The Investigation (AWR Checklist)
==================Before resizing anything, check Instance Efficiency and Load Profile:
Soft Parse %
If this is low, you don’t have a memory problem — you have a SQL design problem.
Execute to Parse %
Are statements being parsed almost as often as they are executed?
Parse CPU vs. Total CPU
If parsing consumes 40%+ of CPU, the application is choking the Shared Pool.
The Root Cause: Literal Chaos
==================
When applications use literals
WHERE ID = 123
instead of bind variables
WHERE ID = :b1
Oracle treats each execution as a new SQL statement.
Result: A flood of hard parses
Impact:
=======
Useful cursors get aged out
High object reloading
Increased latch and mutex contention
Pro-Level Tuning Steps
=================
+Find the Culprits
Use FORCE_MATCHING_SIGNATURE in V$SQL to group statements differing only by literals.
+Identify “Heavy” Objects
Query V$DB_OBJECT_CACHE. Frequently reloading large PL/SQL objects create serious shared pool pressure.
+The KEEP Strategy
Use DBMS_SHARED_POOL.KEEP to pin large, frequently used packages and prevent unnecessary reloads and contiguous memory allocation pressure.
+Consult the Advisor (Last Step)
After fixing SQL, check V$SHARED_POOL_ADVICE.
If parse time savings are negligible with more memory — do not increase the Shared Pool.
Technical Takeaway
==================
A larger Shared Pool does not fix bad SQL. In fact, it can lead to:
Longer LRU scans
Higher latch contention
Fix the SQL design first. Tune memory second.
