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Oracle Data Guard 26ai adds JSON output support to DGMGRL.
Previously, you needed Linux tools like sed, awk, or grep to extract information from DGMGRL command-line output.
Now, you can display DGMGRL results as JSON, making them easy to parse with standard libraries or tools such as jq.
While you can programmatically interact with the broker using PL/SQL or REST, JSON output modernizes existing scripts and simplifies automation.

Enable JSON output by running DGMGRL in batch mode with the -json argument, or toggle it on and off in interactive mode with set json on and set json off.

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Before 26ai, updating properties or parameters across many members in a Data Guard configuration was a multi-step process. You had to run a separate command for each member, changing the DB unique name in each of them, and issuing the command for every one. Similarly, verification required running “show database” for each member.

Before 26ai:

Here’s the documentation for the new commands:

• EDIT ALL MEMBERS RESET (Parameter)
• EDIT ALL MEMBERS RESET (Property)
• EDIT ALL MEMBERS SET (Parameter)
• EDIT ALL MEMBERS SET (Property)
• SHOW ALL MEMBERS (Parameter)
• SHOW ALL MEMBERS (Property)

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Beyond SQLcl, DGMGRL, and the PL/SQL API, another key tool for managing Data Guard is Oracle REST Data Services (ORDS).

If your primary databases are set up with ORDS and accessible via REST (HTTPS), you can use REST calls to manage the Data Guard broker configuration. You can create configurations, add standby databases, modify properties, and more, all through standard REST requests.

Introduced in database version 21c and available with ORDS 21.4, this feature lets you easily automate Data Guard management within CI/CD pipelines or any tool that supports REST calls. ORDS also adds flexibility: by exposing only an HTTPS endpoint, you can manage Data Guard without opening SQL*Net ports, which may be preferable for security in some environments.

The REST API documentation for Data Guard is available here: https://docs.oracle.com/en/database/oracle/oracle-rest-data-services/25.4/orrst/api-data-guard.html

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The PL/SQL APIs published for general use in 26ai have also powered SQLcl’s command line for some time. SQLcl has supported Data Guard commands since version 21c. It’s another nice addition that you get when upgrading from 19c.

SQLcl is a robust Java-based command line tool that excels at running SQL and can also (incomplete list):

• launch Data Pump jobs
• retrieve metadata
• run Liquibase commands
• manage projects
• operate as an MCP server for natural language queries
• and now, execute Data Guard commands

To run a Data Guard command, simply prefix a standard DGMGRL command with “DG ” in your SQLcl session.

While not every Data Guard feature is available—some validation commands are missing—core management commands work without issue. The real advantage is convenience: you can use both SQL and Data Guard commands from the same interface, no need to switch tools.

Looking ahead, we plan to add more features, and SQLcl will eventually become our recommended tool for Data Guard. Today, you can switch between SQLcl and DGMGRL as needed. Note: Observer functionality remains unique to DGMGRL; SQLcl does not yet support it.

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Most people don’t know that, until recently, Data Guard broker relied on internal C routines to execute operations inside the database.

Now, more and more of these routines have moved into “fixed packages”: PL/SQL packages embedded directly in the Oracle binary. These packages are accessible even when databases are mounted (but not necessarily open).

The broker now uses these fixed packages, and so do other components outside the “dgmgrl” tool. With Oracle 26ai, we are exposing these internal PL/SQL functions through the wrapper package DBMS_DG.
Starting with Oracle 26ai, you can use over 30 PL/SQL functions (all documented in the Data Guard Broker Administration Guide) to programmatically configure and manage Data Guard broker configurations.

This change makes automation more robust: instead of shell commands like AWK or grep, you can rely on programmatic PL/SQL APIs. This should simplify integration with automation tools and CI/CD pipelines.

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I’ve previously blogged about how in 26ai you can automatically prepare the primary database using DGMGRL’s command “PREPARE DATABASE FOR DATA GUARD“; you can find the link here for more details.

Many Oracle ACEs and bloggers also blogged about it:

• https://oracle-base.com/articles/21c/data-guard-prepare-database-for-data-guard-21c
• https://www.dbi-services.com/blog/oracle-20c-the-new-prepare-database-for-data-guard/
• https://gavinsoorma.com.au/knowledge-base/prepare-database-for-data-guard/

As of version 23.26.0 (released October ’25), the “PREPARE DATABASE FOR DATA GUARD” command supports both primary and standby databases. While you’ll still need to create your standby database manually, the command now recognizes the role of the database and automatically sets recommended parameters, creates required components, and enforces the necessary settings for joining a Data Guard configuration.

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With a Data Guard asynchronous configuration, there is usually one asynchronous process that handles redo for multiple standby databases. If one or more standby databases are much slower at receiving redo than the others, Data Guard can automatically create extra asynchronous processes for the slower ones.

Before version 26ai, each asynchronous process could open only one connection for each standby database. This limited throughput when network setups restricted bandwidth per connection.

Starting in 26ai, an asynchronous process can notice when a connection is using all its available bandwidth and then open extra connections for better throughput and less lag. This change helps especially in cloud environments, where each connection often has its own bandwidth limit. On the receiving side, there is one RFS process for every connection.

You do not need to change any configuration to get this benefit. Just upgrade to 26ai and the improvement automatically works.

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Just like observer priorities, this is another cool feature first introduced in Oracle 21c.

If your Fast-Start Failover setup doesn’t have a third site for the observer, the usual advice is to place it with your primary database. But if you want true high availability, making sure the observer is always up, you’ll want two observers on the primary site and two on the secondary site (for when the standby takes over as primary).

Before 21c, you could only use up to three observers, which made this setup impossible.

Starting with 21c (and, of course, 26ai), you can now configure four observers. That solves the high-availability challenge when you’ve only got two sites.

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Technically, this is a 21c feature, but it’s worth calling out some of those 21c improvements, because most customers are still running on 19c or earlier. That means when you upgrade to 26ai, you’ll pick up all the 21c goodies too!

Here’s one: Fast-Start Failover observer’s priority. In 19c, you could list preferred observers for each possible primary with the property PreferredObserverHosts , but you couldn’t actually assign an observer priority based, for example, to the observer’s location.

21c fixes that. Now, you can give each observer a priority by adding a colon and a number right after the hostname. The lower the number, the higher the priority. This lets you spell out exactly which observers should be chosen first if a promotion is needed.

The diagram below shows an example: the external site’s observer is set as the top pick for both databases at the primary and the secondary sites, with each site’s local observer as the backup (which is our recommendation in this case).

You can create that setup now—thanks to observer priorities.