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Finding Indexing Opportunities
Oracle Tips by Burleson
 

The process of locating opportunities for indexing is an easy and rewarding activity. Because most SQL developers do not look to see if indexes are available, there are many opportunities for speeding up queries by adding indexes. Better still, adding new indexes is easy and requires no changes to the existing SQL (unless your are using optimizer plan stability).

In general, searching for indexing opportunities involves the following steps:

  • Go fishing the large-table full-table scans  You can check the v$sqlarea view or the stats$sql_summary table to identify tables that experience large-table full-table scans. This is done by executing the access.sql script to explain all of the SQL and generate the full-table scan report. This report will show you the table names, the number of full-table scans, and the number of blocks in the table. As a general rule, tables with full-table scans that have more than 100 blocks warrant investigation.

  • Extract the SQL statement  Once a table name is identified as having large-table full-table scans, you can extract the SQL that accesses these tables.

  • Add the index  Add the index to the database in a test environment. Remember, adding an index could affect the execution plans of many SQL statements, and the index should not be added into the production environment until the speed improvement has been verified.

  • Evaluate the legitimacy of the full-table scan  Next, we test the number of data blocks returned from the query to determine if the query would run faster with an index.

  • Test the execution speed  If the large-table full-table scan has a non-indexed column, create an index on the column and time the SQL using the set timing on SQL*Plus command.

  • Predict the benefit to the whole database  Once the index is created in the test environment, you can use the STATSPACK historical SQL table, to see how the new index would affect the whole database.

The first step in looking for indexing opportunities is to locate those large tables that experience frequent full-table scans. Using the access.sql script from Chapter 5, we can begin by observing the full-table scan report that was produced by analyzing all of the SQL that was in the library cache. From this report, we can check the number of rows returned from each SQL statement to see if the large-table full-table scan is legitimate.

Mon Jan 29                                                             page    1
                          full table scans and counts
                Note that "C" indicates in the table is cached.
             The “K” indicates that the table in in the KEEP pool.

OWNER          NAME                         NUM_ROWS C K   BLOCKS  NBR_FTS
-------------- ------------------------ ------------ - - -------- --------
SYS            DUAL                                  N          2   97,237
EMPDB1         PAGER                       3,450,209 N    932,120    9,999
EMPDB1         RWU_PAGE                          434 N          8    7,355
EMPDB1         STAR_IMAGE                     18,067 N      1,104    5,368
EMPDB1         SUBSCRIPTION                      476 N   K    192    2,087
EMPDB1         PRINT_PAGE_RANGE                   10 N   K     32      874
ARSD           JANET_BOOKS                        20 N          8       64
PERFSTAT       STATS$TAB_STATS                       N         65       10

Here, we must make the subjective judgment about what tables warrant further investigation. As you know, small-table full-table scan tables are placed in the KEEP pool, so we are only concerned with large tables.

In the preceding example, you see that the pager and star_image tables consist of more than 1000 data blocks, and both have more than 5000 full-table scans. These are clear candidates for further investigation, and our next step is to extract these SQL statements and evaluate the legitimacy of the full-table scans.

Extract the SQL Statement

Once we have located a table that has large-table full-table scans, the next step is to extract all SQL statements that reference the query. If you were using access.sql to explain all of the SQL in your library cache, you can run a query against the v$sqlarea view to extract the SQL statements that reference the table.

set lines 2000;

select
   sql_text
from
   v$sqlarea
where
   sql_text like ‘% pager %’;

The set lines 2000; SQL*Plus command will allow most SQL statements to be displayed, and you can easily cut and paste the SQL statements into a file for further evaluation. Now that we have the SQL, we must evaluate the legitimacy of the SQL statement.

Add the Index

You will next add the index that you think will aid the query in your test environment. Remember, adding an index can change the execution plans for many other SQL statements, so this is always best done in the test environment. The main reason for creating the index is to obtain the clustering_factor from dba_indexes for the index.

When comparing the costs of a full-table scan and an index range scan, it is important to know the clustering_factor of our index to the table. As you know, a low value for clustering_factor (near the number of blocks in the table) means that more sequential data rows will be accessed in each block read (Figure 20-1).

Figure 1: An index with a low clustering_factor

Conversely, a high clustering_factor, where the value approaches the number of rows in the table (num_rows), indicates that the rows are not in the same sequence as the index, and additional I/O will be required for index range scans (Figure 20-2).

Figure 2: An index with a high clustering_factor

Next, let’s look at how to evaluate the legitimacy of a full-table scan.


This is an excerpt from "Oracle High-Performance SQL Tuning" by Donald K. Burleson, published by Oracle Press.

  
 

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