1. MIS 385/MBA 664 Systems Implementation with DBMS/ Database Management
Dave Salisbury
( )
(web site)

2. Physical Database Design
The purpose of the physical design process is to translate the logical description of the data into technical specifications for storing and retrieving data
Goal: create a design that will provide adequate performance and insure database integrity, security, and recoverability
Decisions made in this phase have a major impact on data accessibility, response times, security, and user friendliness.

3. Physical Design Process
Inputs
Normalized relations
Volume estimates
Attribute definitions
Response time expectations
Data security needs
Backup/recovery needs
Integrity expectations
DBMS technology used
Decisions
Attribute data types
Physical record descriptions (doesn’t always match logical design)
File organizations
Indexes and database architectures
Query optimization
Leads to

4. Determining volume and usage
Data volume statistics represent the size of the business
calculated assuming business growth over a period of several years
Usage is estimated from the timing of events, transaction volumes, and reporting and query activity.
Less precise than volume statistics

5. Usage statistics for PVFC
Figure Composite usage map
(Pine Valley Furniture Company)

6. Data Volumes Figure 6.1 - Composite usage map
(Pine Valley Furniture Company)
Data volumes

7. Access Frequencies Figure 6.1 - Composite usage map
(Pine Valley Furniture Company)
Access Frequencies (per hour)

8. One usage analysis Figure 6.1 - Composite usage map
(Pine Valley Furniture Company)
Usage analysis:
140 purchased parts accessed per hour 
80 quotations accessed from these 140 purchased part accesses 
70 suppliers accessed from these 80 quotation accesses

9. Another usage analysis
Figure Composite usage map
(Pine Valley Furniture Company)
Usage analysis:
75 suppliers accessed per hour 
40 quotations accessed from these 75 supplier accesses 
40 purchased parts accessed from these 40 quotation accesses

10. Physical Design Decisions
Specify the data type for each attribute from the logical data model
minimize storage space and maximize integrity
Specify physical records by grouping attributes from the logical data model
Specify the file organization technique to use for physical storage of data records
Specify indexes to optimize data retrieval
Specify query optimization strategies

11. Field: smallest unit of data in database Field design
Designing Fields
Field: smallest unit of data in database
Field design
Choosing data type
Coding, compression, encryption
Controlling data integrity

12. Choosing Data Types CHAR – fixed-length character
VARCHAR2 – variable-length character (memo)
LONG – large number
NUMBER – positive/negative number
INTEGER – positive/negative numbers up to 38 digits
DATE – actual date – stores c/y/m/d/h/m/s
BLOB – binary large object (good for graphics, sound clips, etc.)

13. Data type selection goals
Data Format
Data type selection goals
minimize storage
represent all possible values
eliminate illegal values
improve integrity
support manipulation
Note: these have different relative importance

14. Data coding e.g., C(OAK), B(MAPLE) , etc
Implement by creating a look-up table
There is a trade-off in that you must create and store a second table and you must access this table to look up the code value
Consider using when a field has a limited number of possible values, each of which occupies a relatively large amount of space, and the number of records is large and/or the number of record accesses is small

15. Example code-look-up table at PVFC
Code saves space, but costs an additional lookup to obtain actual value.

16. Data Format decisions (integrity)
Data integrity controls
Default value
Range control
Null value control
Referential integrity
Missing data
substitute an estimate
report missing data
sensitivity testing
Triggers can be used to perform these operations

17. For example...
Suppose you were designing the age field in a student record at your university. What decisions would you make about:
data type
integrity (range, default, null)
How might your decision vary by other characteristics about the student such as degree sought?

18. Physical Records
Physical Record: A group of fields stored in adjacent memory locations and retrieved together as a unit
Page: The amount of data read or written in one I/O operation
Blocking Factor: The number of physical records per page

19. Denormalization
Transforming normalized relations into unnormalized physical record specifications
Benefits:
Can improve performance (speed) be reducing number of table lookups (i.e reduce number of necessary join queries)
Costs (due to data duplication)
Wasted storage space
Data integrity/consistency threats
Common denormalization opportunities
One-to-one relationship
Many-to-many relationship with attributes
Reference data (1:N relationship where 1-side has data not used in any other relationship)

20. Denormalization: two entities with one-to-one relationship

21. Denormalization: many-to-many relationship with nonkey attributes
Extra table access required
Null description possible

22. Denormalization: reference data
Extra table access required
Data duplication

23. Consider the following normalized relations
STORE(Store_Id, Region, Manager_Id, Square_Feet)
EMPLOYEE(Emp_Id, Store_Id, Name, Address)
DEPARTMENT(Dept#, Store_ID, Manager_Id, Sales_Goal)
SCHEDULE(Dept#, Emp_Id, Date, hours)
What opportunities might exist for denormalization?

24. Partitioning
Horizontal Partitioning: Distributing the rows of a table into several separate files
Useful for situations where different users need access to different rows
Three types: Key Range Partitioning, Hash Partitioning, or Composite Partitioning
Vertical Partitioning: Distributing the columns of a table into several separate files
Useful for situations where different users need access to different columns
The primary key must be repeated in each file
Combinations of Horizontal and Vertical
Partitions often correspond with User Schemas (user views)

25. Partitioning Advantages of Partitioning:
Records used together are grouped together
Each partition can be optimized for performance
Security, recovery
Partitions stored on different disks: contention
Take advantage of parallel processing capability
Disadvantages of Partitioning:
Slow retrievals across partitions
Complexity

26. Data Replication
Purposely storing the same data in multiple locations of the database
Improves performance by allowing multiple users to access the same data at the same time with minimum contention
Sacrifices data integrity due to data duplication
Best for data that is not updated often

27. Designing Physical Files
A named portion of secondary memory allocated for the purpose of storing physical records
Constructs to link two pieces of data:
Sequential storage.
Pointers.
File Organization:
How the files are arranged on the disk.
Access Method:
How the data can be retrieved based on the file organization.

28. Sequential file organization
If not sorted
Average time to find desired record = n/2. 1 2 n
If sorted – every insert or delete requires resort
Records of the file are stored in sequence by the primary key field values.

29. Sequential Retrieval
Consider a file of 10,000 records each occupying 1 page
Queries that require processing all records will require 10,000 accesses
e.g., Find all items of type 'E'
Many disk accesses are wasted if few records meet the condition
However, very effective if most or all records will be accessed (e.g., payroll)

30. Indexed File Organizations
Index – a separate table that contains organization of records for quick retrieval – like an index in a book.
Primary keys are automatically indexed
Oracle has a CREATE INDEX operation, and MS ACCESS allows indexes to be created for most field types
Indexing approaches:
B-tree index
Bitmap index
Hash Index
Join Index

31. Tree Search Fig. 6-7b – B-tree index
uses a tree search
Average time to find desired record = depth of the tree
Leaves of the tree are all at same level 
consistent access time

32. Hashing
A technique for reducing disk accesses for direct access
Avoids an index
Number of accesses per record can be close to one
The hash field is converted to a hash address by a hash function

33. Hashed File Organization
Hashing Algorithm: Converts a primary key value into a record address
Division-remainder method is common hashing algorithm

34. Hashing Hash algorithm
Usually uses division-remainder to determine record position. Records with same position are grouped in lists.

35. Hashing
Hash address = remainder after dividing SSN by (in this case)

36. Shortcomings of Hashing
Different hash fields may convert to the same hash address
these are called Synonyms
store the colliding record in an overflow area
Long synonym chains degrade performance
There can be only one hash field per record
The file can no longer be processed sequentially
More collisions between synonyms leads to reduced access speed

37. Bitmap index organization
Bitmap saves on space requirements
Rows - possible values of the attribute
Columns - table rows
Bit indicates whether the attribute of a row has the values

38. Join Index – speeds up join operations

39. Comparing File Organizations

40. Clustering Files
In some relational DBMSs, related records from different tables can be stored together in the same disk area
Useful for improving performance of join operations
Primary key records of the main table are stored adjacent to associated foreign key records of the dependent table
e.g. Oracle has a CREATE CLUSTER command

41. Indexing
An index is a table file that is used to determine the location of rows in another file that satisfy some condition

42. Querying with an Index
Read the index into memory
Search the index to find records meeting the condition
Access only those records containing required data
Disk accesses are substantially reduced when the query involves few records

43. Maintaining an Index
Adding a record requires at least two disk accesses:
Update the file
Update the index
Trade-off:
Faster queries
Slower maintenance (additions, deletions, and updates of records)
Thus, more static databases benefit more overall

44. Rules for Using Indexes
Use on larger tables
Index the primary key of each table
Index search fields (fields frequently in WHERE clause)
Fields in SQL ORDER BY and GROUP BY commands
When there are >100 values but not when there are <30 values

45. Rules for Using Indexes
DBMS may have limit on number of indexes per table and number of bytes per indexed field(s)
Null values will not be referenced from an index
Use indexes heavily for non-volatile databases; limit the use of indexes for volatile databases
Why? Because modifications (e.g. inserts, deletes) require updates to occur in index files

46. Rules for Adding Derived Columns
Use when aggregate values are regularly retrieved.
Use when aggregate values are costly to calculate.
Permit updating only of source data.
Create triggers to cascade changes from source data.

47. Another rule of thumb to increase performance
Consider contriving a shorter field or selecting another candidate key to substitute for a long, multi-field primary key (and all associated foreign keys)

48. RAID
Redundant Arrays of Inexpensive Disks
Exploits economies of scale of disk manufacturing for the computer market
Can give greater security
Increases fault tolerance of systems
Not a replacement for regular backup

49. RAID
The operating system sees a set of physical drives as one logical drive
Data are distributed across physical drives
All levels, except 0, have data redundancy or error-correction features
Parity codes or redundant data are used for data recovery

50. Mirroring Write Read Read error Tradeoffs
Identical copies of file are written to each drive in array
Read
Alternate pages are read simultaneously from each drive
Pages put together in memory
Access time is reduced by approximately the number of disks in the array
Read error
Read required page from another drive
Tradeoffs
Provides data security
Reduces access time
Uses more disk space

51. Mirroring
Complete Data Set
Complete Data Set
No parity

52. Striping Three drive model Write Read Read error Tradeoffs
Half of file to first drive
Half of file to second drive
Parity bit to third drive
Read
Portions from each drive are put together in memory
Read error
Lost bits are reconstructed from third drive’s parity data
Tradeoffs
Provides data security
Uses less storage space than mirroring
Not as fast as mirroring

53. Striping
One-Half Data Set
One-Half Data Set
Parity Codes

54. RAID with four disks and striping
Here, pages 1-4 can be read/written simultaneously

55. Raid Types Raid 0 Raid 1 Raid 2 Raid 3 Raid 4 Raid 5
Maximized parallelism
No redundancy
No error correction
no fault-tolerance
Raid 1
Redundant data – fault tolerant
Most common form
Raid 2
One record spans across data disks
Error correction in multiple disks– reconstruct damaged data
Raid 3
Error correction in one disk
Record spans multiple data disks (more than RAID2)
Not good for multi-user environments,
Raid 4
Multiple records per stripe
Parallelism, but slow updates due to error correction contention
Raid 5
Rotating parity array
Error correction takes place in same disks as data storage
Parallelism, better performance than Raid4

56. Database Architectures
Legacy
Systems
Current
Technology
Data
Warehouses

57. Query Optimization Parallel Query Processing
Override Automatic Query Optimization
Data Block Size -- Performance tradeoffs:
Block contention
Random vs. sequential row access speed
Row size
Overhead
Balancing I/O Across Disk Controllers

58. Query Optimizer Factors
Type of Query
Highly selective.
All or most of the records of a file.
Unique fields
Size of files
Indexes
Join Method
Nested-Loop
Merge-Scan (Both files must be ordered or indexed on the join columns.)

59. Query Optimization
Wise use of indexes
Compatible data types
Simple queries
Avoid query nesting
Temporary tables for query groups
Select only needed columns
No sort without index