Just upload your Oracle AWR Report to chatGPT for analysis! Oracle’s Automatic Workload Repository (AWR) report is a powerful tool for understanding the performance and health of your Oracle database. It provides deep insights into various aspects like CPU usage, wait events, memory utilization, and SQL execution statistics. But reviewing these detailed reports can be overwhelming. Fortunately, with the help of ChatGPT

After thoroughly reviewing the AWR report, here are some of the key performance issues and recommendations for improvement:

1. High CPU Usage

The report showed significant CPU usage, with 34.18% of the total database time spent on DB CPU. Additionally, SQL execution accounted for 94% of DB time, suggesting that optimizing SQL queries could reduce CPU overhead.

Recommendations:

• Identify the top SQL statements contributing to high CPU consumption and optimize them.
• Use execution plan analysis tools, add appropriate indexes, and consider partitioning large tables.
• Ensure that statistics are up to date for better execution plans.

2. Scheduler Wait Events

The top wait event was identified as resmgr:cpu quantum, consuming a massive 44.7% of DB time. This indicates that Resource Manager policies might be limiting CPU availability, causing contention.

Recommendations:

• Review Resource Manager settings and ensure that CPU allocation is appropriate for workloads.
• If Resource Manager policies are unnecessary, consider disabling or modifying them to remove CPU throttling.
• If CPU resources are insufficient, consider increasing the allocated CPU resources.

3. I/O Wait Events

Significant time was spent on User I/O wait events, such as cell smart table scan and direct path read/write temp. These indicate large I/O operations due to full table scans or temp space usage for sorts and joins.

Recommendations:

• Optimize SQL statements that perform heavy I/O operations. Add appropriate indexes and reduce full table scans where possible.
• Ensure efficient use of temp space by adjusting PGA and reducing excessive temp I/O.

4. SQL Optimization

SQL execution consumed a significant amount of database time, with over 151,667 seconds spent on executing SQL queries. This suggests that optimizing SQL queries should be a high priority.

Recommendations:

• Focus on tuning SQL queries that consume the most database time, utilizing Oracle’s SQL Tuning Advisor.
• Consider using parallelism for complex queries that involve large data sets, if applicable.

5. Memory Management

The Shared Pool and Database Cache seem to be appropriately sized, but it’s important to monitor memory usage over time to ensure there are no bottlenecks.

Recommendations:

• Regularly monitor the SGA and PGA usage, adjusting them as necessary to optimize memory performance.
• If there are frequent memory-related waits, consider increasing memory allocations for these areas.

6. Leveraging Exadata Optimizations

Since the system uses Exadata, it’s critical to leverage the platform’s capabilities, such as Smart Scan optimizations. The presence of cell smart table scan events indicates that some queries may benefit from Exadata’s advanced features.

Recommendations:

• Ensure queries are optimized to take full advantage of Exadata’s Smart Scan by minimizing row-based operations.
• Review queries that involve large full table scans and ensure they are optimized for Exadata’s hardware capabilities.

Remember, regular monitoring and fine-tuning of your Oracle database are key to maintaining optimal performance!