Chapter 11: Storage and File Structure

Chapter 11: Storage and File Structure

اسلاید 1: Chapter 11: Storage and File Structure

اسلاید 2: Chapter 11: Storage and File StructureOverview of Physical Storage MediaMagnetic DisksRAIDTertiary Storage Storage AccessFile OrganizationOrganization of Records in FilesData-Dictionary StorageStorage Structures for Object-Oriented Databases

اسلاید 3: Classification of Physical Storage MediaSpeed with which data can be accessedCost per unit of dataReliabilitydata loss on power failure or system crashphysical failure of the storage deviceCan differentiate storage into:volatile storage: loses contents when power is switched offnon-volatile storage: Contents persist even when power is switched off. Includes secondary and tertiary storage, as well as batter-backed up main-memory.

اسلاید 4: Physical Storage MediaCache – fastest and most costly form of storage; volatile; managed by the computer system hardware.Main memory:fast access (10s to 100s of nanoseconds; 1 nanosecond = 10–9 seconds)generally too small (or too expensive) to store the entire databasecapacities of up to a few Gigabytes widely used currentlyCapacities have gone up and per-byte costs have decreased steadily and rapidly (roughly factor of 2 every 2 to 3 years)Volatile — contents of main memory are usually lost if a power failure or system crash occurs.

اسلاید 5: Physical Storage Media (Cont.)Flash memory Data survives power failureData can be written at a location only once, but location can be erased and written to again Can support only a limited number (10K – 1M) of write/erase cycles.Erasing of memory has to be done to an entire bank of memory Reads are roughly as fast as main memoryBut writes are slow (few microseconds), erase is slowerCost per unit of storage roughly similar to main memory Widely used in embedded devices such as digital camerasIs a type of EEPROM (Electrically Erasable Programmable Read-Only Memory)

اسلاید 6: Physical Storage Media (Cont.)Magnetic-diskData is stored on spinning disk, and read/written magneticallyPrimary medium for the long-term storage of data; typically stores entire database.Data must be moved from disk to main memory for access, and written back for storageMuch slower access than main memory (more on this later)direct-access – possible to read data on disk in any order, unlike magnetic tapeHard disks vs floppy disksCapacities range up to roughly 400 GB currentlyMuch larger capacity and cost/byte than main memory/flash memoryGrowing constantly and rapidly with technology improvements (factor of 2 to 3 every 2 years)Survives power failures and system crashesdisk failure can destroy data, but is rare

اسلاید 7: Physical Storage Media (Cont.)Optical storage non-volatile, data is read optically from a spinning disk using a laser CD-ROM (640 MB) and DVD (4.7 to 17 GB) most popular formsWrite-one, read-many (WORM) optical disks used for archival storage (CD-R, DVD-R, DVD+R)Multiple write versions also available (CD-RW, DVD-RW, DVD+RW, and DVD-RAM)Reads and writes are slower than with magnetic disk Juke-box systems, with large numbers of removable disks, a few drives, and a mechanism for automatic loading/unloading of disks available for storing large volumes of data

اسلاید 8: Physical Storage Media (Cont.)Tape storage non-volatile, used primarily for backup (to recover from disk failure), and for archival datasequential-access – much slower than disk very high capacity (40 to 300 GB tapes available)tape can be removed from drive  storage costs much cheaper than disk, but drives are expensiveTape jukeboxes available for storing massive amounts of data hundreds of terabytes (1 terabyte = 109 bytes) to even a petabyte (1 petabyte = 1012 bytes)

اسلاید 9: Storage Hierarchy

اسلاید 10: Storage Hierarchy (Cont.)primary storage: Fastest media but volatile (cache, main memory).secondary storage: next level in hierarchy, non-volatile, moderately fast access timealso called on-line storage E.g. flash memory, magnetic diskstertiary storage: lowest level in hierarchy, non-volatile, slow access timealso called off-line storage E.g. magnetic tape, optical storage

اسلاید 11: Magnetic Hard Disk MechanismNOTE: Diagram is schematic, and simplifies the structure of actual disk drives

اسلاید 12: Magnetic DisksRead-write head Positioned very close to the platter surface (almost touching it)Reads or writes magnetically encoded information.Surface of platter divided into circular tracksOver 50K-100K tracks per platter on typical hard disksEach track is divided into sectors. A sector is the smallest unit of data that can be read or written.Sector size typically 512 bytesTypical sectors per track: 500 (on inner tracks) to 1000 (on outer tracks)To read/write a sectordisk arm swings to position head on right trackplatter spins continually; data is read/written as sector passes under headHead-disk assemblies multiple disk platters on a single spindle (1 to 5 usually)one head per platter, mounted on a common arm.Cylinder i consists of ith track of all the platters

اسلاید 13: Magnetic Disks (Cont.)Earlier generation disks were susceptible to head-crashesSurface of earlier generation disks had metal-oxide coatings which would disintegrate on head crash and damage all data on diskCurrent generation disks are less susceptible to such disastrous failures, although individual sectors may get corruptedDisk controller – interfaces between the computer system and the disk drive hardware.accepts high-level commands to read or write a sector initiates actions such as moving the disk arm to the right track and actually reading or writing the dataComputes and attaches checksums to each sector to verify that data is read back correctlyIf data is corrupted, with very high probability stored checksum won’t match recomputed checksumEnsures successful writing by reading back sector after writing itPerforms remapping of bad sectors

اسلاید 14: Disk SubsystemMultiple disks connected to a computer system through a controllerControllers functionality (checksum, bad sector remapping) often carried out by individual disks; reduces load on controllerDisk interface standards familiesATA (AT adaptor) range of standardsSATA (Serial ATA) SCSI (Small Computer System Interconnect) range of standardsSeveral variants of each standard (different speeds and capabilities)

اسلاید 15: Performance Measures of DisksAccess time – the time it takes from when a read or write request is issued to when data transfer begins. Consists of: Seek time – time it takes to reposition the arm over the correct track. Average seek time is 1/2 the worst case seek time.Would be 1/3 if all tracks had the same number of sectors, and we ignore the time to start and stop arm movement4 to 10 milliseconds on typical disksRotational latency – time it takes for the sector to be accessed to appear under the head. Average latency is 1/2 of the worst case latency.4 to 11 milliseconds on typical disks (5400 to 15000 r.p.m.)Data-transfer rate – the rate at which data can be retrieved from or stored to the disk.25 to 100 MB per second max rate, lower for inner tracksMultiple disks may share a controller, so rate that controller can handle is also importantE.g. ATA-5: 66 MB/sec, SATA: 150 MB/sec, Ultra 320 SCSI: 320 MB/sFiber Channel (FC2Gb): 256 MB/s

اسلاید 16: Performance Measures (Cont.)Mean time to failure (MTTF) – the average time the disk is expected to run continuously without any failure.Typically 3 to 5 yearsProbability of failure of new disks is quite low, corresponding to a “theoretical MTTF” of 500,000 to 1,200,000 hours for a new diskE.g., an MTTF of 1,200,000 hours for a new disk means that given 1000 relatively new disks, on an average one will fail every 1200 hoursMTTF decreases as disk ages

اسلاید 17: Optimization of Disk-Block AccessBlock – a contiguous sequence of sectors from a single track data is transferred between disk and main memory in blocks sizes range from 512 bytes to several kilobytesSmaller blocks: more transfers from diskLarger blocks: more space wasted due to partially filled blocksTypical block sizes today range from 4 to 16 kilobytesDisk-arm-scheduling algorithms order pending accesses to tracks so that disk arm movement is minimized elevator algorithm : move disk arm in one direction (from outer to inner tracks or vice versa), processing next request in that direction, till no more requests in that direction, then reverse direction and repeat

اسلاید 18: Optimization of Disk Block Access (Cont.)File organization – optimize block access time by organizing the blocks to correspond to how data will be accessedE.g. Store related information on the same or nearby cylinders.Files may get fragmented over timeE.g. if data is inserted to/deleted from the fileOr free blocks on disk are scattered, and newly created file has its blocks scattered over the diskSequential access to a fragmented file results in increased disk arm movementSome systems have utilities to defragment the file system, in order to speed up file access

اسلاید 19: Nonvolatile write buffers speed up disk writes by writing blocks to a non-volatile RAM buffer immediatelyNon-volatile RAM: battery backed up RAM or flash memoryEven if power fails, the data is safe and will be written to disk when power returnsController then writes to disk whenever the disk has no other requests or request has been pending for some timeDatabase operations that require data to be safely stored before continuing can continue without waiting for data to be written to diskWrites can be reordered to minimize disk arm movementLog disk – a disk devoted to writing a sequential log of block updates Used exactly like nonvolatile RAMWrite to log disk is very fast since no seeks are requiredNo need for special hardware (NV-RAM)File systems typically reorder writes to disk to improve performanceJournaling file systems write data in safe order to NV-RAM or log diskReordering without journaling: risk of corruption of file system dataOptimization of Disk Block Access (Cont.)

اسلاید 20: RAIDRAID: Redundant Arrays of Independent Disks disk organization techniques that manage a large numbers of disks, providing a view of a single disk of high capacity and high speed by using multiple disks in parallel, and high reliability by storing data redundantly, so that data can be recovered even if a disk fails The chance that some disk out of a set of N disks will fail is much higher than the chance that a specific single disk will fail. E.g., a system with 100 disks, each with MTTF of 100,000 hours (approx. 11 years), will have a system MTTF of 1000 hours (approx. 41 days)Techniques for using redundancy to avoid data loss are critical with large numbers of disksOriginally a cost-effective alternative to large, expensive disksI in RAID originally stood for ``inexpensive’’Today RAIDs are used for their higher reliability and bandwidth. The “I” is interpreted as independent

اسلاید 21: Improvement of Reliability via RedundancyRedundancy – store extra information that can be used to rebuild information lost in a disk failureE.g., Mirroring (or shadowing)Duplicate every disk. Logical disk consists of two physical disks.Every write is carried out on both disksReads can take place from either diskIf one disk in a pair fails, data still available in the otherData loss would occur only if a disk fails, and its mirror disk also fails before the system is repairedProbability of combined event is very small Except for dependent failure modes such as fire or building collapse or electrical power surgesMean time to data loss depends on mean time to failure, and mean time to repairE.g. MTTF of 100,000 hours, mean time to repair of 10 hours gives mean time to data loss of 500*106 hours (or 57,000 years) for a mirrored pair of disks (ignoring dependent failure modes)

اسلاید 22: Improvement in Performance via ParallelismTwo main goals of parallelism in a disk system: 1.Load balance multiple small accesses to increase throughput2.Parallelize large accesses to reduce response time.Improve transfer rate by striping data across multiple disks.Bit-level striping – split the bits of each byte across multiple disksIn an array of eight disks, write bit i of each byte to disk i.Each access can read data at eight times the rate of a single disk.But seek/access time worse than for a single diskBit level striping is not used much any moreBlock-level striping – with n disks, block i of a file goes to disk (i mod n) + 1Requests for different blocks can run in parallel if the blocks reside on different disksA request for a long sequence of blocks can utilize all disks in parallel

اسلاید 23: RAID LevelsSchemes to provide redundancy at lower cost by using disk striping combined with parity bitsDifferent RAID organizations, or RAID levels, have differing cost, performance and reliability characteristicsRAID Level 1: Mirrored disks with block stripingOffers best write performance. Popular for applications such as storing log files in a database system.RAID Level 0: Block striping; non-redundant. Used in high-performance applications where data lost is not critical.

اسلاید 24: RAID Levels (Cont.)RAID Level 2: Memory-Style Error-Correcting-Codes (ECC) with bit striping.RAID Level 3: Bit-Interleaved Parity a single parity bit is enough for error correction, not just detection, since we know which disk has failedWhen writing data, corresponding parity bits must also be computed and written to a parity bit diskTo recover data in a damaged disk, compute XOR of bits from other disks (including parity bit disk)

اسلاید 25: RAID Levels (Cont.)RAID Level 3 (Cont.)Faster data transfer than with a single disk, but fewer I/Os per second since every disk has to participate in every I/O. Subsumes Level 2 (provides all its benefits, at lower cost). RAID Level 4: Block-Interleaved Parity; uses block-level striping, and keeps a parity block on a separate disk for corresponding blocks from N other disks.When writing data block, corresponding block of parity bits must also be computed and written to parity diskTo find value of a damaged block, compute XOR of bits from corresponding blocks (including parity block) from other disks.

اسلاید 26: RAID Levels (Cont.)RAID Level 4 (Cont.)Provides higher I/O rates for independent block reads than Level 3 block read goes to a single disk, so blocks stored on different disks can be read in parallelProvides high transfer rates for reads of multiple blocks than no-stripingBefore writing a block, parity data must be computed Can be done by using old parity block, old value of current block and new value of current block (2 block reads + 2 block writes)Or by recomputing the parity value using the new values of blocks corresponding to the parity blockMore efficient for writing large amounts of data sequentiallyParity block becomes a bottleneck for independent block writes since every block write also writes to parity disk

اسلاید 27: RAID Levels (Cont.)RAID Level 5: Block-Interleaved Distributed Parity; partitions data and parity among all N + 1 disks, rather than storing data in N disks and parity in 1 disk.E.g., with 5 disks, parity block for nth set of blocks is stored on disk (n mod 5) + 1, with the data blocks stored on the other 4 disks.

اسلاید 28: RAID Levels (Cont.)RAID Level 5 (Cont.)Higher I/O rates than Level 4. Block writes occur in parallel if the blocks and their parity blocks are on different disks.Subsumes Level 4: provides same benefits, but avoids bottleneck of parity disk.RAID Level 6: P+Q Redundancy scheme; similar to Level 5, but stores extra redundant information to guard against multiple disk failures. Better reliability than Level 5 at a higher cost; not used as widely.

اسلاید 29: Choice of RAID LevelFactors in choosing RAID levelMonetary costPerformance: Number of I/O operations per second, and bandwidth during normal operationPerformance during failurePerformance during rebuild of failed diskIncluding time taken to rebuild failed diskRAID 0 is used only when data safety is not important E.g. data can be recovered quickly from other sourcesLevel 2 and 4 never used since they are subsumed by 3 and 5Level 3 is not used anymore since bit-striping forces single block reads to access all disks, wasting disk arm movement, which block striping (level 5) avoidsLevel 6 is rarely used since levels 1 and 5 offer adequate safety for almost all applicationsSo competition is between 1 and 5 only

اسلاید 30: Choice of RAID Level (Cont.)Level 1 provides much better write performance than level 5Level 5 requires at least 2 block reads and 2 block writes to write a single block, whereas Level 1 only requires 2 block writesLevel 1 preferred for high update environments such as log disksLevel 1 had higher storage cost than level 5disk drive capacities increasing rapidly (50%/year) whereas disk access times have decreased much less (x 3 in 10 years)I/O requirements have increased greatly, e.g. for Web serversWhen enough disks have been bought to satisfy required rate of I/O, they often have spare storage capacity so there is often no extra monetary cost for Level 1!Level 5 is preferred for applications with low update rate, and large amounts of data Level 1 is preferred for all other applications

اسلاید 31: Hardware IssuesSoftware RAID: RAID implementations done entirely in software, with no special hardware supportHardware RAID: RAID implementations with special hardwareUse non-volatile RAM to record writes that are being executedBeware: power failure during write can result in corrupted diskE.g. failure after writing one block but before writing the second in a mirrored systemSuch corrupted data must be detected when power is restoredRecovery from corruption is similar to recovery from failed diskNV-RAM helps to efficiently detected potentially corrupted blocksOtherwise all blocks of disk must be read and compared with mirror/parity block

اسلاید 32: Hardware Issues (Cont.)Hot swapping: replacement of disk while system is running, without power downSupported by some hardware RAID systems, reduces time to recovery, and improves availability greatlyMany systems maintain spare disks which are kept online, and used as replacements for failed disks immediately on detection of failureReduces time to recovery greatlyMany hardware RAID systems ensure that a single point of failure will not stop the functioning of the system by using Redundant power supplies with battery backupMultiple controllers and multiple interconnections to guard against controller/interconnection failures

اسلاید 33: Optical DisksCompact disk-read only memory (CD-ROM)Removable disks, 640 MB per disk Seek time about 100 msec (optical read head is heavier and slower)Higher latency (3000 RPM) and lower data-transfer rates (3-6 MB/s) compared to magnetic disksDigital Video Disk (DVD) DVD-5 holds 4.7 GB , and DVD-9 holds 8.5 GB DVD-10 and DVD-18 are double sided formats with capacities of 9.4 GB and 17 GBSlow seek time, for same reasons as CD-ROM Record once versions (CD-R and DVD-R) are populardata can only be written once, and cannot be erased.high capacity and long lifetime; used for archival storage Multi-write versions (CD-RW, DVD-RW, DVD+RW and DVD-RAM) also available

اسلاید 34: Magnetic TapesHold large volumes of data and provide high transfer ratesFew GB for DAT (Digital Audio Tape) format, 10-40 GB with DLT (Digital Linear Tape) format, 100 GB+ with Ultrium format, and 330 GB with Ampex helical scan formatTransfer rates from few to 10s of MB/sCurrently the cheapest storage medium Tapes are cheap, but cost of drives is very highVery slow access time in comparison to magnetic disks and optical disks limited to sequential access.Some formats (Accelis) provide faster seek (10s of seconds) at cost of lower capacityUsed mainly for backup, for storage of infrequently used information, and as an off-line medium for transferring information from one system to another.Tape jukeboxes used for very large capacity storage(terabyte (1012 bytes) to petabye (1015 bytes)

اسلاید 35: Storage AccessA database file is partitioned into fixed-length storage units called blocks. Blocks are units of both storage allocation and data transfer.Database system seeks to minimize the number of block transfers between the disk and memory. We can reduce the number of disk accesses by keeping as many blocks as possible in main memory.Buffer – portion of main memory available to store copies of disk blocks.Buffer manager – subsystem responsible for allocating buffer space in main memory.

اسلاید 36: Buffer ManagerPrograms call on the buffer manager when they need a block from disk.If the block is already in the buffer, buffer manager returns the address of the block in main memoryIf the block is not in the buffer, the buffer managerAllocates space in the buffer for the blockReplacing (throwing out) some other block, if required, to make space for the new block.Replaced block written back to disk only if it was modified since the most recent time that it was written to/fetched from the disk.Reads the block from the disk to the buffer, and returns the address of the block in main memory to requester.

اسلاید 37: Buffer-Replacement PoliciesMost operating systems replace the block least recently used (LRU strategy)Idea behind LRU – use past pattern of block references as a predictor of future referencesQueries have well-defined access patterns (such as sequential scans), and a database system can use the information in a user’s query to predict future referencesLRU can be a bad strategy for certain access patterns involving repeated scans of data e.g. when computing the join of 2 relations r and s by a nested loops for each tuple tr of r do for each tuple ts of s do if the tuples tr and ts match …Mixed strategy with hints on replacement strategy provided by the query optimizer is preferable

اسلاید 38: Buffer-Replacement Policies (Cont.)Pinned block – memory block that is not allowed to be written back to disk.Toss-immediate strategy – frees the space occupied by a block as soon as the final tuple of that block has been processedMost recently used (MRU) strategy – system must pin the block currently being processed. After the final tuple of that block has been processed, the block is unpinned, and it becomes the most recently used block.Buffer manager can use statistical information regarding the probability that a request will reference a particular relationE.g., the data dictionary is frequently accessed. Heuristic: keep data-dictionary blocks in main memory bufferBuffer managers also support forced output of blocks for the purpose of recovery (more in Chapter 17)

اسلاید 39: File OrganizationThe database is stored as a collection of files. Each file is a sequence of records. A record is a sequence of fields.One approach:assume record size is fixedeach file has records of one particular type only different files are used for different relationsThis case is easiest to implement; will consider variable length records later.

اسلاید 40: Fixed-Length RecordsSimple approach:Store record i starting from byte n  (i – 1), where n is the size of each record.Record access is simple but records may cross blocksModification: do not allow records to cross block boundariesDeletion of record I: alternatives:move records i + 1, . . ., n to i, . . . , n – 1move record n to ido not move records, but link all free records on a free list

اسلاید 41: Free ListsStore the address of the first deleted record in the file header.Use this first record to store the address of the second deleted record, and so onCan think of these stored addresses as pointers since they “point” to the location of a record.More space efficient representation: reuse space for normal attributes of free records to store pointers. (No pointers stored in in-use records.)

اسلاید 42: Variable-Length RecordsVariable-length records arise in database systems in several ways:Storage of multiple record types in a file.Record types that allow variable lengths for one or more fields.Record types that allow repeating fields (used in some older data models).

اسلاید 43: Variable-Length Records: Slotted Page StructureSlotted page header contains:number of record entriesend of free space in the blocklocation and size of each recordRecords can be moved around within a page to keep them contiguous with no empty space between them; entry in the header must be updated.Pointers should not point directly to record — instead they should point to the entry for the record in header.

اسلاید 44: Organization of Records in FilesHeap – a record can be placed anywhere in the file where there is spaceSequential – store records in sequential order, based on the value of the search key of each recordHashing – a hash function computed on some attribute of each record; the result specifies in which block of the file the record should be placedRecords of each relation may be stored in a separate file. In a multitable clustering file organization records of several different relations can be stored in the same fileMotivation: store related records on the same block to minimize I/O

اسلاید 45: Sequential File OrganizationSuitable for applications that require sequential processing of the entire file The records in the file are ordered by a search-key

اسلاید 46: Sequential File Organization (Cont.)Deletion – use pointer chainsInsertion –locate the position where the record is to be insertedif there is free space insert there if no free space, insert the record in an overflow blockIn either case, pointer chain must be updatedNeed to reorganize the file from time to time to restore sequential order

اسلاید 47: Multitable Clustering File OrganizationStore several relations in one file using a multitable clustering file organization

اسلاید 48: Multitable Clustering File Organization (cont.)Multitable clustering organization of customer and depositor:good for queries involving depositor customer, and for queries involving one single customer and his accountsbad for queries involving only customerresults in variable size recordsCan add pointer chains to link records of a particular relation

اسلاید 49: Data Dictionary StorageInformation about relationsnames of relationsnames and types of attributes of each relationnames and definitions of viewsintegrity constraintsUser and accounting information, including passwordsStatistical and descriptive datanumber of tuples in each relationPhysical file organization informationHow relation is stored (sequential/hash/…)Physical location of relation Information about indices (Chapter 12) Data dictionary (also called system catalog) stores metadata: that is, data about data, such as

اسلاید 50: Data Dictionary Storage (Cont.)Catalog structureRelational representation on diskspecialized data structures designed for efficient access, in memoryA possible catalog representation:Relation_metadata = (relation_name, number_of_attributes, storage_organization, location) Attribute_metadata = (attribute_name, relation_name, domain_type, position, length)User_metadata = (user_name, encrypted_password, group)Index_metadata = (index_name, relation_name, index_type, index_attributes)View_metadata = (view_name, definition)

اسلاید 51: End of Chapter

اسلاید 52: Record RepresentationRecords with fixed length fields are easy to representSimilar to records (structs) in programming languagesExtensions to represent null valuesE.g. a bitmap indicating which attributes are nullVariable length fields can be represented by a pair (offset,length) where offset is the location within the record and length is field length. All fields start at predefined location, but extra indirection required for variable length fieldsExample record structure of account recordaccount_numberbranch_namebalancePerryridgeA-10240010

اسلاید 53: File Containing account Records

اسلاید 54: File of Figure 11.6, with Record 2 Deleted and All Records Moved

اسلاید 55: File of Figure 11.6, With Record 2 deleted and Final Record Moved

اسلاید 56: Byte-String Representation of Variable-Length Records

اسلاید 57: Clustering File Structure

اسلاید 58: Clustering File Structure With Pointer Chains

اسلاید 59: The depositor Relation

اسلاید 60: The customer Relation

اسلاید 61: Clustering File Structure

اسلاید 62:

اسلاید 63: Figure 11.4

اسلاید 64: Figure 11.7

اسلاید 65: Figure 11.8

اسلاید 66: Figure 11.100

اسلاید 67: Figure 11.20

اسلاید 68: Byte-String Representation of Variable-Length RecordsByte string representationAttach an end-of-record () control character to the end of each recordDifficulty with deletionDifficulty with growth

اسلاید 69: Fixed-Length RepresentationUse one or more fixed length records: reserved spacepointersReserved space – can use fixed-length records of a known maximum length; unused space in shorter records filled with a null or end-of-record symbol.

اسلاید 70: Pointer MethodPointer method A variable-length record is represented by a list of fixed-length records, chained together via pointers.Can be used even if the maximum record length is not known

اسلاید 71: Pointer Method (Cont.)Disadvantage to pointer structure; space is wasted in all records except the first in a a chain.Solution is to allow two kinds of block in file:Anchor block – contains the first records of chainOverflow block – contains records other than those that are the first records of chairs.

اسلاید 72: Mapping of Objects to FilesMapping objects to files is similar to mapping tuples to files in a relational system; object data can be stored using file structures.Objects in O-O databases may lack uniformity and may be very large; such objects have to managed differently from records in a relational system.Set fields with a small number of elements may be implemented using data structures such as linked lists. Set fields with a larger number of elements may be implemented as separate relations in the database.Set fields can also be eliminated at the storage level by normalization.Similar to conversion of multivalued attributes of E-R diagrams to relations

اسلاید 73: Mapping of Objects to Files (Cont.)Objects are identified by an object identifier (OID); the storage system needs a mechanism to locate an object given its OID (this action is called dereferencing).logical identifiers do not directly specify an object’s physical location; must maintain an index that maps an OID to the object’s actual location.physical identifiers encode the location of the object so the object can be found directly. Physical OIDs typically have the following parts:1. a volume or file identifier2. a page identifier within the volume or file3. an offset within the page

اسلاید 74: Management of Persistent PointersPhysical OIDs may be a unique identifier. This identifier is stored in the object also and is used to detect references via dangling pointers.

اسلاید 75: Management of Persistent Pointers (Cont.)Implement persistent pointers using OIDs; persistent pointers are substantially longer than are in-memory pointers Pointer swizzling cuts down on cost of locating persistent objects already in-memory.Software swizzling (swizzling on pointer deference)When a persistent pointer is first dereferenced, the pointer is swizzled (replaced by an in-memory pointer) after the object is located in memory.Subsequent dereferences of of the same pointer become cheap.The physical location of an object in memory must not change if swizzled pointers pont to it; the solution is to pin pages in memoryWhen an object is written back to disk, any swizzled pointers it contains need to be unswizzled.

اسلاید 76: Hardware SwizzlingWith hardware swizzling, persistent pointers in objects need the same amount of space as in-memory pointers — extra storage external to the object is used to store rest of pointer information.Uses virtual memory translation mechanism to efficiently and transparently convert between persistent pointers and in-memory pointers.All persistent pointers in a page are swizzled when the page is first read in. thus programmers have to work with just one type of pointer, i.e., in-memory pointer.some of the swizzled pointers may point to virtual memory addresses that are currently not allocated any real memory (and do not contain valid data)

اسلاید 77: Hardware SwizzlingPersistent pointer is conceptually split into two parts: a page identifier, and an offset within the page.The page identifier in a pointer is a short indirect pointer: Each page has a translation table that provides a mapping from the short page identifiers to full database page identifiers.Translation table for a page is small (at most 1024 pointers in a 4096 byte page with 4 byte pointer)Multiple pointers in page to the same page share same entry in the translation table.

اسلاید 78: Hardware Swizzling (Cont.)Page image before swizzling (page located on disk)

اسلاید 79: Hardware Swizzling (Cont.)When system loads a page into memory the persistent pointers in the page are swizzled as described belowPersistent pointers in each object in the page are located using object type informationFor each persistent pointer (pi, oi) find its full page ID Pi If Pi does not already have a virtual memory page allocated to it, allocate a virtual memory page to Pi and read-protect the pageNote: there need not be any physical space (whether in memory or on disk swap-space) allocated for the virtual memory page at this point. Space can be allocated later if (and when) Pi is accessed. In this case read-protection is not required.Accessing a memory location in the page in the will result in a segmentation violation, which is handled as described later Let vi be the virtual page allocated to Pi (either earlier or above)Replace (pi, oi) by (vi, oi) Replace each entry (pi, Pi) in the translation table, by (vi, Pi)

اسلاید 80: Hardware Swizzling (Cont.)When an in-memory pointer is dereferenced, if the operating system detects the page it points to has not yet been allocated storage, or is read-protected, a segmentation violation occurs.The mmap() call in Unix is used to specify a function to be invoked on segmentation violationThe function does the following when it is invokedAllocate storage (swap-space) for the page containing the referenced address, if storage has not been allocated earlier. Turn off read-protectionRead in the page from diskPerform pointer swizzling for each persistent pointer in the page, as described earlier

اسلاید 81: Hardware Swizzling (Cont.)Page with short page identifier 2395 was allocated address 5001. Observe change in pointers and translation table.Page with short page identifier 4867 has been allocated address 4867. No change in pointer and translation table.Page image after swizzling

اسلاید 82: Hardware Swizzling (Cont.)After swizzling, all short page identifiers point to virtual memory addresses allocated for the corresponding pagesfunctions accessing the objects are not even aware that it has persistent pointers, and do not need to be changed in any way!can reuse existing code and libraries that use in-memory pointersAfter this, the pointer dereference that triggered the swizzling can continueOptimizations:If all pages are allocated the same address as in the short page identifier, no changes required in the page!No need for deswizzling — swizzled page can be saved as-is to diskA set of pages (segment) can share one translation table. Pages can still be swizzled as and when fetched (old copy of translation table is needed).A process should not access more pages than size of virtual memory — reuse of virtual memory addresses for other pages is expensive

اسلاید 83: Disk versus Memory Structure of ObjectsThe format in which objects are stored in memory may be different from the formal in which they are stored on disk in the database. Reasons are:software swizzling – structure of persistent and in-memory pointers are differentdatabase accessible from different machines, with different data representationsMake the physical representation of objects in the database independent of the machine and the compiler.Can transparently convert from disk representation to form required on the specific machine, language, and compiler, when the object (or page) is brought into memory.

اسلاید 84: Large ObjectsLarge objects : binary large objects (blobs) and character large objects (clobs)Examples include: text documentsgraphical data such as images and computer aided designs audio and video dataLarge objects may need to be stored in a contiguous sequence of bytes when brought into memory.If an object is bigger than a page, contiguous pages of the buffer pool must be allocated to store it.May be preferable to disallow direct access to data, and only allow access through a file-system-like API, to remove need for contiguous storage.

اسلاید 85: Modifying Large ObjectsIf the application requires insert/delete of bytes from specified regions of an object:B+-tree file organization (described later in Chapter 12) can be modified to represent large objectsEach leaf page of the tree stores between half and 1 page worth of data from the objectSpecial-purpose application programs outside the database are used to manipulate large objects:Text data treated as a byte string manipulated by editors and formatters.Graphical data and audio/video data is typically created and displayed by separate applicationcheckout/checkin method for concurrency control and creation of versio