Oracle Database 26ai introduces AI-driven index and partition recommendations that go beyond traditional static advisor tools. Instead of analyzing a single SQL or snapshot in time, Oracle 26ai evaluates real workload behavior over time and provides recommendations aligned with actual database usage.
What Makes This Different from Traditional Advisors
Traditional tools like SQL Tuning Advisor or Index Advisor:
- Work on isolated SQL statements
- Rely heavily on statistics at a point in time
- Require manual DBA interpretation
- Often lead to over-indexing or ignored recommendations
Oracle 26ai takes a workload-aware approach.
It continuously learns from:
- Query execution frequency and access paths
- Join patterns and filter selectivity
- DML intensity and write overhead
- Time-based data access trends
- Hot vs cold data usage
Intelligent Index Recommendations
Oracle 26ai recommends indexes that:
- Benefit multiple high-impact queries, not just one
- Balance read performance against write cost
- Identify redundant or unused indexes automatically
- Avoid indexes that increase contention or maintenance overhead
This results in fewer but more effective indexes, improving performance without hurting OLTP throughput.
Intelligent Partition Recommendations
Partitioning decisions are traditionally risky and complex. Oracle 26ai simplifies this by identifying:
- Tables with natural data aging patterns
- Queries that consistently access data ranges
- High-volume delete or archival workloads
- Scenarios where partition pruning provides measurable benefit
Based on observed behavior, Oracle 26ai can suggest:
- Range or interval partitioning
- Partitioning keys aligned with query filters
- Local index strategies
- Maintenance-friendly designs (purge, backup, stats)
Key Benefits
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Targeted performance gains based on real workload data
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Reduced DBA effort in interpreting advisor outputs
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Indexing and partitioning aligned to actual load, not assumptions
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More predictable performance and maintenance behavior
DBA Impact
Oracle 26ai shifts DBAs from reactive tuning to proactive design optimization.
AI handles continuous analysis, while DBAs retain control over implementation decisions.
This feature significantly improves confidence in indexing and partitioning changes, especially in high-volume OLTP and mixed workloads.
That’s my current understanding and perspective on this topic. Happy to discuss further or correct anything if I’ve missed or misunderstood something. Still learning.
-Chetan