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Oracle 19c Automatic Indexing: Poor Data Clustering With Autonomous Databases Part II (Wild Is The Wind) August 10, 2020

Posted by Richard Foote in 19c, 19c New Features, Attribute Clustering, Automatic Indexing, Autonomous Data Warehouse, Autonomous Database, Autonomous Transaction Processing, Clustering Factor, Oracle Indexes, Performance Tuning.
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In my previous post, I discussed a scenario in which Oracle Automatic Indexing refused to create a VALID index, because the resultant index was too inefficient to access the necessary rows due to the poor clustering of data within the table.

If the performance of such an SQL were critical for business requirements, there is a way to address this scenario, by re-clustering the data within the table to align itself with the index. Although the re-clustering table operation can now be very easily performed online since Oracle Database 12.2 (without having to use the dbms_redefinition process), this is NOT automatically performed within the Autonomous Database self-tuning framework (yet).

But it’s an activity we can perform manually to improve the performance of such critical SQLs as follows:

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

Table altered.

SQL> alter table nickcave move online;

Table altered.

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

INDEX_NAME           AUT CON VISIBILIT COMPRESSION   STATUS     NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR
-------------------- --- --- --------- ------------- -------- ---------- ----------- -----------------
SYS_AI_dh8pumfww3f4r YES NO  INVISIBLE DISABLED      UNUSABLE          0           0                 0

 

With the data in the table now perfectly aligned with the index, we would ordinarily now expect the index to be more efficient method to retrieve this 1% of the data.

However, if we now re-run the previously executed SQLs each a number of times:

 

SQL> select * from nickcave where code=24;

SQL> select * from nickcave where code=42;

SQL> select * from nickcave where code=13;

And now wait until next Automatic Indexing process period:

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

...

We notice that the Automatic Index is still NOT mentioned in the Automatic Indexing reports and still remains UNUSABLE:

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

INDEX_NAME           AUT CON VISIBILIT COMPRESSION   STATUS     NUM_ROWS LEAF_BLOCKS CLUSTERING_FACTOR
-------------------- --- --- --------- ------------- -------- ---------- ----------- -----------------
SYS_AI_dh8pumfww3f4r YES NO  INVISIBLE DISABLED      UNUSABLE          0           0                 0

 

So what’s going on?

In order to prevent the same SQLs from being continually re-evaluated to see if an index might be preferable, the Automatic Indexing process puts previously evaluated SQLs on a type of blacklist and therefore don’t get subsequently re-evaluated.

So although the new clustering of the data within the table would now likely warrant the creation of a new index, if we just run the some SQLs as previously, nothing changes. No Automatic Index is created and the SQLs remain in their current “sub-optimal” state.

In Part III, we’ll look at how to finally get Automatic Indexing to create these indexes and improve the performance of these queries…

Comments»

1. Oracle 19c Automatic Indexing: Poor Data Clustering With Autonomous Databases Part III (Star) | Richard Foote's Oracle Blog - August 11, 2020

[…] Part II we improved the data clustering but the previous SQLs could still not generate a new Automatic […]

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2. Oracle 19c Automatic Indexing: Poor Data Clustering With Autonomous Databases Part III (Star) | Richard Foote's Oracle Blog - August 11, 2020

[…] Part II we improved the data clustering but the previous SQLs could still not generate a new Automatic […]

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