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Star Schema: Indexing ProblemPeriod dimension has a compound key, a hierarchy of day, week, month, quarter, year.Problems with compound key:It requires multiple metadata definitionsFact table must carry all key componentsThe size of the index increases, performance decreasesAlternatives:Concatenate the keys into a single key (solve the first two problems)Use artificial (generated) key. Move the compound key into non-key attributes (The Best Approach)Star Schema: Level Indicator ProblemThe dimensional table design often includes a level of hierarchy indicator for every record (whether the record stores details or aggregates).The best alternative to using the level indicator is the snowflake schema. In this schema, aggregate fact tables are created separately from detail fact tables. In addition to the main fact tables, the snowflake schema contains separate fact tables for each level of aggregation.STARjoin and STARindexSTARjoin is a high-speed, single-pass, parallelizable multitable join, invented by Red Brick. Red Brick’s DBMS can join more than two tables in a single operation.The core technology in STARjoin is an innovative approach to indexing.Red Brick’s RDBMS supports the creation of specialized indexes, called STARindexes, to dramatically accelerate join performance.STARindex: The ConceptSTARindexes are created on one or more foreign key columns of a fact table.Unlike traditional indexes that contain information to translate a column value to a list of rows with that value, a STARindex contains highly compressed information that relates the dimensions of a fact table to the rows that contain those dimensionsSTARindex vs Traditional IndexTypical Multicolumn index references a single table whereas the STARindex can reference multiple tables.With multicolumn indexes, if a query’s WHERE clause does not constrain on all the columns in the composite index, the index cannot be fully used unless the specified columns are a leading subset. On the other hand, STARindex can be fully utilized regardless of patterns of constraint processing.STARindex ExampleAssume there are 500 possible PRODUCTS, 200 MARKETS, 300 PERIODS, and one million FACTS in the data warehouse database. Further assume that a particular query selects 50 PRODUCTS, 20 MARKETS, 30 PERIODS that ultimately will select 1000 of the FACTS.A traditional pairwise join strategy would generate 111,000 rows. A cartesian product would perform better in generating 50x20x30=30,000 intermediate rows plus 1000 FACTS rows = 31,000 rows.A well-constrained STARjoin would generate only slightly more combinations than exist in the selected rows of the FACTS table, on average about 10 percent more, resulting 1100 rows.Bitmapped IndexingSYBASE IQ is an example of a product that uses a bitmapped index structure of the data stored in the SYBASE DBMS.The Indexing Technology is developed by Expressway Technologies, which Sybase acquired in 1994.SYBASE IQ is a stand-alone database that targeted as an “ideal” data mart solution that is optimized to handle multiuser ad hoc queries.Data CardinalityBitmap indexes are used to optimized queries against low-cardinality data – the total number of potential values is relatively low. For example, gender cardinality is only 2 (male and female).Bitmap indexes can become cumbersome and even unsuitable for high-cardinality data where the range of potential values is high. For example, values like “income” may have an almost infinite number of values.SYBASE IQ uses a patented technique called Bit-Wise technology to build bitmap indexes for high-cardinality data.Shortcomings of indexingNo updatesLack of core RDBMS featuresLess advantageous for planned queriesHigh memory usage
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