Real-World Performance Training Dimensional Queries

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2 Real-World Performance Training al Queries Real-World Performance Team

3 Agenda The DW/BI Death Spiral Parallel Execution Loading Data Exadata and Database In-Memory al Queries

4 al Queries The al Model and Star Queries Star Query Execution Star Query Prescription Edge Conditions and Extreme Performance

5 al Queries The al Model and Star Queries Star Query Execution Star Query Prescription Edge Conditions and Extreme Performance

6 The al Model Bill Inmon s Paradigm Ralph Kimball s Paradigm The Data warehouse is one part of an overall business intelligence platform An enterprise has one data warehouse, and data marts source their information from the data warehouse Information is stored in 3 rd normal form The Data Warehouse is a conglomerate of all data marts in an enterprise Information is stored in the dimensional model

7 al Model References The Data Warehouse Toolkit: The Definitive Guide to al Modeling, 3 rd Edition Ralph Kimball Margy Ross /productcd html#

8 Why a al Model? Predictable and standard format Star join framework withstands unexpected changes in user behavior Performance Well established performance algorithms for this model A single fact table row contains all required measures, with correlated attributes stored in-row as join columns to dimension tables Fact

9 The al Model al models are deployed in dimensional schemas al schemas comprised of fact and dimension tables Fact tables represent business events; i.e., sales, orders, logins, etc. These are referred to as measures Fact s contain reference information about facts; i.e., who, what, when, how, why, etc. Fact tables generally contain numerical values and dimension tables contain descriptive information

10 Star Schema Characteristics Fact tables are generally: Large Append-only Historical in nature tables are generally: Small compared to Fact Fact Static or slowly changing

11 al Schemas Attribute Fact Fact Attribute Attribute Attribute Star Schema Snowflake Schema

12 al Model Example: Retail Sales Schema Date Date Key (PK) Date Day of Week Calendar Week Ending Date Calendar Month Calendar Year - Month Calendar Quarter Store Store Key (PK) Store Name Store Number Store District Store Region First Open Date Last Remodel Date POS Retail Sales Transaction Fact Date Key (FK) Product Key (FK) Store Key (FK) Promotion Key (FK) POS Transaction Number (DD) Sales Quantity Sales Amount Cost Amount Gross Profit Product Product Key (PK) Product Description SKU Number Brand Description Subcategory Description Category Description Department Description Promotion Promotion Key (PK) Promotion Name Promotion Media Type Promotion Begin Date Promotion End Date

13 al Model Example: Telecommunications Customer Customer Key (PK) Customer ID (Natural Key) Customer Name Customer City Customer State Customer Zip Date of 1 st Service Sales Rep Sales Rep Key (PK) Sales Rep Number (Natural Key) Sales Rep Name Sales Organization ID Sales Organization Name Sales Channel ID Sales Channel Name Billing Fact Bill Date Key (FK) Customer Key (FK) Service Line Key (FK) Sales Rep Key (FK) Rate Plan Key (FK) Bill Number (DD) Number of Calls Number of Total Minutes Number of Roam Minutes Number of Long-Distance Mins Monthly Service Charge Roaming Charge Long-Distance Charge Taxes Regulatory Charge Bill Date Bill Date Key (PK) Bill Date Bill Year Service Line Service Line Key (PK) Service Line Number (Natural Key) Service Line Area Code Rate Plan Rate Plan Key (PK) Rate Plan Code (Natural Key) Rate Plan Abbreviation Rate Plan Description

14 al Model Example: Financial Services Account Account Key (PK) Account Number (Natural Key) Primary Account Holder Name Secondary Account Holder Name Account Address Attributes Account Open Date Account Type Description Household Household Key (PK) Head of Household Name Household Address Attributes Household Type Household Income Household Children Monthly Account Snapshot Fact Month End Date Key (FK) Branch Key (FK) Product Key (FK) Account Key (FK) Account Status (FK) Household Key (FK) Primary Month End Balance Average Daily Balance Number of Transactions Interest Paid Interest Charged Fees Charged Month Month End Date Key (PK) Month Attributes Branch Branch Key (PK) Branch Address Attributes Branch Rollup Attributes Branch Format Description Product Product Key (PK) Product Description Product Type Product Category Account Status Account Status Key (PK) Account Status Description Account Status Group

15 al Model Application Vendors Many Vendors Work With al Data Models

16 Star Queries Star queries access data in star schemas Star schemas comprised of fact tables and dimension tables Fact tables store measures tables store attributes to describe facts Tables are joined using keys Filtering is performed on dimension table attributes table attributes are used for aggregation and sorting

17 Shape and Structure of a Typical al Query SELECT d_sellingseason, p_category, s_region, SUM(lo_extendedprice) FROM lineorder JOIN customer ON lo_custkey = c_custkey JOIN date_dim ON lo_orderdate = d_datekey JOIN part ON lo_partkey = p_partkey JOIN supplier ON lo_suppkey = s_suppkey WHERE d_year IN (1993, 1994, 1995) AND p_container in ('JUMBO PACK') GROUP BY d_sellingseason, p_category, s_region ORDER BY d_sellingseason, p_category, s_region Choose your fact table Complete the star by defining relationships with joins to dimension tables Choose filter criteria based upon dimension attributes Choose measures for aggregation Choose segmentation/roll up columns Choose grouping requirements Choose ordering requirements

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