Chapter 17 Database File Indexing Techniques, BT Trees, and d B+Trees Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Ch t 17 Outline Chapter O tli Type of Single-Level Ordered Indexes Multilevel Indexes Dynamic Multilevel Indexes Using B-Trees and d B+-Trees T Indexes de es on o Multiple u t p e Keys eys Other Types of Indexes Some general Issues Concerning Indexing Copyright © 2011 Ramez Elmasri and Shamkant Navathe I d Indexes as Access A P Paths th The idea behind an ordered index access structure is similar to that behind the index used in a textbook. A single-level i l l l iindex d is i an auxiliary ili fil file that th t makes k it more efficient to search for a record in the data file The index inde is usually s all specified on one field of the file file, called an indexing field (or indexing attribute), (although it could be specified on several fields) One form of an index is a file of entries <field value, pointer to record record>,, which is ordered by field value The index is called an access path on the field Copyright © 2011 Ramez Elmasri and Shamkant Navathe I d Indexes as Access A Paths P th (cont’d.) ( t’d ) 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 file. A sparse (or nondense) index, on the other hand, has index entries for only some of the search values Copyright © 2011 Ramez Elmasri and Shamkant Navathe Indexes as Access Paths (cont’d.) ( ) Example: p Given the following g data file EMPLOYEE(NAME, SSN, ADDRESS, JOB, SAL, ... ) Suppose that: record size R=150 bytes y r=30000 records block size B=512 bytes y Then, we get: g blocking factor Bfr= B div R= 512 div 150= 3 records/block number of file blocks b= (r/Bfr)= (30000/3)= 10000 blocks Copyright © 2011 Ramez Elmasri and Shamkant Navathe Indexes as Access Paths (cont’d.) ( ) For an index on the SSN field field, assume the field size VSSN=9 bytes, assume the record pointer size PR=7 bytes Then: bytes. 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 bI = (r/ BfrI) = (30000/32) (30000/32)=938 938 blocks binaryy search needs log g2bI = log g2938 = 10 block accesses Copyright © 2011 Ramez Elmasri and Shamkant Navathe Indexes as Access Paths (cont’d.) (cont d.) For an index on the SSN field field, assume the field size VSSN=9 bytes, assume the record pointer size PR=7 bytes Then (cont’d bytes. (cont d.): ): This is compared to an average linear search cost of: • (b/2)= 10000/2 (b/2) 10000/2= 5000 block accesses If the file records are ordered, the binary search cost would be: • log2b= log210000= 14 block accesses Copyright © 2011 Ramez Elmasri and Shamkant Navathe T Types off Single-Level Si l L l Indexes I d Primary Index Clustering Indexes on the ordering nonkey field of a file Secondary Indexes on the ordering key field of an ordered file on any y nonordering g field of a file A file can have at most one physical ordering field, so it can have at most one primary index or one clustering index, but not both Copyright © 2011 Ramez Elmasri and Shamkant Navathe Pi Primary Index I d Primary Index Defined on an ordered data file The data file is ordered on a key field Includes one index entryy 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. Ap primary y index is a nondense ((sparse) p ) 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. Copyright © 2011 Ramez Elmasri and Shamkant Navathe Pi Primary Index I d (cont’d) ( t’d) The index file for a primary index occupies a much smaller space than does the data file for two reasons: There are fewer index entries than there are records in the data file E h iindex Each d entry t iis ttypically i ll smaller ll iin size i th than a d data t record because it has only two fields Copyright © 2011 Ramez Elmasri and Shamkant Navathe Primary Index on the Ordering Key Field Copyright © 2011 Ramez Elmasri and Shamkant Navathe Pi Primary Index I d Example E l Example 1: suppose that we have an ordered file with r=30 r=30,000 000 records stored on a disk block size B=1,024 bytes. File records are fixed size and are unspanned, with record length R=100 bytes. What is the blocking factor for the file? bfr= ⎣B/R⎦ = ⎣1024/100⎦=10, b= ⎡r/bfr ⎤ = ⎡30000/10⎤ =3000 How manyy block accesses are needed to retrieve a record using ga binary search based on the ordered key field? ⎡log ⎡ 23000⎤=12 ⎤ Suppose that the ordering key field of the file is V=9 bytes long, a block pointer is P=6 bytes long, and a primary index is constructed for the file file. What is the blocking factor for the index? bfri =⎣B/Ri⎦ =⎣1024/15⎦ =68 What is the total number of blocks needed to store the index? bi=⎡r ⎡ i/bfri⎤⎤=⎡3000/68⎤=45 ⎡ ⎤ How many block accesses are needed to retrieve a record using the index if a binary search is applied to the index file? ⎡log245⎤+1=7 Copyright © 2011 Ramez Elmasri and Shamkant Navathe P bl Problems off Primary Pi Indexes I d The major problem with a primary index – as with any ordered file – is insertion and deletion of records Inserting a record into its correction requires not only to move records to make space for the new record, but also change g some index entries Using an ordered overflow file can reduce the problem Use a linked list of overflow records for each block in the data fil records file; d within ithi each h bl block k and d itits overflow fl lilinked k d lilistt can be sorted to improve retrieval time Deleting g a record can be handled using g deletion makers Copyright © 2011 Ramez Elmasri and Shamkant Navathe Cl t i Index Clustering I d Defined on an ordered data file The data file is ordered on a non-key field unlike primary i d index, which hi h requires i th thatt th the ordering d i fifield ld off th the d data t fil 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 eco ds with that a field e d value a ue records It (Figure 17.3) is another example of nondense index g where Insertion and Deletion is relativelyy straightforward with a clustering index By reserving a whole block (or a cluster of contiguous blocks) for each h value l off the th cluster l t field fi ld Copyright © 2011 Ramez Elmasri and Shamkant Navathe A Clustering Index Example Copyright © 2011 Ramez Elmasri and Shamkant Navathe Cl t i Index Clustering I d (cont’d.) ( t’d ) To alleviate the record insertion and deletion problems Reserve a whole block (or a cluster of contiguous blocks) for each value of the clustering field; all records with that value are placed in the block (or block cluster) (see Figure 17.3) An index is somewhat similar to the directory structures used for extensible hashing. Both are searched to find a pointer to data block containing the desired record A index An i d search h uses th the values l off th the search h fifield ld it itself, lf whereas a hash directory search uses the hash value Copyright © 2011 Ramez Elmasri and Shamkant Navathe Another Clustering Index Example Copyright © 2011 Ramez Elmasri and Shamkant Navathe Secondary Index A secondary index provides a secondary means of accessing a file for which some primary access already l d exists i t The data file can be ordered, unordered, or hashed. 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 k with ith d duplicate li t values l Copyright © 2011 Ramez Elmasri and Shamkant Navathe Secondary Index (cont’d (cont d.)) The index is an ordered file with two fields The first field is of the same data type as some nonordering d i field fi ld off the th data d t file fil that th t is i an indexing i d i fifield ld 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 hence, Copyright © 2011 Ramez Elmasri and Shamkant Navathe Example off a Dense D Secondary Index Copyright © 2011 Ramez Elmasri and Shamkant Navathe S Secondary d Index I d (cont’d.) ( t’d ) Example 2: consider the file in Example 1 with rr=30,000 30 000 fixed length records of size R=100 bytes stored o a disk block size B=1024 bytes. The file has b=3000 blocks as calculated l l t d iin E Example l 1 1. How many number of block accesses required for doing a linear search on the file? b/2=3000/2=1500 Suppose that a dense secondary index is constructed on a norordering key field of the file that is V=9 bytes long and a block pointer is P=6 bytes y long. g What is the blocking g factor for the index? bfri = ⎣1024/15⎦= ⎣ ⎦ 68 What is the total number of blocks needed to store the index? bi=⎡ri/bfri⎤=⎡30000/68⎤=442 How many block accesses are needed to retrieve a record using the index if a binary search is applied to the index file? ⎡log g2442⎤+1=10 Copyright © 2011 Ramez Elmasri and Shamkant Navathe S Secondary d Index I d (cont’d.) ( t’d ) Options for implementing a secondary index on a nonkey, nonordering field of a file Option 1: Include several index entries (duplicate index entries) with the ( ) value – one for each record same K(i) This would be a dense index Option 2: (nondense) Have variable-length records for the index entries, with a repeating field for the pointer Keep a list of pointers <P(i <P(i,1), 1) …,P(i,k)> P(i k)> in the index entry for K(i), where one pointer to each block that contains a record whose index field value is K(i) Copyright © 2011 Ramez Elmasri and Shamkant Navathe S Secondary d Index I d (cont’d.) ( t’d ) O ti 3: Option 3 more commonly l used d Keep the index entries themselves at fixed length and have a single entry for each index field value Create an extra level of indirection to handle the multiple pointers This would be a nondense index The block p pointer P(i) ( ) in each index entry y <K(i), ( ), P(i)> () p points to a block of record pointers that lead to one of the records with value K(i) (see Figure 17.5) If the K(i) ( ) occurs in manyy records that cannot fit in a single g disk block, a cluster or linked list of blocks is used Retrieval via the index requires one or more additional block accesses A secondary index provides a logical ordering on the records by the index field. Copyright © 2011 Ramez Elmasri and Shamkant Navathe Example off Secondary Index Copyright © 2011 Ramez Elmasri and Shamkant Navathe Properties of Index Types Copyright © 2011 Ramez Elmasri and Shamkant Navathe Multi-Level Indexes Because a single-level index is an ordered file, we can create a primary index to the index itself; IIn this thi case, the th original i i l iindex d fil file iis called ll d the th firstfi t level index and the index to the index is called the second-level 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 anyy type yp of first-level index (primary, secondary, clustering) as long as the first-level index consists of more than one disk block Copyright © 2011 Ramez Elmasri and Shamkant Navathe Multi-Level Indexes (cont (cont’d d.)) The binary search requires approximately (log2bi) block accesses for an index bi block because it reduces the part of index file that we continue to search by a factor of 2 The idea behind a multilevel index is to reduce the part of index file that we continue to search by bfri, the blocking factor for index, which is larger than 2 The e value a ue o of b bfri iss called ca ed the e fan-out a out o of the e multilevel u e e index and is denoted as fo (bfri=fo) Searching g a multilevel index requires q approximately pp y (logfobi) block accesses for an index bi block Copyright © 2011 Ramez Elmasri and Shamkant Navathe M lti L Multi-Level l Indexes I d ( (cont’d.) t’d ) A multilevel index considers the index file, now refer to the first (or base) level of the a multilevel index, as an ordered file with a distinct value for each K(i) Hence, we can create a primary index for the first level; this index to the first level is called the second level of the multilevel index The e seco second d level e e has as o one ee entry y for o eac each b block oc o of the e first s level. If the first level has r1 entries and the blocking factor for the index is bfri=fo, then the first level needs ⎡(r1/fo)⎤ blocks, which is the number of entries r2 needed at the second level of the index Copyright © 2011 Ramez Elmasri and Shamkant Navathe M lti L Multi-Level l Indexes I d ( (cont’d.) t’d ) We can repeatt the W th process for f the th second d level. l l The Th third thi d level, which the primary index of the second level, has an entry y for each second-level block,, so the number of thirdlevel entries is r3= ⎡(r2/fo)⎤ A second level is required only if the first level needs more than th one bl block k off di diskk storage, t and, d similarly, i il l a thi third d level is required only if the second level needs more than one o eb block oc We can repeat the process until all the entries of some index level t fit in a single block. This block at the tth level i called is ll d th the top t index i d llevel. l Th Thus, 1 1≤(r ( 1/fo /f t) and, d h hence, t = ⎡logfor1⎤ Copyright © 2011 Ramez Elmasri and Shamkant Navathe M lti L Multi-Level l Indexes I d ( (cont’d.) t’d ) The multilevel Th l il l scheme h can be b used d on any type off iindex, d whether h h iit iis primary, clustering, or secondary – as long as the first-level index has distinct values for K(i) and fixed-length entries. Example 3: Suppose that the dense secondary index of Example 2 is converted into a multilevel index. What is the fan fan-out out fo for the multilevel index? fo=⎣1024/15⎦ =68 What are the number of first-level block b1, the number of secondlevel b2, and the number of third-level block b3? b1= ⎡ 1/fo⎤=⎡30000/68⎤=442, ⎡r ⎤ ⎡ ⎤ b2=⎡b ⎡ 1/fo⎤=⎡442/68⎤=7, ⎤ ⎡ ⎤ b3= ⎡7/68⎤=1 ⎡ ⎤ What is the number of the top level of the index t? t=3 How many block accesses are needed to retrieve a record using the multilevel index? t+1=3+1=4 Copyright © 2011 Ramez Elmasri and Shamkant Navathe A Two-Level Two Level Primary I d Index Copyright © 2011 Ramez Elmasri and Shamkant Navathe Multi-Level Indexes (cont (cont’d d.)) We can have W h a multilevel l il l primary i iindex, d which hi h would ld b be nondense. d 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 A common file organization is an ordered file with a multilevel primary index on its ordering key field field. Such an organization is called indexed sequential file. The insertion can be handled by some form of overflow file and recreate the index during the file reorganization. reorganization IBM ISAM (indexed Sequential Access Method): two-level index (cylinder, track) To retain the benefits of using multilevel indexing while reducing index insertion and deletion problem, a multilevel index that leaves some space in each of its blocks for inserting entries can be adopted. This is called a dynamic multilevel index and is often implemented by using B-tree and B+-tree. Copyright © 2011 Ramez Elmasri and Shamkant Navathe Dynamic Multilevel Indexes Using BT B-Trees and d B+-Trees B T Most multi-level M lil l iindexes d use B B-tree or B+-tree B d data structures because of the insertion and deletion problem This leaves space p 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 values. A search tree is a special type of tree that is used to guide the search for a record, given the value of record field. The multilevel index can be though of as a variation of search tree (slightly different); each node in the multilevel index can have as many as fo pointers and fo key values, where fo is the index fan-out The index field (or search field) in each node guide us to the next node, until we reach the data file block that contains the required record. eco d Copyright © 2011 Ramez Elmasri and Shamkant Navathe Dynamic Multilevel Indexes Using BT B-Trees and d B+-Trees B T (contd.) ( td ) In B-Tree and B+-Tree data structures, each node corresponds to a disk block E h node Each d iis kkeptt b between t h half-full lf f ll and d completely l t l ffullll An insertion into a node that is not full is quite efficient If a node d is i ffullll th the iinsertion ti causes a split lit iinto t ttwo nodes d Splitting may propagate to other tree levels A deletion d l ti iis quite it efficient ffi i t if a node d d does nott b become lless than half full If a deletion causes a node to become less than half full full, it must be merged with neighboring nodes Copyright © 2011 Ramez Elmasri and Shamkant Navathe A Tree Data Structure that Shows an Unbalanced U b l d Tree T Copyright © 2011 Ramez Elmasri and Shamkant Navathe Dynamic Multilevel Indexes Using BT B-Trees and d B+-Trees B T (contd.) ( td ) Search Trees A search tree of order p is a tree such that each node contains at most p p−1 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 Ki is a search value from some ordered set of values Two constraints must hold Within each Withi h node, d K1 < K2 < … < Kq-1 For all values X in the subtree pointed at by Pi, we have Ki-1< X < Ki for 1< i < q; X < Ki for i=1;; and Ki-1 q i 1< X for i=q Copyright © 2011 Ramez Elmasri and Shamkant Navathe A Node in a Search Tree with Pointers to Subtrees Below It Copyright © 2011 Ramez Elmasri and Shamkant Navathe Copyright © 2011 Ramez Elmasri and Shamkant Navathe Bt B-tree Structures St t AB B-tree tree of order p can be defined as follo follows: s Each internal node in the B-tree (Figure 14.10a) is of the form <P1, <K1, Pr1>, P2, <K2, Pr2>, …, <Kq-1, Prq-1>, Pq> where q ≤ p. Each Pi is a tree pointer to another node in the B-tree. Each Pri is a data pointer to the record whose search key field value is equal to Ki Within each node node, K1 < K2 < … < Kq-1 For all search key field values X in the subtree pointed by Pi, 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 ≤ p, has q-1 search field values All leaf nodes are at the same level. Leaf nodes have the same structure as internal nodes Copyright © 2011 Ramez Elmasri and Shamkant Navathe B-tree B tree Structures Copyright © 2011 Ramez Elmasri and Shamkant Navathe Dynamic Multilevel Indexes Using BT B-Trees and d B+-Trees B T (contd.) ( td ) Mostt implementations M i l t ti off a dynamic d i multilevel ltil l iindex d use a variation of B-tree data structure called B+-tree. In B+-tree, B+-tree data pointers are stored only at the leaf nodes of the tree; hence the structure of leaf nodes differs from the structure of internal node The leaf nodes have an entry for every value of the search field, among with a data pointer to the record (or to the block that contains this record) if the search field is a key field. For a nonkeyy search field, the pointer p p points to a block containing pointers to the data file records, creating an extra level of indirection Copyright © 2011 Ramez Elmasri and Shamkant Navathe B t B+-tree Structures St t (cont’d.) ( t’d ) The structure Th t t off the th internal i t l nodes d off a B+-tree B t off order d P is Each internal node is of the form <P1, K1, P2, K2, …, Pq-1, Kq-1, Pq> where q ≤ p. Each Pi is a tree pointer Within each internal node, K1 < K2 < … < Kq-1 For all search key field values X in the subtree pointed by Pi, we have Ki-1< X ≤ Ki for 1 < i < q; X ≤ Ki for i=1; and Ki-1 < X for i=q Each internal node has at most p tree pointers E h iinternal Each t l node, d exceptt th the roott and d lleaff nodes, d h has att lleastt ⎡(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 ≤ p, has q-1 search field values Copyright © 2011 Ramez Elmasri and Shamkant Navathe B t B+-tree Structures St t (cont’d.) ( t’d ) The structure of the leaf nodes of a B+-tree of order p is Each leaf node is of the form <<K1, Pr1>, > <K2, Pr2>, > …, <Kq-1, Prq-1>, > Pnext> where q ≤ p. Each Pri is a data pointer, and Pnex points to the next leaf node of the B+-tree Within each leaf node, K1 < K2 < … < Kq-1 , q ≤ 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 Copyright © 2011 Ramez Elmasri and Shamkant Navathe Difference between B-tree 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 leaf-level A B+-tree can have less levels (or higher capacity of search values) than the corresponding B B-tree tree Copyright © 2011 Ramez Elmasri and Shamkant Navathe The Nodes of a B+-tree tree Copyright © 2011 Ramez Elmasri and Shamkant Navathe Copyright © 2011 Ramez Elmasri and Shamkant Navathe Copyright © 2011 Ramez Elmasri and Shamkant Navathe I d Indexes on Multiple M lti l Keys K If a certain combination of attributes is used very frequently, it is advantageous to set up an access structure to provide efficient access by a key value that is a combination of those attributes Example: an EMPLOYEE file Consider a query “List the employees in department g is 59”,, where both DNO and number 4 whose age AGE are nonkey attributes The search strategies g can be Assume DNO has an index, but AGE does not, access the records having DNO=4 using the index then select from among them those records that satisfy AGE=59 Copyright © 2011 Ramez Elmasri and Shamkant Navathe I d Indexes on Multiple M lti l Keys K (cont’d.) ( t’d ) Alt Alternately, t l AGE has h an index, i d but b t DNO d does not, t access th the records d having AGE=59 using the index then select from among them those records that satisfy DNO=4 If both DNO and AGE have indexes; use both index to retrieve a set of records (or a set of pointers) and an intersection of these set of records (or pointers) yields those records that satisfy both condition However, if the set of records that meet each condition However (DNO=4 or AGE=59) individually are large, yet only a few records satisfy the combined condition, then none of above is a very efficient technique for the given search request We can consider the combination <DNO, AGE> or <AGE, DNO> as a search key made up of multiple attributes We refer the keys containing multiple attributes as composite keys Copyright © 2011 Ramez Elmasri and Shamkant Navathe Ordered Index on Multiple Att ib t Attributes In general, if an index is created on attributes <A1, A2, …, An>, the search key values are tuples with n values: <v1, v2, …, vn> E.g., <DNO, AGE> = <4, 59> A lexicographic ordering of these tuples values establishes an order on this composite search key E.g., E g <3 <3, n> proceeds <4 <4, m> for any values of m and n An index on a composite key of n attributes work similarly to any single attribute index Copyright © 2011 Ramez Elmasri and Shamkant Navathe P titi Partitioned d Hashing H hi Partitioned hashing is an extension of static external hashing that allows access on multiple keys. It is suitable only for equality comparison; rang queries are not supported. t d In partitioned hashing, for a key consisting of n components, p , the hash function is designed g to p produce a result with n separate hash addresses. The bucket address is a concatenation of these n addresses Example: DNO=4 has a hash value “100” 100 and AGE=59 has a hash value “10101”, then the bucket address will be 10010101 Th partitioned The titi dh hashing hi can b be easily il extended t d d tto any number of attributes, but cannot handle range queries on any of the component attributes Copyright © 2011 Ramez Elmasri and Shamkant Navathe G id Files Grid Fil If we wantt to t access a file fil on two t keys, k we can construct t t a grid array with one linear scale (or dimension) for each g 14.14)) of the search attributes ((see Figure The scales are made in a way as to achieve a uniform distribution of that attribute Each cell of the grid array points to some bucket address where the records corresponding to the cell are stored The method is particularly useful for range queries that would map into a set of cells corresponding to a group of g the linear scales. values along The grid file concept may be applied to any number of search keys Copyright © 2011 Ramez Elmasri and Shamkant Navathe Example of a Grid Array on DNO and d AGE Attributes Att ib t Copyright © 2011 Ramez Elmasri and Shamkant Navathe Using Hashing and Other Data St Structures t as Indexes I d It is also possible to create access structure similar to indexes that are based on hashing Th index The i d entries t i <K, <K P> can b be organized i d as a dynamically expandable hash file; searching for an entry uses the hash search algorithm on K E.g., extensible hashing (directory) and linear hashing Once an entry is found found, the pointer P is used to locate the corresponding record in the data file Copyright © 2011 Ramez Elmasri and Shamkant Navathe L i l versus Physical Logical Ph i l Indexes I d Physical Ph i l iindex d h has th the di disadvantage d t th thatt th the pointer i t mustt b be changed if the record is moved to another disk location To remedy this situation, we can use a structure called a logical index,, whose index entries are of the form <K,, Kp> e.g., a primary file organization based on linear hashing or extensible hashing Each entry has one value K for the secondary indexing field matched with the value Kp of the field used for the primary file organization By searching the secondary index K, we first locate Kp and use Kp to access the record through the primary file organization Logical indexes introduce an additional level of indirection between the access structure and the data Copyright © 2011 Ramez Elmasri and Shamkant Navathe Di Discussion i An index can be created and discarded dynamically A secondary index is usually created to avoid physical ordering g of the records in the data file It is more expensive and much harder to create primary indexes and clustering indexes dynamically because of physical ordering on the indexing field It is common to use an index to enforce a key constraint on an attribute A file fil that h h has a secondary d iindex d on every one off iits fifields ld is often called a fully inverted file. Because all indexes are secondary, new records are inserted at the end of the file; therefore the data file itself is an unordered (heap) file Copyright © 2011 Ramez Elmasri and Shamkant Navathe Summary Types of Single-level Ordered Indexes Primary Indexes Clustering Indexes Secondary y Indexes Multilevel Indexes Dynamic Multilevel Indexes Using B B-Trees Trees and B+-Trees I d Indexes on Multiple M lti l K Keys Copyright © 2011 Ramez Elmasri and Shamkant Navathe
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