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12.1.0.2 Introduction to Zone Maps Part III (Little By Little) November 24, 2014

Posted by Richard Foote in 12c, Attribute Clustering, Oracle Indexes, Zone Maps.
1 comment so far

I’ve previously discussed the new Zone Map database feature and how they work in a similar manner to Exadata Storage indexes.

Just like Storage Indexes (and conventional indexes for that manner), they work best when the data is well clustered in relation to the Zone Map or index. By having the data in the table ordered in the same manner as the Zone Map, the ranges of the min/max values for each 8M “zone” in the table can be as narrow as possible, making them more likely to eliminate zone accesses.

On the other hand, if the data in the table is not well clustered, then the min/max ranges within the Zone Map can be extremely wide, making their effectiveness limited.

In my previous example on the ALBUM_ID column in my first article on this subject, the data was extremely well clustered and so the associated Zone Map was very effective. But what if the data is poorly clustered ?

To illustrate, I’m going to create a Zone Map based on the poorly clustered ARTIST_ID column, which has its values randomly distributed throughout the whole table:

SQL> create materialized zonemap big_bowie_artist_id_zm on big_bowie(artist_id);
 create materialized zonemap big_bowie_artist_id_zm on big_bowie(artist_id)
 *
 ERROR at line 1:
 ORA-31958: fact table "BOWIE"."BIG_BOWIE" already has a zonemap
 "BOWIE"."BIG_BOWIE_ALBUM_ID_ZM" on it

Another difference between an index and Zone Map is that there can only be the one Zone Map defined per table, but a Zone Map can include multiple columns. As I already have a Zone Map defined on just the ALBUM_ID column, I can’t just create another.

So I’ll drop the current Zone Map and create a new one based on both the ARTIST_ID and ALBUM_ID columns:

SQL> drop materialized zonemap big_bowie_album_id_zm;

Materialized zonemap dropped.

SQL> create materialized zonemap big_bowie_zm on big_bowie(album_id, artist_id);

Materialized zonemap created.
    
 SQL> select measure, position_in_select, agg_function, agg_column_name
 from dba_zonemap_measures where zonemap_name='BIG_BOWIE_ZM';

MEASURE              POSITION_IN_SELECT AGG_FUNCTION  AGG_COLUMN_NAME
 -------------------- ------------------ ------------- --------------------
 "BOWIE"."BIG_BOWIE".                  5 MAX           MAX_2_ARTIST_ID
 "ARTIST_ID"

"BOWIE"."BIG_BOWIE".                  4 MIN           MIN_2_ARTIST_ID
 "ARTIST_ID"

"BOWIE"."BIG_BOWIE".                  3 MAX           MAX_1_ALBUM_ID
 "ALBUM_ID"

"BOWIE"."BIG_BOWIE".                  2 MIN           MIN_1_ALBUM_ID
 "ALBUM_ID"

So this new Zone Map has min/max details on each zone in the table for both the ARTIST_ID and ALBUM_ID columns.

The min/max ranges of a Zone Map provides an excellent visual representation of the clustering of the data. If I select Zone Map details of the ALBUM_ID column (see partial listing below):

SQL> select zone_id$, min_1_album_id, max_1_album_id, zone_rows$ from big_bowie_zm;

ZONE_ID$ MIN_1_ALBUM_ID MAX_1_ALBUM_ID ZONE_ROWS$
 ---------- -------------- -------------- ----------
 3.8586E+11              1              2      66234
 3.8586E+11              5              6      56715
 3.8586E+11              7              7      76562
 3.8586E+11              7              8      76632
 3.8586E+11              8              9      76633
 3.8586E+11             21             22      75615
 3.8586E+11             29             29      75582
 3.8586E+11             31             32      75545
 3.8586E+11             35             36      75617
 3.8586E+11             43             44      75615
 ...

3.8586E+11             76             77      75615
 3.8586E+11             79             80      75615
 3.8586E+11             86             87      75616
 3.8586E+11             88             89      75618
 3.8586E+11             97             97      75771
 3.8586E+11            100            100      15871

134 rows selected.

As the data in the table is effectively ordered based on the ALBUM_ID column (and so is extremely well clustered in relation to this column), the min/max ranges for each zone is extremely narrow. Each zone basically only contains one or two different values of ALBUM_ID and so if I’m just after a specific ALBUM_ID value, the Zone Map is very effective in eliminating zones from having to be accessed. Just what we want.

However, if we look at the Zone Map details of the poorly clustered ARTIST_ID column (again just a partial listing):

SQL> select zone_id$, min_2_artist_id, max_2_artist_id, zone_rows$ from big_bowie_zm;

ZONE_ID$ MIN_2_ARTIST_ID MAX_2_ARTIST_ID ZONE_ROWS$
 ---------- --------------- --------------- ----------
 3.8586E+11            3661           98244      66234
 3.8586E+11               1          100000      56715
 3.8586E+11            5273           81834      76562
 3.8586E+11               1          100000      76632
 3.8586E+11               1          100000      76633
 3.8586E+11               1          100000      75615
 3.8586E+11            2383           77964      75582
 3.8586E+11               1          100000      75545
 3.8586E+11               1          100000      75617
 3.8586E+11               1          100000      75615
 ...

3.8586E+11               1          100000      75615
 3.8586E+11               1          100000      75615
 3.8586E+11               1          100000      75615
 3.8586E+11               1          100000      75615
 3.8586E+11               1          100000      75616
 3.8586E+11               1          100000      75618
 3.8586E+11            4848           80618      75771
 3.8586E+11           84130          100000      15871

134 rows selected.

We notice the ranges for most of the zones is extremely large, with many actually having a min value of 1 (the actual minimum) and a max of 100000 (the actual maximum). This is a worst case scenario as a specific required value could potentially reside in most of the zones, thereby  forcing Oracle to visit most zones and making the Zone Map totally ineffective.

If we run a query searching for a specific ARTIST_ID:

SQL> select * from big_bowie where artist_id=42;

100 rows selected.

Elapsed: 00:00:00.69

Execution Plan
 ----------------------------------------------------------
 Plan hash value: 1980960934

----------------------------------------------------------------------------------------------------
 | Id  | Operation                              | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
 ----------------------------------------------------------------------------------------------------
 |   0 | SELECT STATEMENT                       |           |    99 |  9108 |  3291  (13)| 00:00:01 |
 |*  1 |  TABLE ACCESS STORAGE FULL WITH ZONEMAP| BIG_BOWIE |    99 |  9108 |  3291  (13)| 00:00:01 |
 ----------------------------------------------------------------------------------------------------
 Predicate Information (identified by operation id):
 ---------------------------------------------------

1 - storage("ARTIST_ID"=42)
 filter(SYS_ZMAP_FILTER('/* ZM_PRUNING */ SELECT "ZONE_ID$", CASE WHEN
 BITAND(zm."ZONE_STATE$",1)=1 THEN 1 ELSE CASE WHEN (zm."MIN_2_ARTIST_ID" > :1 OR
 zm."MAX_2_ARTIST_ID" < :2) THEN 3 ELSE 2 END END FROM "BOWIE"."BIG_BOWIE_ZM" zm WHERE
 zm."ZONE_LEVEL$"=0 ORDER BY zm."ZONE_ID$"',SYS_OP_ZONE_ID(ROWID),42,42)<3 AND
 "ARTIST_ID"=42)

      
 Statistics
 ----------------------------------------------------------
 141  recursive calls
 0  db block gets
 101614  consistent gets
 0  physical reads
 0  redo size
 5190  bytes sent via SQL*Net to client
 618  bytes received via SQL*Net from client
 8  SQL*Net roundtrips to/from client
 0  sorts (memory)
 0  sorts (disk)
 100  rows processed

We notice we are forced to perform a very high number of consistent gets (101,614) when returning just 100 rows, much higher than the 2,364 consistent gets required to return a full 100,000 rows for a specific ALBUM_ID and not far from the 135,085 consistent gets when performing a full table scan.

We need to improve the performance of these queries based on the ARTIST_ID column …

Let’s drop this zone map:

SQL> drop materialized zonemap big_bowie_zm;

Materialized zonemap dropped.

and change the physical clustering of the data in the table so that the data is primarily now clustered in ARTIST_ID order:

 

SQL> alter table big_bowie add clustering by linear order(artist_id, album_id) with materialized zonemap;

Table altered.

So we have added a clustering attribute to this table (previously discussed here) and based a new Zone Map on this clustering at the same time.

SQL> select zonemap_name from dba_zonemaps where fact_table='BIG_BOWIE';

ZONEMAP_NAME
---------------
ZMAP$_BIG_BOWIE

SQL> select zonemap_name, pruning, with_clustering, invalid, stale, unusable
from dba_zonemaps where zonemap_name = 'ZMAP$_BIG_BOWIE';

ZONEMAP_NAME    PRUNING  WITH_CLUSTERING INVALID STALE   UNUSABLE
--------------- -------- --------------- ------- ------- --------
ZMAP$_BIG_BOWIE ENABLED  YES             NO      NO      NO

However, as we haven’t actually reorganized the table, the rows in the table are still clustered the same as before:

SQL> select zone_id$, min_2_album_id, max_2_album_id, zone_rows$ from zmap$_big_bowie;

ZONE_ID$ MIN_2_ALBUM_ID MAX_2_ALBUM_ID ZONE_ROWS$
---------- -------------- -------------- ----------
3.8586E+11             43             44      75615
3.8586E+11              1              2      66234
3.8586E+11             81             82      75615
3.8586E+11             29             29      75582
3.8586E+11             50             50      75481
3.8586E+11             90             91      75484
3.8586E+11              5              6      56715
3.8586E+11              7              8      76632
3.8586E+11              8              9      76633
3.8586E+11             16             16      75481
...

3.8586E+11             44             44      75480
3.8586E+11             82             83      75616
3.8586E+11            100            100      15871
3.8586E+11             34             35      75576
3.8586E+11             14             15      75615
3.8586E+11             33             34      75616
3.8586E+11              3              5      75707

134 rows selected.

SQL> select zone_id$, min_1_artist_id, max_1_artist_id, zone_rows$ from zmap$_big_bowie;

ZONE_ID$ MIN_1_ARTIST_ID MAX_1_ARTIST_ID ZONE_ROWS$
---------- --------------- --------------- ----------
3.8586E+11               1          100000      75545
3.8586E+11               1          100000      75616
3.8586E+11               1          100000      75617
3.8586E+11               1          100000      75911
3.8586E+11               1          100000      75616
3.8586E+11               1          100000      75616
3.8586E+11               1          100000      75615
3.8586E+11               1          100000      75616
3.8586E+11             132           75743      75612
3.8586E+11               1          100000      75615
...

3.8586E+11               1          100000      66296
3.8586E+11               1          100000      75615
3.8586E+11            2360           96960      75701
3.8586E+11               1          100000      75615
3.8586E+11               1          100000      75616
3.8586E+11           23432           98911      75480
3.8586E+11               1          100000      75791
3.8586E+11           21104           96583      75480

134 rows selected.

But if we now reorganise the table so that the clustering attribute can take effect:

SQL> alter table big_bowie move;

Table altered.

We notice the characteristics of the Zone Map has change dramatically. The previously well clustered ALBUM_ID now has a totally ineffective Zone Map with all the ranges effectively consisting of the full min/max values:

SQL> select zone_id$, min_2_album_id, max_2_album_id, zone_rows$ from zmap$_big_bowie;

ZONE_ID$ MIN_2_ALBUM_ID MAX_2_ALBUM_ID ZONE_ROWS$
---------- -------------- -------------- ----------
3.9704E+11              1            142      21185
3.9704E+11              1            100       9452
3.9704E+11              1            100      76516
3.9704E+11              1            100      75501
3.9704E+11              1            100      75497
3.9704E+11              1            100      75501
3.9704E+11              1            100      75499
3.9704E+11              1            100      75504
3.9704E+11              1            100      75500
3.9704E+11              1            100      75501
...

3.9704E+11              1            100      75503
3.9704E+11              1            100      75498
3.9704E+11              1            100      75501
3.9704E+11              1            100      75501
3.9704E+11              1            100      75501
3.9704E+11              1            100      75501
3.9704E+11              1            100      75794

144 rows selected.

While the previously ineffective Zone Map on the ARTIST_ID column is now much more effective with significantly smaller min/max ranges for each zone:

SQL> select zone_id$, min_1_artist_id, max_1_artist_id, zone_rows$ from zmap$_big_bowie;

ZONE_ID$ MIN_1_ARTIST_ID MAX_1_ARTIST_ID ZONE_ROWS$
---------- --------------- --------------- ----------
3.9704E+11              67            1036      21185
3.9704E+11            2359            2453       9452
3.9704E+11            8341            9106      76516
3.9704E+11           18933           19688      75501
3.9704E+11           22708           23463      75497
3.9704E+11           26483           27238      75501
3.9704E+11           27238           27993      75499
3.9704E+11           33278           34033      75504
3.9704E+11           36674           40449      75500
3.9704E+11           38563           39318      75501
...

3.9704E+11           49888           50643      75503
3.9704E+11           62723           63478      75498
3.9704E+11           77824           78579      75501
3.9704E+11           82354           83109      75501
3.9704E+11           88394           89149      75501
3.9704E+11           93679           94434      75501
3.9704E+11           98211           98969      75794

144 rows selected.

The same query now runs so much faster as the Zone Map can eliminate almost all zones from being accessed:

SQL> select * from big_bowie where artist_id=42;

100 rows selected.

Elapsed: 00:00:00.02

Execution Plan
----------------------------------------------------------
Plan hash value: 1980960934

----------------------------------------------------------------------------------------------------
| Id  | Operation                              | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                       |           |    99 |  9108 |  3291  (13)| 00:00:01 |
|*  1 |  TABLE ACCESS STORAGE FULL WITH ZONEMAP| BIG_BOWIE |    99 |  9108 |  3291  (13)| 00:00:01 |
----------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------

1 - storage("ARTIST_ID"=42)
filter(SYS_ZMAP_FILTER('/* ZM_PRUNING */ SELECT "ZONE_ID$", CASE WHEN
BITAND(zm."ZONE_STATE$",1)=1 THEN 1 ELSE CASE WHEN (zm."MIN_1_ARTIST_ID" > :1 OR
zm."MAX_1_ARTIST_ID" < :2) THEN 3 ELSE 2 END END FROM "BOWIE"."ZMAP$_BIG_BOWIE" zm WHERE
zm."ZONE_LEVEL$"=0 ORDER BY zm."ZONE_ID$"',SYS_OP_ZONE_ID(ROWID),42,42)<3 AND
"ARTIST_ID"=42)
Statistics
----------------------------------------------------------
187  recursive calls
0  db block gets
175  consistent gets
0  physical reads
0  redo size
5190  bytes sent via SQL*Net to client
618  bytes received via SQL*Net from client
8  SQL*Net roundtrips to/from client
9  sorts (memory)
0  sorts (disk)
100  rows processed

Consistent gets has reduced dramatically down to just 175 from the previously massive 101,614.

As is common with changing the clustering of data, what improves one thing makes something else significantly worse. The previously efficient accesses based on the ALBUM_ID column is now nowhere near as efficient as before:

SQL> select * from big_bowie where album_id = 42;

100000 rows selected.

Elapsed: 00:00:01.27

Execution Plan
----------------------------------------------------------
Plan hash value: 1980960934

----------------------------------------------------------------------------------------------------
| Id  | Operation                              | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                       |           |   100K|  8984K|  3269  (12)| 00:00:01 |
|*  1 |  TABLE ACCESS STORAGE FULL WITH ZONEMAP| BIG_BOWIE |   100K|  8984K|  3269  (12)| 00:00:01 |
----------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------

1 - storage("ALBUM_ID"=42)
filter(SYS_ZMAP_FILTER('/* ZM_PRUNING */ SELECT "ZONE_ID$", CASE WHEN
BITAND(zm."ZONE_STATE$",1)=1 THEN 1 ELSE CASE WHEN (zm."MIN_2_ALBUM_ID" > :1 OR
zm."MAX_2_ALBUM_ID" < :2) THEN 3 ELSE 2 END END FROM "BOWIE"."ZMAP$_BIG_BOWIE" zm WHERE
zm."ZONE_LEVEL$"=0 ORDER BY zm."ZONE_ID$"',SYS_OP_ZONE_ID(ROWID),42,42)<3 AND "ALBUM_ID"=42)

Statistics
----------------------------------------------------------
187  recursive calls
0  db block gets
141568  consistent gets
0  physical reads
0  redo size
4399566  bytes sent via SQL*Net to client
73878  bytes received via SQL*Net from client
6668  SQL*Net roundtrips to/from client
9  sorts (memory)
0  sorts (disk)
100000  rows processed

We now have to perform a whopping 141,568 consistent gets up from the previous 2,364 consistent gets.

So Zone Maps, like database indexes and Exadata Storage Indexes, can be extremely beneficial in reducing I/O but their effectiveness is very much dependant on the clustering of the underlining data.

12.1.0.2 Introduction to Attribute Clustering (The Division Bell) August 26, 2014

Posted by Richard Foote in 12c, Attribute Clustering, Clustering Factor, Oracle Indexes.
5 comments

One of the really cool new features introduced in 12.1.0.2 is Attribute Clustering. This new table based attribute allows you to very easily cluster data in close physical proximity based on the content of specific columns.

As I’ve discussed many times, indexes love table data that is physically clustered in a similar manner to the index as it can significantly improve the efficiency of such indexes. A low Clustering Factor (CF) makes an index more viable and is one of the more important considerations in CBO calculations.

But not only database indexes benefit from well cluster data. Other index structures such as Exadata Storage Indexes and the new Zone Maps (to be discussed in future articles) all benefit from well clustered data. Additionally, compression is likely to be much more effective with data that is well clustered and this in turns also impacts the efficiency of In-memory data (again, to be discussed in future articles).

So having the capability to now easily cluster data in regular heap tables has potentially many benefits.

To illustrate, I’m first going to create a table with data that is not well clustered at all. The CODE column has data that is basically evenly distributed throughout the whole table structure:

SQL> create table ziggy (id number, code number, name varchar2(30));

Table created.

SQL> insert into ziggy select rownum, mod(rownum,100), 'DAVID BOWIE' from dual connect by level >= 2000000;

2000000 rows created.

SQL> commit;

Commit complete.

SQL> exec dbms_stats.gather_table_stats(ownname=>user, tabname=>'ZIGGY', estimate_percent=>null,
method_opt=>'FOR ALL COLUMNS SIZE 1');

PL/SQL procedure successfully completed.

I’ll next create an index on this CODE column and check out its default CF:

SQL> create index ziggy_code_i on ziggy(code);

Index created.

SQL> select index_name, clustering_factor, num_rows from user_indexes
where index_name='ZIGGY_CODE_I';

INDEX_NAME           CLUSTERING_FACTOR   NUM_ROWS
-------------------- ----------------- ----------
ZIGGY_CODE_I                    703133    2000000

For a table with 2 million rows, a CF of some 703,133 is very high and the index is going to be very inefficient when retrieving high numbers of rows.

Let’s run a query that returns a specific CODE value, approx. 1% of all the data (note I’ve set a large arraysize to minimize unnecessary fetches and resultant consistent  gets):

SQL> set arraysize 5000

SQL> select * from ziggy where code = 42;

20000 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 2421001569

-----------------------------------------------------------------------------------
| Id  | Operation                 | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT          |       | 20000 |   390K|   383  (17)| 00:00:01 |
|*  1 |  TABLE ACCESS STORAGE FULL| ZIGGY | 20000 |   390K|   383  (17)| 00:00:01 |
-----------------------------------------------------------------------------------

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

1 - storage("CODE"=42)
filter("CODE"=42)

Statistics
----------------------------------------------------------
1  recursive calls
0  db block gets
15212  consistent gets
0  physical reads
0  redo size
211208  bytes sent via SQL*Net to client
585  bytes received via SQL*Net from client
5  SQL*Net roundtrips to/from client
0  sorts (memory)
0  sorts (disk)
20000  rows processed

The CBO has chosen a Full Table Scan and has decided to not use the index. If we hint the SQL:

SQL> select /*+ index (ziggy, ziggy_code_i) */ * from ziggy where code = 42;

20000 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 3294205578

----------------------------------------------------------------------------------------------------
| Id  | Operation                           | Name         | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                    |              | 20000 |   390K|  7081   (1)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED| ZIGGY        | 20000 |   390K|  7081   (1)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN                  | ZIGGY_CODE_I | 20000 |       |    43   (3)| 00:00:01 |
----------------------------------------------------------------------------------------------------

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

2 - access("CODE"=42)

Statistics
----------------------------------------------------------
1  recursive calls
0  db block gets
7081  consistent gets
41  physical reads
0  redo size
511195  bytes sent via SQL*Net to client
585  bytes received via SQL*Net from client
5  SQL*Net roundtrips to/from client
0  sorts (memory)
0  sorts (disk)
20000  rows processed

At a cost of 7081, the index is way more expensive than the 383 cost for the FTS. The poor clustering of the CODE data within the table has made the index non viable.

Let’s now create another table, but this one with a clustering attribute set on the CODE column:

SQL> create table ziggy2 (id number, code number, name varchar2(30))

clustering by linear order (code) without materialized zonemap;

Table created.

The CLUSTERING BY LINEAR ORDER clause orders data in the table based on the specified columns, in this case the CODE column. Up to 10 columns can be included using this particular technique (there are other attribute clustering options which I’ll again cover in later articles, yes I’ll be writing quite a few new articles) :) WITHOUT MATERIALIZED ZONEMAP means I don’t want to create these new Zone Maps index structures at this stage which could potentially reduce the amount of table storage needed to be accessed (again, I’ll discuss these at another time).

You must use a direct path insert to make use of attribute clustering (or reorganize the table as we’ll see).

So lets insert the exact same data into this new ZIGGY2 table via a straight direct path sub-select:

SQL> insert /*+ append */ into ziggy2 select * from ziggy;

2000000 rows created.

SQL> SELECT * FROM TABLE(dbms_xplan.display_cursor);

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
SQL_ID  0arqdyc9vznpg, child number 0
-------------------------------------
insert /*+ append */ into ziggy2 select * from ziggy

Plan hash value: 1975011999

--------------------------------------------------------------------------------------------------
| Id  | Operation                        | Name  | Rows  | Bytes |TempSpc| Cost(%CPU)| Time     |
--------------------------------------------------------------------------------------------------
|   0 | INSERT STATEMENT                 |       |       |       |       | 10596 (100)|          |
|   1 |  LOAD AS SELECT                  |       |       |       |       |            |          |
|   2 |   OPTIMIZER STATISTICS GATHERING |       |  2000K|    38M|       | 10596   (3)| 00:00:01 |
|   3 |    SORT ORDER BY                 |       |  2000K|    38M|    61M| 10596   (3)| 00:00:01 |
|   4 |     TABLE ACCESS STORAGE FULL    | ZIGGY |  2000K|    38M|       |   376  (16)| 00:00:01 |
--------------------------------------------------------------------------------------------------

SQL> commit;

Commit complete.

Notice the SORT ORDER BY step in the insert execution plan. This implicitly sorts the incoming data in CODE order to satisfy the attribute clustering requirement.

If we create an index on this table and examine the CF:

SQL> exec dbms_stats.gather_table_stats(ownname=>user, tabname=>'ZIGGY2',
estimate_percent=>null, method_opt=>'FOR ALL COLUMNS SIZE 1');

PL/SQL procedure successfully completed.

SQL> select index_name, clustering_factor, num_rows
from user_indexes where index_name='ZIGGY2_CODE_I';

INDEX_NAME           CLUSTERING_FACTOR   NUM_ROWS
-------------------- ----------------- ----------
ZIGGY2_CODE_I                     7072    2000000

We notice the default CF is indeed significantly lower at just 7072 than the previous value of 703133.

If we now run the equivalent query as before on this table:

SQL> select * from ziggy2 where code=42;

20000 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 16801974

-----------------------------------------------------------------------------------------------------
| Id  | Operation                           | Name          | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                    |               | 20000 |   390K|   114   (1)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED| ZIGGY2        | 20000 |   390K|   114   (1)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN                  | ZIGGY2_CODE_I | 20000 |       |    43   (3)| 00:00:01 |
-----------------------------------------------------------------------------------------------------

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

2 - access("CODE"=42)

Statistics
----------------------------------------------------------
1  recursive calls
0  db block gets
121  consistent gets
41  physical reads
0  redo size
511195  bytes sent via SQL*Net to client
585  bytes received via SQL*Net from client
5  SQL*Net roundtrips to/from client
0  sorts (memory)
0  sorts (disk)
20000  rows processed

We notice the CBO has now decided to use the index. This is due to the cost of the index based execution plan being just 114, significantly lower than the previous index cost of 7081 or the FTS at a cost of 383. Just as importantly, the resultant number of consistent gets has also significantly reduced to just 121, significantly less than the previous 7081 consistent gets when using the index. So the index is indeed much more efficient to use and the CBO costs for this is just about spot on. The end result is that performance has improved.

So how to now likewise improve the performance of the first table? Simple add the attribute clustering and reorganize the table:

SQL> alter table ziggy add clustering by linear order(code) without materialized zonemap;

Table altered.

SQL> alter table ziggy move;

Table altered.

SQL> alter index ziggy_code_i rebuild;

Index altered.

SQL> select index_name, clustering_factor, num_rows from user_indexes where index_name='ZIGGY_CODE_I';

INDEX_NAME      CLUSTERING_FACTOR   NUM_ROWS
--------------- ----------------- ----------
ZIGGY_CODE_I                 7134    2000000

So as expected, the CF has likewise reduced. So if we now run the query:

SQL> select * from ziggy where code=42;

20000 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 3294205578

----------------------------------------------------------------------------------------------------
| Id  | Operation                           | Name         | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                    |              | 20000 |   390K|   115   (1)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED| ZIGGY        | 20000 |   390K|   115   (1)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN                  | ZIGGY_CODE_I | 20000 |       |    43   (3)| 00:00:01 |
----------------------------------------------------------------------------------------------------

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

2 - access("CODE"=42)

Statistics
----------------------------------------------------------
0  recursive calls
0  db block gets
121  consistent gets
0  physical reads
0  redo size
511195  bytes sent via SQL*Net to client
585  bytes received via SQL*Net from client
5  SQL*Net roundtrips to/from client
0  sorts (memory)
0  sorts (disk)
20000  rows processed

The query likewise uses the index and with far less consistent gets and performance is significantly better.

So attribute clustering provides a nice mechanism by which data in a heap table (or importantly within a partition or sub-partition) can be physically clustered in a manner that can be potentially beneficial in various scenarios. Of course, the decision on how to actually cluster the data and on which columns is somewhat crucial :)

12.1.0.2 Released With Cool Indexing Features (Short Memory) July 25, 2014

Posted by Richard Foote in 12c, Advanced Index Compression, Attribute Clustering, Database In-Memory, Zone Maps.
2 comments

Oracle Database 12.1.0.2 has finally been released and it has a number of really exciting goodies from an indexing perspective which include:

  • Database In-Memory Option, which enables specific portions of the database to be in dual format, in both the existing row based format and additionally into an efficient memory only columnar based format. This in turn enables analytical based processing to access the real-time data in the In-Memory Store extremely fast, potentially faster and more effectively than via standard analytical based database indexes.
  • Advanced Index Compression, which allows Oracle to automatically choose the appropriate compression method for each individual leaf block, rather than having to manually select a single compression method across the whole index. This makes compressing an index a breeze and much more effective than previously possible.
  • Zone Maps, which enables Storage Index like capabilities to be manually configured and physically implemented inside the database, to eliminate unnecessary accesses of table storage via much smaller objects than conventional database indexes.
  • Attribute Clustering, a new table attribute which enables much better clustering of table data and we all know how both compression and index structures love table data to be well clustered.

These are all topics I’ll be covering in the coming weeks so stay tuned :)

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