Library Cache: The Library Cache is a critical portion of the Shared Pool responsible for storing and managing the parsed representations of SQL statements and their execution plans. When a SQL statement is executed, Oracle’s SQL processing engine first performs parsing, which involves syntactic and semantic analysis of the statement. The parsed representation, also known as the “cursor,” is then stored in the Library Cache for reuse.

Key Benefits of Optimizing Oracle Shared Pool

1. Optimizing the Oracle Shared Pool can yield numerous advantages that significantly enhance your database’s performance and overall efficiency. In this section, we will explore the key benefits of Shared Pool optimization and understand how it positively impacts your Oracle database operations:
2. Improved Response Times: One of the primary benefits of optimizing the Shared Pool is the noticeable improvement in response times for SQL queries. When frequently executed SQL statements are stored in the Shared Pool as shared cursors, subsequent executions can reuse the parsed execution plan, eliminating the need for time-consuming parsing. This results in faster data retrieval and reduced query execution times, delivering a seamless and more responsive user experience.
3. Reduced Parsing Overhead: By caching frequently used SQL statements and their parsed representations, Shared Pool optimization significantly reduces parsing overhead. Parsing involves complex syntactic and semantic analysis of SQL statements, which can be resource-intensive, especially during periods of high user activity. With Shared Pool optimization, parsing is minimized, freeing up valuable CPU resources and lowering the overall system load.
4. Enhanced Memory Utilization: An optimized Shared Pool ensures efficient memory usage by allowing multiple users to share the same parsed SQL statements through shared cursors. This prevents unnecessary duplications and optimizes the use of available memory, maximizing the number of SQL statements that can be cached within the limited memory space.
5. Consistent Performance: Optimizing the Shared Pool promotes the reuse of execution plans for identical SQL statements, leading to consistent query performance. When multiple users execute the same SQL statement, they all benefit from a single cached execution plan, ensuring uniform and predictable response times across the system.
6. Minimized Disk I/O: The caching of frequently accessed data dictionary information in the Shared Pool reduces the need for repeated disk reads to fetch metadata. This minimizes disk I/O operations related to data dictionary access, resulting in faster execution of administrative queries and reduced contention for disk resources.
7. Lower CPU Consumption: Since parsing is minimized through Shared Pool optimization, the database system expends less CPU time on repetitive parsing tasks. This CPU savings can be diverted to other critical database processes, leading to better overall system performance and resource utilization.
8. Scalability and Performance Stability: An optimized Shared Pool contributes to the scalability of the Oracle database. As the number of concurrent users and SQL statements increases, the Shared Pool efficiently handles the caching and sharing of resources, maintaining stable performance even during peak workloads.

Sizing the Shared Pool Appropriately

Properly sizing the Oracle Shared Pool is a critical task that directly impacts the performance and efficiency of your database. An appropriately sized Shared Pool ensures efficient memory utilization and minimizes parsing overhead, leading to optimal query response times. In this section, we will delve into the essential considerations and best practices for sizing the Shared Pool to match your database’s workload.

1. Understand Your Database Workload: Before determining the Shared Pool size, it’s crucial to have a thorough understanding of your database’s workload. Analyze the typical number of concurrent users, the frequency of SQL statement execution, and the size and complexity of SQL queries. Workload patterns can vary throughout the day, so consider peak periods when sizing the Shared Pool.
2. Monitor Shared Pool Usage: Use monitoring tools, such as Oracle Enterprise Manager (OEM) or Automatic Workload Repository (AWR), to gain insights into Shared Pool utilization. Observe the library cache hit ratio, shared pool advisory statistics, and the rate of library cache misses. These metrics provide valuable information about how well the Shared Pool is serving the database’s needs and help in making informed sizing decisions.
3. Enable Automatic Shared Memory Management (ASMM): Oracle offers Automatic Shared Memory Management (ASMM), a feature that automates the sizing of different memory components, including the Shared Pool. When ASMM is enabled, Oracle dynamically adjusts the Shared Pool size based on the workload, allocating more memory when needed and releasing it during periods of lower demand. ASMM simplifies the process of sizing the Shared Pool and allows the database to adapt to changing workloads efficiently.
4. Configure Shared Pool Parameters: If you choose not to use ASMM, you can manually configure the Shared Pool parameters based on your workload analysis. The critical parameters to consider are “SHARED_POOL_SIZE” and “SHARED_POOL_RESERVED_SIZE.” The former represents the size of the Shared Pool, while the latter ensures a reserved portion of the Shared Pool for critical database objects, preventing them from being aged out under memory pressure.
5. Implement Automatic Memory Management (AMM): Alternatively, you can opt for Automatic Memory Management (AMM), which not only automates the sizing of the Shared Pool but also manages other memory components like the Buffer Cache and PGA (Program Global Area). AMM is suitable for environments where you want Oracle to handle the entire memory allocation, simplifying the configuration process.
6. Start with Conservative Sizing: When in doubt, it’s better to start with a conservative Shared Pool size and then monitor its usage over time. A conservative approach ensures that you have enough memory for other SGA components and prevents excessive memory allocation to the Shared Pool, which could impact other areas of the database.
7. Regularly Review and Adjust: Database workloads can change over time due to application updates, data growth, or increasing user activity. As part of routine database maintenance, regularly review the Shared Pool’s performance and adjust its size accordingly to meet the evolving demands.

Implementing Memory Management in Shared Pool

Memory management in the Oracle Shared Pool is a critical aspect of optimizing database performance. Two primary approaches to manage Shared Pool memory are Automatic Shared Memory Management (ASMM) and Manual Shared Memory Management (MSMM). In this section, we will explore these memory management techniques and guide you on choosing the most suitable approach for your database.

Automatic Shared Memory Management (ASMM):

ASMM is an automated memory management feature introduced in Oracle 10g and later versions. When ASMM is enabled, Oracle dynamically manages the sizes of various memory components, including the Shared Pool, based on the current database workload. The key benefits of ASMM include:

a. Simplified Configuration: ASMM reduces the complexity of manually configuring Shared Pool and other memory areas. The database instance automatically adjusts the memory allocations as per the workload, avoiding the need for constant manual tuning.

b. Flexible Memory Allocation: ASMM intelligently distributes memory among different components based on the priority of each area. It ensures that frequently used components like the Shared Pool receive sufficient memory to handle workloads efficiently.

c. Adaptive Memory Tuning: ASMM monitors memory usage and automatically reallocates memory as needed. It dynamically adjusts the Shared Pool size to accommodate changes in application demands.

Manual Shared Memory Management (MSMM):

In MSMM, the DBA manually configures the memory parameters, including the Shared Pool size, based on their understanding of the database workload and resource requirements. While MSMM offers greater control over memory allocation, it requires careful monitoring and constant adjustments to maintain optimal performance. Some considerations for MSMM include:

a. Precise Control: With MSMM, the DBA can precisely allocate memory to the Shared Pool based on specific needs. This can be beneficial in environments with predictable workloads and where precise memory management is essential.

b. Potential for Over/Under Allocation: MSMM requires the DBA to have a deep understanding of the database’s memory requirements. Incorrect sizing of the Shared Pool can lead to overallocation (depriving other components of memory) or underallocation (leading to performance issues).

c. Manual Tuning Efforts: In contrast to ASMM, where memory management is automated, MSMM demands ongoing manual tuning and monitoring. As workload patterns change, the DBA needs to adjust the Shared Pool size accordingly.

Choosing the Most Suitable Approach:

Selecting the right memory management approach depends on the complexity of your database environment, the level of control you require, and the expertise of your DBAs. Consider the following guidelines when making your decision:

Use ASMM if:

•    Your database workload is variable and subject to change.
•    You prefer automated memory management and want to simplify the configuration process.
•    You have limited resources or expertise for manual memory tuning.

Use MSMM if:

•   Your database workload is stable and predictable.
•   You have skilled DBAs who can efficiently manage memory configurations.
•   You need precise control over memory allocation and prefer a hands-on approach to memory   management.

Implementing effective memory management in the Oracle Shared Pool is essential for maximizing database performance. ASMM provides automated and adaptive memory tuning, while MSMM offers greater control over memory allocation but requires manual tuning efforts. Carefully assess your database workload and resource availability to choose the most suitable memory management approach that aligns with your organization’s needs and expertise.

Monitoring and Tuning Shared Pool

Effectively monitoring and tuning the Oracle Shared Pool is essential for maintaining peak database performance over time. In this section, we will explore performance metrics, diagnostic tools, and practical tips to help you keep your Shared Pool in top shape.

Performance Metrics for Shared Pool:

a. Library Cache Hit Ratio: This metric indicates the percentage of times Oracle found the required SQL or PL/SQL statement in the Shared Pool without needing to perform additional parsing. A higher hit ratio (close to 100%) suggests efficient memory utilization and better performance.

b. Library Cache Miss Ratio: Opposite to the hit ratio, this metric shows the percentage of times Oracle failed to find a required SQL or PL/SQL statement in the Shared Pool. A lower miss ratio is desirable, as it reduces the need for costly parsing operations.

c. Shared Pool Free Memory: Monitor the amount of free memory available in the Shared Pool. Insufficient free memory could lead to frequent overwriting of cached data and an increased likelihood of contention.

Diagnostic Tools for Shared Pool:

a. Oracle Enterprise Manager (OEM): Utilize OEM to access a graphical interface for monitoring database performance, including Shared Pool-related metrics. OEM provides real-time monitoring and alerts for any potential issues.

b. Automatic Workload Repository (AWR): AWR captures and stores performance statistics over time. Use AWR reports to analyze Shared Pool utilization patterns, identify potential bottlenecks, and make informed tuning decisions.

c. V$ Views: Oracle provides several V$ views related to the Shared Pool (e.g., V$SGASTAT, V$LIBRARYCACHE). Query these views to gather valuable information about Shared Pool usage and performance.

Practical Tips for Shared Pool Tuning:

a. Optimal Shared Pool Size: Based on workload analysis and monitoring, adjust the Shared Pool size to accommodate frequently used SQL statements and avoid unnecessary resizing overhead. Use ASMM or manual tuning (MSMM) as discussed earlier.

b. Consideration for Reserved Pool: If using manual Shared Pool management, allocate a portion of the pool as a reserved area for critical database objects (using the SHARED_POOL_RESERVED_SIZE parameter). This prevents essential data from being aged out during memory pressure.

c. Set Appropriate Cursor Sharing: The CURSOR_SHARING parameter determines how Oracle handles SQL statements with bind variables. Consider setting it to “FORCE” to promote cursor sharing and increase Shared Pool efficiency.

d. Regular Shared Pool Flushing: Occasionally, flushing the Shared Pool (using ALTER SYSTEM FLUSH SHARED_POOL) can help manage memory fragmentation and release unused space. However, use this sparingly, as frequent flushing can cause performance degradation.

e. Monitor and Address Library Cache Contention: Monitor the occurrence of library cache contention, which can happen when multiple users are trying to access the same objects simultaneously. Implement database design and coding practices that minimize contention issues.

f. Monitor Application Code and SQL Performance: Poorly designed SQL statements or applications can negatively impact the Shared Pool. Regularly review and optimize application code and SQL queries to reduce unnecessary parsing and improve performance.

Identifying and Resolving Fragmentation in the Shared Pool

Fragmentation within the Oracle Shared Pool can lead to inefficient memory utilization and performance degradation. In this section, we will explore the issue of fragmentation and guide you through the steps to identify and resolve it, ensuring smooth performance even during peak workloads.

Understanding Fragmentation in the Shared Pool:

Fragmentation occurs when the available memory in the Shared Pool becomes fragmented into smaller chunks, making it challenging to allocate contiguous memory blocks for new SQL statements and data dictionary entries. This results in an increased number of library cache misses and excessive memory reclamation, leading to performance issues.

Identifying Fragmentation:

a. Library Cache Miss Ratio: A rising library cache miss ratio is often a strong indication of fragmentation. An increase in cache misses suggests that the Shared Pool is unable to accommodate new SQL statements or frequently used ones due to fragmentation.

b. V$SGASTAT View: Monitor the V$SGASTAT view to analyze the shared pool’s heap utilization and identify if fragmentation is causing excessive free memory in small, non-contiguous pieces.

c. Query Performance: Fragmentation can lead to increased parsing times and slower query execution. Monitor SQL performance to detect signs of degradation.

Resolving Fragmentation:

a. Increase Shared Pool Size: If fragmentation is detected, consider increasing the Shared Pool size. A larger pool allows for more contiguous memory allocation and reduces the likelihood of fragmentation.

b. Use Automatic Shared Memory Management (ASMM): Enabling ASMM allows Oracle to dynamically manage memory components, including the Shared Pool. ASMM automatically adjusts the pool size to accommodate the workload and minimize fragmentation.

c. Reduce Memory Allocation for Other SGA Components: In some cases, excessive memory allocation to other SGA components like the Buffer Cache or Large Pool can leave insufficient contiguous memory for the Shared Pool. Adjusting the sizes of these components can alleviate fragmentation.

d. Flushing the Shared Pool: In extreme cases of fragmentation, consider flushing the Shared Pool (using ALTER SYSTEM FLUSH SHARED_POOL). While this can help temporarily, it should be used judiciously, as it can lead to increased parsing overhead.

e. Regular Shared Pool Resizing: Monitor the Shared Pool’s utilization and adjust its size regularly based on workload patterns. Regular resizing can help prevent fragmentation before it becomes a significant issue.

f. Optimize SQL Statements: Poorly written SQL statements can exacerbate fragmentation by consuming more memory than necessary. Regularly optimize and tune SQL queries to reduce memory consumption in the Shared Pool.

g. Maintain an Up-to-Date Database: Regularly apply Oracle’s patches, updates, and patches to address known issues related to memory management and fragmentation.

Managing Contention in Shared Pool

Contention in the Oracle Shared Pool can occur when multiple concurrent requests from different users compete for access to the same resources within the pool. This contention can lead to performance bottlenecks and degraded database response times. In this section, we will explore strategies to deal with contention problems in the Shared Pool and optimize resource utilization.

Identifying Shared Pool Contention:

a. Library Cache Lock Waits: Monitor the V$SESSION_WAIT view for events related to “library cache lock.” These waits indicate contention, with sessions waiting for access to shared cursors or objects stored in the Library Cache.

b. High “Get Requests” and “Pins”: Elevated numbers of “Get Requests” and “Pins” in the V$LIBRARYCACHE view may suggest increased contention, indicating that multiple sessions are trying to acquire access to shared objects.

Strategies to Mitigate Contention:

a. Cursor Sharing: Enforce cursor sharing by setting the “CURSOR_SHARING” parameter to “FORCE.” This encourages the reuse of existing cursors and can significantly reduce contention for new executions of similar SQL statements.

b. Monitor Cursor Cache: Regularly monitor the “CURSOR_SPACE_FOR_BIND” statistic in V$SQL_SHARED_CURSOR. A high value indicates that the cursor cache is efficiently reusing cursors, reducing the likelihood of contention.

c. Increase Shared Pool Size: If contention persists, consider increasing the size of the Shared Pool. More memory allows for a larger number of shared cursors to coexist, reducing contention for resources.

d. Separate PL/SQL and SQL Areas: In Oracle 12c and later versions, you can use the “PLSQL_CODE_TYPE” parameter to separate PL/SQL and SQL areas in the Library Cache. This can help mitigate contention between these two types of objects.

e. Optimize Parsing: Frequent parsing and reparsing of SQL statements can exacerbate contention. Optimize application code and SQL statements to minimize the number of distinct SQL statements.

f. Use Bind Variables: Encourage the use of bind variables in application code instead of hard-coded values. This promotes cursor sharing and reduces the number of uniquely parsed SQL statements.

g. Shared Cursor Pool: In Oracle 11g and later, you can enable the “SHARED_POOL_SIZE” parameter to create a separate subpool exclusively for shared cursors. This can help reduce contention and improve cursor reuse.

h. Address Locking and Blocking: Contention may also arise due to locking and blocking issues unrelated to the Shared Pool. Address any locking or blocking problems promptly to prevent further contention.

i. Review Application Architecture: Evaluate the application architecture to identify if any specific design decisions are contributing to the contention issue. Modifying the application architecture may help alleviate the contention problem.

You can read more about efficiency on Oracle Shared Pool

FAQ

Q1. Common ORA Errors Related to Shared Pool ?

ORA-04031: Unable to allocate x bytes of shared memory (“shared pool”,”unknown object”,”sga heap”,”state objects”)

This error occurs when there is insufficient free space in the Shared Pool to accommodate new data or SQL statements. It         may result from improper Shared Pool sizing or excessive memory usage by SQL statements.

Resolution:

• Increase the Shared Pool size appropriately based on workload analysis.
• Optimize SQL statements to reduce memory consumption.
• Enable Automatic Shared Memory Management (ASMM) to dynamically manage memory components, including the Shared Pool.

ORA-04033: Insufficient memory to grow pool (“shared pool”,”object name”,”heap name”,”request size”)

This error indicates that the Shared Pool is unable to expand to accommodate a specific request due to insufficient free memory.

 Resolution:

• Increase the Shared Pool size if the current size is not sufficient to handle the request.
• Ensure that other SGA components are not consuming excessive memory, leaving insufficient space for the Shared Pool.

Q2. How to Flushing the oracle Shared Pool?

To flush the Shared Pool and remove all objects and data from it, execute the following command as a privileged user:

ALTER SYSTEM FLUSH SHARED_POOL;

Q3. What is Result Cache in Oracle and Its Relation to the Shared Pool ?

The Result Cache is a feature in Oracle that allows storing the results of queries in memory to improve query response times.    The cached results are stored in the Shared Pool. When a query is executed, Oracle first checks if the same query with the        same parameters exists in the Result Cache. If found, the cached result is returned instead of executing the query, saving         processing time.

Q4. How to Keeping Objects in Shared Pool and Flushing from Shared Pool ?

Objects in the Shared Pool, such as SQL statements and execution plans, are automatically managed by Oracle. However, you can influence object retention using features like Result Cache or pinning specific objects in the Shared Pool.

• Result Cache: To use Result Cache, you can enable it for specific queries or functions. Oracle automatically caches the results in the Shared Pool, making them available for subsequent executions.
• Pinning: You can pin specific objects in the Shared Pool using the DBMS_SHARED_POOL package or by specifying the “KEEP” pool in Oracle 12c and later. Pinning ensures that the specified objects stay in the Shared Pool, even under memory pressure.

To flush a specific object from the Shared Pool, you can use the following command: