The Problem: When PGA Memory Goes Wild
Recently, a customer approached us with a common yet challenging issue: “We have many queries consuming a lot of PGA memory. Can Foglight help us find which queries consume the most?”
This question touches on a critical aspect of Oracle performance tuning that many DBAs struggle with daily. Let’s explore how Foglight can help identify and troubleshoot PGA memory issues.
Understanding PGA Memory in Oracle
- PGA (Program Global Area) stores session-specific data:
- Sort operations
- Hash joins
- Bitmap operations
- Session variables
- High PGA usage can lead to:
- Increased CPU consumption
- More disk I/O operations
- System performance degradation
- ORA-04030 errors in extreme cases
The Foglight Approach: Indirect but Effective
While Foglight doesn’t directly track PGA memory at the session level, it provides powerful indirect indicators through Performance Investigator (PI).
Key Metrics That Signal PGA Memory Issues
Metrics affected by high PGA usage:
1. CPU Usage - Memory allocation/deallocation overhead
2. User I/O Wait - Memory-to-disk spilling
3. Direct Writes - Temp tablespace usage
4. Disk Reads - Insufficient memory for operations
5. Buffer Gets - Memory operation delays
Step-by-Step: Finding Memory-Hungry Queries
Step 1: Navigate to Performance Investigator
- Open your Oracle instance in Foglight
- Click on Performance Investigator tab
Step 2: Filter by Relevant Wait Categories
- Start with CPU filter - high PGA often manifests as CPU spikes
- Check User I/O filter - indicates memory spilling to disk
Step 3: Analyze the Workload Tree
- Focus on Statements dimension
- Click Select Metrics to add memory-related columns:
- Executions (fewer = potential memory waits)
- Disk Reads (higher = potential memory shortage)
- Direct Writes (higher = temp space usage)
- Rows Processed (efficiency indicator)
Step 4: Identify Problematic Statements
Look for statements with:
- High CPU time relative to executions
- Excessive disk reads for the data volume
- High direct writes indicating temp usage
- Poor rows-processed-to-execution ratio
Real-World Example
During the customer investigation, we found:
-- Top CPU consumer (40.62 seconds in last hour): SELECT * FROM (SELECT t.tablespace_name, t.BLOCK... -- This query showed signs of memory-intensive operations -- Memory-heavy aggregation: SELECT owner, SUM(BYTES)/1024/1024 MB FROM DBA_EXTENTS GROUP BY owner -- High disk reads and direct writes indicated PGA spilling
Monitoring Strategy: Proactive PGA Management
Create Custom Alerts
- Set thresholds for:
- CPU spikes correlated with specific users/programs
- Excessive direct writes to temp
- Disk read patterns indicating memory shortage
Regular Review Points
- Daily: Check top CPU consumers in PI
- Weekly: Review statement efficiency metrics • Monthly: Analyze trends in temp tablespace usage
Oracle-Side Validation
While Foglight provides the performance view, validate findings with:
-- Current PGA usage by session SELECT s.sid || ' - ' || s.username AS session_info, ROUND(p.pga_used_mem/1024/1024, 2) AS pga_used_mb, ROUND(p.pga_alloc_mem/1024/1024, 2) AS pga_alloc_mb, s.sql_id, s.event FROM v$session s JOIN v$process p ON s.paddr = p.addr WHERE s.type = 'USER' ORDER BY p.pga_alloc_mem DESC; -- Historical PGA usage by SQL SELECT sql_id, ROUND(AVG(pga_allocated)/1024/1024, 2) AS avg_pga_mb, ROUND(MAX(pga_allocated)/1024/1024, 2) AS max_pga_mb, COUNT(*) AS execution_count FROM v$active_session_history WHERE pga_allocated > 0 GROUP BY sql_id ORDER BY max_pga_mb DESC;
Best Practices for PGA Troubleshooting
- Correlate Multiple Metrics: Don’t rely on single indicators
- Time-Based Analysis: Compare peak vs. off-peak patterns
- User/Program Patterns: Identify repeat offenders
- Statement Efficiency: Focus on execution-to-resource ratios
Future Considerations
While the current approach is effective, direct PGA memory tracking at the session level would enhance Foglight’s capabilities. This enhancement request highlights the evolving needs of Oracle performance monitoring.
Conclusion
Foglight’s Performance Investigator provides a comprehensive framework for identifying PGA memory issues through: • CPU and I/O pattern analysis • Statement-level resource consumption tracking • Multi-dimensional performance correlation
By understanding these indirect indicators and following the systematic approach outlined above, DBAs can effectively identify and resolve PGA memory bottlenecks before they impact production systems.
Have you encountered PGA memory challenges in your Oracle environment? Share your experiences and let us know how you’ve leveraged Foglight for performance troubleshooting.