Difference between revisions of "OLAP"
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| Line 36: | Line 36: | ||
: Item(itemID, category, brand, color, size) | : Item(itemID, category, brand, color, size) | ||
: Customer(custID, name, address) | : Customer(custID, name, address) | ||
| + | |||
| + | === OLAP Queries === | ||
| + | ; Join -> Filter -> Group -> Aggregate | ||
| + | ; Performance | ||
| + | :# Inherently very slow: special indexes, query processing techniques | ||
| + | :# Extensive use of materialized views | ||
Revision as of 04:53, 13 November 2013
Two Types of Database Activity
- OLTP (Online Transaction Processing)
- short transactions
- simple queries
- touch small portions of data
- frequent update
- OLAP (Online Analytical Processing)
- long transactions
- complex queries
- touch large portions of the data
- infrequent updates
Terminologies
- Data Warehousing
Bring data from operational(OLTP) sources into a single "warehouse" for (OLAP) analysis
- Decision Support System (DSS)
Infrastructure for data analysis, e.g., data warehouse tuned for OLAP
Star Schema
- Fact table
Updated frequently, often append-only, very large
e.g. sales transactions, course enrollments, page view
- Dimension tables
Updated infrequently, not as large
e.g. stores, items, customers, students, courses, webpages, users, advertisers
- Fact table references dimension tables. Hence, the star shape.
Example
- Fact table
- Sales(storeID, itemID, custID, qty, price)
- dimension attributes: storeID, itemID, custID (=> foreign keys)
- dependent attributes: qty, price
- Dimenstion tables
- Store(storeID, city, state)
- Item(itemID, category, brand, color, size)
- Customer(custID, name, address)
OLAP Queries
- Join -> Filter -> Group -> Aggregate
- Performance
-
- Inherently very slow: special indexes, query processing techniques
- Extensive use of materialized views