1. Chapter 16-17 File Structures, Hashing, Indexing, and Physical Database Design

2. Oracle SQL*Loader

3. Storage Primary storage (main memory) Secondary storage
Can be operated on directly by computer CPU
small, fast
Secondary storage
Can not be operated on directly by computer CPU
Magnetic disks, optical disks, tapes, etc.
Larger capacities, inexpensive, slower than main memory

4. Table Types of Storage with Capacity, Access Time, Max Bandwidth (Transfer Speed), and Commodity Cost

5. Table Specifications of Typical High-End Enterprise Disks from Seagate (a) Seagate Enterprise Performance 10 K HDD GB
continued on next slide

6. Storage capacity units
Kilobytes – 1000 bytes
Megabytes – 1 million bytes
Gigabytes (Gbytes) – 1 billion bytes
Terabytes – 1000 gigabytes

7. Memory Hierarchies and Storage Devices
Primary storage
Cache (static RAM)– most expensive, fast, used by CPU to speed up execution programs
Main memory (dynamic RAM) – work area for CPU

8. Secondary storage (Mass storage)
CD-ROM
Tapes
Disks
Main memory database: entire database is stored in main memory

9. Figure 16. 1 (a) A single-sided disk with read/write hardware
Figure (a) A single-sided disk with read/write hardware. (b) A disk pack with read/write

10. Figure 16. 2 Different sector organizations on disk
Figure Different sector organizations on disk. (a) Sectors subtending a fixed angle. (b) Sectors maintaining a uniform recording density.

11. Tracks
The part of a disk which passes under one read/write head while the head is stationary. The number of tracks on a disk surface therefore corresponds to the number of different radial positions of the head(s). The collection of all tracks on all surfaces at a given radial position is known a cylinder and each track is divided into sectors.

12. Cylinder
The set of tracks on a multi-headed disk that may be accessed without head movement. That is, the collection of disk tracks which are the same distance from the spindle about which the disks rotate.

13. Sector
one sector lies within a continuous range of rotational angle of the disk

14. Data transfer between main memory and disks
(in blocks)
Hardware Address of a block
Surface number
Track number
Block number
Time requires
Seek time
Rotational delay time (latency)
Block transfer time

15. Table 16.2 (continued) Specifications of Typical High-End Enterprise Disks from Seagate (a) Seagate Enterprise Performance 10 K HDD GB
continued on next slide

16. Table 16.2 (continued) Specifications of Typical High-End Enterprise Disks from Seagate (a) Seagate Enterprise Performance 10 K HDD GB

18. Figure 16.3 Interleaved concurrency versus parallel execution.

19. Figure 16.4 Use of two buffers, A and B, for reading from disk.

20. Figure 16. 5 Three record storage formats
Figure Three record storage formats. (a) A fixed-length record with six fields and size of 71 bytes. (b) A record with two variable-length fields and three fixed-length fields. (c) A variable-field record with three types of separator characters.

21. Figure 16.6 Types of record organization. (a) Unspanned. (b) Spanned.

22. Figure Some blocks of an ordered (sequential) file of EMPLOYEE records with Name as the ordering key field.

23. Table 16.3 Average Access Times for a File of b Blocks under Basic File Organizations

24. File organization Heap file (unordered file)
place new records in no order at the end of the file
Sorted file ( sequential file)
keeps the records ordered by the value of a particular file
Hashed file
Uses hash function applied to a field (hash key) to determine a record’s placement on disk
B-trees, B+ trees – use tree structure

25. Static hashing – hash address space is fixed
Hashing techniques
Static hashing – hash address space is fixed
Extendible hashing
Linear hashing

26. Hashing algorithm

27. Hash Table (Wikipedia) http://en.wikipedia.org/wiki/Hash_table

29. Figure 16. 8 Internal hashing data structures
Figure Internal hashing data structures. (a) Array of M positions for use in internal hashing. (b) Collision resolution by chaining records.

30. Figure 16.9 Matching bucket numbers to disk block addresses.

31. Figure 16.10 Handling overflow for buckets by chaining.

32. Figure 16.11 Structure of the extendible hashing scheme.

34. Figure 16.11 Structure of the extendible hashing scheme.

35. Figure 16.12 Structure of the dynamic hashing scheme.

36. Figure 16. 13 Striping of data across multiple disks
Figure Striping of data across multiple disks. (a) Bit-level striping across four disks. (b) Block-level striping across four disks.

37. Figure 16. 14 Some popular levels of RAID
Figure Some popular levels of RAID. (a) RAID level 1: Mirroring of data on two disks. (b) RAID level 5: Striping of data with distributed parity across four disks.

39. A search tree of order p is a tree such that each node contains at most p - 1 search values and p pointers in the order < P1, K1, P2, K2, ..., Pq-1, Kq-1, Pq >, where q 1 p; each Pi is a pointer to a child node (or a null pointer); and each Ki is a search value from some ordered set of values.

42. B tree of order p Each internal node in the B-tree is of the form
<P1, <K1, Pr1> , P2, <K2, Pr2> , ..., <Kq-1,Prq-1> , Pq> where q 1 p. Each Pi is a tree pointer—a pointer to another node in the B-tree. Each Pri is a data pointer —a pointer to the record whose search key field value is equal to Ki (or to the data file block containing that record).
Within each node, K1 <K2 < ... < Kq-1.
3. For all search key field values X in the subtree pointed at by Pi (the ith subtree, see Figure 06.10a), we have:
Ki-1 < X < Ki for 1 < i < q; X < Ki for i = 1; and Ki-1 < X for i = q.
Each node has at most p tree pointers.
Each node, except the root and leaf nodes, has at least (p/2) tree pointers. The root node has at least two tree pointers unless it is the only node in the tree.
A node with q tree pointers, q 1 p, has q - 1 search key field values (and hence has q - 1 data pointers).
All leaf nodes are at the same level. Leaf nodes have the same structure as internal nodes except that all of their tree pointers Pi are null.

43. Root: 1 node 15 entries 16 pointers
EXAMPLE 5: Suppose that the search field of Example 4 is a nonordering key field, and we construct a B-tree on this field. Assume that each node of the B-tree is 69 percent full. Each node, on the average, will have p * 0.69 = 23 * 0.69 or approximately 16 pointers and, hence, 15 search key field values. The average fan-out fo =16. We can start at the root and see how many values and pointers can exist, on the average, at each subsequent level:
Root: node entries pointers
Level 1: nodes entries 256 pointers
Level 2: nodes entries pointers
Level 3: nodes 61,440 entries pointers
Level 4: nodes 983,040 entries

44. B+ Trees
The structure of the internal nodes of a B+-tree of order p is as follows:
Each internal node is of the form <P1, K1, P2, K2, ..., Pq-1, Kq-1, Pq>
where q 1 p and each Pi is a tree pointer.
Within each internal node, K1 < K2 < ... <Kq-1.
For all search field values X in the subtree pointed at by Pi, we have Ki-1 < X 1 Ki for 1 < i < q; X 1 Ki for i = 1; and Ki-1 < X for i = q.
Each internal node has at most p tree pointers.
Each internal node, except the root, has at least (p/2) tree pointers. The root node has at least two tree pointers if it is an internal node.
An internal node with q pointers, q 1 p, has q - 1 search field values.

45. The structure of the leaf nodes of a B+-tree of order p (Figure 14
The structure of the leaf nodes of a B+-tree of order p (Figure 14.11b) is as follows:
Each leaf node is of the form
<<K1, Pr1> , <K2, Pr2>, ..., <Kq-1, Prq-1>, Pnext>
Where q 1 p, each Pri is a data pointer, and Pnext points to the next leaf node of the B+-tree.
Within each leaf node, K1 < K2 < ... < Kq-1, q 1 p.
Each Pri is a data pointer that points to the record whose search field value is Ki or to a file block containing the record (or to a block of record pointers that point to records whose search field value is Ki if the search field is not a key).
Each leaf node has at least (p/2) values.
All leaf nodes are at the same level.

48. Root: 1 node 22 entries 23 pointers
EXAMPLE 7: Suppose that we construct a B+-tree on the field of Example 6. To calculate the approximate number of entries of the B+-tree, we assume that each node is 69 percent full. On the average, each internal node will have 34 * 0.69 or approximately 23 pointers, and hence 22 values. Each leaf node, on the average, will hold 0.69 * pleaf = 0.69 * 31 or approximately 21 data record pointers. A B+-tree will have the following average number of entries at each level:
Root: node entries 23 pointers
Level 1: nodes entries 529 pointers
Level 2: nodes 11,638 entries 12,167 pointers
Level 3: 12,167nodes 255,507entries 279,841 pointers
Level 4: 279,841 nodes 5,876,661entries