How to add a redo log member to an existing redo log group in an Oracle database | Redo log files Multiplexed

Add a redo log member to an existing redo log group in an Oracle database

SQL> select group#,status,member from v$logfile;

    GROUP# STATUS

---------- -------

MEMBER

--------------------------------------------------------------------------------

         3

/u01/app/oracle/oradata/DB1/redo03.log

         2

/u01/app/oracle/oradata/DB1/redo02.log

         1

/u01/app/oracle/oradata/DB1/redo01.log

SQL>

SQL> select group#,members,archived,status from v$log;

    GROUP#    MEMBERS ARC STATUS

---------- ---------- --- ----------------

         1          1 NO  CURRENT

         2          1 YES INACTIVE

         3          1 YES INACTIVE

SQL>

SQL> alter database add logfile member '/u01/app/oracle/oradata/DB1/redo01b.log' to group 1;

Database altered.

SQL> select member from v$logfile;

MEMBER

--------------------------------------------------------------------------------

/u01/app/oracle/oradata/DB1/redo03.log

/u01/app/oracle/oradata/DB1/redo02.log

/u01/app/oracle/oradata/DB1/redo01.log

/u01/app/oracle/oradata/DB1/redo01b.log

SQL> 

Best Practices & Notes

Always add members on different disks for redundancy.

The new log file must not exist already on disk.

All members of a group are written to simultaneously — they are identical.

Adding a member does not affect the database availability.

Two key features of Oracle Exadata Machine

Smart Scan (Exadata Smart Storage):

Offloads query processing to the storage layer, reducing the amount of data sent to the database servers.

Enables operations like filtering, column projection, and join processing to be performed directly on the storage servers, improving query performance significantly.

Hybrid Columnar Compression (HCC):

Provides advanced data compression techniques to reduce storage requirements and improve I/O performance.

Optimized for both OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) workloads, enabling faster query execution and efficient storage utilization.

Steps for Onboarding Oracle Database to Azure

Assessment and Planning:

Evaluate the Oracle database version, size, and features.

Identify the target Azure service (e.g., Azure Virtual Machines, Azure Database for PostgreSQL if migrating to an open-source database, or Azure SQL Managed Instance for re-platforming).

Use tools like:

Azure Migrate: For assessing workloads.

Oracle SQL Developer Migration Workbench: For schema and data migration.

Data Migration Assistant (DMA): For compatibility checks if re-platforming.

Choose the Deployment Model:

Decide whether to run Oracle on:

Azure Virtual Machines: For lift-and-shift migrations, running Oracle Database on Azure VMs.

Azure Database Services: If re-platforming to a different database engine.

Provision the Target Environment:

Set up the required Azure infrastructure, such as virtual networks, storage, and compute resources.

Migrate the Database:

Use tools like Oracle Data Pump, RMAN, or third-party tools for data migration.

What is Agentic AI ?

Agentic AI 

Agentic AI refers to artificial intelligence systems that exhibit a degree of autonomy, decision-making, and goal-directed behavior. These systems are designed to act as "agents" that can perceive their environment, make decisions based on their programming or learned knowledge, and take actions to achieve specific objectives.

Key Characteristics of Agentic AI:

Autonomy:

Operates independently without requiring constant human intervention.

Can make decisions and execute tasks based on its understanding of the environment.

Goal-Oriented Behavior:

Designed to achieve specific goals or objectives.

Uses algorithms to determine the best course of action to meet those goals.

Perception and Adaptation:

Can perceive its environment through sensors or data inputs.

Adapts its behavior based on changes in the environment or feedback.

Action Execution:

Takes actions in the real or virtual world to influence outcomes.

Examples include controlling robots, automating workflows, or making recommendations.

Examples of Agentic AI:

Autonomous Vehicles:

Self-driving cars that perceive their surroundings, make driving decisions, and navigate to destinations.

Virtual Assistants:

AI systems like Siri, Alexa, or Google Assistant that perform tasks based on user commands and context.

Robotics:

Robots in manufacturing or healthcare that perform tasks autonomously, such as assembling products or assisting patients.

Game AI:

Non-player characters (NPCs) in video games that act autonomously to challenge or assist players.

AI in Finance:

Trading bots that analyze market data and execute trades autonomously based on predefined strategies.

Challenges of Agentic AI:

Ethical Concerns:

Ensuring that autonomous decisions align with ethical and societal norms.

Safety:

Preventing unintended actions that could cause harm.

Accountability:

Determining responsibility for decisions made by autonomous systems.

Complexity:

Designing systems that can handle unpredictable environments effectively.