Using Oracle Data Storage Structures Efficiently pptx

14
Copyright © Oracle Corporation, 2002. All rights reserved.
Using Oracle Data Storage
Structures Efficiently
14-2
Copyright © Oracle Corporation, 2002. All rights reserved.
Objectives
After completing this lesson, you should be able to
do the following:
•
Compare and evaluate the different storage
structures
•
Examine different data access methods
•
Implement different partitioning methods
14-3
Copyright © Oracle Corporation, 2002. All rights reserved.
Data Storage Structures
Cluster Index-
organized
table
Heap table
Organization by value
Heap SortedClustered
Partitioned
table
14-4
Copyright © Oracle Corporation, 2002. All rights reserved.
Selecting the Physical Structure
Factors affecting the selection:

•
Rows read in groups
•
SELECT or DML statements
•
Table size
•
Row size, row group, and block size
•
Small or large transactions
•
Using parallel queries to load or for SELECT
statements
14-6
Copyright © Oracle Corporation, 2002. All rights reserved.
Data Access Methods
To enhance performance, you can use the following
data access methods:
•
Clusters
•
Indexes
•
B-tree (normal or reverse key)
•
Bitmap
•
Function based
•
Index-organized tables

•
Materialized views
14-7
Copyright © Oracle Corporation, 2002. All rights reserved.
Clusters
Clustered orders and
order_item tables
Cluster Key
(ORD_NO)
101 ORD_DT CUST_CD
05-JAN-97 R01
PROD QTY
A4102 20
A5675 19
W0824 10
102 ORD_DT CUST_CD
07-JAN-97 N45
PROD QTY
A2091 11
G7830 20
N9587 26
Unclustered orders and
order_item tables
ORD_NO PROD QTY

101 A4102 20
102 A2091 11
102 G7830 20
102 N9587 26
101 A5675 19

101 W0824 10
ORD_NO ORD_DT CUST_CD

101 05-JAN-97 R01
102 07-JAN-97 N45
14-8
Copyright © Oracle Corporation, 2002. All rights reserved.
Cluster Types
Index cluster
Hash cluster
Hash function
14-9
Copyright © Oracle Corporation, 2002. All rights reserved.
Situations Where Clusters Are Useful
Criterion
Uniform key distribution
Evenly distributed key values
Rarely updated key
Often joined master-detail tables
Predictable number of key values
Queries using equality predicate on key
Hash
X
X
X
X
X
Index
X
X

X
14-10
Copyright © Oracle Corporation, 2002. All rights reserved.
Partitioning Methods
The following partitioning methods are available:
•
Range
•
Hash
•
List
•
Composite
Range
partitioning
Hash
partitioning
Composite
partitioning
List
partitioning
14-12
Copyright © Oracle Corporation, 2002. All rights reserved.
1
2
3
CREATE TABLE sales
(acct_no NUMBER(5),
person VARCHAR2(30),
sales_amount NUMBER(8),

week_no NUMBER(2))
PARTITION BY RANGE (week_no)
(PARTITION P1 VALUES LESS THAN (4) TABLESPACE data0,
PARTITION P2 VALUES LESS THAN (8) TABLESPACE data1,
…
PARTITION P13 VALUES LESS THAN (53)TABLESPACE data12
);
The partition key is week_no.
VALUES LESS THAN must be specified as a literal.
Physical attributes can be set per partition.
1
2
3
Range Partitioning Example
14-13
Copyright © Oracle Corporation, 2002. All rights reserved.
Hash Partitioning Overview
•
Easy to Implement
•
Enables better performance for PDML and
partition-wise joins
•
Inserts rows into partitions automatically based on
the hash of the partition key
•
Supports (hash) local indexes
•
Does not support (hash) global indexes
14-14

Copyright © Oracle Corporation, 2002. All rights reserved.
List Partitioning Example
SQL> CREATE TABLE locations
2 (location_id, street_address,
3 postal_code, city, state_province,
4 country_id)
5 STORAGE(INITIAL 10K NEXT 20K)
6 TABLESPACE users
7 PARTITION BY LIST (state_province)
8 (PARTITION region_east
9 VALUES('MA','NY','CT','ME','MD'),
10 PARTITION region_west
11 VALUES('CA','AZ','NM','OR','WA'),
12 PARTITION region_south
13 VALUES('TX','KY','TN','LA','MS'),
14 PARTITION region_central
15 VALUES('OH','ND','SD','MO','IL'));
14-15
Copyright © Oracle Corporation, 2002. All rights reserved.
Default Partition for List Partitioning
Create a DEFAULT list partition for all values not
covered by other partitions:
CREATE TABLE customer

PARTITION BY LIST (state)
(PARTITION p1 VALUES ('CA','CO'),
PARTITION p2 VALUES ('FL','TX'),
PARTITION p3 VALUES (DEFAULT)
);
14-16

Copyright © Oracle Corporation, 2002. All rights reserved.
Composite Partitioned Table Overview
•
Ideal for both historical data and data placement
•
Provides high availability and manageability, like
range partitioning
•
Improves performance for parallel DML and
supports partition-wise joins
•
Allows more granular
partition elimination
•
Supports composite local indexes
•
Does not support composite
global indexes
14-17
Copyright © Oracle Corporation, 2002. All rights reserved.
Global
Nonpartitioned index
Local partitioned index
Global Partitioned Index
Partitioned Indexes for Scalable Access
Table
partition
Table
partition
Table

partition
Table
partition
14-19
Copyright © Oracle Corporation, 2002. All rights reserved.
Partition Pruning
Partition pruning: Only the relevant
partitions are accessed.
99-May
99-Apr
99-Feb
99-Jan
99-Mar
99-Jun
sales
SQL> SELECT SUM(sales_amount)
2 FROM sales
3 WHERE sales_date BETWEEN
4 TO_DATE(‘01-MAR-1999’,
5 ‘DD-MON-YYYY’) AND
6 TO_DATE(‘31-MAY-1999’,
7 ‘DD-MON-YYYY’);
14-20
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Nonpartition-wise join
Full partition-wise join
Partial partition-wise join
Query slave Partition
Partitioned table
1

2
3
Partition-Wise Join
14-22
Copyright © Oracle Corporation, 2002. All rights reserved.
Statistics Collection for
Partitioned Objects
•
You can gather object-, partition-, or subpartition
level statistics.
•
There are GLOBAL or NON-GLOBAL statistics.
•
The dbms_stats package can gather global
statistics at any level for tables only.
•
It is not possible to gather:
–
Global histograms
–
Global statistics for indexes
14-23
Copyright © Oracle Corporation, 2002. All rights reserved.
CALL dbms_stats.gather_table_stats (
ownname => ‘o901’,
tabname => ‘sales’,
partname => ‘feb99’,
granularity => ‘partition’);
CALL dbms_stats.gather_index_stats (
ownname => ‘o901’,

indname => ‘isales’,
partname => ‘s1’);
Some dbms_stats Examples
14-25
Copyright © Oracle Corporation, 2002. All rights reserved.
Summary
In this lesson, you should have learned how to do the
following:
•
Compare and evaluate the different storage
structures
•
Examine different data access methods
•
Implement different partitioning methods