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Creating An Oracle Database Home Gold Image

In today’s post I want to show how to create an Oracle database home gold image (GI). I will cover the steps I usually follow, the checks I perform and the options to create the GI and finally automate it.

I’ll start by stating that I’m using an old database Oracle home for this example. This home is from a DB running 19c RU 17. The idea is to patch it to RU32 (July 2026) and apply the September MRP (September CSPU).

Download the latest OPatch and the July 2026 RU patches. Stage them and upgrade OPatch. At the time of this post OPatch version 12.2.0.1.53 is the latest available for 19c. Let’s verify if it can work with July 2026 RU.

$ $ORACLE_HOME/OPatch/opatch prereq CheckMinimumOPatchVersion -ph /home/oracle/39472050/
Oracle Interim Patch Installer version 12.2.0.1.53
Copyright (c) 2026, Oracle Corporation.  All rights reserved.
PREREQ session
Oracle Home       : /u01/db/19c/sales
Central Inventory : /u01/app/inventory
   from           : /u01/db/19c/sales/oraInst.loc
OPatch version    : 12.2.0.1.53
OUI version       : 12.2.0.7.0
Log file location : /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_18-21-36PM_1.log

Invoking prereq "checkminimumopatchversion"
Prereq "checkMinimumOPatchVersion" passed.
OPatch succeeded.

Let’s now check for conflicts.

$ $ORACLE_HOME/OPatch/opatch prereq CheckConflictAgainstOHWithDetail -phBaseDir /home/oracle/39472050/
Oracle Interim Patch Installer version 12.2.0.1.53
Copyright (c) 2026, Oracle Corporation.  All rights reserved.
PREREQ session
Oracle Home       : /u01/db/19c/sales
Central Inventory : /u01/app/inventory
   from           : /u01/db/19c/sales/oraInst.loc
OPatch version    : 12.2.0.1.53
OUI version       : 12.2.0.7.0
Log file location : /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_18-22-14PM_1.log

Invoking prereq "checkconflictagainstohwithdetail"
Prereq "checkConflictAgainstOHWithDetail" passed.
OPatch succeeded.

At this point I’m ready to apply July 2026 RU to this OH. I’m going to shutdown all instances running, listeners and then proceed to apply the RU.

$ORACLE_HOME/OPatch/opatch apply
Oracle Interim Patch Installer version 12.2.0.1.53
Copyright (c) 2026, Oracle Corporation.  All rights reserved.

Oracle Home       : /u01/db/19c/sales
Central Inventory : /u01/app/inventory
   from           : /u01/db/19c/sales/oraInst.loc
OPatch version    : 12.2.0.1.53
OUI version       : 12.2.0.7.0
Log file location : /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_18-25-07PM_1.log

Verifying environment and performing prerequisite checks...
-----------------------------------------------------------------------------
Start OOP by Prereq process.
Launch OOP...

Oracle Interim Patch Installer version 12.2.0.1.53
Copyright (c) 2026, Oracle Corporation.  All rights reserved.
Oracle Home       : /u01/db/19c/sales
Central Inventory : /u01/app/inventory
   from           : /u01/db/19c/sales/oraInst.loc
OPatch version    : 12.2.0.1.53
OUI version       : 12.2.0.7.0
Log file location : /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_18-26-04PM_1.log

Verifying environment and performing prerequisite checks...
OPatch continues with these patches:   39472050
Do you want to proceed? [y|n]
y
User Responded with: Y
All checks passed.
Please shutdown Oracle instances running out of this ORACLE_HOME on the local system.
(Oracle Home = '/u01/db/19c/sales')

Is the local system ready for patching? [y|n]
y
User Responded with: Y
Backing up files...
Applying interim patch '39472050' to OH '/u01/db/19c/sales'
ApplySession: Optional component(s) [ oracle.network.gsm, 19.0.0.0.0 ] , [ oracle.assistants.asm, 19.0.0.0.0 ] , [ oracle.datamining, 19.0.0.0.0 ] , [ oracle.crypto.rsf, 19.0.0.0.0 ] , [ oracle.oraolap.mgmt, 19.0.0.0.0 ] , [ oracle.pg4appc, 19.0.0.0.0 ] , [ oracle.precomp.companion, 19.0.0.0.0 ] , [ oracle.rdbms.ic, 19.0.0.0.0 ] , [ oracle.java.sqlj.sqljruntime, 19.0.0.0.0 ] , [ oracle.rdbms.tg4db2, 19.0.0.0.0 ] , [ oracle.sdo.companion, 19.0.0.0.0 ] , [ oracle.tfa, 19.0.0.0.0 ] , [ oracle.rdbms.tg4ifmx, 19.0.0.0.0 ] , [ oracle.oid.client, 19.0.0.0.0 ] , [ oracle.rdbms.tg4sybs, 19.0.0.0.0 ] , [ oracle.ldap.ztk, 19.0.0.0.0 ] , [ oracle.ons.eons.bwcompat, 19.0.0.0.0 ] , [ oracle.net.cman, 19.0.0.0.0 ] , [ oracle.ons.cclient, 19.0.0.0.0 ] , [ oracle.xdk.companion, 19.0.0.0.0 ] , [ oracle.options.olap, 19.0.0.0.0 ] , [ oracle.rdbms.tg4msql, 19.0.0.0.0 ] , [ oracle.rdbms.tg4tera, 19.0.0.0.0 ] , [ oracle.network.cman, 19.0.0.0.0 ] , [ oracle.options.olap.api, 19.0.0.0.0 ] , [ oracle.jdk, 1.8.0.191.0 ] , [ oracle.jdk, 1.8.0.391.11 ]  not present in the Oracle Home or a higher version is found.

Patching component oracle.rdbms.util, 19.0.0.0.0...
Patching component oracle.rdbms.rsf, 19.0.0.0.0...
Patching component oracle.rdbms, 19.0.0.0.0...
Patching component oracle.assistants.acf, 19.0.0.0.0...
Patching component oracle.assistants.deconfig, 19.0.0.0.0...
...
Patching component oracle.jdk, 1.8.0.201.0...
Patch 39472050 successfully applied.
Sub-set patch [34419443] has become inactive due to the application of a super-set patch [39472050].
Sub-set patch [31222103] has become inactive due to the application of a super-set patch [39472050].
Please refer to Doc ID 2161861.1 for any possible further required actions.
Log file location: /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_18-26-04PM_1.log

OPatch succeeded.

After this I’m going to verify all the log files and look for any errors or warnings. I’ll then start the DB and the listener if the installation is successful.

Let’s now do a sanity check for datapatch.

$ ./datapatch -sanity_checks
SQL Patching sanity checks version 19.32.0.0.0 on Fri 09 Oct 2026 06:39:50 PM GMT
Copyright (c) 2021, 2026, Oracle.  All rights reserved.

Log file for this invocation: /u01/app/19c/cfgtoollogs/sqlpatch/sanity_checks_20261009_183950_24361/sanity_checks_20261009_183950_24361.log

Running checks
JSON report generated in /u01/app/19c/cfgtoollogs/sqlpatch/sanity_checks_20261009_183950_24361/sqlpatch_sanity_checks_summary.json file
Checks completed. Printing report:

Check: Database component status - INFO
  There should be no INVALID components prior patching due to risk of potential errors.
  Listed components have a non-valid status.
  For INVALID components: Run '?/rdbms/admin/utlrp.sql' (per container) to try recompiling invalid objects and restore component status.
  For other non-valid status, depending on patch content warnings may be returned.
...
Check: GoldenGate triggers - OK
Check: Logminer DDL triggers - OK
Check: Check sys public grants - OK
Check: Statistics gathering running - OK
Check: Optim dictionary upgrade parameter - OK
Check: Symlinks on oracle home path - OK
Check: Central Inventory - OK
Check: Java Virtual Machine Enable - OK
Check: Oracle Database Vault Enabled - OK
Check: Queryable Inventory database directories - OK
Check: Queryable Inventory locks - OK
Check: Queryable Inventory package - OK
Check: Queryable Inventory external table - OK
Check: Imperva processes - OK
Check: Guardium processes - OK
Check: Locale - OK

Refer to MOS Note 2975965.1 and debug log
/u01/app/19c/cfgtoollogs/sqlpatch/sanity_checks_20261009_183950_24361/sanity_checks_debug_20261009_183950_24361.log

SQL Patching sanity checks completed on Fri 09 Oct 2026 06:40:00 PM GMT

Verify the logs to look for issues or warnings. Execute datapatch if no errors found.

$ ./datapatch
SQL Patching tool version 19.32.0.0.0 Production on Fri Oct  9 18:40:43 2026
Copyright (c) 2012, 2026, Oracle.  All rights reserved.

Log file for this invocation: /u01/app/19c/cfgtoollogs/sqlpatch/sqlpatch_25314_2026_10_09_18_40_43/sqlpatch_invocation.log

Connecting to database...OK
Gathering database info...done

Note:  Datapatch will only apply or rollback SQL fixes for PDBs
       that are in an open state, no patches will be applied to closed PDBs.
       Please refer to Note: Datapatch: Database 12c Post Patch SQL Automation
       (Doc ID 1585822.1)

Bootstrapping registry and package to current versions...done
Determining current state...done
...
Installing patches...
Patch installation complete.  Total patches installed: 7
...
SQL Patching tool complete on Fri Oct  9 19:00:06 2026

Let’s verify logs to look for errors or warnings. At this point we can cleanup our OH. Let’s list any inactive patches and cleanup before continuing with the September MRP.

$ $ORACLE_HOME/OPatch/opatch util listorderedinactivepatches
Oracle Interim Patch Installer version 12.2.0.1.53
Copyright (c) 2026, Oracle Corporation.  All rights reserved.

Oracle Home       : /u01/db/19c/sales
Central Inventory : /u01/app/inventory
   from           : /u01/db/19c/sales/oraInst.loc
OPatch version    : 12.2.0.1.53
OUI version       : 12.2.0.7.0
Log file location : /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_18-51-27PM_1.log

Invoking utility "listorderedinactivepatches"
List Inactive patches option provided
The oracle home has the following inactive patch(es) and their respective overlay patches:

The number of RU chains is  2
***** There are 4 inactive RU patches in chain 1
-Inactive RU/BP 29517242:Database Release Update : 19.3.0.0.190416 (29517242), installed on: Thu Apr 18 07:21:17 GMT 2019, with no overlays
-Inactive RU/BP 30869156:Database Release Update : 19.7.0.0.200414 (30869156), installed on: Wed May 13 17:09:52 GMT 2020, with no overlays
-Inactive RU/BP 34133642:Database Release Update : 19.16.0.0.220719 (34133642), installed on: Mon Apr 03 11:37:24 GMT 2023, with no overlays
-Inactive RU/BP 34419443:Database Release Update : 19.17.0.0.221018 (34419443), installed on: Mon Apr 03 12:41:37 GMT 2023, with overlays: 31222103
-Active RU/BP 39472050:Database Release Update : 19.32.0.0.260721 (39472050), installed on: Fri Oct 09 18:30:36 GMT 2026, with no overlays

***** There are 2 inactive RU patches in chain 2
-Inactive RU/BP 30805684:OJVM RELEASE UPDATE: 19.7.0.0.200414 (30805684), installed on: Wed May 13 17:13:27 GMT 2020, with no overlays
-Inactive RU/BP 34086870:OJVM RELEASE UPDATE: 19.16.0.0.220719 (34086870), installed on: Mon Apr 03 11:32:07 GMT 2023, with no overlays
-Active RU/BP 34411846:OJVM RELEASE UPDATE: 19.17.0.0.221018 (34411846), installed on: Mon Apr 03 12:37:43 GMT 2023, with no overlays

OPatch succeeded.

$ $ORACLE_HOME/OPatch/opatch util deleteinactivepatches
Oracle Interim Patch Installer version 12.2.0.1.53
Copyright (c) 2026, Oracle Corporation.  All rights reserved.

Oracle Home       : /u01/db/19c/sales
Central Inventory : /u01/app/inventory
   from           : /u01/db/19c/sales/oraInst.loc
OPatch version    : 12.2.0.1.53
OUI version       : 12.2.0.7.0
Log file location : /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_18-53-58PM_1.log

Invoking utility "deleteinactivepatches"
Inactive Patches Cleanup option provided
Delete Inactive Patches .......
***** There are 4 inactive RU patches in chain 1
***** 3 inactive patches will be deleted
-To be deleted inactive RU/BP 29517242:Database Release Update : 19.3.0.0.190416 (29517242), installed on: Thu Apr 18 07:21:17 GMT 2019, with no overlays
-To be deleted inactive RU/BP 30869156:Database Release Update : 19.7.0.0.200414 (30869156), installed on: Wed May 13 17:09:52 GMT 2020, with no overlays
-To be deleted inactive RU/BP 34133642:Database Release Update : 19.16.0.0.220719 (34133642), installed on: Mon Apr 03 11:37:24 GMT 2023, with no overlays
-To be retained inactive RU/BP 34419443:Database Release Update : 19.17.0.0.221018 (34419443), installed on: Mon Apr 03 12:41:37 GMT 2023, with overlays: 31222103
-Active RU/BP 39472050:Database Release Update : 19.32.0.0.260721 (39472050), installed on: Fri Oct 09 18:30:36 GMT 2026, with no overlays
***** There are 2 inactive RU patches in chain 2
***** 1 inactive patches will be deleted
-To be deleted inactive RU/BP 30805684:OJVM RELEASE UPDATE: 19.7.0.0.200414 (30805684), installed on: Wed May 13 17:13:27 GMT 2020, with no overlays
-To be retained inactive RU/BP 34086870:OJVM RELEASE UPDATE: 19.16.0.0.220719 (34086870), installed on: Mon Apr 03 11:32:07 GMT 2023, with no overlays
-Active RU/BP 34411846:OJVM RELEASE UPDATE: 19.17.0.0.221018 (34411846), installed on: Mon Apr 03 12:37:43 GMT 2023, with no overlays
Do you want to proceed? [y|n]
y
User Responded with: Y
Deleted RU/BP patch: 29517242
Deleted RU/BP patch: 30869156
Deleted RU/BP patch: 34133642
Deleted RU/BP patch: 30805684

OPatch succeeded.

Let’s apply the September 2026 MRP on top of the July 2026 RU. Shutdown the instances and listener. Then verify for conflicts and apply the MRP.

$ $ORACLE_HOME/OPatch/opatch prereq CheckConflictAgainstOHWithDetail -ph /home/oracle/39971265/
Oracle Interim Patch Installer version 12.2.0.1.53
Copyright (c) 2026, Oracle Corporation.  All rights reserved.
PREREQ session

Oracle Home       : /u01/db/19c/sales
Central Inventory : /u01/app/inventory
   from           : /u01/db/19c/sales/oraInst.loc
OPatch version    : 12.2.0.1.53
OUI version       : 12.2.0.7.0
Log file location : /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_19-14-19PM_1.log

Invoking prereq "checkconflictagainstohwithdetail"
Prereq "checkConflictAgainstOHWithDetail" passed.
OPatch succeeded.

$ $ORACLE_HOME/OPatch/opatch napply .
Oracle Interim Patch Installer version 12.2.0.1.53
Copyright (c) 2026, Oracle Corporation.  All rights reserved.
Oracle Home       : /u01/db/19c/sales
Central Inventory : /u01/app/inventory
   from           : /u01/db/19c/sales/oraInst.loc
OPatch version    : 12.2.0.1.53
OUI version       : 12.2.0.7.0
Log file location : /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_19-15-44PM_1.log
Verifying environment and performing prerequisite checks...
OPatch continues with these patches:   39487170  39570655  39570736  39661089  39750798  39761039  39761126  39763853  39779336  39833225  39890510  39897693  39901922  39935553  39951966

Do you want to proceed? [y|n]
y
User Responded with: Y
All checks passed.

Please shutdown Oracle instances running out of this ORACLE_HOME on the local system.
(Oracle Home = '/u01/db/19c/sales')
Is the local system ready for patching? [y|n]
y
User Responded with: Y
Backing up files...
Applying interim patch '39487170' to OH '/u01/db/19c/sales'

Patching component oracle.rdbms.rsf, 19.0.0.0.0...
Patching component oracle.xdk.rsf, 19.0.0.0.0...
Patching component oracle.rdbms, 19.0.0.0.0...
...
Patches 39487170,39570655,39570736,39661089,39750798,39761039,39761126,39763853,39779336,39833225,39890510,39897693,39901922,39935553,39951966 successfully applied.
Log file location: /u01/db/19c/sales/cfgtoollogs/opatch/opatch2026-10-09_19-15-44PM_1.log

OPatch succeeded.

Verify the logs. Start the instances and listener. Then execute a datapatch sanity and a datapatch on the DB.

$ ./datapatch
SQL Patching tool version 19.32.0.0.0 Production on Fri Oct  9 19:31:20 2026
Copyright (c) 2012, 2026, Oracle.  All rights reserved.

Log file for this invocation: /u01/app/19c/cfgtoollogs/sqlpatch/sqlpatch_22646_2026_10_09_19_31_20/sqlpatch_invocation.log

Connecting to database...OK
Gathering database info...done
Note:  Datapatch will only apply or rollback SQL fixes for PDBs
       that are in an open state, no patches will be applied to closed PDBs.
       Please refer to Note: Datapatch: Database 12c Post Patch SQL Automation
       (Doc ID 1585822.1)

Bootstrapping registry and package to current versions...done
Determining current state...done
...
SQL Patching tool complete on Fri Oct  9 19:31:44 2026

Let’s create the gold image. We can use the standard runInstaller method or we can use an automation tool like Oracle Enterprise Manager Fleet Maintenance.

For the first gold image we are going to use the runInstaller method.

$ $ORACLE_HOME/runInstaller -silent  -createGoldImage -destinationLocation /home/oracle
Launching Oracle Database Setup Wizard...

Successfully Setup Software.
Gold Image location: /home/oracle/db_home_2026-10-09_07-01-14PM.zip

After this we already have an Oracle home gold image created that we can use for later deployment. Let’s also show an automated way to create and manage the gold images using Oracle Enterprise Manager (EM).

Using the Oracle Database Lifecycle Management pack, we can easily manage gold image using the console. If you navigate to the Database targets page and click on the Administrator menu and then select Fleet Maintenance Hub, you can access the Fleet Maintenance functionality.

Then navigate to Patch Recommendations for Images.

Enterprise Manager integrates with My Oracle Support (MOS) to identify recommended and missing patches for existing Gold Images, helping DBAs maintain current, secure, and consistent software baselines. Administrators can create and manage Gold Image versions, promote approved versions, and deploy them across subscribed targets using automated, out-of-place patching workflows. This approach reduces configuration drift, simplifies patch compliance tracking, minimizes manual effort, and enables organizations to adopt a more frequent and consistent security patching cadence.

MORE info…

This covered few options of creating and maintaining Oracle database home gold images.

Thanks,
Alfredo

In-Place vs. Out-of-Place Patching: That’s The Question

Oracle patching is becoming more frequent. With quarterly Release Updates (RUs) complemented by Monthly Recommended Patches (MRPs) and CSPUs, the traditional model of patching a few times a year is becoming harder to sustain.

That makes one question increasingly important: should Oracle Database and Grid Infrastructure be patched in-place or out-of-place?

The two approaches are technically supported, but they represent very different operating models.

In-place

In-place patching modifies the existing Oracle Home directly. The primary advantage is simplicity. There is no second Oracle Home to build, no additional storage requirement, and no home-switching process. For certain environments, and particularly for supported online/hot patches, this can be a practical approach. Oracle continues to support OPatch and OPatchAuto for these scenarios.

The concern is that production software is being changed before the new state has been fully proven.

Rollback can also become more complicated as the Oracle Home accumulates RUs, MRPs, OJVM updates, merge patches, and application-specific one-offs. As an example, the MRP/CSPU is made up of individual patches that may need to be rolled back separately, followed by datapatch execution.

There is another issue: patch conflicts. When a home contains multiple one-offs and MRPs, applying the next RU can require conflict analysis and merge patch requests.

Over time, the result can be a highly customized Oracle Home that is difficult to standardize and automate.

Out-of-place

Out-of-place patching takes a different approach. The existing Oracle Home remains untouched while the new home is prepared and validated.

This creates significant advantages.

Risk isolation. A failed patch doesn’t immediately alter the production software.

Rollback. Instead of reconstructing the previous software state, the instance can be returned to the retained Oracle Home.

Standardization. The new Oracle Home can become a controlled gold image and be reused across many systems.

Oracle recommends out-of-place patching as the preferred approach for Database and Grid Infrastructure, with gold-image workflows and Database Lifecycle Management or Fleet Patching and Provisioning (FPP) recommended for RAC, Data Guard, and Exadata environments automation. There’s also Exadata Fleet Update for Oracle Cloud environments.

Throw monthly security updates in the picture

The difference becomes much larger as patch frequency increases. Oracle’s current recommendation is essentially to stay on the current RU and apply the latest available MRP/CSPU, rather than relying on a long-running n-1 strategy.

That makes the ability to repeatedly create, test, and deploy a known software image increasingly valuable.

Out-of-place patching reduces production risk by building, patching, and validating a new Oracle Database or Grid Infrastructure home before moving workloads to it, while preserving the existing home as a controlled fallback. It simplifies rollback, identifies patch conflicts before the maintenance window, and reduces downtime by completing most preparation and validation in advance.

Testing time can also be significantly reduced by creating standardized, pre-patched gold images that are qualified once and then promoted consistently across development, test, and production environments, rather than rebuilding and retesting different patch combinations for every Oracle Home.

This approach prevents the complexity that accumulates when RUs, MRPs, OJVM patches, and one-offs are repeatedly layered onto existing homes, while enabling greater automation, rolling maintenance, fleet-wide consistency, and auditability making it particularly well suited to today’s more frequent security patching cadence.

In-place can still be appropriate when:

a supported online/hot patch is available
storage is highly constrained
a legacy application requires it
or a specific technical limitation prevents an out-of-place workflow.

The key is to make those intentional exceptions, rather than making in-place the default simply because it has always been the process.

Thanks,
Alfredo

Building Oracle Database and Grid Infrastructure Images in OCI with the latest CSPU

The arrival of frontier AI models is changing an assumption that has shaped enterprise patching for decades: that defenders generally have time between publication of a vulnerability and practical exploitation.

That window is getting smaller.

Frontier AI models can accelerate vulnerability discovery, analyze software changes, reverse-engineer security patches, and help identify potential attack paths at unprecedented speed and scale. They can also combine weaknesses across different layers of an application and data stack into attacks that might not have been obvious when each vulnerability was considered independently.

Oracle is responding with a faster patch cadence and the inclusion of monthly Critical Security Patches (CSPUs). This has several implications to database administrator teams, including the need to deploy these CSPUs as soon as they are available. For customers running on Oracle Cloud Infrastructure, Oracle provides software images for both databases and Grid Infrastructure every 3 months. These Oracle provided images contain the latest RU for that quarter.

The question now is. How can I have a software image that contains the latest CSPU?

Oracle-provided versus custom software images

Oracle-provided software images contain generally available Oracle Database or Grid Infrastructure releases and updates.

Custom software images start with a supported Oracle Release Update and allow the customer to add the specific interim or one-off patches required by their environment. OCI stores these images in Oracle-managed Object Storage and makes them available as reusable resources for provisioning and updating database infrastructure. That makes custom software images particularly useful as Oracle moves toward a faster security-patching cadence.

How to create custom software images?

Let’s use Oracle Database 19c as an example.

Our starting baseline is:
Oracle Database 19c 19.32.0.0.260721 July 2026 RU

The August 2026 security release then introduces targeted fixes on top of that baseline. August DB MRP Patch for 19c on Linux is 39834034.

This will create the DB software image containing the July 2026 RU plus the August 2026 CSPU (MRP).

There is another useful method.

Suppose you’ve already created and tested a Database Home containing exactly the patch combination you want.

Run:

$ORACLE_HOME/OPatch/opatch lsinventory > /tmp/db19c_aug2026_lsinventory.txt

OCI allows this inventory file to be uploaded while creating the custom software image. OCI can use the inventory to ensure that the new image contains, at minimum, the patches already present in that Oracle Home.

You can create software images for Base Database Service and for Exadata Cloud. So, be sure you create the right one for your specific DB estate.

Below I show the images I created for both database and GI software with the August 2026 CSPU (MRP).

more here…

Thanks,
Alfredo

EM Database Job Error After Upgrade to 24ai

Today I want to talk about an error that I haven’t seen before until I upgraded my demo Enterprise Manager environment to the latest 24ai version. I went through the standard process of upgrading the OMs and then upgraded the agents. Everything went fine expect that any jobs executed against a database or a host were failing with:

Failed to ascii decode encrypted-text (status=1)

Some jobs finished correctly, however that error was still present on the job log file. I came to know by using KB656298 – Failed to ascii decode encrypted-text (status=1) After EM Upgrade that I totally forgot to execute root.sh script after the upgrade.

One thing I need to mention is that some agents worked fine and some of them I needed to restart after executing root.sh.

The KB note doesn’t mention anything about the need of restarting the agent. Next time I need to make sure I execute all the needed steps after upgrading the agents.

Thanks,
Alfredo

Patch & Protect Your Oracle Database From AI-Enabled Threats

We hear more and more about the benefits of using AI models in terms of efficiency and speed innovation for the software industry. One thing that we also started hearing last month is that new models also offer new ways to exploit security vulnerabilities by bad actors.

Oracle Database Engineering is actively working with leading AI model providers in order to continuously discover vulnerabilities using advanced AI techniques to rapidly remediate and deliver fixes via the existing database release updates.

What does this mean to you?

Patch, patch and patch TODAY!
You must apply the quarterly release update (RU) promptly, this is RU31 for 19c (19.31) or RU2 for 26ai (23.26.2). If you are not in 19c or 26ai yet you must upgrade your database as soon as possible. Also make sure your client libraries are upgraded to 19c or 26ai.

Changes are also coming on the way and frequency Oracle delivers security patches. From the Accelerating Vulnerability Detection and Response blog:

Oracle is expanding how security fixes are delivered to customers with a monthly Critical Security Patch Update (CSPU), starting in May 2026. CSPUs provide targeted fixes for critical security issues, allowing customers to address high-priority vulnerabilities without waiting for the next quarterly release. Each CSPU is smaller and more focused, making it easier to apply critical fixes quickly. Quarterly Critical Patch Updates will continue to include all fixes released in prior CSPUs.

This approach enables customers to apply critical fixes more quickly on premises, while continuing to support established quarterly patching cycles through cumulative updates. All patches are applied automatically in Oracle-managed cloud environments.

Review recent communication information

This information is now available through several channels including blog posts & MOS.

What can you do to patch easier & faster?

As Mike Dietrich mentioned is his post, we live in an interesting and challenging era that we can’t stop but we should rather be prepared. For Oracle Database Administrators this means to have all your Oracle databases upgraded and patched to either 19.31 or 23.26.2. Patch NOW!

To make this process easier use automation tools like Oracle AutoUpgrade, FPP or Oracle Database Lifecycle Management in OEM.

If you have additional questions contact your Oracle Sales representative or your Oracle Architect.

UPDATE (May 5th):

Integrated Cyber Center issued a note on the upcoming CSPUs.

Beginning May 28, 2026, Oracle will deliver a Critical Security Patch Update (CSPU) each month. CSPUs provide targeted fixes for critical vulnerabilities in a smaller, more focused format, allowing customers to address high-priority issues without waiting for the next quarterly release.

Support has also issued Product-Specific guidance notes. I recommend to take a look at the general note as there are links to several specific products:

Protect Oracle Products from AI Security Threats ALERT28

Thanks,
Alfredo

From AWR to Answers: Building a Local RAG Assistant with Ollama and Oracle AI Database 26ai

Oracle’s database instrumentation generates a huge amount of performance telemetry and AWR reports can still feel like a wall of text when you just want clear, targeted answers. In this post, I show how I turned a 24‑hour AWR report into an interactive performance tuning assistant using a local LLM (via Ollama on Windows 11) and Oracle AI Database 26ai running inside a VirtualBox VM.

This experiment was inspired by Lorenzo De Marchis’ “From theory to practice: building a RAG pipeline with LangChain and Oracle AI Database 26ai in Python,” where Oracle AI Database 26ai acts as the vector store behind a RAG pipeline. I kept the same core idea—Oracle as the vector database, Python as the glue—and code but adapted it to a hybrid lab setup with Ollama, VirtualBox, and an AWR‑specific use case. But also inspired by several customers that during last year’s Oracle AI World mentioned they are uploading AWR reports to online AI LLM’s to ask questions about them.

lorenzo’s post

Rather than manually scrolling through an AWR HTML report, I can now ask questions like “Summarize the top performance issues in this 24‑hour period” and let the assistant answer using only the information embedded from that report.

Architecture overview

At a high level, the architecture combines a Windows host, a VirtualBox VM, and Oracle 26ai’s native vector search.

Windows 11 gaming PC
Runs Ollama as the local inference server. I chose this PC because I have an NVIDIA RTX3060 GPU and may retrieve results faster.
VirtualBox VM running Oracle Linux 9
Hosts Oracle AI Database 26ai, which stores the AWR report, embedding vectors, and metadata in regular tables plus a vector index and hosts the Python application that orchestrates retrieval and generation.
Python RAG layer
Connects to Oracle AI Database 26ai via the Python LangChain driver, performs similarity search using SQL over the vector column, and calls Ollama’s HTTP API to generate grounded answers.

Conceptually, the design mirrors Lorenzo’s rag.py module: the database is responsible for similarity search, and the language model is responsible for natural‑language generation. This keeps data, embeddings, and retrieval inside Oracle, while the LLM remains fully local on your own hardware.

This is very important if you want to prevent your data from leaving your premises or avoid token fees.

Why AWR plus a local RAG assistant?

AWR reports are extremely rich: they summarize instance statistics, wait events, time model data, and “Top SQL” sections between two snapshot IDs. They are necessary for serious performance diagnostics, but they are not always friendly to read under time pressure.

A local RAG assistant addresses three pain points:

Navigating complexity: Instead of remembering exactly which section covers which metric, you can ask natural questions such as “Which wait events dominate DB time?” or “Do we have CPU or I/O bottlenecks?” and let retrieval pick the right section.
Keeping data on‑premises: AWR content is operationally sensitive. By running Oracle AI Database 26ai in a VirtualBox VM and Ollama on a local PC, you keep all data and prompts within your controlled environment.
Experimenting with models: Ollama makes it easy to try different LLMs (Llama 3, Mistral, etc.) against the same AWR corpus by changing a model name, without rebuilding your pipeline.

The result is not meant to replace expert tuning but to act as a fast‑feedback assistant that helps you spot patterns and narrow down questions more quickly.

Preparing the AWR data

Generating a 24‑hour AWR report

First, I collected the baseline AWR report from the test database. Oracle documents the standard scripts for generating AWR reports, such as awrgrpt.sql and awrrpt.sql, which can output HTML or text. However, I’m using the DBMS_WORKLOAD_REPOSITORY package to generate the report in text format.

From SQL*Plus on the test database:

SQL> SELECT * FROM TABLE(DBMS_WORKLOAD_REPOSITORY.awr_report_text(db_id, inst_id, start_snap, end_snap));

This produces a text output that I can easily spool into a file like awr_123_145.txt that contains all the usual sections (Instance Efficiency, Load Profile, Top Timed Events, SQL ordered by Elapsed Time, and so on).

I then copy this TXT file into a working directory accessible from the machine where I run the Python ingestion script (in my case, the Linux host where I also run Oracle and the Python RAG code).

Ingesting and chunking the AWR report with Python and LangChain

Next, we use Lorenzo’s Python ingest.py to:

Parse the AWR text file.
Split it into meaningful chunks.
Generate embeddings for each chunk.
Insert the chunks and embeddings into the Oracle AI Database using the Oracle Python driver.

I also needed to modify the ingest.py file because Ollama LLM is hosted in a deifferent machine. So, I installed Ollama locally in the Linux host and then modified the configuration setting the OLLAMA_HOST variable in order to connect to my AIServer hosts (Windows 11).

OLLAMA_HOST=http://AIServer:11434

In a production‑quality version, you would parse the report header to extract snapshot timestamps and normalize them into TIMESTAMP columns. You might also refine the chunking strategy to align with specific AWR sections such as “SQL ordered by Elapsed Time” or “Top Timed Events” for more precise retrieval.

Building the retrieval query in Oracle AI Database 26ai

At query time, I use Lorenzo’s rag.py script. I just pass the prompt and receive the output from the LLM using RAG augmentation from the Oracle database.

$ python3.11 -m app.rag "Summarize the main performance issues highlighted in this AWR"

Example questions for the AWR assistant

Once the pipeline is in place, you can treat the AWR report as a conversational knowledge base. Here are some practical queries that work well:

“Summarize the main performance issues highlighted in this AWR.”
“Which wait events contributed most to DB time, and during which hours were they highest?”
“List the top 5 SQL statements by elapsed time and explain in plain language what they are doing.”
“Does the report suggest CPU bottlenecks, I/O bottlenecks, or something else?”
“Based on this report, what are three concrete tuning actions I should consider first?”

A typical interaction might look like this (paraphrased):

Question: “What are the top three tuning actions suggested by this AWR?”
Assistant: “The top three tuning actions suggested by this AWR are:
Investigate the cause for high “Disk file I/O Calibration” waits.
Investigate the cause for high “Disk file I/O Calibration” waits in Module “DBMS_SCHEDULER”.
Investigate the cause for high “Disk file I/O Calibration” waits in Service “SYS$USERS”.”

Because the assistant is restricted to the retrieved chunks, its answers remain grounded in actual AWR sections rather than hallucinated generic advice.

Here’s the final prompt after tuning it several times.

You are an expert Oracle Database Administrator with deep expertise in performance analysis and AWR report interpretation. Your task is to thoroughly analyze the provided AWR report and deliver a comprehensive performance analysis.

ANALYSIS REQUIREMENTS:

TOP EVENTS IDENTIFICATION:

Extract and rank the top 10 wait events from the AWR report
Identify the top 5 SQL statements by elapsed time and CPU time
Highlight any significant changes in system metrics compared to previous snapshots

ROOT CAUSE ANALYSIS:

For each top event, explain:
- What type of wait event it is (CPU, I/O, Latch, Lock, etc.)
- Why it’s occurring (resource contention, inefficient queries, configuration issues)
The impact on database performance
Analyze the correlation between events and SQL statements

DETAILED RECOMMENDATIONS:

Provide specific, actionable steps to resolve each issue
Include SQL optimization suggestions for top SQL statements
Recommend database configuration changes if needed
Suggest hardware or infrastructure improvements if applicable
Include monitoring recommendations to prevent recurrence

REPORT STRUCTURE:

Executive summary of key findings
Detailed event analysis with root cause explanation
Prioritized remediation steps
Performance improvement estimates
Follow-up actions and monitoring requirements

OUTPUT FORMAT:

Clear, technical language appropriate for DBA audience
Include specific Oracle commands where applicable
Provide justification for each recommendation
Highlight critical issues requiring immediate attention

INPUT: AWR data is already ingested. Use RAG.

Provide a comprehensive analysis that enables the DBA to quickly identify, understand, and resolve performance bottlenecks.

**Performance Analysis Report**

**Top Events Identification**

1. Top 10 wait events:
        * db file sequential read (14.34%)
        * db file scattered read (12.55%)
        * latch: cache buffers chains (8.42%)
        * latch: enqueue (5.67%)
        * CPU time (4.22%)
        * latch: shared pool (3.91%)
        * wait on library cache (3.56%)
        * enq: TX - row lock contention (2.95%)
        * latch: redo allocation (2.73%)
        * log file sync (2.46%)
2. Top 5 SQL statements by elapsed time:
        * SQL_ID "r6swge3rhwsa3" (96% of database time)
        * SQL_ID "ws4dr3ag24rfg" (100% of database time)
        * SQL_ID "5tm1ruvkx3wsx" (4% of database time)
        * SQL_ID "tf4dus7dsfgh" (2% of database time)
        * SQL_ID "gv3dsh4jklmn" (1% of database time)
3. Significant changes in system metrics:
        * Total database time was 7 seconds.
        * Average number of active sessions was 0.

**Root Cause Analysis**

1. db file sequential read (14.34%):
        * This wait event indicates that the database is spending a significant amount of time waiting for sequential reads from disk.
        * Possible causes:
                + Inefficient queries or indexes.
                + Insufficient disk space or slow disk performance.
                + Configuration issues (e.g., inadequate block size).
        * Impact on performance: High wait times can lead to increased response times and decreased throughput.
2. db file scattered read (12.55%):
        * This wait event indicates that the database is spending a significant amount of time waiting for scattered reads from disk.
        * Possible causes:
                + Inefficient queries or indexes.
                + Insufficient disk space or slow disk performance.
                + Configuration issues (e.g., inadequate block size).
        * Impact on performance: High wait times can lead to increased response times and decreased throughput.
3. latch: cache buffers chains (8.42%):
        * This wait event indicates that the database is spending a significant amount of time waiting for latches on the cache buffers chains.
        * Possible causes:
                + Insufficient cache size or inadequate memory allocation.
                + Inefficient queries or indexes.
                + Configuration issues (e.g., inadequate latch size).
        * Impact on performance: High wait times can lead to increased response times and decreased throughput.
4. latch: enqueue (5.67%):
        * This wait event indicates that the database is spending a significant amount of time waiting for latches on the enqueue structure.
        * Possible causes:
                + Insufficient enqueue size or inadequate memory allocation.
                + Inefficient queries or indexes.
                + Configuration issues (e.g., inadequate latch size).
        * Impact on performance: High wait times can lead to increased response times and decreased throughput.

**Detailed Recommendations**

1. Investigate the PL/SQL statement with SQL_ID "r6swge3rhwsa3" for possible performance improvements.
        * Supplement the information given here with an ASH report for this SQL_ID.
2. Optimize the queries and indexes related to the SQL statements with SQL_ID "r6swge3rhwsa3" and "ws4dr3ag24rfg".
        * Use the SQL Tuning Advisor to identify and optimize these queries.
3. Review and adjust the database configuration to ensure adequate cache size, memory allocation, and latch size.
4. Consider adding more disk space or upgrading the disk performance to reduce wait times for sequential and scattered reads.
5. Review and adjust the database configuration to ensure adequate enqueue size and memory allocation.

**Monitoring Recommendations**

1. Monitor the wait times and latch waits for the identified SQL statements and queries.
2. Monitor the cache size, memory allocation, and latch size to ensure they are adequate.
3. Monitor the disk space and performance to ensure they are sufficient.

**Performance Improvement Estimates**

1. Estimated benefit of optimizing the PL/SQL statement with SQL_ID "r6swge3rhwsa3" is 0.02 active sessions and 15.56% of total activity.

**Follow-up Actions and Monitoring Requirements**

1. Schedule regular monitoring and analysis of the identified SQL statements and queries.
2. Review and adjust the database configuration as needed to ensure adequate resources.
3. Consider adding more disk space or upgrading the disk performance to reduce wait times for sequential and scattered reads.

Note: This report is a summary of the top events and recommendations. For a detailed analysis, please refer to the original AWR report and ADDM report.

Thanks,
Alfredo

OEM Incidents and AI Vector Search

In this post I want to provide a practical use of the AI Vector search functionality in the Oracle database. The use case is to be able to use AI Vector search on data that Oracle Enterprise Manager (EM) produces. In this case incidents data. The idea is to be able to search incidents using vectors in Oracle.

But what is Vector search and why we want to use it?
Vector search is a feature that enables searching data by semantics or meanings and values. We want to use it in combination with an LLM in order to ask questions about our data. What kind of data? In this case EM’s incident data.

more here…

There are several challenges that we’ll have to work on in order to be able to use AI Vector search. The first challenge is that AI Vector search is a feature of the 26AI (former 23AI) database. Oracle EM doesn’t support 26AI as a repository database. Oracle EM 24AI only supports 19.22 as a repository database as of today. So the question is, how we can me this work?

We will have to use an intermediate Oracle database for the AI Vector functionality. I decided to use the 26AI free version of the Oracle database. You can download a VirtualBox appliance image and run it in your personal computer. There are several limitations of the 26AI free version but it works for my use case.

download here…

Now that I have the 26AI appliance and database running, I need to bring the Oracle EM Incident data over. There are several ways to extract the data:

Connect through SQL Plus to the repository database
Use REST APIs
Use EMCLI

I decided to use REST APIs as it gives me the data in JSON format. Then I can format the in any shape or form needed.

Oracle EM exposes several REST APIs so we can consume data on external systems. There’s a whole framework behind these REST APIs.

more here…

We are going to call below REST API from the host where the 26AI free database is running.

$ curl -X GET -u sysman:<password> -H "Content-Type:application/json"  https://EM_HOST:EM_CONSOLE_HTTPS_PORT/em/api/incidents --insecure

This produces a JSON output with the first set of all open incidents with the most recently created incidents listed first.

I can create a Shell script that executes this CURL command, obtains the data in JSON format and then executes jq in order to convert it into CSV format. It will look similar to this:

#!/bin/bash

# Fetch JSON data using curl

JSON_DATA=$(curl -X GET -u sysman:<password> -H "Content-Type:application/json"  https://EM_HOST:EM_CONSOLE_HTTPS_PORT/em/api/incidents --insecure)

# Define the jq filter to extract and format data into CSV
# @csv: Convert the array into a CSV-formatted string
JQ_FILTER='.[].results | [.id, .displayId, .message, .severity, .status, .isOpen, .timeCreated, .timeUpdated, .target_names] | @csv' # Example filter

# Extract data and format as CSV using jq
CSV_CONTENT=$(echo "$JSON_DATA" | jq -r "$JQ_FILTER")

# Define CSV header (adjust to match your selected fields)
CSV_HEADER="id,displayId,message,severity, status, isOpen, timeCreated, timeUpdated, target_names"

# Output to a CSV file
echo "$CSV_HEADER" > incidents.csv
echo "$CSV_CONTENT" >> incidents.csv

echo "CSV data saved to incidents.csv"

We need now to insert this data into the 26AI free database and create the Vectors. Before we do that we need to find a way to generate the vectors from our incident data. For that we will use a model provided by Oracle named all-MiniLM-L12-v2.

Let’s download and load the model into our 26AI free database.

$ mkdir -p /home/oracle/onnx
$ cd /home/oracle/onnx

$ wget https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/VBRD9P8ZFWkKvnfhrWxkpPe8K03-JIoM5h_8EJyJcpE80c108fuUjg7R5L5O7mMZ/n/adwc4pm/b/OML-Resources/o/all_MiniLM_L12_v2_augmented.zip

$ unzip -oq all_MiniLM_L12_v2_augmented.zip
$ sqlplus sys as sysdba

SQL> create user em_inc identified by password quota unlimited on users;
SQL> grant create session, db_developer_role, create mining model to em_inc;

SQL> create or replace directory model_onnx as '/home/oracle/onnx';
SQL> grant read, write on directory model_onnx to em_inc;
SQL> begin
  dbms_vector.drop_onnx_model (
    model_name => 'ALL_MINILM_L12_V2',
    force => true);

  dbms_vector.load_onnx_model (
    directory  => 'model_onnx',
    file_name  => 'all_MiniLM_L12_v2.onnx',
    model_name => 'ALL_MINILM_L12_V2');
end;
/
SQL> select model_name, algorithm, mining_function from   user_mining_models where  model_name = 'ALL_MINILM_L12_V2';

MODEL_NAME                     ALGORITHM  MINING_FUNCTION
------------------------------ ---------- ---------------
ALL_MINILM_L12_V2              ONNX       EMBEDDING

At this point I can generate the Vector embeddings for the incident data.

more here…

We will now import our incident data from the CSV file and create the vector.

SQL> conn em_inc/password
SQL> create table incidents as
select id, displayId, message, severity, status, isOpen, timeCreated, timeUpdated, target_names
from   external (
         (
           id  varchar2(40),
           displayId        number(6),
           message   varchar2(500),
           severity   varchar2(100),
           status  varchar(400),
           isOpen  varchar(50),
           timeCreated varchar(400),
           timeUpdated varchar(400),
           target_names varchar(1500)
         )
         type oracle_loader
         default directory model_dir
         access parameters (
           records delimited by newline
           skip 1
           badfile model_dir
           logfile model_dir:'incidents_ext_tab_%a_%p.log'
           discardfile model_dir
           fields csv with embedded terminated by ',' optionally enclosed by '"'
           missing field values are null
        )
        location ('incidents.csv')
        reject limit unlimited
      );

SQL> alter table incidents add (
  incidents_vector vector
);

SQL> update incidents
set incidents_vector = vector_embedding(all_minilm_l12_v2 using message as data);

SQL> commit;

Let’s now perform a Vector similarity search using the VECTOR_DISTANCE function. This first query will do a search on “Incidents related to agents” query Vector.

SQL> SELECT vector_distance(incidents_vector, (vector_embedding(all_minilm_l12_v2 using 'Incidents related to agents' as data)), EUCLIDEAN) as distance,
       message
FROM   incidents
order by distance
FETCH EXACT FIRST 10 ROWS ONLY;

DISTANCE	MESSAGE
0.98592836033379427	Process em_agent crashed unexpectedly.
0.98592836033379427	Process em_agent crashed unexpectedly.
0.98592836033379427	Process em_agent crashed unexpectedly.
1.08642161802020090	Number of Active Agents crossed the critical threshold (1). Current value: 0
1.09352931649992050	Agent Unreachable (REASON = Unable to connect to the agent at https://host:3872/emd/main/ [Connection refused]). Host is reachable.
1.09352931649992050	Agent Unreachable (REASON = Unable to connect to the agent at https://host:3872/emd/main/ [Connection refused]). Host is reachable.
1.09352931649992050	Agent Unreachable (REASON = Unable to connect to the agent at https://host:3872/emd/main/ [Connection refused]). Host is reachable.
1.23613168789133000	Process ora_pmon crashed unexpectedly.
1.23613168789133000	Process ora_pmon crashed unexpectedly.

Let’s analyze these results. The shortest distance in the Similarity Vector search is for the message containing the same exact word “agent”, the second closest distance is for messages having the word “Agent” and the third one is for messages not having the word “a(A)gent”.

more here…

With this functionality we can perform Similarity Vector searches on our EM incident data.

Thanks,
Alfredo

Oracle EM Agent Preferred Connect Descriptor

The USE_SID_AS_SERVICE_LISTENER=ON saga

If you are migrating to multi-tenant architecture of the Oracle database, you’ll find out that in order to connect to the pluggable database (PDB) you must use the service name for the PDB. But, what if you have a legacy application that can only use SID instead of the service name?

For that, you can enable the parameter USE_SID_AS_SERVICE_LISTENER in the listener configuration, so the service name will act as an SID.

Problem solved, right? Well, the problem is now with applications that require the service name and not the SID. One of them is Oracle Enterprise Manager (EM). If you turn on this parameter at the listener level then, you won’t be able to connect to the databases and they will show down (red) in the EM console.

So, what can we do? In EM 13.5 RU16 or higher there’s functionality that allows EM to connect to the database by using an Agent preferred connect descriptor. This way, you can provide the connect descriptor using the service name.

One important note to mention is that you must have RU16 or higher applied to both, the OMS and the Agents in order to enable this functionality.

Follow the My Oracle Support Note “EM 13c: How To Enable Agent Preferred Connect Descriptor Support For RAC Instance, Single Instance DB (Doc ID 2963079.1) ” to enable this functionality. You will have to restart your OMS, so be prepared.

After you enable this setting, navigate to the Monitoring Configuration of your target and look for the new Agent preferred descriptor section. It looks like the image below.

Add the proper connect descriptor and viola! EM is now able to connect to your target.

Is always recommended to turn the USE_SID_AS_SERVICE_LISTENER parameter off after migrations and upgrades. Sometimes this is not an option if you have legacy applications though. This feature allows you connect your EM when the parameter USE_SID_AS_SERVICE_LISTENER must be turned on.

Thanks,
Alfredo

Monitor Oracle DBs in RDS With Enterprise Manager

In this blog I want to provide some important steps if you want to monitor Oracle databases running on AWS RDS.

The first step is to have an Oracle Enterprise Manager (EM) installation. This can be on-prem or running on EC2. If you need to setup EM in EC2 you can use one of the pre-built images that already contains an Oracle database. You are going to need this database for the EM repository.
One quick example can be the Oracle database 19c image by cloudimg running on Oracle Linux. Just make sure you add enough space in the filesystem so you can also install the EM software.

Once the EC2 instance is up and running, go ahead and patch the Oracle database with the latest RU (RU28 as of today).

Download the Oracle EM 13.5 software from oracle.com and also download the latest RU available (RU27 as of today).

Install EM 13.5 and patch it with RU27. Follow below videos if you need additional guidance on how to install and patch EM.

Install EM…

Patch em …

Next we are going to follow the instructions that RDS has available for the EM agent installation. Remember that because there’s no actual access to the RDS host, we can’t install the EM agent using the traditional way.

click here…

Verify all the network pre-reqs so your RDS instance can actually reach your EM installation running on EC2. If you EM is running on-prem then a VPN setup will be required.

Now is time to create an option group once all the network pre-reqs are met. The option group is how we actually ask the RDS console to provide us the option to deploy an EM agent. The option for the option group needs to be OEM_AGENT.

Before you deploy the option group to your RDS instance, you will have to provide information about your EM instance, including:

EM Host Name
EM upload port
EM registration key

Then deploy the option group to your RDS instance.

Once the deployment finishes, you will actually have the EM agent up and running in your RDS instance and showing up in the EM console.

The next step is to discover your DB instance and the listener. Follow below link for the documentation.

click here…

Hope this set of steps help you with the monitoring setup of your RDS database with Oracle Enterprise Manager.

Thanks,
Alfredo

Upgrade Enterprise Manager to 24ai

In this post I want to share my experience while executing the upgrade of Oracle Enterprise Manager from 13.5 to 24ai.

As always. Please go ahead and read the Oracle documentation on how to upgrade EM to 24ai.

click here…

My EM demo environment was running EM 13.5 RU21 and the EM repository database 19c RU18. As per of the EM pre-reqs you need to be at least on EM13.5 RU22 and the DB needs to have the latest RU available.

So before even trying to upgrade, I needed to patch my EM. I decided to skip the patch but the UI is really good at reminding it.

So, I patched my EM to RU25 and the EM repository DB to RU26.

Let me execute the 24ai installer and follow the instructions.

Here I decided to upgrade my 13.5 environment instead of creating a new one.
There are 2 options though. Upgrade end-to-end or just install the binaries and proceed with the upgrade later.

NOTE: I chose end-to-end first, but ended going back and selecting the second option. The wizard gives a failure in the pre-reqs as it knows that RU 1 is already available for 24ai and you must install it before attempting the upgrade. I’ll share a MOS note on how to deploy it in subsequent steps.

I skipped the software updates.

Then the UI ran the pre-checks.

The pre-checks failed due the fact that 24ai RU1 needed to be applied.

I had to use below MOS note in order to apply RU1 with the bitonly option.

13.5: How to Apply Release Update on the OMS During the Install/Upgrade (Doc ID 2810169.1)

Select your new 24ai home location.

Type the EM repository connection details.

I performed to upgrade using the SYS DB account.

I had an issue with Plug-ins. My environment had 2 plug-ins deployed that are no longer supported in 24ai. I had to use EMCLI commands to un-deploy them.

click here…

After un-deploying the, I was able to continue.