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In this post we will discuss about new AI features and its usage in oracle database 23AI.
Oracle Database 23AI brings revolutionary AI-driven features that ease database administration, enhance performance, and boost security. These advancements enable DBAs to automate mundane tasks, optimize workloads, and anticipate problems before they occur.
Let's delve into the main new DBA features in Oracle Database 23AI with practical use cases.
1. AI-Driven Autonomous Health Framework (AHF) Enhancements
Oracle 23AI enhances its Autonomous Health Framework (AHF) with AI-based diagnostics and remediation.
Key Features:
✅ Automatic Anomaly Detection – AI detects abnormal performance patterns (e.g., spike in CPU usage, I/O bottlenecks).
✅ Self-Healing Recommendations – Recommends corrective actions for general problems (e.g., SQL tuning, memory tuning).
✅ Predictive Failure Analysis – Alerts on upcoming storage/network failure before it actually happens.
Example:
-- Look at AI-predicted health insights
SELECT * FROM V$AUTONOMIC_HEALTH_ADVISOR;
Use Case:
Proactively alert and fix performance degradation.
Lower manual troubleshooting effort.
2. Autonomous Indexing with AI (Auto-Indexing)
Oracle 23AI brings AI-driven automatic indexing, where the database automatically identifies and creates the best indexes without DBA intervention.
Highlights:
✅ Ongoing Index Monitoring – AI monitors query patterns and recommends new indexes.
✅ Automatic Creation/Dropping of Indexes – Creates or drops indexes as the workload changes.
✅ Protective Rollback Mechanism – If a new index causes performance to decrease, it is rolled back automatically.
Illustration:
-- Turn on Auto-Indexing
ALTER SYSTEM SET autonomous_indexing = ON;
-- See AI-optimized indexes
SELECT index_name, table_name, auto_created FROM dba_indexes WHERE auto_created = 'YES';
Use Case:
Enhance query performance without hand-tuning.
Save storage space from unnecessary indexes.
3. AI-Guided SQL Tuning Advisor
Oracle 23AI now employs machine learning to suggest improved execution plans through the SQL Tuning Advisor.
Key Features:
✅ Adaptive SQL Plan Management – AI dynamically adapts execution plans.
✅ Automated SQL Profile Generation – Creates optimized SQL profiles without DBA intervention.
✅ Real-Time Performance Feedback – Learns from query executions to improve recommendations.
Example:
-- Execute AI-driven SQL Tuning
EXEC DBMS_SQLTUNE.CREATE_TUNING_TASK(sql_id => 'abc123xyz');
Use Case:
Automatically correct poorly performing SQL.
Minimize manual SQL tuning work.
4. Autonomous Transaction Logging (Blockchain Tables Enhanced)
Oracle 23AI extends Blockchain Tables with AI-based tamper detection and automated auditing.
Key Features:
✅ Intelligent Fraud Detection – AI identifies suspicious transactions.
✅ Automated Cryptographic Validation – Validates data integrity without human intervention.
✅ Immutable Audit Logs – AI identifies unauthorized modifications.
Example:
-- Create a Blockchain table with AI auditing
CREATE BLOCKCHAIN TABLE audit_log (
id NUMBER,
operation VARCHAR2(100),
timestamp TIMESTAMP
) NO DROP UNTIL 365 DAYS IDLE;
Use Case:
Regulatory compliance (GDPR, SOX).
Secure financial/healthcare transactions.
5. AI-Optimized In-Memory Column Store
Oracle 23AI’s In-Memory Column Store (IMCS) now uses AI to prioritize frequently accessed data.
Key Features:
✅ Auto-Data Prioritization – AI identifies "hot" data for in-memory caching.
✅ Dynamic Compression Adjustments – Optimizes storage based on access patterns.
✅ Self-Managing Memory Allocation – Automatically resizes in-memory areas.
Example:
-- Enable AI-driven In-Memory
ALTER SYSTEM SET inmemory_automatic_level = HIGH;
Use Case:
Faster analytics without manual tuning.
Ideal memory utilization for OLAP loads.
6. AI-Powered Security Threat Detection
Oracle 23AI provides AI-based anomaly detection for security threats.
Key Features:
✅ Behavioral Analysis – Identifies suspicious login activity (e.g., brute-force attacks).
✅ Automated Patching – AI recommends security patches from vulnerability scans.
✅ Real-Time Alerting – Alerts DBAs to suspicious actions.
Example:
-- Monitor AI security alerts
SELECT * FROM V$AI_SECURITY_ALERTS;
Use Case:
Avoid SQL injection & unauthorized access.
Automate compliance reporting.
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