1. Physical Database Design
Translate global logical data model for target DBMS
Design file organizations and indexes
Design user views
Design security mechanisms
Consider the introduction of controlled redundancy
Monitor and tune the operation system
Chapter 18(17)

2. Translate global logical data model for target DBMS
Design base relations
define data type,basic constrain of attributes
Design representation of derived data
一些統計數字可以存放的table
Design general constraints

3. Design file organizations and indexes
objective:achieve acceptable performance
transaction throughput
response time
disk storage
System resource:Memory,CPU,Disk,Network
Analyze transactions:Table 18.1(17.1),pp.531(504)
Choose file organizations
Choose indexes
Estimate disk space requirements

4. Choose file organizations
Target DBMSs usually give little or no choice on file organizations.
file types
Unordered Files(Heap)
Hash
Indexed Sequential Access Method(ISAM)
B-tree
Clusters
Appendix F (C)

5. File types(1) Heap:record by record, unordered
insert quickly, small space
deleted by mark record deleted, need reorganized periodically
need linear search
good for
data bulk-loaded into table
only a few records
usually read entire table
table has index

6. File types(2) Hash:use hash function to calculate address of record
good for exact matching
not good for pattern matching, ranges of values
When hash field is frequently updated, DBMS must delete entire record and move it to new location

7. File types(3) Indexed Sequential Access Method(ISAM)
supports exact match, pattern match, range of values, part key specification
But index are static, performance will reduce after file update
B-tree:A tree structure,Appendix F(C).5.5
Dynamic index, but ISAM is more efficient if tables are not frequently updated.
Each node in the tree is actually a disk page

8. File types(4)
Clustered tables:tables physically stored together because they share common columns and often used together.
related columns are called cluster key
indexed cluster
queries retrieve rows over a rang of cluster key values
clustered tables may grow unpredictably
hash cluster
queries retrieve rows based on equality conditions
clustered tables are static or size determined

9. Choose indexes(1) Index file type: B-tree 最常見
some DBMS can select index file type
some DBMS support full-text search index
Bitmap Index:suitable for domain with few possible value
Join Indexes

10. Choose indexes(2) Index overhead
insert must insert records to every indexes
update need to update related indexes
disk space
possible performance reduction during query optimization

11. Choose indexes(3) Guidelines: F(C).7,18(17).3(pp.509) remove indexes
if insert will increase 10% size of table, drop the indexes temporarily
Homework:
design a program to test the performance of each index file type and file type of selected DBMS.
create views for your database
prepare an environment for your APs.

12. Consider the introduction of controlled redundancy
objective:relaxing the normalization rules to improve performance
join operation
draw back of denormalization
makes implementation more complex
often scarifices flexibility
speed up retrievals but slows down updates
if often read, little update, denormalization is a possible option
Step
Implications and solutions
Chapter 19(18)

13. Monitor and Tune the OS
Memory(RAM Disk,shared memory), storage (block size,partition)
benefits
avoid buy additional hardware
possible to downsize the hardware configuration
produces faster response time and throughput
How to monitor(UNIX & Windows)
Memory, CPU, DISK I/O, Network