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Oracle optimizer extended statistics
tips
Oracle extended statistics Tips by Burleson Consulting
December 13, 2007, Updated March 19, 2011 |
One of the most exciting new features
of Oracle 11g is improvements to the dbms_stats package,
specifically the ability to aid complex queries by providing
extended statistics to the cost-based optimizer (CBO).
For details on creating optimizer extended
statistics, see these important notes on
dbms_stats.create_extended_stats.
The 11g extended statistics are intended to improve the optimizers guesses for the
cardinality of combined columns and columns that are modified by a
built-in or user-defined function.
In Oracle 10g we saw how that dynamic
sampling can be used to provide inter-table cardinality estimates,
but dynamic sampling has important limitations. However, the
11g create_extended_stats in dbms_stats relieves much of the
problem of sub-optimal table join orders allowing for extended
statistics on correlated columns.
In the absence of column histograms
and extended statistics, the Oracle cost-based optimizer must be
able to “guess” the size of complex result sets information, and it
sometimes gets it wrong. This is one reason why the ORDERED hint is
one of the most popular SQL tuning hints; using the ORDERED hint
allows you to specify that the tables be joined together in the same
order that they appear in the FROM clause.
In this example, the four-way table
join only returns 18 rows, but the query carries 9,000 rows in
intermediate result sets, slowing-down the SQL execution speed:
A suboptimal table join order
If we
were able to better predict the sizes of the intermediate results by
tipping-off the optimizer with extended statistics, we can
re-sequence the table-join order to carry less “intermediate
baggage” during the four-way table join, in this example carrying
only 3,000 intermediate rows between the table joins:
11g extended statistics help
the CBO predict inter-table join result set sizes
Extended statistics supplemental notes
Oracle guru Guy
Harrison also offers this advice for advanced statistics collection.
"In 11g, I think there are two other ways to get
statistics collected for indexed expressions:
1)
Collect extended statistics directly on the expression. So for instance, if we had a function SALES_CATEGORY, we might do
this:"DBMS_STATS.gather_table_stats
(ownname => USER,
tabname => ‘SALES’,
method_opt => ‘FOR ALL
COLUMNS FOR COLUMNS
(sale_category(amount_sold))’
);
2) Create a virtual column on the
expression, then index that column. So for the same
example as above we might create the following virtual column,
then index the column and collect stats as usual:
ALTER TABLE
SALES
ADD
sales_category
GENERATED
ALWAYS AS
(sale_category(amount_sold));
"I think I like the first method better, because the
statistics will still exist even if the index is dropped and –
unlike the second approach – it doesn’t change the logical
structure of the table."
Arup Nanda has a great article on extended statistics with
dbms_stats, specialty histogram analysis using function-based
columnar data:
"Next, re-gather statistics on the table and
collect the extended statistics on the expression upper(cust_name)."
begin
dbms_stats.gather_table_stats (
ownname => 'ARUP',
tabname => 'CUSTOMERS',
method_opt => 'for all columns size skewonly for columns
(upper(cust_name))'
);
end;
Alternatively you
can define the column group as part of the gather statistics
command.
You do that by
placing these columns in the method_opt parameter of the
gather_table_stats procedure in dbms_stats as shown below:
begin
dbms_stats.gather_table_stats (
ownname => 'ARUP',
tabname => 'BOOKINGS',
estimate_percent=> 100,
method_opt => 'FOR ALL COLUMNS SIZE SKEWONLY FOR COLUMNS
(HOTEL_ID,RATE_CATEGORY)',
cascade => true
This page is related to the book "Oracle
11g New Features" by Rampant TechPress.
See these related
notes on using 11g CBO statistics:
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