www.semantec.de oracle 8i/9i features which support data warehousing author: krasen paskalev...
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www.semantec.de
Oracle 8i/9i features which support Data Warehousing
Author: Krasen Paskalev
Certified Oracle DBA
Semantec GmbH.
D-71083 Herrenberg
www.semantec.de
Agenda
• ETL Features
• Data Warehouse Management
• Data Warehouse Querying
• Parallel Operations
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Agenda
• ETL (Extraction, Transformation, Transportation and Loading)
– Transportable Tablespaces– External Tables– Table Functions– MERGE Statement
• Data Warehouse Management• Data Warehouse Querying• Parallel Operations
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Transportable tablespaces
• The fastest method for moving data between databases
• The tablespeces with all their data are plugged into the data warehouse database
Production Data WarehouseTablespace Tablespace
ftp
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External Tables
• Can be directly queried and joined in SQL, PL/SQL and Java
• Avoid data staging• One step loading and transformation• Save DB space
ASCIIfile
Excelsheet
Read-only virtual tables
External files
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Table Functions• Can take a set of rows as input• Can return a set of rows as output• Can be used in the FROM clause• Can be paralellized• Can be pipelined• User defined in PL/SQL, Java or C
Region %
West
Central
East
30
50
20
Sales
TableFunction
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Table Functions
• Pipelining Data Transformation
TableFunction
TableFunction
Source TargetStep 1 Step 2
Log table
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MERGE statement
id amount
4 3000
8 1000
9 2000
id amount
4 5000
7 3000
8 6000
9 2000
UPDATE
UPDATE
INSERT
new_sales sales
MERGE INTO sales s
USING new_sales n
ON (s.id = n.id)
WHEN MATCHED THEN
UPDATE s.amount = s.amount + n.amount
WHEN NOT MATCHED THEN
INSERT (s.id, s.amount)
VALUES (n.id, n.amount)
id amount
4 2000
7 3000
8 5000
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MERGE Advantages
• Single simple SQL statement
• Can be paralellized
• Can use Bulk DML
• Fewer scans of the base table
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More ETL Features
• Direct-path Interface– SQL*Loader– CREATE AS SELECT– INSERT– Oracle Call Interface
• Multi-table INSERTs
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Agenda
• ETL Features
• Data Warehouse Management– Partitioning– Materialized Views– DBMS_STATS
• Data Warehouse Querying
• Parallel Operations
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Partitioning
Jan‘2002
Tablespace 0102
Feb‘2002
Tablespace 0202
Dec‘2002
Tablespace 1202
...
Table Sales
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Advantages of Partitioning
• Partition independance– LOAD, MOVE, Purge and DROP partitions– MERGE, SPLIT, EXCHANGE partitions– BACKUP, RESTORE, SET READ ONLY
• Partition elimination– SELECT or JOIN only the partition needed
• Parallel Operations– SELECT, UPDATE, DELETE, MERGE
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Partitioning Methods
• Hash Partitioning– Even row distribution by hash function
• Range Patitioning– <01.01.2002 | <01.02.2002 | ... | <01.01.2003
• List Partitioning– Stuttgart, Munich | Manheim, Frankfurt | ...
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Table Compression
• Stores tables or partitions in compressed format
• Reduces disk space requirements• Reduces memory requirements• Speeds up query execution• Speeds up backup and recovery• Very efficient for highly redundant data –
the FACT table• 2 to 4 times compression is usual
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Materialized Views
revenue_sum
region month revenue
sales
region month invc_sum...
SELECT region, month,
sum(invc_sum) revenue
FROM sales
GROUP BY region, month
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Advantages of Materialized Views
• Improved query/reporting performance for:– Summaries– Agregates– Joins
• Fast Refresh– Data change tracking– Partition change tracking
• No application change needed – their usage is automatic
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DBMS_STATS
• New package for gathering table and index statistics
• Gathers statistics in parallel
• Can export and import statistics
ProductionData Warehouse
DevelopmentData Warehouse
Statistics
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More Data Warehouse Management Features
• Index-organized tables
• Online index rebuild
• Online table rebuild
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Agenda
• ETL Features• Data Warehouse Management• Data Warehouse Querying
– Bitmap Indexing
– Star Query Transformation
– Agregation – ROLLUP, CUBE, Grouping Sets
– Analytic functions
• Parallel Operations
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Bitmap IndexesRegion east central west NULL
rowid 1 0 0 0
rowid 0 0 1 0
... 0 0 0 1
rowid 0 1 0 0
1
0
0
0
0
1
0
0
0
0
1
0
OR AND NOT( ) =
1
1
0
0
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Advantages of Bitmap Indexes
• Reduced response time for ad-hoq queries• Uses much less space than a B-tree index• Dramatic performance gains for large class
of queries:– Multiple AND, OR and NOT conditions– IS NULL conditions– COUNT– NOT IN - Bitmap MINUS– BETWEEN - Bitmap UNION
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Star Query Transformation• The query is re-written for efficient execution
sales cust_id prod_id amountq_id
cust_id name prod_id name q_id name
customers products quarters • Steps:1. Filter all
dimentions
2. Combine the bitmap indexes of the fact table‘s foreign keys
3. Retrieve fact and dimention other rows
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Agregation Operators
• Oracle extends the GROUP BY clause by:– ROLLUP– CUBE– Grouping Sets
2500 8000
4000
6500
10500
SELECT SUM(amount)
FROM sales
GROUP BY county, quarter
Q1
Q2
UK US
1000 3000
1500 5000
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ROLLUP and CUBE
ROLLUP(country, department, quarter)
(country, department, quarter)
(country, department)
(country)
() - Grand Total
CUBE(country, department, quarter)
(country, department, quarter)
(country, department)
(country, quarter)
(department, quarter)
(country)
(department)
(quarter)
() - Grand Total
ROLLUP – subtotals at increasing levels of agregation – from right to left
CUBE – subtotals on all combinations
n+1
2n
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Agregation Operators Advantages
• Applicable on many agregation functions:– SUM, AVG, COUNT– MIN, MAX– STDDEV, VARIANCE
• Flexible agregation groups and levels
• Runs in parallel
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Analytic functions
• Significantly improved performance for complex reports as:– Ranking – Find top 10 sales in each region– Moving agregates – What is the 90 day moving
sales average?– Period-over-period comparison – What are the
revenues from January 2002 compared to January 2001?
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Example – Moving WindowSELECT c.cust_id, t.month,
SUM(amount_sold) SALES,
AVG(SUM(amount_sold))
OVER (ORDER BY c.cust_id, t.month ROWS 2 PRECEDING) MOV_3_MONTH
FROM sales s, times t, customers c
WHERE s.time_id = t.time_id AND
s.cust_id = c.cust_id AND
t. year = 1999 AND
c.cust_id IN (6380)
GROUP BY c.cust_id, t.month
ORDER BY c.cust_id, t.month;
CUST_ID MONTH SALES MOV_3_MONTH
------- ------- ------- -----------
6380 1999-01 19,642 19,642
6380 1999-02 19,324 19,483
6380 1999-03 21,655 20,207
6380 1999-04 27,091 22,690
6380 1999-05 16,367 21,704
6380 1999-06 24,755 22,738
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More Data Warehouse Querying Features
• Function-based Indexes• Optimizer Plan Stability• Statistics for Long Running Operations• Resumable Statements• Full Outer Join• With Operator• Oracle Text “Advanced Searching with Oracle Text”
14.11.2002, 2nd Conference day
11:50-12:30, Konferenzraum EG
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Agenda
• ETL Features
• Data Warehouse Management
• Data Warehouse Querying
• Parallel Operations
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Parallel Operations
• Dramatically reduce execution time of data intensive operations
• Loading– Direct Path Load
• DDL Statements– CREATE AS SELECT, CREATE INDEX– REBUILD INDEX, REBUILD INDEX PARTITION– MOVE, SPLIT, COALESCE PARTITION
• DML Statements– INSERT AS SELECT– UPDATE, DELETE and MERGE
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Parallel Operations
• Access methods– Table and index range and full scans
• Join methods– Nested loops, Sort merge, Hash, Star transformation
• SQL operations– GROUP BY, ROLLUP , CUBE
– DISTINCT, UNION, UNION ALL
– Agregate functions
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Parallel System Requirements
• Symetric Multiprocessor Systems, Clusters or Massively Parallel Systems
• Sufficient I/O Bandwidth
• Sufficient (Underutilized) CPUs
• Sufficient Memory
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Summary
• Effective handling of multi-terabyte Data Warehouses
• Rich feature set for all Data Warehouse operations
• Flexible agregation and analytical features for high performance queries
• Effective parallelizm
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Want to know more?
Telephone:
Telephone:
Fax:
E-Mail:
Internet:
Company:Name:
Address:
Semantec GmbH.
Krasen Paskalev, Armin Singer, Peter Kopecki
Benzstr. 32D-71083 Herrenberg, Germany
Meet us here -> booth 2C at the ground floor
+49(7032)9130-0
+49(7032)9130-12
+49(7032)9130-22
krasen.paskalev@semantec.bg
singer@semantec.de
www.semantec.de
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