Peter Ramm's Oracle performance analysis stuff

As of Oracle DB release 19.12 it is possible to use the new and the previous password for user logon simultaneously for a limited period. For details see New features guide . This is a great and long-awaited feature that lowers the barriers to changing passwords with less operational risk. But there's an obstacle for practical use: You must be able to monitor the users who are within this rollover period and are still using the old password. The only way of monitoring the use of old passwords known to me so far is to: Use Unified Auditing Create a policy for the LOGON action Check for the keyword "VERIFIER=12C-OLD" in the column AUTHENTICATION_TYPE of the logon records in unified audit trail. The following SQL provides an overview over all the users who are currently within a password rollover period, combined with info about old password usage: SELECT u.Account_Status, u.Password_Change_Date, u.Profile, a.*, u.Password_Change_Date + NUMT

The release 19.19 of the Oracle DB got the long awaited populating of Access_Predicates and Filter_Predicates in DBA_Hist_SQL_Plan (as backport from 21c). So far so good, after decades of complaints. But as the execution plans already stored in the AWR before 19.19 are without a time reference, Access_Predicates and Filter_Predicates for SQLs are not filled, as long as the plans from before 19.19 still exist. These plans are independent of the AWR retention time and therefore remain in the old state without Access_Predicates and Filter_Predicates forever as long as the SQLs are still contained in upcoming AWR snapshots. A solution for that behaviour is, to manually remove the old plans to trigger storage of current plans from SGA again. If executed once as SYSDBA, this script deletes all the execution plans saved in the AWR for which the Access_Predicates and Filter_Predicates columns are not filled. These plans will be saved again from SGA with the next AWR snapshots, but then

Some times a SQL execution includes all partitions of a partitioned table or index into processing, although there are filter conditions used that contain also the partition key of that partitoned table or index. There are several possible reasons why the optimizer does not recognize that access could be reduced to one or less partitions (a.k.a. partition pruning): The partition key can be obfuscated behind a conversion function The partition key can be compared to a function result that is known only at execution time Being able to use partition pruning can make a huge difference for execution time of the SQL. Imagine you only need to scan one partition of a table instead of several thousands. Often only a small change in the SQL statement can drive the optimizer to use partition pruning. A small example where all partitions are scanned due to comparison of the partition key with a function result: Get the sum of order value for a table interval partitioned by day. SELE

What does the database really do with your optimizer hints in SQL statements? Starting with Oracle 19c the column 'OTHER_XML' of V$SQL_Plan rsp. DBA_Hist_SQL_Plan or Plan_Table was extended by a section 'hint_usage'. This gives information about the state of optimizer hint usage correlated with the line in the execution plan. Example for section 'hint_usage' of OTHER_XML Lets take this simple but useless SQL as example: SELECT /*+ FULL(c) QB_NAME(Outer) NONSENS(c) */ * FROM cust.Customer c WHERE ID_Company IN ( SELECT /*+ USE_NL(cp) QB_NAME(Inner) */ 12 FROM sysp.Company cp WHERE RowNum < 5 ) AND RowNum < 20; The section 'hint_usage' in PLAN_TABLE.OTHER_XML looks like for this example: <hint_usage> <q> <n><![CDATA[INNER]]></n> <h o="EM"> <x><![CDATA[QB_NAME(Inner)]]></x> </h> <

Why Oracle DB allows retrospective evaluation of workload of active sessions by Active Session History (ASH) if you have Enterprise Edition and Diagnostics Pack. The default retention time for ASH is 7 days back from now, usual settings for production are around 30 days. But sometimes you need to analyze the evolution of database load over longer periods like over many years (e.g. as base for hardware planning and capital budgeting). There are less builtin alternatives yet: Increase the ASH retention time up to years leads to a huge amount of data, cumbersome or impossible to handle Using the AWR-warehouse function in an EM Cloud Control installation may fit your needs, but especially for single or less database instances this causes an inappropriate effort. What Panorama-Sampler allows you to extract some information from ASH and store it compressed as summary other periods between one hour and one day. The Panorama-GUI contains table-views and diagrams for evaluatio