Chapter Outline Indexes as additional auxiliary access structure Types of Single-level Ordered Indexes (ordered files) Primary Indexes Clustering Indexes Secondary Indexes Multilevel Indexes Dynamic Multilevel Indexes Using B-Trees and B+-Trees Indexes on Multiple Keys

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 <field value, pointer to record>, which is ordered by field value The index is called an access path on the field.

Indexes as Access Paths (contd.) The index file usually occupies considerably less disk blocks than the data file because its entries are much smaller A binary search on the index yields a pointer to the file record Indexes can also be characterized as dense or sparse A dense index has an index entry for every search key value (and hence every record) in the data file. A sparse (or nondense) index, on the other hand, has index entries for only some of the search values

Indexes Vs non-Index as Access Paths (example) 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= floor(B div R)= 512 div 150= 3 records/block number of file blocks b= (r/Bfr)= (30000/3)= 10000 blocks For an index on the SSN field, assume the field size VSSN=9 bytes, assume the record pointer size PR=7 bytes. Then: index entry size RI=(VSSN+ PR)=(9+7)=16 bytes index blocking factor BfrI= B div RI= 512 div 16= 32 entries/block number of index blocks b= (r/ BfrI)= (30000/32)= 938 blocks binary search needs log2bI= log2938= 10 block accesses This is compared to an average linear search cost of: (b/2)= 30000/2= 15000 block accesses If the file records are ordered, the binary search cost would be: log2b= log230000= 15 block accesses

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. Disadvantage - as any ordered file – insertion/deletion of records. move records to make space for the new record + change some index entries

Primary index on the ordering key field

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 does not have distinct value for each record of the data file 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.

A Clustering Index Example FIGURE 14.2 A clustering index on the DEPTNUMBER ordering non-key field of an EMPLOYEE file.

Another Clustering Index Example

Types of Single-Level Indexes Secondary Index A secondary index provides a secondary means of accessing a file for which some primary access already exists. The secondary index may be on a field which is a candidate key and has a unique value in every record, or a non-key with duplicate values. The index is an ordered file with two fields. The first field is of the same data type as some non-ordering field of the data file that is an indexing field. The second field is either a block pointer or a record pointer. There can be many secondary indexes (and hence, indexing fields) for the same file. Includes one entry for each record in the data file; hence, it is a dense index

Example of a Dense Secondary Index

An Example of a Secondary Index

Secondary Index - Example Example 2. Consider the data file has r = 30,000 fixed-length records of size R = 100 bytes stored on a disk with block size B = 1024 bytes. The file has b =3000 blocks. Suppose we want to search for a record with a specific value for the secondary key—a non-ordering key field of the file that is V = 9 bytes long. Without the secondary index, to do a linear search on the file would require b/2 = 3000/2 = 1500 block accesses on the average. secondary index on nonordering key field of the file - block pointer P = 6 bytes, then each index entry, Ri = V+P = (9 + 6) = 15 bytes. Blocking factor for the index, bfri = ⎣(B/Ri)⎦ = ⎣(1024/15)⎦ = 68 entries per block. In a dense secondary index, the total number of index entries ri = the number of records in the data file, which is 30,000. The #of blocks needed for the index, bi = ⎡(ri /bfri)⎤ = ⎡(3000/68)⎤ = 442 blocks. A binary search on this secondary index needs ⎡(log2bi)⎤ = ⎡(log2442)⎤ = 9 block accesses. To search for a record using the index, we need an additional block access to the data file for a total of 9 + 1 = 10 block accesses—a vast improvement over the 1500 block accesses needed on the average for a linear search, but slightly worse than the 7 block accesses required for the primary index. This difference arose because the primary index was nondense and hence shorter, with only 45 blocks in length.

Properties of Index Types

Multi-Level Indexes Because a single-level index is an ordered file, we can create a primary index to the index itself; In this case, the original index file is called the first-level index and the index to the index is called the second-level index. We can repeat the process, creating a third, fourth, ..., top level until all entries of the top level fit in one disk block A multi-level index can be created for any type of first-level index (primary, secondary, clustering) as long as the first-level index consists of more than one disk block If first level has r1 entries and blocking factor bfri = f0 r2 = ceiling (r1/f0), r3 = ceiling(r2/f0)… Total levels needed t = ceiling(logf0(r1)) or ⎡(logfo(r1))⎤

A Two-level Primary Index

Multi-Level Indexes Such a multi-level index is a form of search tree However, insertion and deletion of new index entries is a severe problem because every level of the index is an ordered file. Thus far - indexing uses an ordered index file, binary search (log2bi) to locate pointers to a disk block/record, algorithm reduces the part of the index file that we continue to search by a factor of 2. multilevel index - reduce the search by bfri (blocking factor for the index which is fan-out (fo) ), which is larger than 2. search space reduced much faster. Instead of two halves at each step, record search space is divided into n-ways (where n = fan-out), requires approximately (logfobi) block accesses, which is a substantially smaller. In most cases, the fan-out is much larger than 2.

Multi-Level Index - Example Example 3. Suppose that the dense secondary index of Example 2 is converted into a multilevel index. We calculated the index blocking factor bfri = 68 index entries per block, which is also the fan-out fo for the multilevel index; #of firstlevel blocks b1 = 442 blocks was also calculated. #of second-level blocks, b2 = ⎡(b1/fo)⎤ = ⎡(442/68)⎤ = 7 blocks #of third-level blocks, b3 = ⎡(b2/fo)⎤ = ⎡(7/68)⎤ = 1 block. Hence, the third level is the top level of the index, and t = 3. To access a record by searching the multilevel index, we must access one block at each level plus one block from the data file, so we need t + 1 = 3 + 1 = 4 block accesses. Compare this to Example 2, where 10 block accesses were needed when a single-level index and binary search were used.

A Node in a Search Tree with Pointers to Subtrees below It 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 ≤ p. Each Pi is a pointer to a child node (or a NULL pointer), and each Ki is a search value

FIGURE 14.9 A search tree of order p = 3.

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

Dynamic Multilevel Indexes Using B-Trees and B+-Trees (contd.) An insertion into a node that is not full is quite efficient If a node is full the insertion causes a split into two nodes Splitting may propagate to other tree levels A deletion is quite efficient if a node does not become less than half full If a deletion causes a node to become less than half full, it must be merged with neighboring nodes

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

B-tree Structures

B-tree Example Example 4. Suppose that the search field is a nonordering key field, and we construct a B-tree on this field with p = 23 pointers. 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: 1 node 15 key entries 16 pointers Level 1: 16 nodes 240 key entries 256 pointers Level 2: 256 nodes 3840 key entries 4096 pointers Level 3: 4096 nodes 61,440 key entries At each level, we calculated the number of key entries by multiplying the total number of pointers at the previous level by 15, the average number of entries in each node. Hence, for the given block size, pointer size, and search key field size, a two level B-tree holds 3840 + 240 + 15 = 4095 entries on the average; a three-level B-tree holds 65,535 entries on the average.

The Nodes of a B+-tree 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.

B+-tree Example Example 5. To calculate the order p of a B+-tree, suppose that the search key field is V = 9 bytes long, the block size is B = 512 bytes, a record pointer is Pr = 7 bytes, and a block pointer is P = 6 bytes. An internal node of the B+-tree can have up to p tree pointers and p – 1 search field values; these must fit into a single block. Hence, we have: (p * P) + ((p – 1) * V) ≤ B (P * 6) + ((P − 1) * 9) ≤ 512 (15 * p) ≤ 521 We can choose p to be the largest value satisfying the above inequality, which gives p = 34. This is larger than the value of 23 for the B-tree

An Example of an Insertion in a B+-tree

An Example of a Deletion in a B+-tree

Summary Types of Single-level Ordered Indexes Multilevel Indexes Primary Indexes Clustering Indexes Secondary Indexes Multilevel Indexes Dynamic Multilevel Indexes Using B-Trees and B+-Trees Indexes on Multiple Keys