Data Warehouses for Decision Support

Transcription

1 Data Warehouses for Decision Support Vera Goebel Department of Informatics, University of Oslo

2 What and Why of Data Warehousing Database Database Datastore Database System Database System Data System Data Extraction and Load Data Warehouse System Datacubes Queries. DSS app workstations What: A very large database containing materialized views of multiple, independent source databases. The views generally contain aggregation data (aka datacubes). Why: The data warehouse (DW) supports read-only queries for new applications, e.g., DSS, OLAP & data mining. 2

3 Data Warehouse (DW) Life Cycle The Life Cycle: Global Schema Definition Data Extraction and Load Query Processing Data Update General Problems: Heavy user demand Problems with source data ownership, format, heterogeneity Underestimating complexity & resources for all phases Boeing Computing Services DW for DSS in airplane repair DW size: 2-3 terabytes Online query services: 24 7 service Data life cycle: retain data for 70+ years (until the airplane is retired) Data update: No nighttime ; concurrent refresh is required Access paths: Support new and old methods for 70+ years 3

4 Global Schema Design Base Tables Fact Table Stores basic facts from the source databases (often denormalized) Data about past events (e.g., sales, deliverings, factory outputs,...) Have a time (or time period) associated with them Data is very unlikely to change at a data source; no updates Very large tables (up to 1 TB) Dimension Table Attributes of one dimension of a fact table (typically denormalized) A chain of dimension tables to describe attributes on other dimension tables, (normalized or denormalized) Data can change at a data source; updates executed occasionally ProductID SupplierID PurchaseDate DeliveryDate CustYrs Fact Table ProductID ProdName ProdDesc ProdStyle ManufSite SupplierID SuppName SuppAddr SuppPhone Date1stOrder Dimension Tables TimeID Quarter Year AuditName AuditID AuditComp Addr AcctName Phone ContractYr 4

5 Schema Design Patterns Star Schema F D3 D1 D2 D1, D2, D3 are denormalized Starflake Schema F D3.1 D3.2 D1 D2 D3 may be normalized or denormalized Snowflake Schema D1.1 D1.2 F1 F D2.1 D1 D3.1 F2 D2.2 D1, D2, D3 are normalized D3.2 Constellation Schema D1 stores attributes about a relationship between F1 and F2 5

6 Summary Tables aka datacubes or multidimensional tables Store precomputed query results for likely queries Reduce on-the-fly join operations Reduce on-the-fly aggregation functions, e.g., sum, avg Stores denormalized data Aggregate data from one or more fact tables and/or one or more dimension tables Discard and compute new summaries as the set of likely queries changes Dim Table#2 Fact Table Dim Table#1 Summary Table Fact Table 6

7 Summary Tables = Datacubes Total Expenses for Parts by Product, Supplier and Quarter Average Price paid to all suppliers of parts for Product P11 in the 1 st quarter GROUP BY product, quarter Supplier Fiscal Quarter All-S S1 S2 Q1 Q2 Q3 Q4 All-Q 1.2M 1.0M 0.2M 0.4M 0.6M 1.0M 2.2M P11 P14 P19 P27 P33 All-P Product Total Expenses paid to all suppliers of parts for Product P19 in the 1 st quarter GROUP BY product, quarter 2.7M 4.6M Total Expenses paid to Supplier S1 for parts for Product P33 in 2nd quarter GROUP BY supplier, product, quarter Total Expenses paid to Supplier S2 for parts for all products in all quarters GROUP BY supplier Typical, pre-computed Measures are: Sum, percentage, average, std deviation, count, min-value, max-value, percentile 7

8 Too Many Summary Tables The Schema Design Problem: Given a finite amount of disk storage, what views (summaries) will you pre-compute in the data warehouse? Factors to be considered: What queries must DW support? What source data are available? What is the time-space trade-off to store versus re-compute joins and measures? Cost to acquire and update the data? An NP-complete optimization problem Use heuristics and approximation algorithms Benefit Per Unit Space (BPUS) } Use a derivation lattice to analyze Pick By Size (PBS) possible materialized views Pick By Size Use (PBS-U) D B G A E ALL/None C H Derivation Lattice of materialized views F 8

9 A Lattice of Summary Tables Derivation Lattice Nodes: The set of attributes that would appear in the group by clause to construct this view Edges: Connect view V2 to view V1 if V1 can be used to answer queries over V2 MetaData: estimated # of records in each view Determine cost and benefit of each view Select a subset of the possible views Typical simplifying assumptions: Query cost # of records scanned to answer the query I/O costs are much larger than CPU cost to compute measures Ignore cost reductions due to using indexes to access records All queries are equally likely to occur PC P 6M PSC PS S ALL/None SC C Derivation Lattice for parts, supplier, & customers 0.1M 9

10 Benefit Per Unit Space (BPUS) D S = { A } B G S = S U {B} S = S U {F} S = S U {D} A E ALL/None C H Derivation Lattice of materialized views F Benefit- round#1 B 50 * 5 = 250 C 25 * 5 = 125 D 80 * 2 = 160 E 70 * 3 = 210 F 60 * 2 = 120 G 99 * 1 = 99 H 90 * 1 = 90 View #MRecs View #MRecs A 100 E 30 B 50 F 40 C 75 G 1 D 20 H 10 S is the set of views we will materialize bf(u,v,s) = min(#w-#v w S and w covers u) Benefit(v, S) = SUM (bf(u,v,s) u=v or v covers u) Savings: read 420M records, not 800M Benefit- round#2 B C 25 * 2 = 50 D 30 * 2 = 60 E 20 * 3 = 60 F = 70 G 49 * 1 = 49 H 40 * 1 = 40 Benefit- round#3 B C 25 * 1 = 25 D 30 * 2 = 60 E 20 * = 50 F G 49 * 1 = 49 H 30 * 1 = 30 10

11 Pick By Size (PBS) Table sizes (in millions of records) Parts+Suppls+Custs (6M) Parts+Custs (6M) Parts+Suppls (0.8M) Suppls+Custs (6M) S = {PSC} While (space > 0) Do v = smallest views If (space - v > 0) Then space = space - v S = S U {v} views = views {v} Else space = 0 Parts (0.2M) Suppls (0.01M) Custs (0.1M) PC P PSC PS S ALL/None SC C Derivation Lattice Storage Savings: Reduced from 19.2M records to 7.2M records Query Savings: Read 19.11M records, not 42M 11

12 Pick By Size-Use (PBS-U) Extends the Pick By Size algorithm to consider the frequency of queries on each possible view Table sizes (#Mrecs) & query frequency (probabilities) Parts+Suppls+Custs (6M, 0.05) Parts (0.2M, 0.1) Parts+Custs (6M, 0.3) Suppls (0.01M, 0.1) Parts+Suppls (0.8M, 0.3) Custs (0.1M, 0.1) Suppls+Custs (6M, 0.05) While (space > 0) Do v = smallest { v / prob(v), where v views } If (space - v > 0) Then space = space - v S = S U {v} views = views {v} Else space = 0 PSC PC PS SC P S C ALL/None Derivation Lattice This query frequency did not change the selected views same savings 12

13 Comparing Schema Design Algorithms All Proposed Algorithms Produce only a near optimal solution Best known is within (0.63 f ) of optimal, where f is the fraction of space consumed by the largest table Make (unrealistic) assumptions e.g., ignore indexed data access Rely heavily on having good metadata e..g., table size and query frequency Algorithmic Performance Benefit Per Unit Space (BPUS) Pick By Size (PBS) O(n log n) runtime O(n 3 ) runtime Limited applicability for PBS? Finds the near optimal solution only for SR-Hypercube lattices A lattice forms an SR-Hypercube when for each v in the lattice, except v = DBT v ((# of direct children of v) * (# of records in the child of v)) 13

14 Data Extraction and Load Step1: Extract and clean data from all sources Select source, remove data inconsistencies, add default values Step2: Materialize the views and measures Reformat data, recalculate data, merge data from multiple sources, add time elements to the data, compute measures Step3: Store data in the DW Create metadata and access path data, such as indexes Major Issue: Failure during extraction and load Approaches: UNDO/REDO logging Too expensive in time and space Incremental Checkpointing When to checkpoint? Modularize and divide the long-running tasks Must use UNDO/REDO logs also; Need high/performance logging 14

15 Materializing Summary Tables Scenario: CompsiQ has factories in 7 cities. Each factory manufactures several of CompsiQ s 30 hardware products. Each factory has 3 types of manufacturing lines: robotic, hand-assembly, and mixed-line. Schema for source data from Factory-A: YieldInfo ProductCode RoboticYield Hand-AssemYield MixedLineYield Week Year ProductInfo ProductCode ProductName ProductType FCS-Date EstProductLife Target summary query: What is last year s yield from Factory-A by product type? 15

16 Materialization using SchemaSQL What is last year s yield from Factory-A by product type? select p.producttype, sum(y.lt) from Factory-A::YieldInfo lt, Factory-A::YieldInfo y, Factory-A::ProductInfo p where lt < > ProductCode and lt < > Week and lt < > Year and y.productcode = p.productcode and y.year = 01 group by p.producttype At execution time, lt ranges over the attribute names in relation YieldInfo YieldInfo ProductCode RoboticYield Hand-AssemYield MixedLineYield Week Year ProductInfo ProductCode ProductName ProductType FCS-Date EstProductLife 16

17 Aggregation Over Irregular Blocks YieldInfo P P P P P P P P ProductInfo P11 ATMCard Net P12 SMILCard Video P13 ATMHub Net P14 MPEGCard Video P15 MP3 Audio YieldInfo ProductCode RoboticYield Hand-AssemYield MixedLineYield Week Year ProductInfo ProductCode ProductName ProductType FCS-Date EstProductLife 17

18 User Queries Retrieve pre-computed data or formulate new measures not materialized in the DW. New user operations on logical datacubes: Roll-up, Drill-down, Pivot/Rotate Slicing and Dicing with a data blade Sorting Selection Derived Attributes Supplier Fiscal Quarter All-S S1 S2 Q1 Q2 Q3 Q4 All-Q P11 P14 P19 P27 P33 Product 18

19 Query Processing Traditional query transformations Index intersection and union Advanced join algorithms Piggy-backed scans Multiple queries with different selection criteria SQL extensions => new operators Red Brick Systems has proposed 8 extensions, including: MovingSum and MovingAvg Rank When RatioToReport Tertiles Create Macro 19

20 Data Update Data sources change over time Must refresh the DW Adds new historical data to the fact tables Updates descriptive attributes in the dimension tables Forces recalculation of measures in summary tables Issues: 1. Monitoring/tracking changes at the data sources 2. Recalculation of aggregated measures 3. Refresh typically forces a shutdown for DW query processing 20

21 Monitoring Data Sources Approaches: 1. Value-deltas - Capture before and after values of all tuples changed by normal DB operations and store them in differential relations. Issues: must take the DW offline to install the modified values 2. Operation-deltas Capture SQL updates from the transaction log of each data source and build a new log of all transactions that effect data in the DW. Advantages: DW can remain online for query processing while executing data updates (using traditional concurrency control) 3. Hybrid use value-deltas and operation-deltas for different data sources or a subset of the relations from a data source. 21

22 Creating a Differential Relation Approaches at the Data Source: 1.Execute the update query 3 times (1) Select and record the before values; (2) Execute the update; (3) Select and record the after values Issues: High cost in time & space; reduces autonomy of the data sources 2. Define and insert DB triggers Triggers fire on insert, delete, and update operations; Log the before and after values Issues: Not all data sources support triggers; reduces autonomy of the data sources 22

23 Creating Operation-Deltas The process: Scan the transaction log at each data source Select pertinent transactions and delta-log them Advantage: Op-delta is much smaller than the value-delta Issues: Must transform the update operation on the data source schema into an update operation on the DW schema not always possible. Hence can not be used in all cases. 23

24 Recalculating Aggregated Measures Delta Tables Assume we have differential relations for the base facts in the data sources (i.e., value deltas) Two processing phases (Propagation & Refresh): 1) Propagation pre-compute all new tuples and all replacement tuples and store them in a delta table Differential Relations Global DW Schema Propagation Process Delta Tables 24

25 Recalculating Aggregated Measures 2) Refresh Scan the DW tuples, replace existing tuples with the pre-computed tuple values, insert new tuples from the delta tables Delta Tables DW Tables Refresh Process Updated DW Tables Issue: Can not pre-compute Delta Table for non-commutative measures Ex: average (without #records), percentiles Must compute these during the refresh phase. 25

26 Data Marting What: Stores a second copy of a subset of a DW Data Warehouse System datacubes Data Extraction and Load Data Mart System Datacubes Data Mart System Queries. Why build a data mart? Datacubes A user group with special needs (dept.) Better performance accessing fewer records To support a different user access tool Queries To enforce access control over different subsets To segment data over different hardware platforms DSS app workstations 26

27 Costs and Benefits of Data Marting System costs: More hardware (servers and networks) Define a subset of the global data model More software to: Extract data from the warehouse Load data into the mart Update the mart (after the warehouse is updated) User benefits: Define new measures not stored in the DW Better performance (mart users and DW users) Support a more appropriate user interface Ex: a browser with forms versus SQL queries Company achieves more reliable access control 27

28 Commercial DW Products Short list of companies with DW products: Informix/Red Brick Systems Oracle Prism Solutions Software AG Typical Products and Tools Specially tuned DB Server DW Developer Tools: data extraction, incremental update, index builder User Tools: ad hoc query and spreadsheet tools for DSS and post-processing (creating graphs, pie-charts, etc.) Application Developer Tools (toolkits for OLAP and DSS): spreadsheet components, statistics packages, trend analysis and forecasting components 28

29 Ongoing Research Problems How to incorporate domain and business rules into DW creation and maintenance Replacing manual tasks with intelligent agents Data acquisition, data cleaning, schema design, DW access paths analysis and index construction Separate (but related) research areas: Tools for data mining and OLAP Providing active database services in the DW 29