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STAR Transformation Process

Question: How does one promote the performance of Oracle Queries using the STAR Transformation Process?
Promoting the performance of Oracle queries using the star transformation process requires a systematic approach, encompassing aspects of database design, index creation, query construction, and optimizer configuration. The star transformation process is particularly beneficial in data warehousing environments with star schemas, where it significantly enhances the speed of complex, ad-hoc queries.
Here are the steps to optimize Oracle queries using the star transformation process:
  1. Design a Star Schema: The foundation for star transformation is a well-designed star schema. This consists of a large fact table containing transactional data, joined to several smaller dimension tables that provide descriptive, categorical data. The fact table should contain foreign keys referencing each dimension table, creating a star-like structure.
  2. Create Bitmap Indexes: Create bitmap indexes on all the foreign key columns in the fact table. These indexes are crucial for the star transformation process. When a star query is executed, Oracle uses these bitmap indexes to quickly identify the rows in the fact table that satisfy the conditions on the dimension tables.
  3. Construct Star Queries: Construct your queries following the star schema model. Star queries typically retrieve data from the fact table and use JOIN clauses to combine this data with relevant information from the dimension tables. The WHERE clause often includes conditions on columns from the dimension tables.
  4. Enable Star Transformation: Ensure that the star transformation feature is enabled in Oracle. This can be done by setting the STAR_TRANSFORMATION_ENABLED initialization parameter to TRUE or TEMP_DISABLE. When set to TRUE, the optimizer considers the cost of the star transformation before deciding whether to use it. When set to TEMP_DISABLE, it uses star transformation if temporary tables can be used for the transformation, which usually leads to more efficient execution.
  5. Optimizer Statistics and Cost-Based Optimization: Maintain accurate and up-to-date optimizer statistics. Star transformation is a cost-based process, and the optimizer uses these statistics to estimate the cost of different execution plans. You can gather statistics using Oracle's DBMS_STATS package.
  6. Leverage Parallel Execution: For large fact tables and high computational workloads, consider using Oracle's parallel execution feature. This allows the bitmap operations involved in star transformation to be performed in parallel, further increasing query performance.
  7. Tune Queries and Indexes: Regularly review and tune your star queries and bitmap indexes. Use Oracle's explain plan feature to understand how your queries are being executed and identify potential bottlenecks. Monitor the usage and performance of your indexes and consider rebuilding or coalescing them if they become fragmented or unbalanced.
  8. Stay Updated with Oracle Versions: Oracle frequently enhances its query optimization features, including star transformation, in new releases. Keeping your Oracle version up-to-date allows you to leverage these improvements.

Remember, every data warehousing environment is unique, and the strategies that work well in one context might not be suitable in another. Understanding your application's data characteristics and query patterns is crucial when considering the use of star transformation for Oracle query performance optimization.
The star transformation is a cost-based query transformation aimed at executing star queries[1] efficiently. Whereas the star optimization works well for schemas with a small number of dimensions and dense fact tables, the star transformation may be considered as an alternative if any of the following holds true:
  1. The number of dimensions is large
  2. The fact table is sparse
  3. There are queries where not all dimension tables have constraining predicates
The star transformation does not rely on computing a Cartesian product of the dimension tables, which makes it better suited for cases where fact table sparsity and/or a large number of dimensions would lead to a large Cartesian product with few rows having actual matches in the fact table. In addition, rather than relying on concatenated indexes, the star transformation is based on combining bitmap indexes on individual fact table columns.
The transformation can thus combine indexes corresponding precisely to the constrained dimensions. There is no need to create many concatenated indexes where the different column orders match different patterns of constrained dimensions in different queries.

[1] star query: In a star query each dimension table is joined to the fact table using a primary key to foreign key join.