SAMPLING in .NET Include UPC-13 in .NET SAMPLING Code 39 for vb

204 SAMPLING using none torender none with web,windows applicationmake code 39 7 . Visual Studio .NET Introduction Accurate statisti cs can usually be gathered without having to read every block in a table or index. Although statistics generated from reading every block will be more accurate, the increase in time taken to analyze the table will be much greater than the increase in accuracy. Consequently, by default DBMS_STAT will read only a sample of rows from each table.

This behavior is controlled by the ESTIMATE_PERCENT option, which will determine the percentage of rows to be included in the sample. The default value of DBMS_STATS.AUTO_SAMPLE_SIZE results in Oracle attempting to find a balance between sampling time and accuracy.

. PARTITION STATIST none for none ICS The GRANULARITY clause enables statistics for a partitioned table to be global across the entire table or to be collected at the partition or subpartition level. In addition, you can use the DBMS_STATS.SET_TABLE_PREFS procedure to establish an INCREMENTAL collection policy for a partitioned table.

If INCREMENTAL is set to TRUE, then statistics are collected only from partitions that have been modified. Because time-range partitioned objects often have only a single active partition, this can lead to significant savings in sampling time..

EXTENDED STATISTI CS Extended statistics are statistics that go beyond the raw data in a single column. Extended statistics can be gathered for columns in combination, or for column values manipulated by functions or expressions. Multicolumn Extended Statistics Multicolumn extended statistics can be collected to calculate the selectivity of multicolumn expressions.

For some columns, you can calculate selectivity of multiple columns by multiplying the selectivity of each. For instance, if 50 percent of the customers are male, and 10 percent of customers are from Australia, then it s probably reasonable to estimate that 5 percent (10 percent of 50 percent) of customers are Australian men. However, sometimes the data in two columns will have a dependency.

For instance if 50 percent of the customers are female and 5 percent are named John, it s not reasonable to assume that 2.5 percent of the customers are women named John! Multicolumn extended statistics allow the optimizer to recognize these column dependencies. Multicolumn extended statistics can be collected by supplying the column combinations to be collected in the METHOD_OPT parameter.

For example, the following DBMS_STATS call gathers statistics on every individual column and also on the combination of gender and first name:. Optimizing the Optimizer BEGIN DBMS_STATS. gather_table_stats (ownname => "SH", tabname => "CUSTOMERS", method_opt => "FOR ALL COLUMNS FOR COLUMNS (CUST_GENDER,CUST_FIRST_NAME)" ); END; Expression Extended Statistics You can also gather extended statistics on an expression, which can help the optimizer calculate query cost when that expression appears in a SQL. For example, consider the following query, which includes a function in the WHERE clause for which a functional index exists: SQL> SELECT COUNT (*), SUM (amount_sold) 2 FROM sales 3 WHERE sale_category (amount_sold) = 1; COUNT(*) SUM(AMOUNT_SOLD) ---------- ---------------2393655 161110431.

----------------- none for none --------------------------. Id Operation Name Rows ---------------- ---------------------------. 0 . SELECT STATEMENT 1 . 1 . SORT AGGREGATE 1 . * 2 . TABLE ACCESS FULL SALES 1238K. ---------------- none none ---------------------------The optimizer makes an attempt to estimate the number of rows that would match the function definition but can t do a particularly good job because it can t know in advance what the output of the function would be for every input value. We can collect statistics for the function using the following syntax: BEGIN DBMS_STATS.gather_table_stats (ownname => USER, tabname => "SALES", method_opt => "FOR ALL COLUMNS FOR COLUMNS (sale_category(amount_sold))" ); END;.

7 Now that the e xtended statistics are created, the optimizer s estimate is far more accurate: SQL> SELECT COUNT (*), SUM (amount_sold) 2 FROM sales 3 WHERE sale_category (amount_sold) = 1; COUNT(*) SUM(AMOUNT_SOLD) ---------- ---------------2393655 161110431. ----------------- none for none --------------------------. Id Operation Name Rows ---------------- ---------------------------. 0 . SELECT STATEMENT 1 . 1 . SORT AGGREGATE 1 . * 2 . TABLE ACCESS FULL SALES 2395K. ---------------- none none ----------------------------. In 11g, consider collecting extended statistics in which columns that are queried together have data dependencies, for functional indexes or for expressions commonly used in the WHERE clause.. Oracle 11g also a none none llows you to create virtual columns, which can be queried as a regular column, but which are defined as an expression. A virtual column can provide the same benefit as using extended statistics because collecting statistics on the virtual column is logically equivalent to collecting extended statistics on the column s expression. For instance, instead of defining the extended statistics collection as previously shown, we can instead create the following virtual column: ALTER TABLE products ADD rounded_list_price GENERATED ALWAYS AS (ROUND(prod_list_price,-2)) Queries that access the virtual column by name or use the exact expression that defines the virtual column can use statistics that the optimizer collects against the virtual column.

Creating virtual columns changes the logical structure of the table however, which might not be permissible in a production environment..
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