Why Doesn't My Oracle SQL Query Use More CPUs, Even with Parallelism Enabled?

Oracle Parallelization: Why Can't I Force More CPU Utilization with SQL Queries?

Oracle 11g offers enhancements for internal query parallelization, but it's crucial to understand why you may still encounter limitations in utilizing multiple CPUs for a single SQL query.

Understanding Oracle Parallelism

Oracle parallelism is a complex mechanism influenced by numerous factors, making optimization challenging. It's essential to identify the number of parallel servers requested, allocated, and meaningfully used.

Proper Usage of Tools

SQL Monitoring with active reports provides detailed execution statistics and identifies performance bottlenecks. An explain plan can also reveal reasons why parallelism was not requested.

Reasons for Limited Parallelism

Many factors can influence the degree of parallelism achieved, including:

• Inter-operation parallelism: Sorting and grouping operations require twice the number of parallel servers as the DOP.
• Query hints: Statement-level hints like "/ parallel /" can overrule object-level hints.
• Recursive SQL: Recursive operations can serialize execution.
• Alter session: Settings like "alter session force parallel" can influence parallelism.
• Table degree: Table size affects the estimated number of rows processed and can impact the DOP.
• Index degree: Number of available indexes can affect the DOP for index scans.
• Index preference: Optimizer may choose an index-access plan over a full table scan with parallelism.

Other Factors

In addition to the aforementioned factors, there are numerous other parameters, such as server edition, automatic tuning, and system load, that can affect parallelism.

Unparallelable Queries

Certain queries may be inherently unparallelable due to their structure or execution requirements. For instance, queries that rely on sequential processing, such as row-by-row operations or recursive calls, may not benefit from parallelism.

Conclusion

Optimizing Oracle parallelism requires a comprehensive approach that involves understanding query structure, monitoring execution performance, and adjusting system parameters. By addressing the complexities of Oracle's parallel execution, you can effectively maximize CPU utilization and improve query performance.

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