Oracle SQL Tuning with AWR

For the most comprehensive treatment of Oracle SQL tuning with AWR, see the book Oracle Tuning: The Definitive Reference and Oracle SQL Tuning: The Definitive Reference. 

The AWR tables contain super-useful information about the time-series execution plans for SQL statements and this repository can be used to display details about the frequency of usage for table and indexes. This article will explore these AWR tables and expose their secrets.

The AWR tables contain super-useful information about the time-series execution plans for SQL statements and this repository can be used to display details about the frequency of usage for table and indexes. This article will explore these AWR tables and expose their secrets.

We have the following AWR tables for SQL tuning.

• dba_hist_sqlstat
• dba_hist_sql_summary
• dba_hist_sql_workarea
• dba_hist_sql_plan
• dba_hist_sql_workarea_hstgrm

These simple tables represent a revolution in Oracle SQL tuning and we can now employ time-series techniques to optimizer SQL with better results than ever before. Let's take a closer look at these views.

dba_hist_sqlstat

This view is very similar to the v$sql view but it contains important SQL metrics for each snapshot. These include important delta (change) information on disk reads and buffer gets, as well as time-series delta information on application, I/O and concurrency wait times.

col c1 heading 'Begin|Interval|time'    format a20
col c2 heading 'SQL|ID'                 format a13
col c3 heading 'Executions|Delta'       format 9,999
col c4 heading 'Buffer|Gets|Delta'      format 9,999
col c5 heading 'Disk|Reads|Delta'       format 9,999
col c6 heading 'IO Wait|Delta'          format 9,999
col c7 heading 'Application|Wait|Delta' format 9,999
col c8 heading 'Concurrency|Wait|Delta' format 9,999

break on c1 skip 2
break on c2 skip 2

select
   begin_interval_time c1,
   sql_id              c2,
   executions_delta    c3,
   buffer_gets_delta   c4,
   disk_reads_delta    c5,
   iowait_delta        c6,
   apwait_delta        c7,
   ccwait_delta        c8
from

   see code depot for full script
   dba_hist_snapshot
natural join
   dba_hist_sqlstat
order by
   c1, c2;

dba_hist_sql_plan

The dba_hist_sql_plan table contains time-series data about each object (table, index, view) involved in the query. The important columns include the cardinality, cpu_cost, io_cost and temp_space required for the object.

The query below will show the main predicates involved for each object component in a SQL execution plan:

col c1 heading 'Begin|Interval|time' format a20
col c2 heading 'SQL|ID'              format a13
col c3 heading 'Object|Name'         format a20
col c4 heading 'Search Columns'      format 99
col c5 heading 'Cardinality'         format 99
col c6 heading 'Access|Predicates'   format a80
col c6 heading 'Filter|Predicates'   format a80

break on c1 skip 2
break on c2 skip 2

select
   begin_interval_time c1,
   sql_id              c2,
   object_name         c3,
   search_columns      c4,
   cardinality         c5,
   access_predicates   c6,
   filter_predicates   c7
from
   see code depot for full script
   dba_hist_snapshot
natural join
   dba_hist_sql_plan
order by
   c1, c2;

But there is lots more information in dba_hist_sql_plan that is useful. The query below will extract importing costing information for all objects involved in each query.

col c1 heading 'Begin|Interval|time' format a20
col c2 heading 'SQL|ID'              format a13
col c3 heading 'Object|Name'         format a20
col c4 heading 'Search Columns'      format 9,999
col c5 heading 'Cardinality'         format 9,999
col c6 heading 'Disk|Reads|Delta'    format 9,999
col c7 heading 'Rows|Processed'      format 9,999

break on c1 skip 2
break on c2 skip 2

select
   begin_interval_time c1,
   sql_id              c2,
   object_name         c3,
   bytes               c4,
   cpu_cost            c5,
   io_cost             c6,
   temp_space          c7
from

   see code depot for full script
   dba_hist_snapshot
natural join
   dba_hist_sql_plan
order by
   c1, c2;

Now that we see the important table structures lets examine how we can get spectacular reports from this AWR data.

Viewing table and index access with AWR

One of the problems in Oracle9i was the single bit-flag that was used to monitor index usage. You could set the flag with the "alter index xxx monitoring usage" command, and see if the index was accessed by querying the v$object_usage view.

The goal of any index access is to use the most selective index for a query, the one that produces the smallest number of rows. The Oracle data dictionary is usually quite good at this, but it is up to you to define the index. Missing function-based indexes are a common source of sub-optimal SQL execution because Oracle will not use an indexed column unless the WHERE clause matches the index column exactly.

col c1 heading 'Begin|Interval|time' format a20
col c2 heading 'SQL|ID' format a13
col c3 heading 'Object|Name' format a20

col c4 heading 'Search Columns' format 999,999
col c5 heading 'Cardinality' format 999,999
col c6 heading 'Disk|Reads|Delta' format 999,999
col c7 heading 'Rows|Processed' format 999,999

break on c1 skip 2
break on c2 skip 2

select
   begin_interval_time c1,
   sql_id c2,
   object_name c3,
   search_columns c4,
   cardinality c5,
   disk_reads_delta c6,
   rows_processed_delta c7  

see code depot for full script
from
   dba_hist_sql_plan
natural join
   dba_hist_snapshot
natural join
   dba_hist_sqlstat;

You can also use the dba_hist_sql_plan table to gather counts about the frequency of participation of objects inside queries.

col c1 heading 'Begin|Interval|time' format a20

col c2 heading 'SQL|ID'              format a13

col c3 heading 'Object|Name'         format a20

col c4 heading 'Object|Count'        format 999,999

break on c1 skip 2
break on c2 skip 2

select
   to_char(begin_interval_time,'yyyy-mm-dd HH24') c1,
   sql_id c2,
   object_name c3,
   count(*) c4
from
   dba_hist_sql_plan
natural join
   dba_hist_snapshot
group by
   to_char(begin_interval_time,'yyyy-mm-dd HH24'),
   sql_id,
  object_name ;

Here we can see the average SQL invocations for every database object, averaged by hour-of-the day or day-of-the-week. Understanding the SQL signature can be extremely useful for determining what objects to place in your KEEP pool, and to determining the most active tables and indexes in your database.

col c1 heading 'Begin|Interval|time' format a20			

col c2 heading 'Object|Name' format a20

col c3 heading 'Search Columns' format 999,999

col c4 heading 'Disk|Reads|Delta' format 999,999

col c5 heading 'Rows|Processed' format 999,999

col c6 heading 'Access|Predicates' format a200
col c7 heading 'Filter|Predicates' format a200

break on c1 skip 2

select
   begin_interval_time  c1,
   object_name          c2,
   search_columns       c3,
   disk_reads_delta     c4,
   rows_processed_delta c5,
   access_predicates    c6,
   filter_predicates    C7
   see code depot for full script
from
   dba_hist_sql_plan
natural join
   dba_hist_snapshot
natural join
   dba_hist_sqlstat;

The new access_predicates and filter_predicates columns are very useful because we no longer need to parse-out the WHERE clause of each SQL statement to see the access and filtering criteria for the SQL statements.

Counting object usage inside SQL

In Oracle10g we can easily see what indexes are used, when they are used and the context where they are used. Here is a simple AWR query to plot index usage:

col c1 heading 'Begin|Interval|time' format a20
col c2 heading 'Search Columns' format 999,999
col c2 heading 'Invocation|Count' format a20

break on c1 skip 2

select
   begin_interval_time c1,
   count(*) c3
from
   dba_hist_sqltext
natural join
   dba_hist_snapshot
where
   lower(sql_text) like lower('%cust_name_idx%')
;

This will produce an output like this, showing a summary count of all indexes used during the snapshot interval.

Conclusion

Oracle SQL tuning is constantly different. As the data changes, Oracle must be able to accommodate the changes with new execution plans. AWR now provides complete time-series SQL execution data and further research is sure to find exciting new ways to tune SQL as the data changes over time.

For more information on SQL tuning with Oracle10g AWR, see my book "Oracle Tuning: The Definitive Reference".  You can save over 30% by getting it directly from the publisher.

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Reader feedback

Oracle tuning guru Earl Shaffer adds this customization to the script above, a sophisticated script to see SQL delta change over time :

REM File: sqlstathist.sql

set echo off feed off lines 100 pages 9999
clear col
clear break
col beginttm      head 'Begin|Interval' format a14
col sqlid         head 'SQL|ID' format a13
col execsdlt      head 'Delta|Execs' format 99990
col bufgetwaitdlt head 'Delta|Buffer|Gets' format 9999990
col dskrdwaitdlt  head 'Delta|Disk|Reads' format 999990
col iowaitdlt     head 'Delta|IO Wait' format 9999990
col appwaitdlt    head 'Delta|Wait|App' format 9999990
col concurwaitdlt head 'Delta|Wait|Concur' format 99990
break on beginttm skip 1

spool sqlstathist.lis


select
   to_char(begin_interval_time,'mm-dd hh24:mi:ss') beginttm,
   sql_id sqlid,
   executions_delta execsdlt,
   buffer_gets_delta bufgetwaitdlt,
   disk_reads_delta dskrdwaitdlt,
   iowait_delta iowaitdlt,
   apwait_delta appwaitdlt,
   ccwait_delta concurwaitdlt
from
   dba_hist_snapshot sn,
   dba_hist_sqlstat ss
where
   ss.snap_id = sn.snap_id and
   begin_interval_time > (sysdate - 4/24)
order by
   beginttm,
   ( executions_delta + buffer_gets_delta +
     disk_reads_delta + iowait_delta +
     apwait_delta + ccwait_delta ) desc
/


spool off
clear break
clear col
set echo on feed on lines 80 pages 60
 

An Oracle professional also offers this script to find SQL over time, by date:

select
s.sql_id,
sum(case
when begin_interval_time = to_date(?14-feb-2009 1100′,?dd-mon-yyyy hh24mi?) then s.executions_total
else 0
end) sum_after,
(sum(case
when begin_interval_time >= to_date(?14-feb-2009 1100′,?dd-mon-yyyy hh24mi?) then s.executions_total
else 0
end) -
sum(case
when begin_interval_time < to_date(?14-feb-2009 1100′,?dd-mon-yyyy hh24mi?) then s.executions_total
else 0
end)) difference
from
   dba_hist_sqlstat s,
   dba_hist_snapshot sn
where
   sn.begin_interval_time between to_date(?05-nov-2008 0001′,?dd-mon-yyyy hh24mi?)
and
   to_date(?05-nov-2008 2359′,?dd-mon-yyyy hh24mi?)
and
   sn.snap_id=s.snap_id
group by
   s.sql_id
order by
   difference desc;

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