About Performance Tuning in TDV

You can tune TDV performance in several ways. Some performance tuning topics are covered in this topic, some elsewhere. The following table summarizes and links to the sections that describe performance tuning.

Tuning Tool

Summary

Topic Reference

Generating query execution plans

The execution plan shows how TDV Server plans to execute the SQL for a view. It can help you understand what you might do to enhance query performance.
• Working with the SQL Execution Plan
• Generating and Displaying a SQL Execution Plan
• Execution Plan Nodes in the Tree Panel
• Updating the Query Execution Plan
• Viewing How Much Data was Processed by a Query
• Refreshing All Execution Plan Caches

Creating cardinality statistics

Cardinality statistics show the distribution of values in a table or view, and estimate the number of rows in the query result. This can help you improve the query plan.

• Creating Cardinality Statistics for Cost-Based Optimization
• Using TDV API Procedures to Refresh or Cancel Resource Statistics

Defining indexes, and primary and foreign keys

An index, or a primary or foreign key, can make data easier to find.

• Use Indexes, and Primary and Foreign Keys, to Improve Query Performance

Using joins

Join strategies can significantly affect query performance.
• Types of Joins
• SQL Join Reordering
• Semijoin Optimization Option
• Star Schema Semijoin

Using SQL hints

You can use these when the default behavior of the query optimizer does not result in the best query performance.
• Using Oracle SQL Optimizer Hints

Data caching

Saving local snapshots of data can improve the performance of some design tasks. Caching temporarily stores a copy of frequently accessed source data.
• TDV Caching

Using nested aggregates

If a data source does not support nested aggregates, you can improve performance by letting TDV know not to look for them.
• Tuning Nested Aggregate Function Behavior

Using data ship

Data ship optimization analyzes queries to determine if performance can be improved by temporarily transferring a small amount of data to a data source target.

• Data Ship Performance Optimization