If Performance Objectives are not being met, Quality of Service Management makes a recommendation. Each recommendation focuses on improving the highest-ranked Performance Class by exceeding its Performance Objective. Submissions may include changing consumer group mappings – and reprioritizing work within existing resource boundaries. For example, changing consumer group mappings may involve promoting a specific workload to get a more significant share of resources, or demoting a competing workload to make additional resources available to the target Performance Class. Another recommendation is to move servers between server pools and reprioritize resources between them to meet workload demands; so effectively, taking a node out of one pool and adding it to another pool gives more resources to the Performance Class running in that pool. And another recommendation is moving CPUs between databases within a server pool – reprioritize CPU resources within existing server pool boundaries. And this is called instance caging, where the CPU count parameter is set to limit the amount of CPUs an instance can use on a node.

The Quality of Service Management recommendations to improve the performance of a particular Performance Class adds more of the bottleneck resource – such as CPU time – for that Performance Class, making the bottleneck resource available more quickly to work requests in the Performance Class. Adding more resources to a Performance Class that is not performing well means taking resources away from another Performance Class. The Performance Class where the resources are removed should be less business-critical than the one being helped. So overall, the reallocation of resources should be beneficial to the business. When generating recommendations, Quality of Service Management evaluates the impact of the system performance as a whole. For example, suppose the improvement for one Performance Class is rather tiny, but the adverse effects on another Performance Class are significant. In that case, Quality of Service Management might report that the performance gain is too small and not recommended. If there is more than one way to resolve the bottleneck, Quality of Service Management advises the best overall recommendation. It is invariable, such as the calculated impact on all the Performance Classes and the predicted disruption and settling time associated with the action. And using Oracle Enterprise Manager, you can view the current and the alternative recommendations. Performance data is sent to Oracle Enterprise Manager for display on the Quality of Service Management Dashboard and Performance History pages. By default, Oracle Database QoS Management does not automatically implement recommendations. Instead, it suggests improving performance, which the administrator must then implement by clicking the Implement button. From version 12.1.0.2, Quality of Service Management allows you to specify authorized automatic actions that it can implement without the intervention of an administrator.

If you require a certain number of backups to be retained, you can set the retention policy based on the redundancy option. This option requires that a specified number of backups be cataloged before any backup is identified as obsolete. The default retention policy has a redundancy of 1, which means that only one backup of a file must exist at any given time. A backup is deemed obsolete when a more recent version of the same file has been backed up. For example, you can use the following command to configure a redundancy recovery policy of 2:

CONFIGURE RETENTION POLICY TO REDUNDANCY 2;

When patching an Oracle home, you might run into problems. OPatch might not behave as expected. It is helpful to know where to look for error messages or find additional information to pass on to Oracle Support if you want to log an SR for your problem. So OPatch maintains logs for apply, rollback, and lsinventory operations. The OPatch log files are located in the $ORACLE_HOME/cfgtoollogs/opatch. Each time you run OPatch, a new log file is created, and each log file is tagged with the operation’s timestamp. OPatch maintains an index of processed commands and log files in the opatch_history.txt file – and that is also in the above-mentioned $ORACLE_HOME/cfgtoollogs/opatch directory. So if you change the directory to $ORACLE_HOME/cfgtoollogs/opatch, you’ll see that every time you run OPatch, a log file is created with a date stamp. And then, at the bottom, you’ll see an opatch_history.txt file. look at the file, you’ll see a record of each time you ran the opatch apply, opatch rollback, or lsinventory command. The DBMS_QOPATCH package provides a PL/SQL or a SQL interface to view the installed database patches.

The package returns the patch and patch metadata information available as part of the “opatch lsinventory -xml” command in real-time. So it’s basically a way to see which patches are applied to your database home but from a PL/SQL or a SQL interface. So you basically get an XML-formatted return of your patch information. The DBMS_QOPATCH package allows users to query what patches are installed from SQL*Plus, write wrapper programs to create reports and do validation checks across multiple environments, and also to check patches installed on cluster nodes from a single location. If you log into SQL*Plus as the sys user and then perform select DBMS_QOPATCH.GET_OPATCH_LSINVENTORY from dual. And you’ll see just lots of XML information – which you can use an XML parser to make sense of it.

You query V$INSTANCE_RECOVERY view and consistently receive an OPTIMAL_LOGFILE_SIZE value that is greater than the size of your smallest online redo log file. The OPTIMAL_LOGFILE_SIZE column of the V$INSTANCE_RECOVERY view can be used to determine the appropriate size for all of the online redo log files in your database. If the value of the OPTIMAL_LOGFILE_SIZE column is greater than the size of your smallest online redo log file, you should change the size of all online redo log files to be at least this value. In addition, the FAST_START_MTTR_TARGET initialization parameter simplifies the configuration of recovery time from instance or system failure. After adjusting the size of your online redo log files, you may be able to adjust the value of this initialization parameter for better performance. This is done by rerunning the MTTR advisor after changing the size of your online redo log file to achieve more optimal results. However, running the MTTR advisor is not the best option in this situation for improving instance recovery performance.

 

You use the DBMS_RESOURCE_MANAGER package to create a CDB resource plan and define the directives for the plan. Then, from the root container of your CDB connects as the SYS user. Then, create a pending area using the CREATE_PENDING_AREA procedure. After the pending area has been completed, you use the CREATE_CDB_PLAN procedure to create the CDB resource plan. Next, create the CDB resource plan directives for the PDBs using the CREATE_CDB_PLAN_DIRECTIVE procedure. Each directive specifies how resources are allocated to a specific PDB. Finally, you validate the pending area and then submit it. This is done using the VALIDATE_PENDING_AREA and SUBMIT_PENDING_AREA procedures, respectively.

 

Starting in Oracle Database 12c, the multithreaded Oracle model enables specific Oracle processes to execute as operating system threads in separate address spaces. Setting up an Oracle database instance for using multi-process multi-thread architecture is done by starting up the Oracle database instance with the THREADED_EXECUTION initialization parameter set to TRUE.

The multi-process multi-thread model requires a password file for SYSDBA authentication while starting the database instance. Without using the SYS password

an  ORA-1031: insufficient privileges error will be triggered on startup.

When the THREADED_EXECUTION parameter is set to TRUE, you must set the DEDICATED_THROUGH_BROKER_LISTENER parameter to ON in your listener.ora file.

When that parameter is set, the listener knows that it should not spawn an OS process when a connect request is received; instead, it passes the request to the database so that a database thread is spawned and answers the connection.

 

 

 

Today, 18th January 2022, The Database patch bundles were released.
All the details on MOS in Doc ID 19202201.9 and Doc ID 21202201.9 are recommended to be installed on production systems.

 

In 19c or higher, it is no longer necessary to enable ‘Shared Servers’ on the catalog database, so Oracle recommends that (if you are using Oracle Native Encryption with sharding) you disable Shared Servers on the catalog database. This can be done by setting the database parameter shared_servers to “0” and restarting Oracle. Note that this is a global parameter set in the Container Database.

Oracle Groundbreakers EMEA 2021 
Michigan Oracle Users Summit 2021 

Oracle Machine Learning for R (OML4R) is an R API that makes the open-source R statistical programming language and environment ready for enterprise and big data. Designed for significant data problems, OML4R integrates R with Oracle Database.
R users can run R commands and scripts for statistical and graphical analyses on data stored in the Oracle Database. In addition, they can develop, refine and deploy R scripts that leverage the parallelism and scalability of the database to automate data analysis.

For more information, see: https://docs.oracle.com/en/database/oracle/machine-learning/oml4r/1.5.1/tasks.html