally Partitioned Index
- Locally Partitioned Index
So the question(s) are how does Automatic Indexing handle scenarios with partitioned objects?
A very important point to make at the start is that based on my research, the answer has already changed significantly since Automatic Indexing was first released. So it’s important to understand that Automatic Indexing is an ever evolving capability, that will advance and improve as time goes on.
I’ll focus on how the feature currently works (as of Oracle Database 19.5), but will mention previously identified behaviour as a reference on how things can easily change.
In my first simple little example, I’m just going to create a range-partitioned table, partitioned based on RELEASE_DATE, with a partition for each year’s worth of data:
SQL> CREATE TABLE big_bowie1(id number, album_id number, country_id number, release_date date,
total_sales number) PARTITION BY RANGE (release_date)
(PARTITION ALBUMS_2013 VALUES LESS THAN (TO_DATE('01-JAN-2014', 'DD-MON-YYYY')),
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 (MAXVALUE));
Table created.
I’ll now add about 8 years worth of data:
SQL> INSERT INTO big_bowie1 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.
As discussed previously, I’ll importantly collect statistics:
SQL> exec dbms_stats.gather_table_stats(ownname=> null, tabname=> 'BIG_BOWIE1'); PL/SQL procedure successfully completed.
I’ll now run the following very selective query based the TOTAL_SALES column that is NOT part of the partitioning key:
SQL> SELECT * FROM big_bowie1 WHERE total_sales = 42;
19 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 2468051548
---------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop |
---------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 20 | 520 | 643 (15)| 00:00:01 | | |
| 1 | PARTITION RANGE ALL | | 20 | 520 | 643 (15)| 00:00:01 | 1 | 8 |
|* 2 | TABLE ACCESS STORAGE FULL| BIG_BOWIE1 | 20 | 520 | 643 (15)| 00:00:01 | 1 | 8 |
---------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - storage("TOTAL_SALES"=42)
filter("TOTAL_SALES"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
44014 consistent gets
9516 physical reads
0 redo size
1107 bytes sent via SQL*Net to client
369 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
19 rows processed
Without an index in place, the CBO has no choice but to use a FTS. But what will Automatic Indexing make of things?
If we look at the next Automatic Indexing report:
SQL> select dbms_auto_index.report_last_activity() from dual;
GENERAL INFORMATION
-------------------------------------------------------------------------------
Activity start : 13-OCT-2020 01:47:48
Activity end : 13-OCT-2020 02:59:48
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) : 184.55 MB (184.55 MB / 0 B)
Indexes dropped : 0
SQL statements verified : 2
SQL statements improved (improvement factor) : 1 (44119.6x)
SQL plan baselines created : 0
Overall improvement factor : 25135.8x
-------------------------------------------------------------------------------
SUMMARY (MANUAL INDEXES)
-------------------------------------------------------------------------------
Unused indexes : 0
Space used : 0 B
Unusable indexes : 0
-------------------------------------------------------------------------------
INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
*: invisible
-------------------------------------------------------------------------------
---------------------------------------------------------------------------------
| Owner | Table | Index | Key | Type | Properties |
---------------------------------------------------------------------------------
| BOWIE | BIG_BOWIE1 | SYS_AI_2zt7rg40mxa4n | TOTAL_SALES | B-TREE | NONE |
---------------------------------------------------------------------------------
-------------------------------------------------------------------------------
VERIFICATION DETAILS
-------------------------------------------------------------------------------
The performance of the following statements improved:
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL ID : chwm2gubm8fx9
SQL Text : SELECT * FROM big_bowie1 WHERE total_sales = 42
Improvement Factor : 44119.6x
Execution Statistics:
-----------------------------
Original Plan Auto Index Plan
---------------------------- ----------------------------
Elapsed Time (s): 4387193 1173
CPU Time (s): 2599423 1037
Buffer Gets: 749507 22
Optimizer Cost: 643 22
Disk Reads: 470976 2
Direct Writes: 0 0
Rows Processed: 323 19
Executions: 17 1
PLANS SECTION
---------------------------------------------------------------------------------------------
- Original
-----------------------------
Plan Hash Value : 2468051548
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | | | 643 | |
| 1 | PARTITION RANGE ALL | | 20 | 520 | 643 | 00:00:01 |
| 2 | TABLE ACCESS STORAGE FULL | BIG_BOWIE1 | 20 | 520 | 643 | 00:00:01 |
-----------------------------------------------------------------------------------
Notes
-----
- dop = 1
- px_in_memory_imc = no
- px_in_memory = no
- With Auto Indexes
-----------------------------
Plan Hash Value : 937174207
--------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
--------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 19 | 494 | 22 | 00:00:01 |
| 1 | TABLE ACCESS BY GLOBAL INDEX ROWID BATCHED | BIG_BOWIE1 | 19 | 494 | 22 | 00:00:01 |
| * 2 | INDEX RANGE SCAN | SYS_AI_2zt7rg40mxa4n | 19 | | 3 | 00:00:01 |
--------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
------------------------------------------
* 2 - access("TOTAL_SALES"=42)
Notes
-----
- Dynamic sampling used for this statement ( level = 11 )
We notice a couple of interesting points.
Firstly, yes Automatic Indexing has created an index based on the TOTAL_SALES column (SYS_AI_2zt7rg40mxa4n) as it improves performance by a reported 44119.6x.
Note also that the Automatic Index is a Non-Partitioned (Global) Index. From a performance perspective, this is the most efficient index to create to improve the performance of this query as the CBO only has the one index structure to navigate (vs. a LOCAL index that would require having to navigate down all 8 index structures for each table partition.
If we look at the index details:
SQL> SELECT index_name, partitioned, auto, visibility, status FROM user_indexes WHERE table_name = 'BIG_BOWIE1'; INDEX_NAME PAR AUT VISIBILIT STATUS ------------------------------ --- --- --------- -------- SYS_AI_2zt7rg40mxa4n NO YES VISIBLE VALID
We notice that this is indeed a Non-Partitioned Index, that is both VISIBLE and VALID and so can be potentially used by any database session.
If we now re-run the query:
SQL> SELECT * FROM big_bowie1 WHERE total_sales = 42;
19 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 937174207
-----------------------------------------------------------------------------------------------------------------------------------
| 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_BOWIE1 | 20 | 520 | 23 (0)| 00:00:01 | ROWID | ROWID |
|* 2 | INDEX RANGE SCAN | SYS_AI_2zt7rg40mxa4n | 20 | | 3 (0)| 00:00:01 | | |
-----------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("TOTAL_SALES"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
23 consistent gets
0 physical reads
0 redo size
1166 bytes sent via SQL*Net to client
369 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
19 rows processed
We can see the query now uses the newly created Automatic Index and is indeed more efficient, performing now just 23 consistent gets (previously 44014 consistent gets).
However, this was NOT previous behaviour.
The documentation previously mentioned that only LOCAL indexes are used when indexing partitioned tables.
If we run the same demo on Oracle Database 19.3, we get the following report:
GENERAL INFORMATION
-------------------------------------------------------------------------------
Activity start : 14-OCT-2020 13:12:07
Activity end : 14-OCT-2020 14:24:07
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) : 192.94 MB (192.94 MB / 0 B)
Indexes dropped : 0
SQL statements verified : 1
SQL statements improved (improvement factor) : 1 (1950.5x)
SQL plan baselines created : 0
Overall improvement factor : 1950.5x
-------------------------------------------------------------------------------
SUMMARY (MANUAL INDEXES)
-------------------------------------------------------------------------------
Unused indexes : 0
Space used : 0 B
Unusable indexes : 0
-------------------------------------------------------------------------------
INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
*: invisible
-------------------------------------------------------------------------------
---------------------------------------------------------------------------------
| Owner | Table | Index | Key | Type | Properties |
---------------------------------------------------------------------------------
| BOWIE | BIG_BOWIE1 | SYS_AI_8armv0hqq73fa | TOTAL_SALES | B-TREE | LOCAL |
---------------------------------------------------------------------------------
-------------------------------------------------------------------------------
VERIFICATION DETAILS
-------------------------------------------------------------------------------
The performance of the following statements improved:
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL ID : 2pp8ypramw30s
SQL Text : SELECT * FROM big_bowie1 WHERE total_sales = 42
Improvement Factor : 1950.5x
Execution Statistics:
-----------------------------
Original Plan Auto Index Plan
---------------------------- ----------------------------
Elapsed Time (s): 6996973 27327
CPU Time (s): 6704215 12819
Buffer Gets: 815306 49
Optimizer Cost: 12793 28
Disk Reads: 2 40
Direct Writes: 0 0
Rows Processed: 475 25
Executions: 19 1
PLANS SECTION
---------------------------------------------------------------------------------------------
- Original
-----------------------------
Plan Hash Value : 4294056405
-----------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
-----------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | | | 12793 | |
| 1 | PARTITION RANGE ALL | | 20 | 520 | 12793 | 00:00:01 |
| 2 | TABLE ACCESS FULL | BIG_BOWIE1 | 20 | 520 | 12793 | 00:00:01 |
-----------------------------------------------------------------------------
- With Auto Indexes
-----------------------------
Plan Hash Value : 3781269341
--------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
--------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 25 | 650 | 28 | 00:00:01 |
| 1 | PARTITION RANGE ALL | | 25 | 650 | 28 | 00:00:01 |
| 2 | TABLE ACCESS BY LOCAL INDEX ROWID BATCHED | BIG_BOWIE1 | 25 | 650 | 28 | 00:00:01 |
| * 3 | INDEX RANGE SCAN | SYS_AI_8armv0hqq73fa | 25 | | 17 | 00:00:01 |
--------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
------------------------------------------
* 3 - access("TOTAL_SALES"=42)
Notes
-----
- Dynamic sampling used for this statement ( level = 11 )
As we can see, in this scenario, the newly created Automatic Index has a “Property” of LOCAL.
If we look at its index details:
SQL> SELECT index_name, partitioned, auto, visibility, status FROM user_indexes WHERE table_name = 'BIG_BOWIE1'; INDEX_NAME PAR AUT VISIBILIT STATUS ------------------------------ --- --- --------- -------- SYS_AI_8armv0hqq73fa YES YES VISIBLE N/A SQL> SELECT index_name, partitioning_type, partition_count, locality FROM user_part_indexes WHERE table_name = 'BIG_BOWIE1'; INDEX_NAME PARTITION PARTITION_COUNT LOCALI ------------------------------ --------- --------------- ------ SYS_AI_8armv0hqq73fa RANGE 8 LOCAL
We can see how a Local Index was previously created.
As such if we re-run an equivalent query:
SQL> SELECT * FROM big_bowie1 WHERE total_sales = 42;
25 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 3781269341
-----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop |
-----------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 20 | 520 | 26 (0)| 00:00:01 | | |
| 1 | PARTITION RANGE ALL | | 20 | 520 | 26 (0)| 00:00:01 | 1 | 8 |
| 2 | TABLE ACCESS BY LOCAL INDEX ROWID BATCHED| BIG_BOWIE1 | 20 | 520 | 26 (0)| 00:00:01 | 1 | 8 |
|* 3 | INDEX RANGE SCAN | SYS_AI_8armv0hqq73fa | 20 | | 17 (0)| 00:00:01 | 1 | 8 |
-----------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
3 - access("TOTAL_SALES"=42)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
50 consistent gets
0 physical reads
0 redo size
1555 bytes sent via SQL*Net to client
409 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
25 rows processed
Although the query is returning 6 more rows (as with the random number generation, has a slightly different data set), it’s more expensive proportionally now having to perform 50 consistent gets as it now has to read 8 index structures rather than just the one.
So (IMHO), Automatic Indexing has improved here, creating a more efficient index structure than previously. So always bear in mind that Automatic Indexing is an evolving beast, improving and adapting as time moves on.
However, note the compromise here is that by having an effectively Global index structure, there may be some additional issues depending on any subsequent structural changes to the table.
More on Automatic Indexing and Partitioning in my next post…
Oracle 19c Automatic Indexing: Indexing With Stale Statistics Part II (Survive) October 7, 2020
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, CBO, Exadata, Full Table Scans, Index Internals, Index statistics, Oracle, Oracle General, Oracle Indexes, Oracle Statistics, Oracle19c, Performance Tuning, Stale Statistics.1 comment so far
In my previous post, I discussed how having stale statistics, usually a bad idea, is especially problematic with regard Automatic Indexes as it usually results in viable automatic indexes only being created in an UNUSABLE/INVISIBLE state.
If we were to now to collect the missing statistics:
SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE_STALE'); PL/SQL procedure successfully completed. SQL> select table_name, num_rows, blocks, last_analyzed from user_tables where table_name='BOWIE_STALE'; TABLE_NAME NUM_ROWS BLOCKS LAST_ANAL -------------------- ---------- ---------- --------- BOWIE_STALE 10000000 39677 06-JUL-20 SQL> select column_name, num_distinct, density, histogram, last_analyzed from user_tab_cols where table_name='BOWIE_STALE'; COLUMN_NAME NUM_DISTINCT DENSITY HISTOGRAM LAST_ANAL -------------------- ------------ ---------- --------------- --------- ID 10000000 0 HYBRID 06-JUL-20 CODE 971092 .000001 HYBRID 06-JUL-20 NAME 1 4.9416E-08 FREQUENCY 06-JUL-20
If we now repeatedly re-run the problematic query many times:
SQL> select * from bowie_stale where code=42;
10 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 65903426
-----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 10 | 230 | 544 (14)| 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL| BOWIE_STALE | 10 | 230 | 544 (14)| 00:00:01 |
-----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE"=42)
filter("CODE"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
39430 consistent gets
39421 physical reads
0 redo size
610 bytes sent via SQL*Net to client
361 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
10 rows processed
The CBO is forced to use the FTS as the current Automatic Index is in an UNUSABLE/INVISIBLE state.
If we wait for the next Automatic Indexing reporting period:
SQL> select dbms_auto_index.report_last_activity('text', 'ALL', 'ALL' ) report from dual;
REPORT
--------------------------------------------------------------------------------
GENERAL INFORMATION
-------------------------------------------------------------------------------
Activity start : 06-JUL-2020 05:12:42
Activity end : 06-JUL-2020 05:13:34
Executions completed : 1
Executions interrupted : 0
Executions with fatal error : 0
-------------------------------------------------------------------------------
SUMMARY (AUTO INDEXES)
-------------------------------------------------------------------------------
Index candidates : 0
Indexes created : 0
Space used : 0 B
Indexes dropped : 0
SQL statements verified : 0
SQL statements improved : 0
SQL plan baselines created : 0
Overall improvement factor : 0x
-------------------------------------------------------------------------------
SUMMARY (MANUAL INDEXES)
-------------------------------------------------------------------------------
Unused indexes : 0
Space used : 0 B
Unusable indexes : 0
-------------------------------------------------------------------------------
We notice that the Automatic Indexing process has nothing to report. Even though the problematic query is repeatedly executed, the SQL is now effectively on a blacklist and is not re-considered by the Automatic Indexing process.
If we look at the index details on the table:
SQL> select index_name, auto, constraint_index, visibility, compression, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE_STALE'; INDEX_NAME AUT CON VISIBILIT COMPRESSION STATUS NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR ---------------------- --- --- --------- ------------- -------- ---------- ----------- ----------------- BOWIE_STALE_PK NO YES VISIBLE DISABLED VALID 10000000 20164 59110 SYS_AI_300kk2unp8tr0 YES NO INVISIBLE ADVANCED LOW UNUSABLE 10000000 23058 4147514
So the Automatic Index (SYS_AI_300kk2unp8tr0) is still UNUSABLE and INVISIBLE and can not be used by the CBO.
NOTE: In earlier patches of Oracle Database 19c (I’m using version 19.5.0.0.0 in this demo), I identified some scenarios after stale statistics when indexes were created in but in a VALID/INVISIBLE state, such that they could still not be used by the CBO in general database sessions.
If we simply re-run the same queries again from the time when the dependant object statistics were stale, any SQL is just ignored by the Automatic Indexing process.
As such, if we now subsequently re-run the problematic query again:
SQL> select * from bowie_stale where code=42;
10 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 65903426
-----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 10 | 230 | 544 (14)| 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL| BOWIE_STALE | 10 | 230 | 544 (14)| 00:00:01 |
-----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE"=42)
filter("CODE"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
39430 consistent gets
39421 physical reads
0 redo size
610 bytes sent via SQL*Net to client
361 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
10 rows processed
Again, the CBO has no choice here with no viable VALID/VISIBLE index present but to perform a FTS, even though its getting the cardinality estimates spot on since statistics gathering.
In Part III I’ll discuss how to get this query to finally use the Automatic Index and improve its performance, although if you’re a regular reader of the blog you should already know the solution…
Oracle 19c Automatic Indexing: Data Skew Fixed By Baselines Part II (Sound And Vision) September 28, 2020
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, Baselines, CBO, Data Skew, Exadata, Explain Plan For Index, Full Table Scans, Histograms, Index Access Path, Index statistics, Oracle, Oracle Blog, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle Statistics, Oracle19c, Performance Tuning.add a comment
In my previous post, I discussed how the Automatic Indexing task by using Dynamic Sampling Level=11 can correctly determine the correct query cardinality estimates and assume the CBO will likewise determine the correct cardinality estimate and NOT use an index if it would cause performance to regress.
However, if other database sessions DON’T use Dynamic Sampling at the same Level=11 and hence NOT determine correct cardinality estimates, newly created Automatic Indexes might get used by the CBO inappropriately and result inefficient execution plans.
Likewise, with incorrect CBO cardinality estimates, it might also be possible for newly created Automatic Indexes to NOT be used when they should be (as I’ve discussed previously).
These are potential issues if the Dynamic Sampling value differs between the Automatic Indexing task and other database sessions.
One potential way to make things more consistent and see how the Automatic Indexing behaves if it detects an execution plan where the CBO would use an Automatic Index that causes performance regression, is to disable Dynamic Sampling within the Automatic Indexing task.
This can be easily achieved by using the following hint which effectively disables Dynamic Sampling with the previous problematic query:
SQL> select /*+ dynamic_sampling(0) */ * from space_oddity where code in (190000, 170000, 150000, 130000, 110000, 90000, 70000, 50000, 30000, 10000);
1000011 rows selected.
Execution Plan
----------------------------------------------------------------------------------
| Id | Operation        | Name        | Rows | Bytes | Cost (%CPU)| Time    |
----------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ |Â Â Â Â Â Â Â Â Â Â Â Â Â |Â 1005K|Â Â 135M| 11411Â Â (1)| 00:00:01 |
|*Â 1 |Â TABLE ACCESS FULL| SPACE_ODDITY |Â 1005K|Â Â 135M| 11411Â Â (1)| 00:00:01 |
----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("CODE"=10000 OR "CODE"=30000 OR "CODE"=50000 OR
"CODE"=70000 OR "CODE"=90000 OR "CODE"=110000 OR "CODE"=130000 OR
"CODE"=150000 OR "CODE"=170000 OR "CODE"=190000)
Statistics
----------------------------------------------------------
0Â recursive calls
0Â db block gets
41169Â consistent gets
0Â physical reads
0Â redo size
13535504Â bytes sent via SQL*Net to client
2705Â bytes received via SQL*Net from client
202Â SQL*Net roundtrips to/from client
0Â sorts (memory)
0Â sorts (disk)
1000011Â rows processed
The query currently has good cardinality estimates (1005K vs 1000011 rows returned) only because we currently have histograms in place for the CODE column. As such, the query correctly uses a FTS.
However, if we now remove the histogram on the CODE column:
SQL> exec dbms_stats.gather_table_stats(null, 'SPACE_ODDITY', method_opt=> 'FOR ALL COLUMNS SIZE 1’); PL/SQL procedure successfully completed.
There is no way for the CBO to now determine the correct cardinality estimate because of the skewed data and missing histograms.
So what does the Automatic Indexing tasks make of things now. If we look at the next activity report:
SQL> select dbms_auto_index.report_last_activity() report from dual; REPORT -------------------------------------------------------------------------------- GENERAL INFORMATION ------------------------------------------------------------------------------- Activity start              : 18-AUG-2020 16:42:33 Activity end                : 18-AUG-2020 16:43:06 Executions completed        : 1 Executions interrupted      : 0 Executions with fatal error : 0 ------------------------------------------------------------------------------- SUMMARY (AUTO INDEXES) ------------------------------------------------------------------------------- Index candidates                            : 0 Indexes created                             : 0 Space used                                  : 0 B Indexes dropped                             : 0 SQL statements verified                     : 1 SQL statements improved                     : 0 SQL plan baselines created (SQL statements) : 1 (1) Overall improvement factor                  : 0x ------------------------------------------------------------------------------- SUMMARY (MANUAL INDEXES) ------------------------------------------------------------------------------- Unused indexes   : 0 Space used       : 0 B Unusable indexes : 0
We can see that it has verified this one new statement and has created 1 new SQL Plan Baseline as a result.
If we look at the Verification Details part of this report:
VERIFICATION DETAILS ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- The following SQL plan baselines were created: ------------------------------------------------------------------------------- Parsing Schema Name    : BOWIE SQL ID                 : 3yz8unzhhvnuz SQL Text               : select /*+ dynamic_sampling(0) */ * from space_oddity where code in (190000, 170000, 150000, 130000, 110000, 90000, 70000, 50000, 30000, 10000) SQL Signature          : 3910785437403172730 SQL Handle             : SQL_3645e6a2952fcf7a SQL Plan Baselines (1) : SQL_PLAN_3cjg6naakzmvu198c05b9
We can see Automatic Indexing has created a new SQL Plan Baseline for our query with Dynamic Sampling set to 0 thanks to the hint.
Basically, the Automatic Indexing task has found a new query and determined the CBO would be inclined to use the index, because it now incorrectly assumes few rows are to be returned. It makes the poor cardinality estimate because there are currently no histograms in place AND because it can’t now use Dynamic Sampling to get a more accurate picture of things on the fly because it has been disabled with the dynamic_sampling(0) hint.
Using an Automatic Index over the current FTS plan would make the performance of the SQL regress.
Therefore, to protect the current FTS plan, Automatic Indexing has created a SQL Plan Baseline that effectively forces the CBO to use the current, more efficient FTS plan.
This can be confirmed by looking at the DBA_AUTO_INDEX_VERIFICATIONS view:
SQL> select execution_name, original_buffer_gets, auto_index_buffer_gets, status from dba_auto_index_verifications where sql_id = '3yz8unzhhvnuz'; EXECUTION_NAMEÂ Â Â Â Â Â Â Â Â Â Â Â ORIGINAL_BUFFER_GETS AUTO_INDEX_BUFFER_GETS STATUS -------------------------- -------------------- ---------------------- --------- SYS_AI_2020-08-18/16:42:33Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 41169Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 410291 REGRESSED
If we now re-run the SQL again (noting we still don’t have histograms on the CODE column):
SQL> select /*+ dynamic_sampling(0) */ * from space_oddity where code in (190000, 170000, 150000, 130000, 110000, 90000, 70000, 50000, 30000, 10000);
1000011 rows selected.
Execution Plan
----------------------------------------------------------------------------------
| Id | Operation        | Name        | Rows | Bytes | Cost (%CPU)| Time    |
----------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ |Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â 32 |Â 4512 | 11425Â Â (2)| 00:00:01 |
|*Â 1 |Â TABLE ACCESS FULL| SPACE_ODDITY |Â Â Â 32 |Â 4512 | 11425Â Â (2)| 00:00:01 |
----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("CODE"=10000 OR "CODE"=30000 OR "CODE"=50000 OR
"CODE"=70000 OR "CODE"=90000 OR "CODE"=110000 OR "CODE"=130000 OR
"CODE"=150000 OR "CODE"=170000 OR "CODE"=190000)
Hint Report (identified by operation id / Query Block Name / Object Alias):
Total hints for statement: 1 (U - Unused (1))
---------------------------------------------------------------------------
1 -Â SEL$1
U -Â dynamic_sampling(0) / rejected by IGNORE_OPTIM_EMBEDDED_HINTS
Note
-----
- SQL plan baseline "SQL_PLAN_3cjg6naakzmvu198c05b9" used for this statement
Statistics
----------------------------------------------------------
9Â recursive calls
4Â db block gets
41170Â consistent gets
0Â physical reads
0Â redo size
13535504Â bytes sent via SQL*Net to client
2705Â bytes received via SQL*Net from client
202Â SQL*Net roundtrips to/from client
0Â sorts (memory)
0Â sorts (disk)
1000011Â rows processed
We can see the CBO is forced to use the SQL Plan Baseline “SQL_PLAN_3cjg6naakzmvu198c05b9” as created by the Automatic Indexing task to ensure the more efficient FTS is used and not the available Automatic Index.
So Automatic Indexing CAN create SQL PLan Baselines to protect SQL from performance regressions caused by inappropriate use of Automatic Indexes BUT it’s really hard and difficult for it to do this effectively if the Automatic Indexing tasks and other database sessions have differing Dynamic Sampling settings as it does by default…
Oracle 19c Automatic Indexing: Data Skew Fixed By Baselines Part I (The Prettiest Star)) September 25, 2020
Posted by Richard Foote in 19c, 19c New Features, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, Baselines, CBO, Data Skew, Exadata, Full Table Scans, Histograms, Index Access Path, Oracle, Oracle Cloud, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle Statistics, Oracle19c, Performance Tuning.1 comment so far
In my previous few blog posts, I’ve been discussing some issues in relation to how Automatic Indexes handle SQL statements that accesses skewed data. In this post, I’m going to setup the scenario in which Automatic Indexing can potentially use Baselines to help address some of these issues. BUT, as we’ll see, I’m having to manufacture things somewhat to make this work due to the problem of the Automatic Indexing task using Dynamic Sampling of level 11, whereas most usual database sessions do not.
To set things up, I’m going recap what I’ve previously discussed (but with a slight difference), by creating a table that has significant data skew on the CODE column, with most values very uncommon, but with a handful of values being very common:
SQL> create table space_oddity (id number constraint space_oddity_pk primary key, code number, name varchar2(142)); Table created. SQL> begin 2Â Â Â Â for i in 1..2000000 loop 3Â Â Â Â Â Â if mod(i,2) = 0 then 4Â Â Â Â Â Â Â Â Â insert into space_oddity values(i, ceil(dbms_random.value(0,1000000)), 'David Bowie is really Ziggy Stardust and his band are called The Spiders From Mars. Then came Aladdin Sane and the rest is history'); 5Â Â Â Â Â Â else 6Â Â Â Â Â Â Â Â Â insert into space_oddity values(i, mod(i,20)*10000, 'Ziggy Stardust is really David Bowie and his band are called The Spiders From Mars. Then came Aladdin Sane and the rest is history.'); 7Â Â Â Â Â Â end if; 8Â Â Â Â end loop; 9Â Â Â Â commit; 10Â end; 11Â / PL/SQL procedure successfully completed.
So most CODE values will only occur a few times if at all, but a few values divisible by 10000 have many many occurrences within the table.
Importantly, we will initially collect statistics with NO histograms on the CODE column, which is the default behaviour anyways if no SQL has previous run with predicates on the column:
SQL> exec dbms_stats.gather_table_stats(null, 'SPACE_ODDITY', method_opt=> 'FOR ALL COLUMNS SIZE 1'); PL/SQL procedure successfully completed.
If we run a query based on a rare value for CODE:
SQL> set arraysize 5000
SQL> select * from space_oddity where code=25;
Execution Plan
----------------------------------------------------------------------------------
| Id | Operation        | Name        | Rows | Bytes | Cost (%CPU)| Time    |
----------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ |Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â 3 |Â Â 423 | 11356Â Â (1)| 00:00:01 |
|*Â 1 |Â TABLE ACCESS FULL| SPACE_ODDITY |Â Â Â Â 3 |Â Â 423 | 11356Â Â (1)| 00:00:01 |
----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("CODE"=25)
Statistics
----------------------------------------------------------
0Â recursive calls
0Â db block gets
40974Â consistent gets
0Â physical reads
0Â redo size
1018Â bytes sent via SQL*Net to client
402Â bytes received via SQL*Net from client
2Â SQL*Net roundtrips to/from client
0Â sorts (memory)
0Â sorts (disk)
2Â rows processed
Without an index, the CBO has no choice at this point but to perform a FTS. BUT note that the 2 rows returned is very similar to the 3 estimated rows, which would make an index likely the way to go if such an index existed.
However, the following SQL accesses many of the common values of CODE and returns many rows:
SQL> select * from space_oddity where code in (10000, 30000, 50000, 70000, 90000, 110000, 130000, 150000, 170000, 190000);
1000011 rows selected.
Execution Plan
----------------------------------------------------------------------------------
| Id | Operation        | Name        | Rows | Bytes | Cost (%CPU)| Time    |
----------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ |Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â 32 |Â 4512 | 11425Â Â (2)| 00:00:01 |
|*Â 1 |Â TABLE ACCESS FULL| SPACE_ODDITY |Â Â Â 32 |Â 4512 | 11425Â Â (2)| 00:00:01 |
----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("CODE"=10000 OR "CODE"=30000 OR "CODE"=50000 OR
"CODE"=70000 OR "CODE"=90000 OR "CODE"=110000 OR "CODE"=130000 OR
"CODE"=150000 OR "CODE"=170000 OR "CODE"=190000)
Statistics
----------------------------------------------------------
0Â recursive calls
0Â db block gets
41169Â consistent gets
0Â physical reads
0Â redo size
13535504Â bytes sent via SQL*Net to client
2678Â bytes received via SQL*Net from client
202Â SQL*Net roundtrips to/from client
0Â sorts (memory)
0Â sorts (disk)
1000011Â rows processed
Again, without an index in place, the CBO has no choice but to perform a FTS but this is almost certainly the way to go regardless. BUT without a histogram on the CODE column, the CBO has got the cardinality estimate way way off and thinks only 32 rows are to be returned and not the actual 1000011 rows.
So what does Automatic Indexing make of things. Let’s wait and have a look at the next Automatic Indexing Report:
SQL> select dbms_auto_index.report_last_activity() report from dual; REPORT -------------------------------------------------------------------------------- GENERAL INFORMATION ------------------------------------------------------------------------------- Activity start              : 18-AUG-2020 15:57:14 Activity end                : 18-AUG-2020 15:58:10 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)             : 35.65 MB (35.65 MB / 0 B) Indexes dropped                              : 0 SQL statements verified                      : 1 SQL statements improved (improvement factor) : 1 (40984.3x) SQL plan baselines created                   : 0 Overall improvement factor                   : 40984.3x ------------------------------------------------------------------------------- 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 | SPACE_ODDITY | SYS_AI_82bdnqs7q8rtm | CODE | B-TREE | NONE      | ----------------------------------------------------------------------------
So Automatic Indexing has indeed created the index (SYS_AI_82bdnqs7q8rtm) on the CODE column BUT this is based on only the one SQL statement:
VERIFICATION DETAILS
-------------------------------------------------------------------------------
The performance of the following statements improved:
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL IDÂ Â Â Â Â Â Â Â Â Â Â Â Â Â : 19sv1g6tt0g1y
SQL Text            : select * from space_oddity where code=25
Improvement Factor  : 40984.3x
Execution Statistics:
-----------------------------
Original Plan                Auto Index Plan
----------------------------Â ----------------------------
Elapsed Time (s):Â 5417408Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 139265
CPU Time (s):Â Â Â Â Â 1771880Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 7797
Buffer Gets:Â Â Â Â Â Â 327876Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 5
Optimizer Cost:Â Â Â 11356Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 5
Disk Reads:Â Â Â Â Â Â Â 649Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 2
Direct Writes:Â Â Â Â 0Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 0
Rows Processed:Â Â Â 16Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 2
Executions:Â Â Â Â Â Â Â 8Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1
The Automatic Indexing task has correctly identified a significant improvement of 40984.3x when using an index on the SQL statement that returned just the 2 rows. The other SQL statement that returns many rows IS NOT MENTIONED.
This is because the Automatic Indexing tasks uses Dynamic Sampling Level=11, meaning it determines the more accurate cardinality estimate on the fly and correctly identifies that a vast number of rows are going to be returned. As a result, it correctly determines that the new Automatic Indexing if used would be detrimental to performance and would not be used by the CBO.
BUT most importantly, it also makes the assumption that the CBO would automatically likewise make this same decision to NOT use any such index in other database sessions and so there’s nothing to protect.
BUT this assumption is incorrect IF other database sessions don’t likewise use Dynamic Sampling with Level=11.
BUT by default, including in Oracle’s Autonomous Database Transaction Processing Cloud environment, the Dynamic Sampling Level is NOT set to 11, but the 2.
Therefore, most database sessions will not be able to determine the correct cardinality estimate on the fly and so will incorrectly assume the number of returned rows is much less than in reality and potentially use any such new Automatic Index inappropriately…
So if we look at the Plans Section of the Automatic Indexing report:
PLANS SECTION
---------------------------------------------------------------------------------------------
- Original
-----------------------------
Plan Hash Value : 2301175572
-----------------------------------------------------------------------------
| Id | Operation          | Name        | Rows | Bytes | Cost | Time    |
-----------------------------------------------------------------------------
|Â 0 | SELECT STATEMENTÂ Â Â |Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â |Â Â Â Â Â Â | 11356 |Â Â Â Â Â Â Â Â Â |
|Â 1 |Â Â TABLE ACCESS FULL | SPACE_ODDITY |Â Â Â 3 |Â Â 423 | 11356 | 00:00:01 |
-----------------------------------------------------------------------------
- With Auto Indexes
-----------------------------
Plan Hash Value : 54782313
-------------------------------------------------------------------------------------------------------
| Id | Operation                            | Name                | Rows | Bytes | Cost | Time    |
-------------------------------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â 3 |Â Â 423 |Â Â Â 5 | 00:00:01 |
|Â Â 1 |Â Â TABLE ACCESS BY INDEX ROWID BATCHED | SPACE_ODDITYÂ Â Â Â Â Â Â Â |Â Â Â 3 |Â Â 423 |Â Â Â 5 | 00:00:01 |
| * 2 |Â Â Â INDEX RANGE SCANÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | SYS_AI_82bdnqs7q8rtm |Â Â Â 2 |Â Â Â Â Â Â |Â Â Â 3 | 00:00:01 |
-------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
------------------------------------------
* 2 - access("CODE"=25)
Notes
-----
- Dynamic sampling used for this statement ( level = 11 )
The new plan for the SQL returning 2 rows when using the new Automatic Index and is much more efficient with a significantly reduced cost (just 3 down from 11356).
But again, the plans for the SQL that returns many rows are not listed as the Automatic Indexing task has already determined that an index would make such a plan significantly less efficient.
If we now rerun the SQL the returns many rows (and BEFORE High Frequency Collection Statistics potentially kicks in):
SQL> select * from space_oddity where code in (10000, 30000, 50000, 70000, 90000, 110000, 130000, 150000, 170000, 190000);
1000011 rows selected.
Execution Plan
-------------------------------------------------------------------------------------------------------------
| Id | Operation                           | Name                | Rows | Bytes | Cost (%CPU)| Time    |
-------------------------------------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â 32 |Â 4512 |Â Â Â 35Â Â (0)| 00:00:01 |
|Â Â 1 |Â INLIST ITERATORÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â |Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â |
|Â Â 2 |Â Â TABLE ACCESS BY INDEX ROWID BATCHED| SPACE_ODDITYÂ Â Â Â Â Â Â Â |Â Â Â 32 |Â 4512 |Â Â Â 35Â Â (0)| 00:00:01 |
|*Â 3 |Â Â Â INDEX RANGE SCANÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | SYS_AI_82bdnqs7q8rtm |Â Â Â 32 |Â Â Â Â Â Â |Â Â Â 12Â Â (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
3 - access("CODE"=10000 OR "CODE"=30000 OR "CODE"=50000 OR "CODE"=70000 OR "CODE"=90000 OR
"CODE"=110000 OR "CODE"=130000 OR "CODE"=150000 OR "CODE"=170000 OR "CODE"=190000)
Statistics
----------------------------------------------------------
0Â recursive calls
0Â db block gets
410422Â consistent gets
0Â physical reads
0Â redo size
145536076Â bytes sent via SQL*Net to client
2678Â bytes received via SQL*Net from client
202Â SQL*Net roundtrips to/from client
0Â sorts (memory)
0Â sorts (disk)
1000011Â rows processed
Note that the cardinality estimate is still way way wrong, thinking that just 32 rows are to be returned, when is fact 1000011 rows are returned.
As a result, the CBO has decided to incorrectly use the new Automatic Index. Incorrectly, in that the number of consistent gets has increased 10x from the previous FTS plan (410,422 now, up from 41,169).
One way to resolve this is to collect histograms on the CODE column (or wait for the High Frequency Stats Collection to kick in):
SQL> exec dbms_stats.gather_table_stats(null, 'SPACE_ODDITY', method_opt=> 'FOR ALL COLUMNS SIZE 2048’); PL/SQL procedure successfully completed.
If we now re-run this SQL:
SQL> select * from space_oddity where code in (190000, 170000, 150000, 130000, 110000, 90000, 70000, 50000, 30000, 10000);
1000011 rows selected.
Execution Plan
----------------------------------------------------------------------------------
| Id | Operation        | Name        | Rows | Bytes | Cost (%CPU)| Time    |
----------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ |Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â 996K|Â Â 133M| 11411Â Â (1)| 00:00:01 |
|*Â 1 |Â TABLE ACCESS FULL| SPACE_ODDITY |Â Â 996K|Â Â 133M| 11411Â Â (1)| 00:00:01 |
----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("CODE"=10000 OR "CODE"=30000 OR "CODE"=50000 OR
"CODE"=70000 OR "CODE"=90000 OR "CODE"=110000 OR "CODE"=130000 OR
"CODE"=150000 OR "CODE"=170000 OR "CODE"=190000)
Statistics
----------------------------------------------------------
0Â recursive calls
0Â db block gets
41169Â consistent gets
0Â physical reads
0Â redo size
13535504Â bytes sent via SQL*Net to client
2678Â bytes received via SQL*Net from client
202Â SQL*Net roundtrips to/from client
0Â sorts (memory)
0Â sorts (disk)
1000011Â rows processed
The cardinality estimate is now much more accurate and the the execution plan now uses the more efficient FTS.
In Part II, we’ll look at how the Automatic Indexing tasks can be made to identify the dangers of a new index to SQLs that might degrade in performance and how it will create a Baseline to protect against any such SQL regressions….
Oracle 19c Automatic Indexing: CBO Incorrectly Using Auto Indexes Part II ( Sleepwalk) September 21, 2020
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, CBO, Data Skew, Dynamic Sampling, Exadata, Explain Plan For Index, Extended Statistics, Hints, Histograms, Index Access Path, Index statistics, Oracle, Oracle Cloud, Oracle Cost Based Optimizer, Oracle Indexes, Oracle19c, Performance Tuning.add a comment
As I discussed in Part I of this series, problems and inconsistencies can appear between what the Automatic Indexing processing thinks will happen with newly created Automatic Indexing and what actually happens in other database sessions. This is because the Automatic Indexing process session uses a much higher degree of Dynamic Sampling (Level=11) than other database sessions use by default (Level=2).
As we saw in Part I, an SQL statement may be deemed to NOT use an index in the Automatic Indexing deliberations, where it is actually used in normal database sessions (and perhaps incorrectly so). Where the data is heavily skewed and current statistics are insufficient for the CBO to accurately detect such “skewness” is one such scenario where we might encounter this issue.
One option to get around this is to hint any such queries with a Dynamic Sampling value that matches that of the Automatic Indexing process (or sufficient to determine more accurate cardinality estimates).
If we re-run the problematic query from Part I (where a new Automatic Index was inappropriately used by the CBO) with such a Dynamic Sampling hint:
SQL> select /*+ dynamic_sampling(11) */ * from iggy_pop where code1=42 and code2=42;
100000 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 3288467
--------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
--------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 100K| 2343K| 575 (15)| 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL| IGGY_POP | 101K| 2388K| 575 (15)| 00:00:01 |
--------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE1"=42 AND "CODE2"=42)
filter("CODE1"=42 AND "CODE2"=42)
Note
-----
- dynamic statistics used: dynamic sampling (level=AUTO)
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
40964 consistent gets
40953 physical reads
0 redo size
1092240 bytes sent via SQL*Net to client
609 bytes received via SQL*Net from client
21 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
100000 rows processed
We can see that the CBO this time correctly calculated the cardinality and hence correctly decided against the use of the Automatic Index.
Although these parameters can’t be changed in the Oracle Autonomous Database Cloud services, on the Exadata platform if using Automatic Indexing you might want to consider setting the OPTIMIZER_DYNAMIC_SAMPLING parameter to 11 (and/or OPTIMIZER_ADAPTIVE_STATISTICS=true)Â in order to be consistent with the Automatic Indexing process. These settings can obviously add significant overhead during parsing and so need to be set with caution.
In this scenario where there is an inherent relationship between columns which the CBO is not detecting, the creation of Extended Statistics can be beneficial.
We currently have the following columns and statistics on the IGGY_POP table:
SQL> select column_name, num_distinct, density, num_buckets, histogram from user_tab_cols where table_name='IGGY_POP'; COLUMN_NAME NUM_DISTINCT DENSITY NUM_BUCKETS HISTOGRAM -------------------- ------------ ---------- ----------- --------------- ID 9705425 0 254 HYBRID CODE1 100 .00000005 100 FREQUENCY CODE2 100 .00000005 100 FREQUENCY NAME 1 5.0210E-08 1 FREQUENCY
If we now collect Extended Statistics on both CODE1, CODE2 columns:
SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'IGGY_POP', method_opt=> 'FOR COLUMNS (CODE1,CODE2) SIZE 254'); PL/SQL procedure successfully completed. SQL> select column_name, num_distinct, density, num_buckets, histogram from user_tab_cols where table_name='IGGY_POP'; COLUMN_NAME NUM_DISTINCT DENSITY NUM_BUCKETS HISTOGRAM ------------------------------ ------------ ---------- ----------- --------------- ID 9705425 0 254 HYBRID CODE1 100 .00000005 100 FREQUENCY CODE2 100 .00000005 100 FREQUENCY NAME 1 5.0210E-08 1 FREQUENCY SYS_STU#29QF8Y9BUDOW2HCDL47N44 99 .00000005 100 FREQUENCY
The CBO now has some idea on the cardinality if both columns are used within a predicate.
If we re-run the problematic query without the hint:
SQL> select * from iggy_pop where code1=42 and code2=42;
100000 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 3288467
--------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
--------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 100K| 2343K| 575 (15)| 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL| IGGY_POP | 100K| 2343K| 575 (15)| 00:00:01 |
--------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE1"=42 AND "CODE2"=42)
filter("CODE1"=42 AND "CODE2"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
40964 consistent gets
40953 physical reads
0 redo size
1092240 bytes sent via SQL*Net to client
581 bytes received via SQL*Net from client
21 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
100000 rows processed
Again, the CBO is correctly the cardinality estimate of 100K rows and so is NOT using the Automatic Index.
However, we can still get ourselves in problems. If I now re-run the query that returns no rows and was previously correctly using the Automatic Index:
SQL> select code1, code2, name from iggy_pop where code1=1 and code2=42;
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 3288467
--------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
--------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 50000 | 878K | 575 (15) | 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL| IGGY_POP | 50000 | 878K | 575 (15) | 00:00:01 |
--------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE1"=1 AND "CODE2"=42)
filter("CODE1"=1 AND "CODE2"=42)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
40964 consistent gets
40953 physical reads
0 redo size
368 bytes sent via SQL*Net to client
377 bytes received via SQL*Net from client
1 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
0 rows processed
We see that the CBO is now getting this execution plan wrong and is now estimating incorrectly that 50,000 rows are to be returned (and not the 1000 rows it estimated previously). This increased estimate is now deemed too expensive for the Automatic Index to retrieve and is now incorrectly using a FTS.
This because with a Frequency based histogram now in place, Oracle assumes that 50% of the lowest recorded frequency within the histogram is returned (100,000 x 0.5 = 50,000) if the values don’t exist but resided within the known min-max range of values.
So we need to be very careful HOW we potentially collect any additional statistics and its potential impact on other SQL statements.
As I’ll discuss next, another alternative to get more consistent behavior with Automatic Indexing in these types of scenarios is to make the Automatic Indexing processing session appear more like other database sessions…
Oracle 19c Automatic Indexing: Data Skew Part III (The Good Son) September 16, 2020
Posted by Richard Foote in 19c, 19c New Features, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, CBO, Data Skew, Index Access Path, Oracle, Oracle Cost Based Optimizer, Oracle General, Oracle Indexes, Oracle Statistics, Oracle19c, Unusable Indexes.add a comment
I’m going to expand just a tad on my previous posts on data skew and run a simple query that returns a few rows based on a column predicate AND another query on the same column that returns many rows.
The following table has a CODE column as with previous posts with the data heavily skewed:
SQL> create table bowie_skew (id number, code number, name varchar2(42)); Table created. SQL> insert into bowie_skew select rownum, 10, 'DAVID BOWIE' from dual connect by level <=1000000; 1000000 rows created. SQL> update bowie_skew set code = 9 where mod(id,3) = 0; 333333 rows updated. SQL> update bowie_skew set code = 1 where mod(id,2) = 0 and id between 1 and 20000; 10000 rows updated. SQL> update bowie_skew set code = 2 where mod(id,2) = 0 and id between 30001 and 40000; 5000 rows updated. SQL> update bowie_skew set code = 3 where mod(id,100) = 0 and id between 300001 and 400000; 1000 rows updated. SQL> update bowie_skew set code = 4 where mod(id,100) = 0 and id between 400001 and 500000; 1000 rows updated. SQL> update bowie_skew set code = 5 where mod(id,100) = 0 and id between 600001 and 700000; 1000 rows updated. SQL> update bowie_skew set code = 6 where mod(id,1000) = 0 and id between 700001 and 800000; 100 rows updated. SQL> update bowie_skew set code = 7 where mod(id,1000) = 0 and id between 800001 and 900000; 100 rows updated. SQL> update bowie_skew set code = 8 where mod(id,1000) = 0 and id between 900001 and 1000000; 100 rows updated. SQL> commit; Commit complete.
I’ll next collect statistics with NO histogram, as I don’t think they’re required at this point:
SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'bowie_skew', estimate_percent=>100, method_opt=>'FOR ALL COLUMNS SIZE 1'); PL/SQL procedure successfully completed.
If we look at the table data:
SQL> select code, count(*) from bowie_skew group by code order by code;
CODEÂ COUNT(*)
---------- ----------
1Â Â Â Â 10000
2Â Â Â Â Â 5000
3Â Â Â Â Â 1000
4Â Â Â Â Â 1000
5Â Â Â Â Â 1000
6Â Â Â Â Â Â 100
7Â Â Â Â Â Â 100
8Â Â Â Â Â Â 100
9Â Â Â Â 327235
10Â Â Â Â 654465
The value “7” only has 100 associated rows, while the value “10” is very common with 654,465 rows.
But I currently have no histograms:
SQL> select column_name, num_buckets, histogram from user_tab_cols where table_name='BOWIE_SKEW'; COLUMN_NAMEÂ Â Â Â NUM_BUCKETS HISTOGRAM --------------- ----------- --------------- IDÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1 NONE CODEÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1 NONE NAMEÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1 NONE
If I run the following query with a CODE=7 predicate just once:
SQL> select * from bowie_skew where code=7; 100 rows selected. Execution Plan -------------------------------------------------------------------------------------------- | Id | Operation                  | Name     | Rows | Bytes | Cost (%CPU)| Time    | -------------------------------------------------------------------------------------------- |  0 | SELECT STATEMENT           |          |  100K| 1953K|  570  (7)| 00:00:01 | |  1 | PX COORDINATOR             |          |     |     |          |        | |  2 |  PX SEND QC (RANDOM)      | :TQ10000  |  100K| 1953K|  570  (7)| 00:00:01 | |  3 |   PX BLOCK ITERATOR       |          |  100K| 1953K|  570  (7)| 00:00:01 | |* 4 |    TABLE ACCESS STORAGE FULL| bowie_skew |  100K| 1953K|  570  (7)| 00:00:01 | --------------------------------------------------------------------------------------------
It uses a Full Table Scan (the CBO has no choice without an index) AND hopelessly gets the cardinality estimate wrong, thinking 100K are going to be returned (and not the 100 actual rows). So the CBO is unlikely to use an index anyways as it would be deemed too expensive to return so many rows.
I’ll now run the following query many times on the CODE=10 predicate that returns many rows:
SQL> select * from bowie_skew where code=10; 654465 rows selected. Execution Plan -------------------------------------------------------------------------------------------- | Id | Operation                  | Name     | Rows | Bytes | Cost (%CPU)| Time    | -------------------------------------------------------------------------------------------- |  0 | SELECT STATEMENT           |          |  100K| 1953K|  570  (7)| 00:00:01 | |  1 | PX COORDINATOR             |          |     |     |          |         | |  2 |  PX SEND QC (RANDOM)      | :TQ10000  |  100K| 1953K|  570  (7)| 00:00:01 | |  3 |   PX BLOCK ITERATOR       |          |  100K| 1953K|  570  (7)| 00:00:01 | |* 4 |    TABLE ACCESS STORAGE FULL| bowie_skew |  100K| 1953K|  570  (7)| 00:00:01 | --------------------------------------------------------------------------------------------
So again, no choice here with a FTS and we likely wouldn’t want to use an index anyways as it would be just too expensive.
If we check out what the Automatic Indexing process has done with such a workload:
SQL> select dbms_auto_index.report_last_activity() report from dual;
REPORT
INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
*: invisible
-------------------------------------------------------------------------------
--------------------------------------------------------------------------
| Owner | Table | Index | Key | Type | Properties |
--------------------------------------------------------------------------
| BOWIE | BOWIE_SKEW | SYS_AI_7psvzc164vbng | CODE | B-TREE | NONE |
--------------------------------------------------------------------------
-------------------------------------------------------------------------------
VERIFICATION DETAILS
-------------------------------------------------------------------------------
The performance of the following statements improved:
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL ID : 6fm3m8cg2jnun
SQL Text : select * from bowie_skew where code=7
Improvement Factor : 46.6x
Execution Statistics:
-----------------------------
Original Plan Auto Index Plan
---------------------------- ----------------------------
Elapsed Time (s): 36653 1992
CPU Time (s): 33899 967
Buffer Gets: 4291 103
Optimizer Cost: 52 4
Disk Reads: 0 2
Direct Writes: 0 0
Rows Processed: 100 100
Executions: 1 1
An Automatic Index on the CODE column is created (SYS_AI_7psvzc164vbng), with ONLY the SQL based on the CODE=7 predicate listed in the report. The other query is indeed too expensive for a new index to be viable and so isn’t listed.
If we look at the Plans Section of the Automatic Indexing report:
PLANS SECTION
---------------------------------------------------------------------------------------------
- Original
-----------------------------
Plan Hash Value : 410492785
--------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
--------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | | | 52 | |
| 1 | TABLE ACCESS STORAGE FULL | BOWIE_SKEW | 100000 | 2000000 | 52 | 00:00:01 |
--------------------------------------------------------------------------------------
Notes
-----
- dop_reason = no expensive parallel operation
- dop = 1
- px_in_memory_imc = no
- px_in_memory = no
- With Auto Indexes
-----------------------------
Plan Hash Value : 140816325
-------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost | Time |
-------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 119 | 2380 | 4 | 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE_SKEW | 119 | 2380 | 4 | 00:00:01 |
| * 2 | INDEX RANGE SCAN | SYS_AI_7psvzc164vbng | 100 | | 3 | 00:00:01 |
-------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
------------------------------------------
* 2 - access("CODE"=7)
Notes
-----
- Dynamic sampling used for this statement ( level = 11 )
The important point to note here is that the cardinality estimates are relatively accurate despite there being no histograms at this stage because the Automatic Indexing session uses Dynamic Sampling Level=11. Missing/inaccurate statistics are calculated on fly and this enables the session to accurately determine the size of the returned data set and that an index is indeed the more efficient access path.
So with mixed workloads, all it takes is one SQL executed once that demonstrably improves thanks to an index for the associated Automatic Index to be created as a VISIBLE/VALID index:
SQL> select index_name, auto, visibility, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='BOWIE_SKEW'; INDEX_NAME AUT VISIBILIT STATUS NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR ------------------------------ --- --------- -------- ---------- ----------- ----------------- SYS_AI_7psvzc164vbng YES VISIBLE VALID 1000000 1537 8534
If we now run the query AFTER the histograms are subsequently created thanks to the High-Frequency Automatic Statistics Collection (see previous post), the new Automatic Index is now used:
SQL> select * from bowie_skew where code=7;
100 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 140816325
------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 100 | 2000 | 4 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED| BOWIE_SKEW | 100 | 2000 | 4 (0)| 00:00:01 |
|* 2 | INDEX RANGE SCAN | SYS_AI_7psvzc164vbng | 100 | | 3 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("CODE"=7)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
104 consistent gets
0 physical reads
0 redo size
2871 bytes sent via SQL*Net to client
359 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
100 rows processed
Note if the histogram is NOT yet collected, the CBO will not determine the correct cardinality estimate and will ignore the new Automatic Index (as previously discussed).
If we run again the query that returns many rows:
SQL> select * from bowie_skew where code=10;
654465 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 410492785
----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 654K| 12M| 52 (16)| 00:00:01 |
|* 1 | TABLE ACCESS STORAGE FULL| BOWIE_SKEW | 654K| 12M| 52 (16)| 00:00:01 |
----------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - storage("CODE"=10)
filter("CODE"=10)
Note
-----
- automatic DOP: Computed Degree of Parallelism is 1 because of no expensive parallel operation
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
3725 consistent gets
0 physical reads
0 redo size
6549708 bytes sent via SQL*Net to client
1790 bytes received via SQL*Net from client
132 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
654465 rows processed
The new Automatic Index is correctly ignored by the CBO, as the query returns too many rows for the index to be viable.
So in this example, Automatic Indexing works exactly as it should. It creates a new Automatic Index for a query where it will indeed improve the performance, while other queries on the same column in which many more rows are returned are also run. For these other queries, the new Automatic Index is correctly not used as such an index would degrade the performance of the query.
In my next post, I’ll look at the first example with data skew where Automatic Indexing can be problematic…
Oracle 19c Automatic Indexing: Data Skew Part I (A Saucerful of Secrets) September 10, 2020
Posted by Richard Foote in 19c, 19c New Features, Automatic Indexing, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, Data Skew, Full Table Scans, Histograms, Index Access Path, Index statistics, Low Cardinality, Oracle Blog, Oracle Indexes, Oracle19c, Performance Tuning.1 comment so far
When it comes to Automatic Indexes, things can become particularly interesting when dealing with data skew (meaning that some columns values are much less common than other column values). The next series of blog posts will look at a number of different scenarios in relation to how Automatic Indexing works with data that is skewed and not uniformly distributed.
I’ll start with a simple little example, that has an interesting little twist at the end.
The following table has a CODE column, which has 10 distinct values that a widely skewed, with some values much less common than others:
SQL> create table bowie_skew (id number, code number, name varchar2(42)); Table created. SQL> insert into bowie_skew select rownum, 10, 'DAVID BOWIE' from dual connect by level <=1000000; 1000000 rows created. SQL> update bowie_skew set code = 9 where mod(id,3) = 0; 333333 rows updated. SQL> update bowie_skew set code = 1 where mod(id,2) = 0 and id between 1 and 20000; 10000 rows updated. SQL> update bowie_skew set code = 2 where mod(id,2) = 0 and id between 30001 and 40000; 5000 rows updated. SQL> update bowie_skew set code = 3 where mod(id,100) = 0 and id between 300001 and 400000; 1000 rows updated. SQL> update bowie_skew set code = 4 where mod(id,100) = 0 and id between 400001 and 500000; 1000 rows updated. SQL> update bowie_skew set code = 5 where mod(id,100) = 0 and id between 600001 and 700000; 1000 rows updated. SQL> update bowie_skew set code = 6 where mod(id,1000) = 0 and id between 700001 and 800000; 100 rows updated. SQL> update bowie_skew set code = 7 where mod(id,1000) = 0 and id between 800001 and 900000; 100 rows updated. SQL> update bowie_skew set code = 8 where mod(id,1000) = 0 and id between 900001 and 1000000; 100 rows updated. SQL> commit; Commit complete.
I’ll collect statistics on this table, but explicitly NOT collect histograms, so that the CBO will have no idea that the data is actually skewed. Note if I collected data with the default size, there would still be no histograms, as the column has yet to be used within an SQL predicate and so has no column usage recorded.
SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE_SKEW', estimate_percent=>100, method_opt=>'FOR ALL COLUMNS SIZE 1'); PL/SQL procedure successfully completed.
We can clearly see that some CODE values (such as “6”) have relatively few values, with only 100 occurrences:
SQL> select code, count(*) from bowie_skew group by code order by code;
CODE COUNT(*)
---------- ----------
1Â Â Â Â 10000
2Â Â Â Â Â 5000
3Â Â Â Â Â 1000
4Â Â Â Â Â 1000
5Â Â Â Â Â 1000
6Â Â Â Â Â Â 100
7Â Â Â Â Â Â 100
8Â Â Â Â Â Â 100
9Â Â Â Â 327235
10Â Â Â 654465
As I explicitly collected statistics with SIZE 1, we currently have NO histograms in the table:
SQL> select column_name, num_buckets, histogram from user_tab_cols where table_name='BOWIE_SKEW'; COLUMN_NAMEÂ Â Â NUM_BUCKETS HISTOGRAM --------------- ----------- --------------- IDÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1 NONE CODEÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1 NONE NAMEÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1 NONE
Let’s now run the following query with a predicate on CODE=6, returning just 100 rows:
SQL> select * from bowie_skew where code=6;
100 rows selected.
Execution Plan
-------------------------------------------------------------------------------------------
| Id | Operation                    | Name       | Rows | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â Â |Â Â 100K|Â 1953K|Â Â 570Â Â (7)| 00:00:01 |
|Â Â 1 |Â PX COORDINATOR Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â |Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â |
|Â Â 2 |Â Â PX SEND QC (RANDOM)Â Â Â Â Â Â Â Â | :TQ10000Â Â |Â Â 100K|Â 1953K|Â Â 570Â Â (7)| 00:00:01 |
|Â Â 3 |Â Â Â PX BLOCK ITERATORÂ Â Â Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â Â |Â Â 100K|Â 1953K|Â Â 570Â Â (7)| 00:00:01 |
|*Â 4 |Â Â Â Â TABLE ACCESS STORAGE FULL| BOWIE_SKEW |Â Â 100K|Â 1953K|Â Â 570Â Â (7)| 00:00:01 |
-------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - storage("CODE"=6)
filter("CODE"=6)
Statistics
----------------------------------------------------------
6Â recursive calls
0Â db block gets
3781Â consistent gets
0Â physical reads
0Â redo size
2796Â bytes sent via SQL*Net to client
654Â bytes received via SQL*Net from client
8Â SQL*Net roundtrips to/from client
0Â sorts (memory)
0Â sorts (disk)
100Â rows processed
The CBO has no choice but to use a FTS as I currently have no indexes on the CODE column. Note also that the CBO has got its cardinality estimates way wrong, expecting 100,000 rows and not the actual 100 rows, as I have no histograms on the CODE column.
So let’s now wait 15 minutes or so and see what the Automatic Indexing process decides to do. Following are portions of the next Auto Indexing report:
INDEX DETAILS
-------------------------------------------------------------------------------
The following indexes were created:
--------------------------------------------------------------------------
| Owner | Table     | Index              | Key | Type  | Properties |
--------------------------------------------------------------------------
| BOWIE | BOWIE_SKEW | SYS_AI_7psvzc164vbng | CODE | B-TREE | NONEÂ Â Â Â Â Â |
--------------------------------------------------------------------------
VERIFICATION DETAILS
-------------------------------------------------------------------------------
The performance of the following statements improved:
-------------------------------------------------------------------------------
Parsing Schema Name : BOWIE
SQL ID Â Â Â Â Â Â Â Â Â Â Â Â Â : fn4shnphu4bvj
SQL Text           : select * from bowie_skew where code=6
Improvement Factor  : 41.1x
Execution Statistics:
-----------------------------
Original Plan               Auto Index Plan
----------------------------Â ----------------------------
Elapsed Time (s):Â 119596Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 322
CPU Time (s):Â Â Â Â 100781Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 322
Buffer Gets:Â Â Â Â Â 11347Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 103
Optimizer Cost:Â Â Â 570Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 4
Disk Reads:Â Â Â Â Â Â 0Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 0
Direct Writes:Â Â Â Â 0Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 0
Rows Processed:Â Â Â 100 Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 100
Executions:Â Â Â Â Â Â 1Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1
So we can see that yes, Auto Indexing has decided to create a new index here on the CODE column (“SYS_AI_7psvzc164vbng“) as it improves the performance of the query by a factor of 41.1x.
If we look further down the Auto Indexing report and compare the execution plans:
PLANS SECTION
---------------------------------------------------------------------------------------------
- Original
-----------------------------
Plan Hash Value : 3374004665
-----------------------------------------------------------------------------------------
| Id | Operation                    | Name     | Rows  | Bytes  | Cost | Time   |
-----------------------------------------------------------------------------------------
|Â 0 | SELECT STATEMENT Â Â Â Â Â Â Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â |Â Â Â Â Â Â Â |Â 570 |Â Â Â Â Â Â Â Â |
|Â 1 |Â PX COORDINATOR Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â |Â Â Â Â Â Â Â |Â Â Â Â Â |Â Â Â Â Â Â Â Â |
|Â 2 |Â Â PX SEND QC (RANDOM)Â Â Â Â Â Â Â | :TQ10000Â Â | 100000 | 2000000 |Â 570 | 00:00:01 |
|Â 3 |Â Â Â PX BLOCK ITERATORÂ Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â | 100000 | 2000000 |Â 570 | 00:00:01 |
|Â 4 |Â Â Â Â TABLE ACCESS STORAGE FULL | BOWIE_SKEW | 100000 | 2000000 |Â 570 | 00:00:01 |
-----------------------------------------------------------------------------------------
- With Auto Indexes
-----------------------------
Plan Hash Value : 140816325
-------------------------------------------------------------------------------------------------------
| Id | Operation                           | Name               | Rows | Bytes | Cost | Time    |
-------------------------------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â 100 |Â 2000 |Â Â Â 4 | 00:00:01 |
|Â Â 1 |Â Â TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE_SKEWÂ Â Â Â Â Â Â Â Â |Â 100 |Â 2000 |Â Â Â 4 | 00:00:01 |
| * 2 |Â Â Â INDEX RANGE SCANÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | SYS_AI_7psvzc164vbng |Â 100 |Â Â Â Â Â Â |Â Â Â 3 | 00:00:01 |
-------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
------------------------------------------
* 2 - access("CODE"=6)
Notes
-----
- Dynamic sampling used for this statement ( level = 11 )
We can see that new execution plan indeed uses the index BUT interestingly, it has a correct cardinality estimate of 100 and not 100,000 as per the original plan.
Now this can be explained in that the Automatic Indexing process uses a Dynamic Sampling level of 11, meaning it can calculate the correct cardinality on the fly and can cause difficulties between what the Automatic Indexing process thinks the CBO costs will be vs. the CBO costs in a default database session that uses the (usually default) Dynamic Sampling level of 2 (as I’ve discussed previously).
BUT when I now rerun the SQL query again:
SQL> select * from bowie_skew where code=6;
100 rows selected.
Execution Plan
---------------------------------------------------------------------------------------------------
| Id | Operation                           | Name               | Rows | Bytes | Cost (%CPU)|
---------------------------------------------------------------------------------------------------
|Â Â 0 | SELECT STATEMENTÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â 100 |Â 2000 |Â Â Â Â 4Â Â (0)|
|Â Â 1 |Â PX COORDINATOR Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â Â |Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â |
|Â Â 2 |Â Â PX SEND QC (RANDOM)Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | :TQ10001Â Â Â Â Â Â Â Â Â Â Â |Â Â 100 |Â 2000 |Â Â Â Â 4Â Â (0)|
|Â Â 3 |Â Â Â TABLE ACCESS BY INDEX ROWID BATCHED| BOWIE_SKEWÂ Â Â Â Â Â Â Â Â |Â Â 100 |Â 2000 |Â Â Â Â 4Â Â (0)|
|Â Â 4 |Â Â Â Â BUFFER SORT Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â |Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â |
|Â Â 5 |Â Â Â Â Â PX RECEIVE Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â 100 |Â Â Â Â Â Â |Â Â Â Â 3Â Â (0)|
|Â Â 6 |Â Â Â Â Â Â PX SEND HASH (BLOCK ADDRESS)Â Â Â | :TQ10000Â Â Â Â Â Â Â Â Â Â Â |Â 100 |Â Â Â Â Â Â |Â Â Â Â 3Â Â (0)|
|Â Â 7 |Â Â Â Â Â Â Â PX SELECTORÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â |Â Â Â Â Â |Â Â Â Â Â Â |Â Â Â Â Â Â Â Â Â Â Â |
|*Â 8 | Â Â Â Â Â Â Â Â Â INDEX RANGE SCANÂ Â Â Â Â Â Â Â Â Â Â | SYS_AI_7psvzc164vbng |Â 100 |Â Â Â Â Â Â |Â Â Â Â 3Â Â (0)|
---------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
8 - access("CODE"=6)
Statistics
----------------------------------------------------------
12Â recursive calls
0Â db block gets
103Â consistent gets
0Â physical reads
0Â redo size
2796Â bytes sent via SQL*Net to client
654Â bytes received via SQL*Net from client
8Â SQL*Net roundtrips to/from client
2Â sorts (memory)
0Â sorts (disk)
100Â rows processed
We notice the new Automatic Index is now used BUT also that the CBO has now determined the correct cardinality estimate of 100. But how is this possible when I haven’t recalculated the table statistics?
I’ll explain in my next post.
Oracle 19c Automatic Indexing: Poor Data Clustering With Autonomous Databases Part I (Don’t Look Down) August 6, 2020
Posted by Richard Foote in 19c, 19c New Features, Attribute Clustering, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, Clustering Factor, Full Table Scans, Index Rebuild, Index statistics, Oracle, Oracle Cloud, Oracle Cost Based Optimizer, Oracle Indexes, Oracle19c, Performance Tuning.4 comments
I’ve discussed many times the importance of data clustering in relation to the efficiency of indexes. With respect to the efficiency of Automatic Indexes including their usage within Oracle’s Autonomous Database environments, data clustering is just as important.
The following demo was run on an Oracle 19c database within the Oracle Autonomous Database Transaction Processing Cloud environment.
I begin by creating a simple table that has the key column CODE, in which data is populated in a manner where the data is very poorly clustered:
SQL> create table nickcave (id number, code number, name varchar2(42));
Table created.
SQL> insert into nickcave select rownum, mod(rownum, 100), 'Nick Cave and the Bad Seeds'
from dual connect by level <= 10000000;
10000000 rows created.
SQL> commit;
Commit complete.
SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'NICKCAVE');
PL/SQL procedure successfully completed.
So we have 100 evenly distributed distinct CODE values but they’re all distributed throughout the table.
The following SQL statement is basically returning just 1% of the data and is executed a number of times:
SQL> select * from nickcave where code=42; 100000 rows selected. Execution Plan ----------------------------------------------------------------------------------------- | Id | Operation                  | Name   | Rows  | Bytes | Cost (%CPU)| Time  | ----------------------------------------------------------------------------------------- |  0 | SELECT STATEMENT           |        |  100K| 3613K| 9125  (5)| 00:00:01| |  1 | PX COORDINATOR             |        |       |     |          |        | |  2 |  PX SEND QC (RANDOM)      | :TQ10000 |  100K| 3613K| 9125  (5)| 00:00:01| |  3 |   PX BLOCK ITERATOR       |        |  100K| 3613K| 9125  (5)| 00:00:01| |* 4 |    TABLE ACCESS STORAGE FULL| NICKCAVE |  100K| 3613K| 9125  (5)| 00:00:01| ------------------------------------------------------------------------------------------
Without an index, the CBO currently has no choice but to use a Full Table Scan to access the table. So we wait for the next Automatic Index process to kick in:
SQL> select dbms_auto_index.report_last_activity() report from dual;
The Automatic Indexing report makes no mention of Automatic Indexes on the NICKCAVE table…
If we look to see if any indexes have actually been created:
SQL> select index_name, auto, constraint_index, visibility, compression, status, num_rows, leaf_blocks, clustering_factor
from user_indexes where table_name='NICKCAVE';
INDEX_NAMEÂ Â Â Â Â Â Â Â Â AUT CON VISIBILIT COMPRESSIONÂ Â STATUSÂ Â Â NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR
-------------------- --- --- --------- ------------- -------- ---------- ----------- -----------------
SYS_AI_dh8pumfww3f4r YES NOÂ INVISIBLE DISABLEDÂ Â Â Â Â UNUSABLEÂ 10000000Â Â Â Â Â Â 20346Â Â Â Â Â Â Â Â Â 4158302
SQL> select index_name, column_name, column_position from user_ind_columns where table_name='NICKCAVE'
order by index_name, column_position;
INDEX_NAMEÂ Â Â Â Â Â Â Â COLUMN_NAMEÂ Â Â Â Â Â Â Â Â COLUMN_POSITION
-------------------- -------------------- ---------------
SYS_AI_dh8pumfww3f4r CODEÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 1
We can see that yes, an Automatic Index (SYS_AI_dh8pumfww3f4r) has been created on the CODE column of the NICKCAVE table BUT it remains in an INVISIBLE, UNUSABLE state.
So Automatic Indexing considered an index on CODE, created it in an INVISIBLE, USABLE state but when testing it, failed in that it found it to be less efficient than the current FTS and so reverted the Automatic Index back to an UNUSABLE index.
Therefore, if we run a bunch of other similar SQL statements such as the following:
SQL> select * from nickcave where code=24;
SQL> select * from nickcave where code=42;
SQL> select * from nickcave where code=13;
They all use the FTS as again, the CBO has no choice with no VALID index on the CODE column available.
If we keep checking the Automatic Indexing report:
SQL> select dbms_auto_index.report_last_activity() report from dual;
There’s still no mention of an index on the CODE column. The existing Automatic Index remains in an UNUSABLE state:
SQL> select index_name, auto, constraint_index, visibility, compression, status, num_rows, leaf_blocks, clustering_factor from user_indexes where table_name='NICKCAVE'; INDEX_NAMEÂ Â Â Â Â Â Â Â Â AUT CON VISIBILIT COMPRESSIONÂ Â STATUSÂ Â Â NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR -------------------- --- --- --------- ------------- -------- ---------- ----------- ----------------- SYS_AI_dh8pumfww3f4r YES NOÂ INVISIBLE DISABLEDÂ Â Â Â Â UNUSABLEÂ 10000000Â Â Â Â Â Â 20346Â Â Â Â Â Â Â Â Â 4158302
Basically, the index remains ineffective because with a Clustering Factor of 4158302, it’s just too inefficient to return the 1% (100000 rows) of the table.
Even in an Autonomous Database environment, nothing will automatically change with this scenario.
In my next post, we’ll look at how we can improve the performance of this query and get an Automatic Index to actually kick in with a USABLE index…
