Module Contents Duration Self- Study. Operation on Files: Open, Reset, Find, delete, modify, insert, close, scan

Transcription

1 Mdule-6 Strage and Indexing 6.1. Mtivatin: Crrsin is the disintegratin f an engineered material int its cnstituent atms due t chemical and electrchemical reactins with its surrundings. It includes the different measures which need t be taken fr cntrl f crrsin Syllabus: Mdule Cntents Duratin Self- Study Operatin n Files: Open, Reset, Find, delete, mdify, insert, clse, scan hashing Techniques: i) Internal Hashing ii) External Hashing, iii) Hashing Technique that allw Dynamic file expansin Types f Indexes: Single-Level Ordered Indexes; Multilevel Indexes; Overview f B-Trees and B+-Trees; Indexes n Multiple Keys 3 Lectures 3 Lectures 5 hurs 4 hurs 3 Lecture 5 hurs 6.3. Weightage in university Examinatin: Marks 6.4. Curse Outcme & Learning Outcme: CO: Student shuld be able t Build indexing mechanisms fr efficient retrieval f infrmatin frm a database LO: Implement indexes fr a database using techniques like B r B+ trees Theretical Backgrund: Basic knwledge f perating systems and file systems, knwledge f prgramming Language. any 6.6. Abbreviatins: CPU: - central prcessing unit 6.7. Ntatins: 6.8. Frmulae: 6.9. Key Definitins:

2 1. Indexing: Indexing is a data structure technique t efficiently retrieve recrds frm database files based n sme attributes n which the indexing has been dne. Indexing in database systems is similar t the ne we see in bks. 2. Hashing: Anther type f primary file rganizatin is based n hashing, which prvides very fast access t recrds under certain search cnditins. This rganizatin is usually called a hash file. The search cnditin must be an equality cnditin n a single field, called the hash field. In mst cases, the hash field is als a key field f the file, in which case it is called the hash key. The idea behind hashing is t prvide a functin h, called a hash functin r randmizing functin, which is applied t the hash field value f a recrd and yields the address f the disk blck in which the recrd is stred Intrductin File Organizatin 1. A file is rganized lgically as a sequence f recrds. 2. Recrds are mapped nt disk blcks. 3. Files are prvided as a basic cnstruct in perating systems, s we assume the existence f an underlying file system. 4. Blcks are f a fixed size determined by the perating system. 5. Recrd sizes vary. 6. In relatinal database, tuples f distinct relatins may be f different sizes. 7. One apprach t mapping database t files is t stre recrds f ne length in a given file. 8. An alternative is t structure files t accmmdate variable-length recrds. (Fixedlength is easier t implement.) Curse Cntent: File strage Databases are stred in file frmats, which cntain recrds. At physical level, the actual data is stred in electrmagnetic frmat n sme device. These strage devices can be bradly categrized int three types

3 Primary Strage The memry strage that is directly accessible t the CPU cmes under this categry. CPU's internal memry (registers), fast memry (cache), and main memry (RAM) are directly accessible t the CPU, as they are all placed n the mtherbard r CPU chipset. This strage is typically very small, ultra-fast, and vlatile. Primary strage requires cntinuus pwer supply in rder t maintain its state. In case f a pwer failure, all its data is lst. Secndary Strage Secndary strage devices are used t stre data fr future use r as backup. Secndary strage includes memry devices that are nt a part f the CPU chipset r mtherbard, fr example, magnetic disks, ptical disks (DVD, CD, etc.), hard disks, flash drives, and magnetic tapes. Tertiary Strage Tertiary strage is used t stre huge vlumes f data. Since such strage devices are external t the cmputer system, they are the slwest in speed. These strage devices are mstly used t take the back up f an entire system. Optical disks and magnetic tapes are widely used as tertiary strage. Memry Hierarchy A cmputer system has a well-defined hierarchy f memry. A CPU has direct access t it main memry as well as its inbuilt registers. The access time f the main memry is bviusly less than the CPU speed. T minimize this speed mismatch, cache memry is intrduced. Cache memry prvides the fastest access time and it cntains data that is mst frequently accessed by the CPU. The memry with the fastest access is the cstliest ne. Larger strage devices ffer slw speed and they are less expensive, hwever they can stre huge vlumes f data as cmpared t CPU registers r cache memry.

4 Magnetic Disks Hard disk drives are the mst cmmn secndary strage devices in present cmputer systems. These are called magnetic disks because they use the cncept f magnetizatin t stre infrmatin. Hard disks cnsist f metal disks cated with magnetizable material. These disks are placed vertically n a spindle. A read/write head mves in between the disks and is used t magnetize r de-magnetize the spt under it. A magnetized spt can be recgnized as 0 (zer) r 1 (ne). Hard disks are frmatted in a well-defined rder t stre data efficiently. A hard disk plate has many cncentric circles n it, called tracks. Every track is further divided int sectrs. A sectr n a hard disk typically stres 512 bytes f data. Redundant Array f Independent Disks RAID r Redundant Array f Independent Disks, is a technlgy t cnnect multiple secndary strage devices and use them as a single strage media. RAID cnsists f an array f disks in which multiple disks are cnnected tgether t achieve different gals. RAID levels define the use f disk arrays. RAID 0 In this level, a striped array f disks is implemented. The data is brken dwn int blcks and the blcks are distributed amng disks. Each disk receives a blck f data t write/read in parallel. It enhances the speed and perfrmance f the strage device. There is n parity and backup in Level 0. RAID 1 RAID 1 uses mirrring techniques. When data is sent t a RAID cntrller, it sends a cpy f data t all the disks in the array. RAID level 1 is als called mirrring and prvides 100% redundancy in case f a failure.

5 RAID 2 RAID 2 recrds Errr Crrectin Cde using Hamming distance fr its data, striped n different disks. Like level 0, each data bit in a wrd is recrded n a separate disk and ECC cdes f the data wrds are stred n a different set disks. Due t its cmplex structure and high cst, RAID 2 is nt cmmercially available. RAID 3 RAID 3 stripes the data nt multiple disks. The parity bit generated fr data wrd is stred n a different disk. This technique makes it t vercme single disk failures. RAID 4 In this level, an entire blck f data is written nt data disks and then the parity is generated and stred n a different disk. Nte that level 3 uses byte-level striping, whereas level 4 uses blck-level striping. Bth level 3 and level 4 require at least three disks t implement RAID. RAID 5 RAID 5 writes whle data blcks nt different disks, but the parity bits generated fr data blck stripe are distributed amng all the data disks rather than string them n a different dedicated disk.

6 RAID 6 RAID 6 is an extensin f level 5. In this level, tw independent parities are generated and stred in distributed fashin amng multiple disks. Tw parities prvide additinal fault tlerance. This level requires at least fur disk drives t implement RAID. Relative data and infrmatin is stred cllectively in file frmats. A file is a sequence f recrds stred in binary frmat. A disk drive is frmatted int several blcks that can stre recrds. File recrds are mapped nt thse disk blcks. File Organizatin File Organizatin defines hw file recrds are mapped nt disk blcks. We have fur types f File Organizatin t rganize file recrds Heap File Organizatin When a file is created using Heap File Organizatin, the Operating System allcates memry area t that file withut any further accunting details. File recrds can be placed anywhere in that memry area. It is the respnsibility f the sftware t manage the recrds. Heap File des nt supprt any rdering, sequencing, r indexing n its wn.

7 Sequential File Organizatin Every file recrd cntains a data field (attribute) t uniquely identify that recrd. In sequential file rganizatin, recrds are placed in the file in sme sequential rder based n the unique key field r search key. Practically, it is nt pssible t stre all the recrds sequentially in physical frm. Hash File Organizatin Hash File Organizatin uses Hash functin cmputatin n sme fields f the recrds. The utput f the hash functin determines the lcatin f disk blck where the recrds are t be placed. Clustered File Organizatin Clustered file rganizatin is nt cnsidered gd fr large databases. In this mechanism, related recrds frm ne r mre relatins are kept in the same disk blck, that is, the rdering f recrds is nt based n primary key r search key File Operatins Operatins n database files can be bradly classified int tw categries Update Operatins Retrieval Operatins Update peratins change the data values by insertin, deletin, r update. Retrieval peratins, n the ther hand, d nt alter the data but retrieve them after ptinal cnditinal filtering. In bth types f peratins, selectin plays a significant rle. Other than creatin and deletin f a file, there culd be several peratins, which can be dne n files. Open A file can be pened in ne f the tw mdes, read mde r write mde. In read mde, the perating system des nt allw anyne t alter data. In ther wrds, data is read nly. Files pened in read mde can be shared amng several entities. Write mde allws data mdificatin. Files pened in write mde can be read but cannt be shared. Lcate Every file has a file pinter, which tells the current psitin where the data is t be read r written. This pinter can be adjusted accrdingly. Using find (seek) peratin, it can be mved frward r backward.

8 Read By default, when files are pened in read mde, the file pinter pints t the beginning f the file. There are ptins where the user can tell the perating system where t lcate the file pinter at the time f pening a file. The very next data t the file pinter is read. Write User can select t pen a file in write mde, which enables them t edit its cntents. It can be deletin, insertin, r mdificatin. The file pinter can be lcated at the time f pening r can be dynamically changed if the perating system allws t d s. Clse This is the mst imprtant peratin frm the perating system s pint f view. When a request t clse a file is generated, the perating system remves all the lcks (if in shared mde), saves the data (if altered) t the secndary strage media, and releases all the buffers and file handlers assciated with the file. The rganizatin f data inside a file plays a majr rle here. The prcess t lcate the file pinter t a desired recrd inside a file varius based n whether the recrds are arranged sequentially r clustered Index We knw that data is stred in the frm f recrds. Every recrd has a key field, which helps it t be recgnized uniquely. Indexing is a data structure technique t efficiently retrieve recrds frm the database files based n sme attributes n which the indexing has been dne. Indexing in database systems is similar t what we see in bks. Indexing is defined based n its indexing attributes. Indexing can be f the fllwing types Primary Index Primary index is defined n an rdered data file. The data file is rdered n a key field. The key field is generally the primary key f the relatin. Secndary Index Secndary index may be generated frm a field which is a candidate key and has a unique value in every recrd, r a nn-key with duplicate values.

9 Clustering Index Clustering index is defined n an rdered data file. The data file is rdered n a nn-key field. Ordered Indexing is f tw types Dense Index Sparse Index Dense Index In dense index, there is an index recrd fr every search key value in the database. This makes searching faster but requires mre space t stre index recrds itself. Index recrds cntain search key value and a pinter t the actual recrd n the disk. Sparse Index In sparse index, index recrds are nt created fr every search key. An index recrd here cntains a search key and an actual pinter t the data n the disk. T search a recrd, we first prceed by index recrd and reach at the actual lcatin f the data. If the data we are lking fr is nt where we directly reach by fllwing the index, then the system starts sequential search until the desired data is fund. Multilevel Index Index recrds cmprise search-key values and data pinters. Multilevel index is stred n the disk alng with the actual database files. As the size f the database grws, s des the size f the indices. There is an immense need t keep the index recrds in the main memry s as t speed up the search peratins. If single-level index is used, then a large size index cannt be kept in memry which leads t multiple disk accesses.

10 Multi-level Index helps in breaking dwn the index int several smaller indices in rder t make the utermst level s small that it can be saved in a single disk blck, which can easily be accmmdated anywhere in the main memry B + Tree B + Tree A B + tree is a balanced binary search tree that fllws a multi-level index frmat. The leaf ndes f a B + tree dente actual data pinters. B + tree ensures that all leaf ndes remain at the same height, thus balanced. Additinally, the leaf ndes are linked using a link list; therefre, a B + tree can supprt randm access as well as sequential access. Structure f B + Tree Every leaf nde is at equal distance frm the rt nde. A B + tree is f the rder n where n is fixed fr every B + tree.

11 Internal ndes Internal (nn-leaf) ndes cntain at least n/2 pinters, except the rt nde. At mst, an internal nde can cntain n pinters. Leaf ndes Leaf ndes cntain at least n/2 recrd pinters and n/2 key values. At mst, a leaf nde can cntain n recrd pinters and n key values. Every leaf nde cntains ne blck pinter P t pint t next leaf nde and frms a linked list. B + Tree Insertin B + trees are filled frm bttm and each entry is dne at the leaf nde. If a leaf nde verflws Split nde int tw parts. Partitin at i = (m+1)/2. First i entries are stred in ne nde. Rest f the entries (i+1 nwards) are mved t a new nde. i th key is duplicated at the parent f the leaf. If a nn-leaf nde verflws Split nde int tw parts. Partitin the nde at i = (m+1)/2. Entries up t i are kept in ne nde. Rest f the entries are mved t a new nde. B + Tree Deletin B + tree entries are deleted at the leaf ndes.

12 The target entry is searched and deleted. If it is an internal nde, delete and replace with the entry frm the left psitin. After deletin, underflw is tested, If underflw ccurs, distribute the entries frm the ndes left t it. If distributin is nt pssible frm left, then Distribute frm the ndes right t it. If distributin is nt pssible frm left r frm right, then Merge the nde with left and right t it Hashing Fr a huge database structure, it can be almst next t impssible t search all the index values thrugh all its level and then reach the destinatin data blck t retrieve the desired data. Hashing is an effective technique t calculate the direct lcatin f a data recrd n the disk withut using index structure. Hashing uses hash functins with search keys as parameters t generate the address f a data recrd. Hash Organizatin Bucket A hash file stres data in bucket frmat. Bucket is cnsidered a unit f strage. A bucket typically stres ne cmplete disk blck, which in turn can stre ne r mre recrds. Hash Functin A hash functin, h, is a mapping functin that maps all the set f search-keys K t the address where actual recrds are placed. It is a functin frm search keys t bucket addresses. Static Hashing In static hashing, when a search-key value is prvided, the hash functin always cmputes the same address. Fr example, if md-4 hash functin is used, then it shall generate nly 5 values. The utput address shall always be same fr that functin. The number f buckets prvided remains unchanged at all times.

13 Operatin Insertin When a recrd is required t be entered using static hash, the hash functin h cmputes the bucket address fr search key K, where the recrd will be stred. Bucket address = h(k) Search When a recrd needs t be retrieved, the same hash functin can be used t retrieve the address f the bucket where the data is stred. Delete This is simply a search fllwed by a deletin peratin. Bucket Overflw The cnditin f bucket-verflw is knwn as cllisin. This is a fatal state fr any static hash functin. In this case, verflw chaining can be used. Overflw Chaining When buckets are full, a new bucket is allcated fr the same hash result and is linked after the previus ne. This mechanism is called Clsed Hashing.

14 Linear Prbing When a hash functin generates an address at which data is already stred, the next free bucket is allcated t it. This mechanism is called Open Hashing Dynamic Hashing 1. As the database grws ver time, we have three ptins: Chse hash functin based n current file size. Get perfrmance degradatin as file grws. Chse hash functin based n anticipated file size. Space is wasted initially. Peridically re-rganize hash structure as file grws. Requires selecting new hash functin, recmputing all addresses and generating new bucket assignments. Cstly, and shuts dwn database. 2. Sme hashing techniques allw the hash functin t be mdified dynamically t accmmdate the grwth r shrinking f the database. These are called dynamic hash functins. Extendable hashing is ne frm f dynamic hashing. Extendable hashing splits and calesces buckets as database size changes.

15 This impses sme perfrmance verhead, but space efficiency is maintained. As rerganizatin is n ne bucket at a time, verhead is acceptably lw. 3. Hw des it wrk? General extendable hash structure. We chse a hash functin that is unifrm and randm that generates values ver a relatively large range. Range is b-bit binary integers (typically b=32). is ver 4 billin, s we dn't generate that many buckets! Instead we create buckets n demand, and d nt use all b bits f the hash initially. At any pint we use i bits where. The i bits are used as an ffset int a table f bucket addresses. Value f i grws and shrinks with the database. Figure shws an extendable hash structure. Nte that the i appearing ver the bucket address table tells hw many bits are required t determine the crrect bucket. It may be the case that several entries pint t the same bucket. All such entries will have a cmmn hash prefix, but the length f this prefix may be less than i. S we give each bucket an integer giving the length f the cmmn hash prefix. This is shwn in Figure 11.9 (textbk 11.19) as. Number f bucket entries pinting t bucket j is then. 4. T find the bucket cntaining search key value : Cmpute. Take the first i high rder bits f. Lk at the crrespnding table entry fr this i-bit string. Fllw the bucket pinter in the table entry. 5. We nw lk at insertins in an extendable hashing scheme.

16 Fllw the same prcedure fr lkup, ending up in sme bucket j. If there is rm in the bucket, insert infrmatin and insert recrd in the file. If the bucket is full, we must split the bucket, and redistribute the recrds. If bucket is split we may need t increase the number f bits we use in the hash. 6. Tw cases exist: 1. If, then nly ne entry in the bucket address table pints t bucket j. Then we need t increase the size f the bucket address table s that we can include pinters t the tw buckets that result frm splitting bucket j. We increment i by ne, thus cnsidering mre f the hash, and dubling the size f the bucket address table. Each entry is replaced by tw entries, each cntaining riginal value. Nw tw entries in bucket address table pint t bucket j. We allcate a new bucket z, and set the secnd pinter t pint t z. Set and t i. Rehash all recrds in bucket j which are put in either j r z. Nw insert new recrd. It is remtely pssible, but unlikely, that the new hash will still put all f the recrds in ne bucket. If s, split again and increment i again. 2. If, then mre than ne entry in the bucket address table pints t bucket j. Then we can split bucket j withut increasing the size f the bucket address table (why?). Nte that all entries that pint t bucket j crrespnd t hash prefixes that have the same value n the leftmst bits. We allcate a new bucket z, and set and t the riginal value plus 1. Nw adjust entries in the bucket address table that previusly pinted t bucket j. Leave the first half pinting t bucket j, and make the rest pint t bucket z. Rehash each recrd in bucket j as befre. Reattempt new insert. 7. Nte that in bth cases we nly need t rehash recrds in bucket j. 8. Deletin f recrds is similar. Buckets may have t be calesced, and bucket address table may have t be halved. 9. Insertin is illustrated fr the example depsit file f Figure bit hash values n bname are shwn in Figure An initial empty hash structure is shwn in Figure We insert recrds ne by ne. We (unrealistically) assume that a bucket can nly hld 2 recrds, in rder t illustrate bth situatins described.

17 As we insert the Perryridge and Rund Hill recrds, this first bucket becmes full. When we insert the next recrd (Dwntwn), we must split the bucket. Since, we need t increase the number f bits we use frm the hash. We nw use 1 bit, allwing us buckets. This makes us duble the size f the bucket address table t tw entries. We split the bucket, placing the recrds whse search key hash begins with 1 in the new bucket, and thse with a 0 in the ld bucket (Figure 11.23). Next we attempt t insert the Redwd recrd, and find it hashes t 1. That bucket is full, and. S we must split that bucket, increasing the number f bits we must use t 2. This necessitates dubling the bucket address table again t fur entries (Figure 11.24). We rehash the entries in the ld bucket. We cntinue n fr the depsit recrds f Figure 11.20, btaining the extendable hash structure f Figure Advantages: Extendable hashing prvides perfrmance that des nt degrade as the file grws. Minimal space verhead - n buckets need be reserved fr future use. Bucket address table nly cntains ne pinter fr each hash value f current prefix length. 11. Disadvantages: Extra level f indirectin in the bucket address table Added cmplexity Liner Hashing This is anther dynamic hashing scheme, an alternative t Extendible Hashing. LH handles the prblem f lng verflw chains withut using a directry, and handles duplicates. Idea: Use a family f hash functins h0, h1, h2,... hi (key) = h(key) md(2 in); N = initial # buckets h is sme hash functin (range is nt 0 t N-1) If N = 2 d0, fr sme d0, hi cnsists f applying h and lking at the last di bits, where di = d0 + i. hi+1 dubles the range f hi (similar t directry dubling) Dat Directry avided in LH by using verflw pages, and chsing bucket t split rund-rbin. Splitting prceeds in `runds. Rund ends when all NR initial (fr rund R) buckets are split. Buckets 0 t Next-1 have been split; Next t NR yet t be split. Current rund number is Level. Search: T find bucket fr data entry r, find hlevel (r): If hlevel (r) in range `Next t NR, r belngs here. Else, r culd belng t bucket hlevel (r) r bucket hlevel (r) + NR ; must apply hlevel+1 (r) t find ut

18 Organizatin The prefix f an entire hash value is taken as a hash index. Only a prtin f the hash value is used fr cmputing bucket addresses. Every hash index has a depth value t signify hw many bits are used fr cmputing a hash functin. These bits can address 2n buckets. When all these bits are cnsumed that is, when all the buckets are full then the depth value is increased linearly and twice the buckets are allcated. Operatin Querying Lk at the depth value f the hash index and use thse bits t cmpute the bucket address. Update Perfrm a query as abve and update the data. Deletin Perfrm a query t lcate the desired data and delete the same. Insertin Cmpute the address f the bucket If the bucket is already full. Add mre buckets. Add additinal bits t the hash value. Re-cmpute the hash functin. Else Add data t the bucket, If all the buckets are full, perfrm the remedies f static hashing. Hashing is nt favrable when the data is rganized in sme rdering and the queries require a range f data. When data is discrete and randm, hash perfrms the best. Hashing algrithms have high cmplexity than indexing. All hash peratins are dne in cnstant time Ordered Indexing Hashing Addresses in the memry are srted fr key value. This key value can be primary Addresses are generated using hash functin n the key value. This key value

19 key r any ther clumn in the table. can be primary key r any ther clumn in the table. Perfrmance f this methd cmes dwn as the data increases in the file. Since it stres the data in a srted frm, when there is insert/delete/update peratin, an extra effrt t srt the recrd is needed. This reduces its perfrmance. Perfrmance f dynamic hashing will be gd when there is a frequent additin and deletin f data. But if the database is very huge, maintenance will be cstlier. Static hashing will be gd fr smaller databases where recrd size id previusly knwn. If there is a grwth in data, it results in serius prblems like bucket verflw. There will be unused data blcks due t delete/update peratin. These data blcks will nt be released fr re-use. Hence peridic maintenance f the memry is required. Else, memry is wasted and perfrmance will als degrade. Als it will be cst verhead t maintain memry. In bth static and dynamic hashing, memry is well managed. Bucket verflw is als handled t better extent in static hashing. Data blcks are designed t shrink and grw in dynamic hashing. But there will be an verhead f maintaining the bucket address table in dynamic hashing when there is a huge database grwth. Preferred fr range retrieval f data- that means when there is retrieval data fr particular range, this methd is best suited. This methd is suitable t retrieve a particular recrd based n the search key. But it will nt perfrm better if the hash functin is nt n the search key Objective Questins: 1) Blck is a) a unit f strage in which recrds are saved. b) a table with a search key by which a recrd can be fine. c) a variable which pints frm index entry t starting address f a recrd. d) all f the abve.

20 2) Index is a) a unit f strage in which recrds are saved. b) a table with a search key by which a blck f recrd can be fine. c) a variable which pints frm index entry t starting address f a Blck. d) all f the abve. 3) Pinter isa) a unit f strage in which recrds are saved. b) a table with a search key by which a blck f recrd can be fine. c) a variable which pints frm index entry t starting address f a Blck. d) all f the abve. 4) Hashing technique which allcates fixed number f buckets is classified as a) dynamic hashing b) static hashing c) external hashing d) internal hashing 5) Hashing technique which allws increase r decrease in number f buckets withut a need f directry is classified as a) glbal depth hashing b) linear hashing c) relative hashing d) lcal depth hashing 6) Special nde in tree structure which has many child ndes and ne parent nde is called a) descendant ndes b) rt nde c) leaf nde d) search nde 7) Tree structure diagram in which pinters f data are stred at leaf ndes f diagram is classified as a) b tree b) b+ tree c) b2 tree d) B* tree 8) In tree structure, nde which is free f child ndes is called descendant ndes a) rt nde b) leaf nde

21 c) search nde 9) In tree structure diagrams, nn leaf nde is called a) search nde b) descendant ndes c) external nde d) internal nde Shrt Answer Questins: 1. When is it preferable t use a dense index rather than a sparse index? Explain yur answer. Ans. It is preferable t use a dense index instead f a sparse index when the file is nt srted n the indexed field such as when the index is a secndary index) r when the index file is small cmpared t the size f memry. 2. What is the difference between a primary index and a secndary index? Ans. The primary index is n the field which specifies the sequential rder f the file. There can be nly ne primary index while there can be many secndary indices. 3. Explain the distinctin between clsed and pen hashing. Discuss the relative merits f each technique in database applicatins. Ans. Open hashing may place keys with the same hash functin value in different buckets. Clsed hashing always places such keys tgether in the same bucket. Thus in this case, different buckets can be f different sizes, thugh the implementatin may be by linking tgether fixed size buckets using verflw chains. Deletin is difficult with pen hashing as all the buckets may have t inspected befre we can ascertain that a key value has been deleted, whereas in clsed hashing nly that bucket whse address is btained by hashing the key value need be inspected. Deletins are mre cmmn in databases and hence clsed hashing is mre apprpriate fr them. Fr a small, static set f data lkups may be mre efficient using pen hashing. The symbl table f a cmpiler wuld be a gd example Lng Answer Questins: Q1.Explain Structure Of File. Q2. Explain Types Of Hashing Q3. Explain Cncept if Indexing University Questins: References: 1. Krth, Slberchatz,Sudarshan, Database System Cncepts, 6th Editin, McGraw Hill 2. Elmasri and Navathe, Fundamentals f Database Systems, 6th Editin, PEARSON Educatin. 3. G. K. Gupta : Database Management Systems, McGraw Hill

22 Practice fr Mdule 1 (Based n university pattern) Q.1 Explain Hashing Technique. (10 Marks) Q.2 Explain B Tree (5Marks) Self-assessment Q.1 Define File System. Explain all pssible peratins n file. Q.2 What is Index? Explain different types f Index.