Chapter 10: Storage and File Structure
■ Overview of Physical Storage Media ■ Magnetic Disks
■ RAID
■ Tertiary Storage
■ Storage Access
■ File Organization
■ Organization of Records in Files ■ Data-Dictionary Storage
Database System Concepts – 6th Edition
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Classification of Physical Storage Media
■ Speed with which data can be accessed ■ Cost per unit of data
■ Reliability
● data loss on power failure or system crash
● physical failure of the storage device ■ Can differentiate storage into:
● volatile storage: loses contents when power is switched off ● non-volatile storage:
! Contents persist even when power is switched off. ! Includes secondary and tertiary storage, as well as batter-
backed up main-memory.
Database System Concepts – 6th Edition
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Physical Storage Media
■ Cache – fastest and most costly form of storage; volatile; managed by the computer system hardware. Several levels of cache.
■ Main memory:
● fast access (10s to 100s of nanoseconds; 1 nanosecond = 10–9
seconds)
● generally too small (or too expensive) to store the entire database
! capacities of up to a few Gigabytes widely used currently ! Capacities 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.
Database System Concepts – 6th Edition
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Physical Storage Media (Cont.)
■ Flash memory
● Data survives power failure
● Data 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 almost as fast as main memory (but not quite)
● But writes are slow (few microseconds), erase is slower
● Actual performance depends on file system and details
● Widely used in embedded devices such as digital cameras,
phones, and USB keys
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Physical Storage Media (Cont.)
■ Magnetic-disk
● Data is stored on spinning disk, and read/written magnetically
● Primary 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 storage
! Much slower access than main memory (more on this later)
● direct-access – possible to read data on disk in any order, unlike
magnetic tape
● Capacities range up to roughly 1.5 TB as of 2009
! Much larger capacity and cost/byte than main memory/flash memory
! Growing constantly and rapidly with technology improvements (factor of 2 to 3 every 2 years)
● Survives power failures and system crashes ! disk failure can destroy data, but is rare
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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 forms
● Blu-ray disks: 27 GB to 54 GB
● Write-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
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Physical Storage Media (Cont.)
■ Tape storage
● non-volatile, used primarily for backup (to recover from
disk failure), and for archival data
● sequential-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 expensive
● Tape jukeboxes available for storing massive amounts of data
! hundreds of terabytes (1 terabyte = 109 bytes) to even multiple petabytes (1 petabyte = 1012 bytes)
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Storage Hierarchy
cache
main memory
flash memory
magnetic disk
optical disk
magnetic tapes
Database System Concepts – 6th Edition
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Storage Hierarchy (Cont.)
■ primary storage: Fastest media but volatile (cache, main memory).
■ secondary storage: next level in hierarchy, non-volatile, moderately fast access time
● also called on-line storage
● E.g. flash memory, magnetic disks
■ tertiary storage: lowest level in hierarchy, non-volatile,
slow access time
● also called off-line storage
● E.g. magnetic tape, optical storage
■ Another issue: remote storage, clouds, etc.
Database System Concepts – 6th Edition
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Magnetic Hard Disk Mechanism
track t
sector s
spindle
arm assembly
cylinder c
platter
read–write head
arm rotation
NOTE: Diagram is schematic, and simplifies the structure of actual disk drives
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Magnetic Disks
■ Read-write head
● Positioned very close to the platter surface (almost touching it)
● Reads or writes magnetically encoded information.
■ Surface of platter divided into circular tracks
● Over 50K-100K tracks per platter on typical hard disks
■ Each track is divided into sectors.
● A sector is the smallest unit of data that can be read or written.
● Sector size typically 512 bytes
● Typical sectors per track: 500 to 1000 (on inner tracks) to 1000 to 2000 (on outer tracks)
■ T o read/write a sector
● disk arm swings to position head on right track
● platter spins continually; data is read/written as sector passes under head
■ Head-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
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Magnetic Disks (Cont.)
■ Earlier generation disks were susceptible to head-crashes
● Surface of earlier generation disks had metal-oxide coatings which
would disintegrate on head crash and damage all data on disk
● Current generation disks are less susceptible to such disastrous failures, although individual sectors may get corrupted
■ Disk 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 data
● Computes and attaches checksums to each sector to verify that data is read back correctly
! If data is corrupted, with very high probability stored checksum won’t match recomputed checksum
● Ensures successful writing by reading back sector after writing it
● Performs remapping of bad sectors
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Disk Subsystem
■ Multiple disks connected to a computer system through a controller ● Controllersfunctionality(checksum,badsectorremapping)often
carried out by individual disks; reduces load on controller
■ Disk interface standards families
● ATA (AT adaptor) range of standards
● SATA (Serial ATA)
● SCSI(SmallComputerSystemInterconnect)rangeofstandards
● SAS(SerialAttachedSCSI)
● Severalvariantsofeachstandard(differentspeedsandcapabilities)
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Performance Measures of Disks
■ Access time – the time it takes from when a read or write request is issued to when data transfer begins. Consists of:
● Seektime–timeittakestorepositionthearmoverthecorrecttrack. ! 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 movement
! 4 to 10 milliseconds on typical disks
● Rotationallatency–timeittakesforthesectortobeaccessedtoappear
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.
● 25to100MBpersecondmaxrate,lowerforinnertracks
● Multipledisksmayshareacontroller,soratethatcontrollercanhandleis
also important
! E.g. SATA: 150 MB/sec, SATA-II 3Gb (300 MB/sec) ! Ultra 320 SCSI: 320 MB/s, SAS (3 to 6 Gb/sec)
! Fiber Channel (FC2Gb or 4Gb): 256 to 512 MB/s
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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 years
● Probability of failure of new disks is quite low, corresponding to a
“theoretical MTTF” of 500,000 to 1,200,000 hours for a new disk
! E.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 hours
● MTTF decreases as disk ages
Disk Subsystem
■ Disks usually connected directly to computer system
■ In Storage Area Networks (SAN), a large number of disks are
connected by a high-speed network to a number of servers
■ In Network Attached Storage (NAS) networked storage provides a file system interface using networked file system protocol, instead of providing a disk system interface
Database System Concepts – 6th Edition
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Optimization of Disk-Block Access
■ Block – a contiguous sequence of sectors from a single track
● dataistransferredbetweendiskandmainmemoryinblocks ● sizesrangefrom512bytestoseveralkilobytes
! Smaller blocks: more transfers from disk
! Larger blocks: more space wasted due to partially filled blocks ! Typical block sizes today range from 4 to 16 kilobytes
■ Disk-arm-scheduling algorithms order pending accesses to tracks so that disk arm movement is minimized
● elevatoralgorithm
■ File organization – optimize block access time by organizing the blocks to
correspond to how data will be accessed
● E.g. Store related information on the same or nearby cylinders.
● Filesmaygetfragmentedovertime
! Sequential access to fragmented file means increased arm movement
● Somesystemshaveutilitiestodefragmentthefilesystem,inorderto
speed up file access
Database System Concepts – 6th Edition
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Optimization of Disk Block Access (Cont.)
■ Nonvolatile write buffers speed up disk writes by writing blocks to a non-volatile RAM buffer immediately
● Non-volatile RAM: battery backed up RAM or flash memory
! Even if power fails, the data is safe and will be written to disk when power
returns
● Controller then writes to disk whenever the disk has no other requests or request has been pending for some time
● Database operations that require data to be safely stored before continuing can continue without waiting for data to be written to disk
● Writes can be reordered to minimize disk arm movement
■ Log disk – a disk devoted to writing a sequential log of block updates
● Used exactly like nonvolatile RAM
! Write to log disk is very fast since no seeks are required ! No need for special hardware (NV-RAM)
■ File systems typically reorder writes to disk to improve performance
● Journaling file systems write data in safe order to NV-RAM or log disk
● Reordering without journaling: risk of corruption of file system data
Database System Concepts – 6th Edition
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RAID
■ RAID: Redundant Arrays of Independent Disks
● diskorganizationtechniquesthatmanagealargenumbersofdisks,
providing a view of a single disk of
! high capacity and high speed by using multiple disks in parallel,
! high reliability by storing data redundantly, so that data can be recovered even if a disk fails
■ We will skip this for now, and I will post a separate presentation this week
Database System Concepts – 6th Edition
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Next Topic: How are Tables Stored on Disk?
■ The database is stored as a collection of files.
■ In fact, each table is stored in one or more files, where each
file consists of multiple 4KB or 16KB pages (blocks).
■ E.g., 1GB relation = 50 files of 20MB each: file1, file2, file3, ..
■ Simple solution: each page has 20 rows of size 200 bytes:
■ But this is too naive:
● How about insertions and deletions?
● How about sorted tables?
● How about variable-length rows?
■ Need to use more complicated schemes
■ Compare to memory allocation (but on disk)
Database System Concepts – 6th Edition
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row 1
row 2
row 3
row 4
row 5
Next Topic: How are Tables Stored on Disk?
■ The database is stored as a collection of files.
■ In fact, each table is stored in one or more files, where each
file consists of multiple 4KB or 16KB pages (blocks).
■ E.g., 1GB relation = 50 files of 20MB each: file1, file2, file3, ..
■ Simple solution: each page has 20 rows of size 200 bytes:
■ But this is too naive:
● How about insertions and deletions?
● How about sorted tables?
● How about variable-length rows?
■ Need to use more complicated schemes
■ Compare to memory allocation (but on disk)
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.20 ©Silberschatz, Korth and Sudarshan
row 1
row 2
row 3
row 4
row 5
File Organization
■ The 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 fixed
● each file has records of one particular type only
● different files are used for different relations
This case is easiest to implement; will consider variable length records later.
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.21 ©Silberschatz, Korth and Sudarshan
Fixed-Length Records
■ Simple approach:
● Storerecordistartingfrombyten*(i–1),wherenisthesizeof
each record.
● Recordaccessissimplebutrecordsmaycrossblocks
! Modification: do not allow records to cross block boundaries
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■ Deletion of record i:
alternatives:
● moverecordsi+1,…,n
to i, . . . , n – 1
● move record n to i
● donotmoverecords,but
link all free records on a
free list
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020
record 0 record 1
record 2 record3 record 4 record 5 record 6 record 7 record 8 record 9 record 10 record 11
10.22 ©Silberschatz, Korth and Sudarshan
Free Lists
■ Store 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
on
■ Can 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.)
header
record 0
record 1
record 2
record 3
record 4
record 5
record 6
record 7
record 8
record 9
record 10
10101
Srinivasan
Comp. Sci.
65000
15151
Mozart
Music
40000
22222
Einstein
Physics
95000
33456
Gold
Physics
87000
58583
Califieri
History
62000
76543
Singh
Finance
80000
76766
Crick
Biology
72000
83821
Brandt
Comp. Sci.
92000
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Kim
Elec. Eng.
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record 11
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020
10.23 ©Silberschatz, Korth and Sudarshan
Variable-Length Records
■ Variable-length records arise in database systems in several ways:
● Storageofmultiplerecordtypesinafile.
● Recordtypesthatallowvariablelengthsforoneormorefieldssuchas strings (varchar)
● Recordtypesthatallowrepeatingfields(usedinsomeolderdata models).
■ Attributes are stored in order
■ Variable length attributes represented by fixed size (offset, length), with
actual data stored after all fixed length attributes
■ Null values represented by null-value bitmap
Null bitmap (stored in 1 byte) 0000
Bytes 0 4 8 12 20 21 26 36 45
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.24 ©Silberschatz, Korth and Sudarshan
21, 5
26, 10
36, 10
65000
10101
Srinivasan
Comp. Sci.
Variable-Length Records: Slotted Page Structure
Block Header
Records
# Entries
Free Space
Size Location
End of Free Space
■ Slotted page header contains:
● numberofrecordentries
● endoffreespaceintheblock
● locationandsizeofeachrecord
■ Records 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.
Database System Concepts – 6th Edition
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Organization of Records in Files
■ Heap – a record can be placed anywhere in the file where there is space
■ Sequential – store records in sequential order, based on the value of the search key of each record
■ Hashing – a hash function computed on some attribute of each record; the result specifies in which block of the file the record should be placed
■ Records 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 file
● Motivation: store related records on the same block to minimize I/O
Database System Concepts – 6th Edition
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Sequential File Organization
■ Suitable for applications that require sequential processing of the entire file
■ The records in the file are ordered by a search-key
10101
Srinivasan
Comp. Sci.
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Wu
Finance
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Mozart
Music
40000
22222
Einstein
Physics
95000
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El Said
History
60000
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Gold
Physics
87000
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Katz
Comp. Sci.
75000
58583
Califieri
History
62000
76543
Singh
Finance
80000
76766
Crick
Biology
72000
83821
Brandt
Comp. Sci.
92000
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Kim
Elec. Eng.
80000
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.27 ©Silberschatz, Korth and Sudarshan
Sequential File Organization (Cont.)
■ Deletion – use pointer chains
■ Insertion –locate the position where the record is to be inserted
● ifthereisfreespaceinsertthere
● ifnofreespace,inserttherecordinanoverflowblock ● Ineithercase,pointerchainmustbeupdated
■ Need to reorganize the file
from time to time to restore
sequential order
10101
Srinivasan
Comp. Sci.
65000
12121
Wu
Finance
90000
15151
Mozart
Music
40000
22222
Einstein
Physics
95000
32343
El Said
History
60000
33456
Gold
Physics
87000
45565
Katz
Comp. Sci.
75000
58583
Califieri
History
62000
76543
Singh
Finance
80000
76766
Crick
Biology
72000
83821
Brandt
Comp. Sci.
92000
98345
Kim
Elec. Eng.
80000
32222
Verdi
Music
48000
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.28 ©Silberschatz, Korth and Sudarshan
Multitable Clustering File Organization
Store several relations in one file using a multitable clustering file organization
department
instructor
Comp. Sci.
Taylor
100000
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Katz
75000
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Srinivasan
65000
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Brandt
92000
Physics
Watson
70000
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multitable clustering of department and instructor
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020
10.29 ©Silberschatz, Korth and Sudarshan
Multitable Clustering File Organization (cont.)
■ good for queries involving department instructor, and for queries involving one single department and its instructors
■ bad for queries involving only department
■ results in variable size records
■ Can add pointer chains to link records of a particular relation
Comp. Sci.
45564
10101
83821
Physics
33456
Taylor
Katz
Srinivasan
Brandt
Watson
Gold
100000
75000
65000
92000
70000
87000
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.30 ©Silberschatz, Korth and Sudarshan
Data Dictionary Storage
The Data dictionary (also called system catalog) stores metadata; that is, data about data, such as
■ Information about relations
● names of relations
● names, types and lengths of attributes of each relation ● names and definitions of views
● integrity constraints
■ User and accounting information, including passwords ■ Statistical and descriptive data
● number of tuples in each relation ■ Physical file organization information
● How relation is stored (sequential/hash/…)
● Physical location of relation
■ Information about indices (Chapter 11)
Database System Concepts – 6th Edition
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Relational Representation of System Metadata
Relation_metadata
relation_name number_of_aributes storage_organization location
Aribute_metadata
relation_name aribute_name domain_type position length
■ Relational representation on disk
■ Specialized data structures
designed for efficient access, in memory
Index_metadata
index_name relation_name index_type index_aributes
User_metadata
user_name encrypted_password group
View_metadata
view_name definition
Database System Concepts – 6th Edition
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10.32 ©Silberschatz, Korth and Sudarshan
Storage Access
■ A 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.
Database System Concepts – 6th Edition
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Buffer Manager
■ Programs call on the buffer manager when they need a block from disk.
1. If the block is already in the buffer, buffer manager returns the address of the block in main memory
2. If the block is not in the buffer, the buffer manager
1. Allocates space in the buffer for the block
1. Replacing (throwing out) some other block, if required, to make space for the new block.
2. 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.
2. Reads the block from the disk to the buffer, and returns the address of the block in main memory to requester.
Database System Concepts – 6th Edition
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Buffer-Replacement Policies
■ Most operating systems replace the block least recently used (LRU strategy)
■ Idea behind LRU – use past pattern of block references as a predictor of future references
■ Queries 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 references
● LRUcanbeabadstrategyforcertainaccesspatternsinvolving repeated scans of data
! For example: 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 …
● Mixedstrategywithhintsonreplacementstrategyprovided
by the query optimizer is preferable
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.35 ©Silberschatz, Korth and Sudarshan
Buffer-Replacement Policies
■ Example where LRU is really bad:
repeated scans of a relation slightly larger than the buffer
buffer
■ LRU will evict data that will be revisited soon!
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.36 ©Silberschatz, Korth and Sudarshan
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 processed
■ Most 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 relation
● E.g., the data dictionary is frequently accessed. Heuristic: keep data-dictionary blocks in main memory buffer
■ Buffer managers also support forced output of blocks for the purpose of recovery (more in Chapter 16)
Database System Concepts – 6th Edition
Modified by T. Suel for CS6083 at NYU Tandon, Spring 2020 10.37 ©Silberschatz, Korth and Sudarshan