Exadata presentations

Well, I'm finally getting my exadata presentations up on my blog.  I will give a synopsis of it so you can decide if you want to look through them.. I also included a some screenshots that are meaningless without some background.

Hardware  (download)

My conclusion on hardware is that the exadata is merely off the shelf hardware.  Yes putting together the hardware in the exadata configuration (with storage cells, and infiniband) does greatly improve performance over most "normal" configurations that use arrays (like Hitachi, IBM, EMC, etc. etc.) over Fiber.

You can build your own server/storage that is even faster using SSD over infininband and you can customize it to your needs balancing the storage and database for YOUR NEEDS.

Software (download)

This is where the solution shines.  As I said above you can build the hardware yourself, but the gain is in the software.. In my test case I took a 276gb table with 1.7billion rows, and scanned it in under a second by combining HCC, storage indexes and flashcache.  Pretty incredible (it only used 6gb of disk space too).

However, the more your application is OLTP like (so it is less likely to HCC compressed, and scanned) the less gain you see with this solution.

Enjoy !

indexing HCC partititions

I have been playing with what happens when you index HCC compressed data .  If you follow the recommended strategy, you will partition your history tables, and end up with some data uncompressed, some OLTP compressed, and some in different stages of HCC compressed.  What if you use an index lookup that spans all these levels of compression ?

First I took a table that started at 176g of data (uncompressed).

OLTP compression took it down to 76G

HCC query compression took it down to 6G.

Now I indexed the HCC copy.. My index (on a single column) ended up being 40g. 

Now I queried the data using the index.  First execution was longer than querying the unindexed data (with storage indexes working their magic).  Second index is much, much faster.

The lesson I learned is that HCC really helps with storage, but start indexing the HCC data, and you end up using a lot more storage.  Also unindexed lookups can be faster than indexed (until they hit the SGA).

Interesting information to help plan what happens when I go to query across all flavors of compression.

UNYOUG meeting 6/10 In Buffalo.

Yes, it's once again time for my shameless plug for the Upstate NY Oracle Users Group meeting.  This time it's in Buffalo at Buff State.

I will will be presentation (again) on the Exadata part II (software).

Val Bedard will be presenting on Golden Gate (Val ROCKS !!)

Cristophe will be presenting on ODI.  Should be a great day.

The information can be found here.

Cardinality and the in clause

I was running a query that had an in clause, and the cardinality turned out to be wrong.. After investigating, I found that the optimizer was double applying the selectivity of the index.

Here is my example

select * from system.tab1 a,system.tab2 b
where a.col1 IN ('WRH$_PARAMETER_PK','WRH$_SEG_STAT','WRH$_DB_CACHE_ADVICE')
and col1=col2;

PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 54186084

------------------------------------------------------------------------------------------
| Id  | Operation                      | Name    | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |         |     3 |   162 |    14   (0)| 00:00:01 |
|   1 |  NESTED LOOPS                  |         |       |       |            |          |
|   2 |   NESTED LOOPS                 |         |     3 |   162 |    14   (0)| 00:00:01 |
|   3 |    INLIST ITERATOR             |         |       |       |            |          |
|   4 |     TABLE ACCESS BY INDEX ROWID| TAB1    |     3 |    75 |     7   (0)| 00:00:01 |
|*  5 |      INDEX UNIQUE SCAN         | TAB1_PK |     3 |       |     4   (0)| 00:00:01 |
|*  6 |    INDEX RANGE SCAN            | TAB2_IX |     1 |       |     2   (0)| 00:00:01 |
|   7 |   TABLE ACCESS BY INDEX ROWID  | TAB2    |     1 |    29 |     3   (0)| 00:00:01 |
------------------------------------------------------------------------------------------

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

   5 - access("A"."COL1"='WRH$_DB_CACHE_ADVICE' OR "A"."COL1"='WRH$_PARAMETER_PK'
              OR "A"."COL1"='WRH$_SEG_STAT')
   6 - access("COL1"="COL2")
       filter("COL2"='WRH$_DB_CACHE_ADVICE' OR "COL2"='WRH$_PARAMETER_PK' OR
              "COL2"='WRH$_SEG_STAT')

23 rows selected.

Notice the cardinality of 3 rows.   What made me wonder, is when I used just an '=' sign

select * from system.tab1 a,system.tab2 b
where col1 ='WRH$_DB_CACHE_ADVICE'
and col1=col2;

------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 1964798218

----------------------------------------------------------------------------------------
| Id  | Operation                    | Name    | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |         |    26 |  1404 |    23   (0)| 00:00:01 |
|   1 |  NESTED LOOPS                |         |    26 |  1404 |    23   (0)| 00:00:01 |
|   2 |   TABLE ACCESS BY INDEX ROWID| TAB1    |     1 |    25 |     2   (0)| 00:00:01 |
|*  3 |    INDEX UNIQUE SCAN         | TAB1_PK |     1 |       |     1   (0)| 00:00:01 |
|   4 |   TABLE ACCESS BY INDEX ROWID| TAB2    |    26 |   754 |    21   (0)| 00:00:01 |
|*  5 |    INDEX RANGE SCAN          | TAB2_IX |    26 |       |     2   (0)| 00:00:01 |
----------------------------------------------------------------------------------------

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

   3 - access("COL1"='WRH$_DB_CACHE_ADVICE')
   5 - access("COL2"='WRH$_DB_CACHE_ADVICE')

Notice the cardinality is 26 ?  why is the optimizer changing the cardinality from 26 to 3, when I include an in clause with one other value ? I would expect the cardinality to be 78.  In fact when I use a union it comes back right.

select * from system.tab1 a,system.tab2 b

where col1 ='WRH$_DB_CACHE_ADVICE'
and col1=col2
union
select * from system.tab1 a,system.tab2 b
where col1 ='WRH$_SEG_STAT'
and col1=col2
union
select * from system.tab1 a,system.tab2 b
where col1 ='WRH$_PARAMETER_PK'
and col1=col2;

------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 2805776637

---------------------------------------------------------------------------------------------------------
| Id  | Operation                          | Name               | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                   |                    |    77 |  7014 |    71  (68)| 00:00:01 |
|   1 |  SORT UNIQUE                       |                    |    77 |  7014 |    71  (68)| 00:00:01 |
|   2 |   UNION-ALL                        |                    |       |       |            |          |
|   3 |    NESTED LOOPS                    |                    |    26 |  1404 |    23   (0)| 00:00:01 |
|   4 |     TABLE ACCESS BY INDEX ROWID    | TAB1               |     1 |    25 |     2   (0)| 00:00:01 |
|*  5 |      INDEX UNIQUE SCAN             | TAB1_PK            |     1 |       |     1   (0)| 00:00:01 |
|   6 |     TABLE ACCESS BY INDEX ROWID    | TAB2               |    26 |   754 |    21   (0)| 00:00:01 |
|*  7 |      INDEX RANGE SCAN              | TAB2_IX            |    26 |       |     2   (0)| 00:00:01 |
|   8 |    NESTED LOOPS                    |                    |       |       |            |          |
|   9 |     NESTED LOOPS                   |                    |    51 |  5610 |    46   (0)| 00:00:01 |
|  10 |      VIEW                          | VW_JF_SET$623BBB07 |     2 |   162 |     4   (0)| 00:00:01 |
|  11 |       SORT UNIQUE                  |                    |     2 |    50 |     4  (50)| 00:00:01 |
|  12 |        UNION-ALL                   |                    |       |       |            |          |
|  13 |         TABLE ACCESS BY INDEX ROWID| TAB1               |     1 |    25 |     2   (0)| 00:00:01 |
|* 14 |          INDEX UNIQUE SCAN         | TAB1_PK            |     1 |       |     1   (0)| 00:00:01 |
|  15 |         TABLE ACCESS BY INDEX ROWID| TAB1               |     1 |    25 |     2   (0)| 00:00:01 |
|* 16 |          INDEX UNIQUE SCAN         | TAB1_PK            |     1 |       |     1   (0)| 00:00:01 |
|* 17 |      INDEX RANGE SCAN              | TAB2_IX            |    26 |       |     2   (0)| 00:00:01 |
|  18 |     TABLE ACCESS BY INDEX ROWID    | TAB2               |    26 |   754 |    21   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------------------

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

   5 - access("COL1"='WRH$_PARAMETER_PK')
   7 - access("COL2"='WRH$_PARAMETER_PK')
  14 - access("COL1"='WRH$_DB_CACHE_ADVICE')
  16 - access("COL1"='WRH$_SEG_STAT')
  17 - access("ITEM_1"="COL2")

So I ran a 10053 trace and this is what I found.

1) the table (tab2) has 1280422 rows with 49,840 distinct values

Table Stats::

Table: TAB2 Alias: B
#Rows: 1280422 #Blks: 5972 AvgRowLen: 29.00 ChainCnt: 0.00
Column (#1): COL2(
AvgLen: 23 NDV: 49840 Nulls: 0 Density: 0.000020
Index Stats::
Index: TAB2_IX Col#: 1
LVLS: 2 #LB: 5787 #DK: 49840 LB/K: 1.00 DB/K: 17.00 CLUF: 884604.00
 
2) oracle takes this cardinality and computes it for the table access (1,280,422/49,840) * 3 = 77
 
SINGLE TABLE ACCESS PATH



Single Table Cardinality Estimation for TAB2[B]
Table: TAB2 Alias: B
Card: Original: 1280422.000000 Rounded: 77 Computed: 77.07 Non Adjusted: 77.07
Access Path: TableScan
 
 
 
 
3) It then takes the cardinality of the first table (tab1) the cardinality for the second table (tab2), and applies the selectivity.
 
Join Card: 2.964306 = = outer (3.000000) * inner (77.071950) * sel (0.012821)
 
Here is my example and my 10053 trace