Database Systems - Lec 10: Data Storage, Indexing Structures for Files - Nguyen Thanh Tung

Nội dung text: Database Systems - Lec 10: Data Storage, Indexing Structures for Files - Nguyen Thanh Tung

1. Data Storage, Indexing Structures for Files 2
2. Disk Storage Devices ▪ Preferred secondary storage device for high storage capacity and low cost. ▪ Data stored as magnetized areas on magnetic disk surfaces. ▪ A disk pack contains several magnetic disks connected to a rotating spindle. ▪ Disks are divided into concentric circular tracks on each disk surface. • Track capacities vary typically from 4 to 50 Kbytes or more 4
3. Disk Storage Devices (contd.) 6
4. Disk Storage Devices (contd.) 8
5. Blocking ▪ Blocking: • Refers to storing a number of records in one block on the disk. ▪ Blocking factor (bfr) refers to the number of records per block. ▪ There may be empty space in a block if an integral number of records do not fit in one block. ▪ Spanned Records: • Refers to records that exceed the size of one or more blocks and hence span a number of blocks. 10
6. Files of Records (contd.) ▪ File records can be unspanned or spanned • Unspanned: no record can span two blocks • Spanned: a record can be stored in more than one block ▪ The physical disk blocks that are allocated to hold the records of a file can be contiguous, linked, or indexed. ▪ In a file of fixed-length records, all records have the same format. Usually, unspanned blocking is used with such files. ▪ Files of variable-length records require additional information to be stored in each record, such as separator characters and field types. • Usually spanned blocking is used with such files. 12
7. Unordered Files ▪ Also called a heap or a pile file. ▪ New records are inserted at the end of the file. ▪ A linear search through the file records is necessary to search for a record. • This requires reading and searching half the file blocks on the average, and is hence quite expensive. ▪ Record insertion is quite efficient. ▪ Reading the records in order of a particular field requires sorting the file records. 14
8. Ordered Files (contd.) 16
9. Hashed Files ▪ Hashing for disk files is called External Hashing ▪ The file blocks are divided into M equal-sized buckets, numbered bucket0, bucket1, , bucketM-1. • Typically, a bucket corresponds to one (or a fixed number of) disk block. ▪ One of the file fields is designated to be the hash key of the file. ▪ The record with hash key value K is stored in bucket i, where i=h(K), and h is the hashing function. ▪ Search is very efficient on the hash key. ▪ Collisions occur when a new record hashes to a bucket that is already full. • An overflow file is kept for storing such records. • Overflow records that hash to each bucket can be linked together. 18
10. Hashed Files (contd.) ▪ To reduce overflow records, a hash file is typically kept 70-80% full. ▪ The hash function h should distribute the records uniformly among the buckets • Otherwise, search time will be increased because many overflow records will exist. ▪ Main disadvantages of static external hashing: • Fixed number of buckets M is a problem if the number of records in the file grows or shrinks. • Ordered access on the hash key is quite inefficient (requires sorting the records). 20
11. Parallelizing Disk Access using RAID Technology. ▪ Secondary storage technology must take steps to keep up in performance and reliability with processor technology. ▪ A major advance in secondary storage technology is represented by the development of RAID, which originally stood for Redundant Arrays of Inexpensive Disks. ▪ The main goal of RAID is to even out the widely different rates of performance improvement of disks against those in memory and microprocessors. 22
12. Use of RAID Technology (contd.) 24
13. Storage Area Networks (contd.) ▪ Advantages of SANs are: • Flexible many-to-many connectivity among servers and storage devices using fiber channel hubs and switches. • Up to 10km separation between a server and a storage system using appropriate fiber optic cables. • Better isolation capabilities allowing non-disruptive addition of new peripherals and servers. ▪ SANs face the problem of combining storage options from multiple vendors and dealing with evolving standards of storage management software and hardware. 26
14. Indexes as Access Paths ▪ A single-level index is an auxiliary file that makes it more efficient to search for a record in the data file. ▪ The index is usually specified on one field of the file (although it could be specified on several fields) ▪ One form of an index is a file of entries , which is ordered by field value ▪ The index is called an access path on the field. 28
15. Indexes as Access Paths (contd.) ▪ Example: Given the following data file: EMPLOYEE(NAME,SSN, ADDRESS,JOB,SAL, ) ▪ Suppose that: • record size R=150 bytes block size B=512 bytes r=30000 records ▪ Then, we get: • blocking factor Bfr= B div R= 512 div 150= 3 records/block • number of file blocks b= (r/Bfr)= (30000/3)= 10000 blocks 30
16. Types of Single-Level Indexes ▪ Primary Index • Defined on an ordered data file • The data file is ordered on a key field • Includes one index entry for each block in the data file; the index entry has the key field value for the first record in the block, which is called the block anchor • A similar scheme can use the last record in a block. • A primary index is a nondense (sparse) index, since it includes an entry for each disk block of the data file and the keys of its anchor record rather than for every search value. 32
17. Types of Single-Level Indexes ▪ Clustering Index • Defined on an ordered data file • The data file is ordered on a non-key field unlike primary index, which requires that the ordering field of the data file have a distinct value for each record. • Includes one index entry for each distinct value of the field; the index entry points to the first data block that contains records with that field value. • It is another example of nondense index where Insertion and Deletion is relatively straightforward with a clustering index. 34
18. Another Clustering Index Example 36
19. Example of a Dense Secondary Index 38
20. Properties of Index Types 40
21. A Two-level Primary Index 42
22. A Node in a Search Tree with Pointers to Subtrees below It ▪ FIGURE 14.8 44
23. Dynamic Multilevel Indexes Using B-Trees and B+-Trees ▪ Most multi-level indexes use B-tree or B+-tree data structures because of the insertion and deletion problem • This leaves space in each tree node (disk block) to allow for new index entries ▪ These data structures are variations of search trees that allow efficient insertion and deletion of new search values. ▪ In B-Tree and B+-Tree data structures, each node corresponds to a disk block ▪ Each node is kept between half-full and completely full 46
24. Difference between B-tree and B+-tree ▪ In a B-tree, pointers to data records exist at all levels of the tree ▪ In a B+-tree, all pointers to data records exists at the leaf-level nodes ▪ A B+-tree can have less levels (or higher capacity of search values) than the corresponding B-tree 48
25. The Nodes of a B+-tree ▪ FIGURE 14.11 The nodes of a B+-tree • (a) Internal node of a B+-tree with q –1 search values. • (b) Leaf node of a B+-tree with q – 1 search values and q – 1 data pointers. 50
26. Q&A 52