DBMS_unit2_Notes.pdf

BCA204T: DATA BASE MANAGEMENT SYSTEMS Page 1 of 49
BCA204T : DATA BASE MANAGEMENT SYSTEMS
Unit - II
Data Modelling Using the Entity-Relationship Model: High level conceptual Data
Models for Database Design with and example., Entity types, Entity sets,
attributes, and Keys, ER Model Concepts, Notation for ER Diagrams, Proper
naming of Schema Constructs, Relationship types of degree higher than two.
Record Storage and Primary File Organization: Secondary Storage Devices.
Buffering of Blocks. Placing file Records on Disk. Operations on Files, File of
unordered Records (Heap files), Files of Ordered Records (Sorted files), Hashing
Techniques, and Other Primary file Organization.
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Unit-II
Data Modeling Using E-R Model
E-R Model
A logical representation of the data for an organization or for a business area is
called E-R Model. It is also called has Entity-Relationship Model.
Entity-Relationship Diagram
A graphical representation of entity-relationship model. Also called E-R diagram or
just ERD.
ER Model: Basic Concepts
Entity relationship model defines the conceptual view of database. It works around
real world entity and association among them. At view level, ER model is
considered well for designing databases.
Entity
An entity is an object that exists and which is distinguishable from other
objects. An entity can be a person, a place, an object, an event, or a concept
about which an organization wishes to maintain data.
For example, in a school database, student, teachers, class and course offered
can be considered as entities. All entities have some attributes or properties that
give them their identity.
An entity set is a collection of similar types of entities. Entity set may contain
entities with attribute sharing similar values. For example, Students set may
contain all the student of a school; likewise Teachers set may contain all the
teachers of school from all faculties. Entities sets need not to be disjoint.
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Attributes
An attribute is a property that describes an entity. All attributes have
values. For example, a student entity may have name, class, age as
attributes. There exists a domain or range of values that can be assigned to
attributes. For example, a student's name cannot be a numeric value. It has
to be alphabetic. A student's age cannot be negative, etc.
Types of attributes:
Simple attribute:
Simple attributes are atomic values, which cannot be divided further. For
example, student's phone-number is an atomic value of 10 digits.
Composite attribute:
Composite attributes are made of more than one simple attribute.
For example, a student's name may have Firstname and Lastname.
Derived attribute:
Derived attributes are attributes, which do not exist physical in the
database, but there values are derived from other attributes presented
in the database.
For another example, Age can be derived from DOB.
Stored attribute:
An attribute whose value cannot be derived from the values of
other attributes is called a stored attribute. For example, DOB
Single-valued attribute:
Single valued attributes contain on single
value. For example: SocialSecurityNumber.
Multi-value attribute:
Multi-value attribute may contain more than one values.
For example, a person can have more than one phone numbers, EmailId etc.
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Entity-set and Keys
Key is an attribute or collection of attributes that uniquely identifies an
entity among entity set.
For example, RegNo of a student makes her/him identifiable among students.
Super Key: Set of attributes (one or more) that collectively identifies
an entity in an entity set.
Candidate Key: Minimal super key is called candidate key that is, supers
keys for which no proper subset are a superkey. An entity set may have
more than one candidate key.
Primary Key: This is one of the candidate key chosen by the
database designer to uniquely identify the entity set.
Relationship
The association among entities is called relationship. For example, employee
entity has relation works_at with department.
Another example is for student who enrolls in some course. Here, Works_at
and Enrolls are called relationship.
Keys
Superkey: an attribute or set of attributes that uniquely identifies an entity.
Composite key: a key requiring more than one attribute.
Candidate key: a superkey such that no proper subset of its attributes is also a
superkey (minimal superkey – has no unnecessary attributes)
Primary key: the candidate key chosen to be used for identifying entities
and accessing records. Unless otherwise noted "key" means "primary key"
Alternate key: a candidate key not used for primary key
Secondary key: attribute or set of attributes commonly used for accessing
records, but not necessarily unique
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Foreign key: term used in relational databases (but not in the E-R model) for an
attribute that is the primary key of another table and is used to establish a
relationship with that table where it appears as an attribute also.
So a foreign key value occurs in the table and again in the other table.
Mapping Cardinalities:
Cardinality defines the number of entities in one entity set which can
be associated to the number of entities of other set via relationship set.
One-to-one: one entity from entity set A can be associated with at most
one entity of entity set B and vice versa.
One-to-many: One entity from entity set A can be associated with
more than one entities of entity set B but from entity set B one entity
can be associated with at most one entity.
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Many-to-one: More than one entities from entity set A can be associated
with at most one entity of entity set B but one entity from entity set B
can be associated with more than one entity from entity set A.
Many-to-many: one entity from A can be associated with more than one
entity from B and vice versa.
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Notations/Symbols used in E-R diagrams
Entity: an entity can be any object, place person or class.
In E-R diagram entity is represented using rectangles.
For example Student is an entity.
Strong Entities are independently from other entity types. They
always possess one or more attributes that uniquely(primary
key) distinguish each occurrence of the entity. For example
Student is an entity.
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Weak Entities depend on another entity.
Weak entity doesn’t have key attribute of their own.
Double rectangle represents weak entity.
Relationship
A relationship describes relations between entities.
Relationship is represented using diamonds.
Attributes:
An attributes describes a property or characteristic of an entity.
An attribute is represented using eclipse.
For example regno, name, course can be the attribute of student entity.
Key Attribute
Key attribute represents the main characteristic of an entity.
It is used to represent Primary key. Ellipse with underlying
lines represent key attribute.
Composite Attribute
An attributes can be sub divided. These attributes are known as composite
attribute.
Multivalued Attribute
Multivalued attributes are those that are capable of
taking on more than one value. It is represented by double Ellipse.
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Derived Attribute
Derived attributes are attributes whose value can be
calculated from related attribute values. They are represented by dotted ellipse.
Example of E-R diagram
Cardinality of Relationships
Cardinality is the number of entity instances to which another entity set can map
under the relationship. This does not reflect a requirement that an entity has to
participate in a relationship. Participation is another concept.
One-to-one: X-Y is 1:1 when each entity in X is associated with at most one
entity in Y, and each entity in Y is associated with at most one entity in X.
One-to-many: X-Y is 1:M when each entity in X can be associated with many
entities in Y, but each entity in Y is associated with at most one entity in X.
Many-to-many: X:Y is M:M if each entity in X can be associated with many
entities in Y, and each entity in Y is associated with many entities in X
("many" =>one or more and sometimes zero)
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ER Diagram for Company Database
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Relationship types of degree higher than two.
superclass and subclass
A superclass is an entity type that has one or more distinct subgroups with
unique attributes.
For example, the entity type PERSON in below fig is a superclass that includes
faculty, staff, and students as its subgroups. The superclass features only those
attributes that are common for all its subgroups. For example, attributes of
PERSON such as SSN, Name, Address, and Email are shared by all its subgroups
regardless of an individual’s position as student, faculty, or staff within the
university.
The subgroups with unique attributes are defined as subclasses. The PERSON
superclass thus has three subclasses: STUDENT, STAFF, and FACULTY. A subclass
entity type STUDENT has attributes of its superclass along with its own attributes
such as Major, GPA, and Class that uniquely identify the subclass. In the below fig
depicts a superclass and subclasses.
Generalization and Specialization Process
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Going up in this structure is called generalization, where entities are clubbed
together to represent a more generalized view. For example, a particular student
named, Nisarga can be generalized along with all the students, the entity shall be
student, and further a student is person. The reverse is called specialization
where a person is student, and that student is Nisarga.
Generalization is the process of defining general entity types from a set of
specialized entity types by identifying their common characteristics. In other
words, this process minimizes the differences between entities by identifying a
general entity type that features the common attributes of specialized entities.
Generalization is a bottom-up approach as it starts with the specialized entity
types (subclasses) and forms a generalized entity type (superclass).
For example, suppose if we have given us the specialized entity types FACULTY,
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STAFF, and STUDENT, and we want to represent these entity types separately in
the E-R model as depicted in above. However, if we examine them closely, we
can observe that a number of attributes are common to all entity types, while
others are specific to a particular entity.
For example, FACULTY, STAFF, and STUDENT all share the attributes Name,
SSN, BirthDate, Address, and Email. On the other hand, attributes such as GPA,
Class, and MajorDept are specific to the STUDENTS; OfficePhone is specific to
FACULTY, and Designation is specific to STAFF. Common attributes suggest that
each of these three entity types is a form of a more general entity type. This
general entity type is simply a PERSON superclass entity with common attributes
of three subclasses (in below fig).
Thus, in the generalization process, we group specialized entity types to form
one general entity type and identify common attributes of specialized entities as
attributes of a general entity type. The general entity type is a superclass of
specialized entity types or subclasses. Generalization is a bottom-up approach.
Specialization
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Specialization is the process of defining one or more subclasses of a superclass
by identifying its distinguishing characteristics. It starts with the general entity
(superclass) and forms specialized entity types (subclasses) based on specialized
attributes or relationships specific to a subclass.
For example, in the above Figure. LIBRARY ITEM is an entity type with several
attributes such as IdentificationNo, RecordingDate, Frequency, and Edition. After
careful review of these items, it should become clear that some items such as
books do not have values for attributes such as Frequency, RecordingDate, and
CourseNo, while Video CDs do not have an Author or an Edition.
In addition, all items have common attributes such as IdentificationNo, Location,
and Subject. Someone creating a library database, then, could use the
specialization process to identify superclass and subclass relationships. In this
case, the original entity LIBRARY ITEM forms a superclass entity type made up of
attributes shared by all items, while specialized items with distinguishing
attributes, such as BOOK, JOURNALS, and VIDEOCD, form subclasses as shown in
below fig. Specialization is thus a top-down approach.
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Definition
Generalization is the process of defining a general entity type from a set of
specialized entity types by identifying their common characteristics.
Specialization is a process of defining one or more subclasses of a superclass by
identifying their distinguishing characteristics.
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Record Storage and Primary File Organization
The collection of data that makes up a computerized database must be stored
physically on some computer storage medium. The DBMS software can then
retrieve, update, and process this data as needed. Computer storage media form
a storage hierarchy that includes two main categories:
Primary storage. This category includes storage media that can be operated on
directly by the computer’s central processing unit (CPU), such as the computer’s
main memory and smaller but faster cache memories. Primary storage usually
provides fast access to data but is of limited storage capacity. Although main
memory capacities have been growing rapidly in recent
years, they are still more expensive and have less storage capacity than
secondary and tertiary storage devices.
Secondary and tertiary storage. This category includes magnetic disks,
optical disks (CD-ROMs, DVDs, and other similar storage media), and
tapes. Hard-disk drives are classified as secondary storage, whereas removable
media such as optical disks and tapes are considered tertiary storage.
These devices usually have a larger capacity, cost less, and provide slower
access to data than do primary storage devices. Data in secondary or
tertiary storage cannot be processed directly by the CPU; first it must be
copied into primary storage and then processed by the CPU.
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Memory Hierarchies and Storage Devices
In a modern computer system, data resides and is transported throughout a
hierarchy of storage media. The highest-speed memory is the most expensive
and is therefore available with the least capacity. The lowest-speed memory is
offline tape storage, which is essentially available in indefinite storage capacity.
At the primary storage level, the memory hierarchy includes at the most expensive
end, cache memory, which is a static RAM (Random Access Memory). Cache
memory is typically used by the CPU to speed up execution of program
instructions using techniques such as prefetching and pipelining.
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The next level of primary storage is DRAM (Dynamic RAM), which provides the
main work area for the CPU for keeping program instructions and data. It is
popularly called main memory. The advantage of DRAM is its low cost, which
continues to decrease; the drawback is its volatility1 and lower speed
compared with static RAM.
At the secondary and tertiary storage level, the hierarchy includes magnetic
disks, as well as mass storage in the form of CD-ROM (Compact Disk–Read-Only
Memory) and DVD (Digital Video Disk or Digital Versatile Disk) devices, and finally
tapes at the least expensive end of the hierarchy. The storage capacity is
measured in kilobytes (Kbyte or 1000 bytes), megabytes (MB or 1 million bytes),
gigabytes (GB or 1 billion bytes), and even terabytes (1000 GB). The word
petabyte (1000 terabytes or 10**15 bytes) is now becoming relevant in the
context of very large repositories of data in physics, astronomy, earth sciences,
and other scientific applications.
Programs reside and execute in DRAM. Generally, large permanent databases
reside on secondary storage, (magnetic disks), and portions of the database are
read into and written from buffers in main memory as needed. Nowadays,
personal computers and workstations have large main memories of hundreds of
megabytes of RAM and DRAM, so it is becoming possible to load a large part of
the database into main memory.
Between DRAM and magnetic disk storage, another form of memory, flash
memory, is becoming common, particularly because it is nonvolatile. Flash
memories are high-density, high-performance memories using EEPROM
(Electrically Erasable Programmable Read-Only Memory) technology.
The advantage of flash memory is the fast access speed; the disadvantage is
that an entire block must be erased and written over simultaneously. Flash
memory cards are appearing as the data storage medium in appliances with
capacities ranging from a few megabytes to a few gigabytes.
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These are appearing in cameras, MP3 players, cell phones, PDAs, and so on.
USB (Universal Serial Bus) flash drives have become the most portable medium
for carrying data between personal computers; they have a flash memory
storage device integrated with a USB interface.
CD-ROM (Compact Disk – Read Only Memory) disks store data optically and are
read by a laser. CD-ROMs contain prerecorded data that cannot be overwritten.
WORM (Write-Once-Read-Many) disks are a form of optical storage used for
archiving data; they allow data to be written once and read any number of times
without the possibility of erasing. They hold about half a gigabyte of data per
disk and last much longer than magnetic disks.
The DVD is another standard for optical disks allowing 4.5 to 15 GB of storage
per disk. Most personal computer disk drives now read CDROM and DVD disks.
Typically, drives are CD-R (Compact Disk Recordable) that can create CD-ROMs
and audio CDs (Compact Disks), as well as record on DVDs.
Finally, magnetic tapes are used for archiving and backup storage of data. Tape
jukeboxes—which contain a bank of tapes that are catalogued and can be
automatically loaded onto tape drives—are becoming popular as tertiary storage
to hold terabytes of data. For example, NASA’s EOS (Earth Observation Satellite)
system stores archived databases. Many large organizations are already finding it
normal to have terabyte-sized databases.
The term very large database can no longer be precisely defined because disk
storage capacities are on the rise and costs are declining. Very soon the term
may be reserved for databases containing tens of terabytes.
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Secondary Storage Devices
Magnetic disks are used for storing large amounts of data. The most basic unit of
data on the disk is a single bit of information. Bits are grouped into bytes (or
characters). Byte sizes are typically 4 to 8 bits, depending on the computer and
the device. We assume that one character is stored in a single byte, and we use
the terms byte and character interchangeably. The capacity of a disk is the
number of bytes it can store, Hard disks for personal computers typically hold
from several hundred MB up to tens of GB; and large disk packs used with
servers and mainframes have capacities of hundreds of GB. Disk capacities
continue to grow as technology improves.
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Whatever their capacity, all disks are made of magnetic material shaped as a thin
circular disk, as shown in Figure(a), and protected by a plastic or acrylic cover.
A disk is single-sided if it stores information on one of its surfaces only and
doublesided if both surfaces are used. To increase storage capacity, disks are
assembled into a disk pack, as shown in Figure (b), which may include many
disks and therefore many surfaces. Information is stored on a disk surface in
concentric circles of small width, each having a distinct diameter. Each circle is
called a track. In disk packs, tracks with the same diameter on the various
surfaces are called a cylinder because of the shape they would form if
connected in space. The concept of a cylinder is important because data stored
on one cylinder can be retrieved much faster than if it were distributed among
different cylinders.
The number of tracks on a disk ranges from a few hundred to a few thousand, and
the capacity of each track typically ranges from tens of Kbytes to 150 Kbytes.
Because a track usually contains a large amount of information, it is divided into
smaller blocks or sectors. The division of a track into sectors is hard-coded on
the disk surface and cannot be changed.
The division of a track into equal-sized disk blocks (or pages) is set by the
operating system during disk formatting (or initialization). Block size is fixed
during initialization and cannot be changed dynamically. Typical disk block sizes
range from 512 to 8192 bytes. A disk with hard-coded sectors often has the
sectors subdivided into blocks during initialization. Blocks are separated by fixed-
size interblock gaps, which include specially coded control information written
during disk initialization. This information is used to determine which block on
the track follows each interblock gap.
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A disk is a random access addressable device. Transfer of data between main
memory and disk takes place in units of disk blocks. The hardware address of a
block—a combination of a cylinder number, track number (surface number within
the cylinder on which the track is located), and block number (within the track) is
supplied to the disk I/O (input/output) hardware.
The address of a buffer—a contiguous reserved area in main storage that holds
one disk block—is also provided. For a read command, the disk block is copied
into the buffer; whereas for a write command, the contents of the buffer are
copied into the disk block. Sometimes several contiguous blocks, called a cluster,
may be transferred as a unit. In this case, the buffer size is adjusted to match the
number of bytes in the cluster.
The actual hardware mechanism that reads or writes a block is the disk
read/write head, which is part of a system called a disk drive.A disk or disk
pack is mounted in the disk drive, which includes a motor that rotates the disks. A
read/write head includes an electronic component attached to a mechanical arm.
Disk packs with multiple surfaces are controlled by several read/write heads—one
for each surface, as shown in Figure (b). All arms are connected to an actuator
attached to another electrical motor, which moves the read/write heads in unison
and positions them precisely over the cylinder of tracks specified in a block
address.
Disk drives for hard disks rotate the disk pack continuously at a constant speed
(typically ranging between 5,400 and 15,000 rpm(revolutions per minute)). Once the
read/write head is positioned on the right track and the block specified in the block
address moves under the read/write head, the electronic component of the read/write
head is activated to transfer the data. Some disk units have fixed read/write heads,
with as many heads as there are tracks. These are called fixed-head disks, whereas
disk units with an actuator are called movable-head disks.
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For fixed-head disks, a track or cylinder is selected by electronically switching to
the appropriate read/write head rather than by actual mechanical movement;
consequently, it is much faster. However, the cost of the additional read/write
heads is quite high, so fixed-head disks are not commonly used.
A disk controller, typically embedded in the disk drive, controls the disk drive
and interfaces it to the computer system. The controller accepts high-level I/O
commands and takes appropriate action to position the arm and causes the
read/write action to take place. To transfer a disk block, given its address, the disk
controller must first mechanically position the read/write head on the correct
track. The time required to do this is called the seek time. Typical seek times are
5 to 10 msec on desktops and 3 to 8 msecs on servers. There is another delay—
called the rotational delay or latency—while the beginning of the desired block
rotates into position under the read/write head. It depends on the rpm of the disk.
Some additional time is needed to transfer the data; this is called the block
transfer time. Hence, the total time needed to locate and transfer an arbitrary
block, given its address, is the sum of the seek time, rotational delay, and block
transfer time.
Buffering of Blocks
When several blocks need to be transferred from disk to main memory and all the
block addresses are known, several buffers can be reserved in main memory to
speed up the transfer. While one buffer is being read or written, the CPU can
process data in the other buffer because an independent disk I/O processor
(controller) exists that, once started, can proceed to transfer a data block
between memory and disk independent of and in parallel to CPU processing.
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Figure (a) illustrates how two processes can proceed in parallel. Processes A and B
are running concurrently in an interleaved fashion, whereas processes C and D
are running concurrently in a parallel fashion. When a single CPU controls
multiple processes, parallel execution is not possible. However, the processes can
still run concurrently in an interleaved way. Buffering is most useful when
processes can run concurrently in a parallel fashion, either because a separate disk
I/O processor is available or because multiple CPU processors exist.
Figure (b) illustrates how reading and processing can proceed in parallel when the
time required to process a disk block in memory is less than the time required to
read the next block and fill a buffer. The CPU can start processing a block once its
transfer to main memory is completed; at the same time, the disk I/O processor can
be reading and transferring the next block into a different buffer. This technique is
called double buffering and can also be used to read a continuous
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stream of blocks from disk to memory. Double buffering permits continuous
reading or writing of data on consecutive disk blocks, which eliminates the seek
time and rotational delay for all but the first block transfer. Moreover, data is
kept ready for processing, thus reducing the waiting time in the programs.
Placing file Records on Disk
Data is usually stored in the form of records. Each record consists of a collection
of related data values or items, where each value is formed of one or more
bytes and corresponds to a particular field of the record. Records usually describe
entities and their attributes.
For example, an EMPLOYEE record represents an employee entity, and each field
value in the record specifies some attribute of that employee, such as
EmployeeID, Name, DOB, Department, Salary.
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A collection of field names and their corresponding data types constitutes a record
type or record format definition.
A data type, associated with each field, specifies the types of values a field
can take.
The data type of a field is usually include numeric (integer, long integer, or
floating point), string of characters (fixed-length or varying), Boolean (having 0
and 1 or TRUE and FALSE values only), and sometimes specially coded date and
time data types.
Files, Fixed-Length Records, and Variable-Length Records
A file is a sequence of records. In many cases, all records in a file are of the same
record type. If every record in the file has exactly the same size (in bytes), the file
is said to be made up of fixed-length records. If different records in the file
have different sizes, the file is said to be made up of variable-length records.
Allocating File Blocks on Disk
There are several standard techniques for allocating the blocks of a file on disk.
In contiguous allocation, the file blocks are allocated to consecutive disk blocks.
This makes reading the whole file very fast using double buffering, but it makes
expanding the file difficult.
In linked allocation, each file block contains a pointer to the next file block.
This makes it easy to expand the file but makes it slow to read the whole file. A
combination of the two allocates clusters of consecutive disk blocks, and the
clusters are linked. Clusters are sometimes called file segments or extents.
In indexed allocation, where one or more index blocks contain pointers to
the actual file blocks. It is also common to use combinations of these techniques.
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File Headers
A file header or file descriptor contains information about a file that is needed
by the system programs that access the file records. The header includes
information to determine the disk addresses of the file blocks as well as to
record format descriptions, which may include field lengths and the order of
fields within a record for fixed-length unspanned records and field type codes,
separator characters, and record type codes for variable-length records.
Operations on Files
Operations on files are usually grouped into retrieval operations and update
operations.
The retrieval operations do not change any data in the file, but only locate
certain records so that their field values can be examined and processed. The
latter change the file by insertion or deletion of records or by modification of field
values. In either case, we may have to select one or more records for retrieval,
deletion, or modification based on a selection condition (or filtering
condition), which specifies criteria that the desired record or records must satisfy.
Actual operations for locating and accessing file records vary from system to
system. DBMS software programs, access records by using these commands.
Open. Prepares the file for reading or writing. Allocates appropriate buffers
(typically at least two) to hold file blocks from disk, and retrieves the file
header. Sets the file pointer to the beginning of the file.
Reset. Sets the file pointer of an open file to the beginning of the file.
Find (or Locate). Searches for the first record that satisfies a search condition.
Transfers the block containing that record into a main memory buffer
(if it is not already there). The file pointer points to the record in the buffer and
it becomes the current record
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Read (or Get). Copies the current record from the buffer to a program
variable in the user program. This command may also advance the current
record pointer to the next record in the file, which may necessitate
reading the next file block from disk.
FindNext. Searches for the next record in the file that satisfies the
search condition. Transfers the block containing that record into a main
memory buffer.
Delete. Deletes the current record and (eventually) updates the file on
disk to reflect the deletion.
Modify. Modifies some field values for the current record and
(eventually) updates the file on disk to reflect the modification.
Insert. Inserts a new record in the file by locating the block where the record
is to be inserted, transferring that block into a main memory buffer (if it is
not already there), writing the record into the buffer, and (eventually) writing
the buffer to disk to reflect the insertion.
Close. Completes the file access by releasing the buffers and performing
any other needed cleanup operations.
The preceding (except for Open and Close) are called record-at-a-time
operations
because each operation applies to a single record. It is possible to streamline
the operations Find, FindNext, and Read into a single operation, Scan, whose
description is as follows:
Scan. If the file has just been opened or reset, Scan returns the first record;
otherwise it returns the next record. If a condition is specified with the
operation, the returned record is the first or next record satisfying the condition.
FindAll. Locates all the records in the file that satisfy a search condition.
Find (or Locate) n. Searches for the first record that satisfies a search
condition and then continues to locate the next n – 1 records satisfying the same
condition. Transfers the blocks containing the n records to the main memory
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buffer (if not already there).
FindOrdered. Retrieves all the records in the file in some specified
order. Reorganize. Starts the reorganization process. As we shall see,
some file organizations require periodic reorganization. An example is to
reorder the file records by sorting them on a specified field.
Files of Unordered Records (Heap Files)
In this simplest and most basic type of organization, records are placed in the
file in the order in which they are inserted, so new records are inserted at the
end of the file. Such an organization is called a heap or pile file.
Inserting a new record is very efficient. The last disk block of the file is copied
into a buffer, the new record is added, and the block is then rewritten back to
disk. The address of the last file block is kept in the file header.
Searching for a record using any search condition involves a linear search
through the file block by block—an expensive procedure. If only one record
satisfies the search condition, then, on the average, a program will read into
memory and search half the file blocks before it finds the record. For a file of b
blocks, this requires searching (b/2) blocks, on average. If no records or
several records satisfy the search condition, the program must read and search
all b blocks in the file.
To delete a record, a program must first find its block, copy the block into a
buffer, delete the record from the buffer, and finally rewrite the block back to
the disk. This leaves unused space in the disk block. Deleting a large number of
records in this way results in wasted storage space. Another technique used for
record deletion is to have an extra byte or bit, called a deletion marker, stored
with each record. A record is deleted by setting the deletion marker to a certain
value.
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Files of Ordered Records (Sorted Files)
We can physically order the records of a file on disk based on the values of one of
their fields—called the ordering field. This leads to an ordered or sequential file. If
the ordering field is also a key field of the file—a field guaranteed to have a unique
value in each record—then the field is called the ordering key for the file.
A binary search for disk files can be done on the blocks rather than on the
records. Suppose that the file has b blocks numbered 1, 2, ..., b; the records are
ordered by ascending value of their ordering key field; and we are searching for a
record whose ordering key field value is K.Assuming that disk addresses of the
file blocks are available in the file header, the binary search can be described by
Algorithm shown below.
Binary Search on an Ordering Key of a Disk File
L 1; U  b; (* b is the number of file blocks *)
while (U ≥ L ) do
begin i (L + U) div 2;
read block i of the file into the buffer;
if K < (ordering key field value of the first record in block i )
then U  i – 1
else if K > (ordering key field value of the last record in block i )
then L  i + 1
else if the record with ordering key field value = K is in the buffer
then goto found
else goto notfound;
end;
goto notfound;
A binary search usually accesses log2(b) blocks, whether the record is found or
not—an improvement over linear searches, where, on the average, (b/2) blocks
_________________________________

are accessed when the record is found and b blocks are accessed when the
record is not found.
Inserting and deleting records are expensive operations for an ordered file
because the records must remain physically ordered. To insert a record, we must
find its correct position in the file, based on its ordering field value, and then make
space in the file to insert the record in that position. For a large file this can be
very time consuming because, on the average, half the records of the file must be
moved to make space for the new record. This means that half the file blocks
must be read and rewritten after records are moved among them. For record
deletion, the problem is less severe if deletion markers and periodic reorganization
are used.
Modifying a field value of a record depends on two factors: the search condition
to locate the record and the field to be modified. If the search condition involves
the ordering key field, we can locate the record using a binary search; otherwise
we must do a linear search. A nonordering field can be modified by changing the
record and rewriting it in the same physical location on disk—assuming fixed-
length records. Modifying the ordering field means that the record can change its
position in the file. This requires deletion of the old record followed by insertion
of the modified record.
Hashing Techniques
Hashing is a method which provides very fast access to records under certain
search conditions. This organization is usually called a hash file. The search
condition must be an equality condition on a single field, called the hash field. In
most cases, the hash field is also a key field of the file, in which case it is called
the hash key. The idea behind hashing is to provide a function h, called a hash
function or randomizing function, which is applied to the hash field value of a
record and yields the address of the disk block in which the record is stored. A
search for the record within the block can be carried out in a main memory buffer.
For most records, we need only a single-block access to retrieve that record.
_________________________________

Hashing is also used as an internal search structure within a program whenever
a group of records is accessed exclusively by using the value of one field.
Hashing can be used for internal files. it can be modified to store external files on
disk. It can also be extended to dynamically growing files.
Internal Hashing
Hashing is typically implemented as a hash table through the use of
an array of records. Let the array index range is from 0 to M–1, as
shown in Figure(a); then we have M slots whose addresses correspond to the
array indexes. We choose a hash function that transforms the hash field value
into an integer between 0 and M-1. One common hash function is the h(K) = K
mod M function, which returns the remainder of an integer hash field value K
after division by M; this value is then used for the record address.
_________________________________

Noninteger hash field values can be transformed into integers before the mod
function is applied. For character strings, the numeric (ASCII) codes associated
with characters can be used in the transformation—for example, by multiplying
those code values. For a hash field whose data type is a string of 20
characters, Algorithm (a) can be used to calculate the hash address.
Algorithm Two simple hashing algorithms: (a) Applying the mod hash
function to a character string K. (b) Collision resolution by open addressing.
(a) temp  1;
for i 1 to 20 do temp  temp * code(K[i ] ) mod M ;
hash_address  temp mod M;
(b) i  hash_address(K); a i;
if location i is occupied
then begin i  (i + 1) mod M;
while (i ≠ a) and location i is occupied
do i  (i + 1) mod M;
if (i = a) then all positions are full
else new_hash_address  i;
end;
Other hashing functions can be used. One technique, called folding, involves
applying an arithmetic function such as addition or a logical function such as
exclusive or to different portions of the hash field value to calculate the hash
address.
A collision occurs when the hash field value of a record that is being inserted
hashes to an address that already contains a different record. In this situation,
we must insert the new record in some other position. The process of finding
another position is called collision resolution.
_________________________________

There are numerous methods for collision resolution, including the following:
Open addressing. Ponce a position specified by the hash address is found to
be occupied, the program checks the subsequent positions in order until an
unused (empty) position is found. Algorithm (b) may be used for this purpose.
Chaining. For this method, various overflow locations are kept, usually by
extending the array with a number of overflow positions. Additionally, a pointer
field is added to each record location. A collision is resolved by placing the new
record in an unused overflow location and setting the pointer of
the occupied hash address location to the address of that overflow location. A
linked list of overflow records for each hash address is thus maintained, as
shown in Figure (b).
Multiple hashing. The program applies a second hash function if the first
results in a collision. If another collision results, the program uses open
addressing or applies a third hash function and then uses open addressing if
necessary.
External Hashing for Disk Files
Hashing for disk files is called external hashing. the target address space is in
external hashing is made of buckets, A bucket is either one disk block or a cluster
of contiguous blocks. The hashing function maps the indexing field’s value into a
relative bucket number. A table maintained in the file header converts the bucket
number into the corresponding disk block address as shown in below Figure.
_________________________________

The hashing scheme is called static hashing if a fixed number of buckets is
allocated.
A major drawback of static hashing is that the number of buckets must be
chosen large enough that can handle large files. That is, it is difficult to expand or
shrink the file dynamically.
Dynamic Hashing
Two hashing techniques are
Extendible hashing and Liner hashing.
Extendible hashing
Basic Idea:
 Maintain a directory of bucket addresses instead of just hashing to
buckets directly. (indirection)

 The directory can grow, but its size is always a power of 2.

 At any time, the directory consists of d levels, and a directory of depth
d has 2d bucket pointers.

- However, not every directory entry (bucket pointer) has to point to a
unique bucket. More than one directory entry can point to the same one.
- Each bucket has a local depth d’ that indicates how many of the d bits of
_________________________________

the hash value are actually used to indicate membership in the bucket.
 The depth d of the directory is based on the # of bits we use from
each hash value.

- The hash function produces an output integer which can be treated as a
sequence of k bits. We use the first d bits in the hash value produced
to look up an entry in the directory. The directory entry points us to the
block that contains records whose keys hash to that value.
Example:
-Assume each hashed key is a
sequence of four binary digits.
-Store values 0001, 1001, 1100.
Insert 0100
Insert 1111
_________________________________

Directory grows one level.
Insert 1101
Directory grows one level.
Delete 1111
_________________________________

Merge Blocks and Shrink Directory
Linear hashing
 Linear hashing allows a hash file to expand and shrink dynamically
without the need of a directory.

- Thus, directory issues like extendible hashing are not present.
 A linear hash table starts with 2d buckets where d is the # of bits used
from the hash value to determine bucket membership.

- The size of the table will grow gradually, but not double in size.
1) Every time there is a bucket overflow.
2) When the load factor of the hash table reaches a given
point. We will examine the second growth method.
Since overflows may not always trigger growth, note that each bucket
may use overflow blocks.
_________________________________

Parallelizing Disk Access Using RAID Technology
RAID (originally redundant array of inexpensive disks; now commonly
redundant array of independent disks) is a data storage that combines
multiple disk drive components into a logical unit for the purposes of data
redundancy or performance improvement.
Disk striping is the process of dividing a body of data into blocks and spreading
the data blocks across several partitions on several hard disks.
Striping can be done at the byte level, or in blocks. Byte-level striping means
that the file is broken into "byte-sized pieces". The first byte of the file is sent to
the first drive, then the second to the second drive, and so on. Sometimes byte-
level striping is done as a sector of 512 bytes.
Block-level striping means that each file is split into blocks of a certain size and
those are distributed to the various drives. The size of the blocks used is also
called the stripe size (or block size, or several other names), and can be selected
from a variety of choices when the array is set up.
_________________________________

RAID 0
RAID 0 performs what is called ―Block Striping‖ across multiple drives. The data
is fragmented, or broken up, into blocks and striped among the drives. This level
increases the data transfer rate and data storage since the controller can access
the hard disk drives simultaneously. However, this level has no redundancy. If
single drive fails, the entire array becomes inaccessible. The more drivers in the
array the higher the data transfer but higher risk of a failure. RAID level 0 requires
2 drives to implement.
Advantages
I/O performance is greatly improved by spreading the I/O load across
many channels and drives
Best performance is achieved when data is striped across multiple
controllers with only one drive per controller
No parity calculation overhead is involved
Very simple design
Easy to implement
_________________________________

Disadvantages
Not a "True" RAID because it is NOT fault-tolerant
The failure of just one drive will result in all data in an array being
lost Should never be used in mission critical environments
Recommended Applications
Video Production and Editing
Image Editing
Pre-Press Applications
Any application requiring high bandwidth
RAID 1
RAID 1 is used to create a complete mirrored set of disks. Usually employing two
disks, RAID 1 writes data as a normal hard drive would but makes an exact copy of
the primary drive with the second drive in the array. This mirrored copy is constantly
updated as data on the primary drive changes. By keeping this mirrored backup, the
array decreases the chance of failure from 5% over three years to
0.25%. Should a drive fail, the failed drive should be replaced as soon as possible
_________________________________

to keep the mirror updated. RAID Level 1 requires a minimum of 2 drives
to implement.
Advantages
Twice the Read transaction rate of single disks, same Write transaction rate
as single disks
100% redundancy of data means no rebuild is necessary in case of a
disk failure, just a copy to the replacement disk
Transfer rate per block is equal to that of a single disk
Under certain circumstances, RAID 1 can sustain multiple simultaneous drive
failures
Simplest RAID storage subsystem design
Disadvantages
Highest disk overhead of all RAID types (100%) - inefficient Typically
the RAID function is done by system software, loading the
CPU/Server and possibly degrading throughput at high activity levels.
Hardware implementation is strongly recommended
May not support hot swap of failed disk when implemented in "software"
Accounting
Payroll
Financial
Any application requiring very high availability
_________________________________

RAID 5
RAID 5 is the most stable of the more advanced RAID levels and offers
redundancy, speed, and the ability to rebuild a failed drive. RAID 5 uses the
same block level striping as other RAID levels but adds another level of data
protection by creating a ―parity block‖. These blocks are stored alongside the other
blocks in the array in a staggered pattern and are used to check that the data has
been written correctly in the drive.
when an error or failure occur, the parity block will be used to locate the
information stored on the other member disks and parity blocks in the array to
rebuild the data correctly. This can be done on-the-fly without interruptions to
applications or other programs running on the computer. The computer will notify
the user of the failed drive, but will continue to operate normally. This state of
operation is known as Interim Data Recovery Mode. Performance may suffer
while the disk is being rebuilt, but operation should continue. The failed drive
should be replaced as soon as possible. RAID Level 5 requires a minimum of 3
drives to implement.
_________________________________

Advantages
Highest Read data transaction rate
Medium Write data transaction rate
Low ratio of Error Correction Code (Parity) disks to data disks means
high efficiency
Good aggregate transfer rate
Disadvantages
Disk failure has a medium impact on
throughput Most complex controller design
Difficult to rebuild in the event of a disk failure (as compared to RAID 1)
Individual block data transfer rate same as single disk
File and Application servers
Database servers
Web, E-mail, and News servers
Intranet servers
Most versatile RAID level
_________________________________

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