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Presentation is about the Basic RDBMS Concepts
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File Processing System
Database(Information in Files Format)
Application Programs
(Programs Written in CPascal etc.)
File System
(Data StructureFile Handling)
Disadvantages of FPS
Data Redundancy and InconsistencyDifficulty in accessing dataData isolationIntegrity ProblemsAtomicity ProblemsConcurrent-access anomaliesSecurity Problems
Data Redundancy and Inconsistency
Customer Information Saving Account
Name Address
ABC BhiwaniDEF Delhi
AccNo Name Address
1002 ABC Bhiwani1005 DEF Jaipur
Difficulty in accessing data
Database(Information Storage
in Files Format)
Application Programs
(Programs Written in CPascal etc.)
File System
(Data StructureFile Handling)
Requirement
Manager
Data Isolation and Integrity Problems
#include <stdio.h>Main()
{-----
}
01 Reserve-rec. 03 saving 05 accno PIC A(2)
--------
Program in C Program in COBOL
New Document
• A mechanism for specification of data and its dependencies (Integrity Constraints) in an integrated fashion.• Prevention of redundancy and inconsistency.• Provision of adequate security and access-rights.• Mechanism for concurrency control.• Mechanism for recovery from failure.
Additionally any DBMS must provide• Schemes for specification of procession rules or application Programs.• Efficient techniques for storage and retrieval of data from the secondary storage (disk).
Requirements of a DBMS
A DBMS has two major components, namely
Structure of Database is called Database Schema.
Instance, which is a state of the database with the actual data loaded.
A set of software tools/programs which access, update and
process the database, called the query and update-mechanism.
DBMS
FileManager
SecondaryStorage
[email protected] Level
Logical Level
View 1 View 2 View n
View Level (External Level)
View of DATA
Internal View
Conceptual View
Data Independence
The ability to modify a schema definition in one level without affecting a schema definition in the next higher level is called data independence.
Physical data independence Logical data independence
Create table emp(empno number(10),
--------------);
Data ModelsA Data Model is a mechanism for describing the data, their interrelationships and the constraints.
Record-based models.Relational ModelNetwork ModelHierarchical Model
Physical data models.
Object-based Conceptual models.Entity-Relationship model
The E-R Model
Entities : An entity is a distinct clearly identifiable object of the database e.g Book
Relationships : A relationship is a mapping between entity sets.
Attribute : Each Entity is characterized by a set of attributes e.g. Acc.No.
Entity Set : Set of all entities having attributes of the same type.
BOOK USERSBorrowed_By
Acc_No Title
Author YearofPub
Card_No Name
Address
Acc_No
Card_No DOI
The Relational Model
AccNo Title Author YearofPub
Relational Model uses a collection of tables to represent both data and relationship among those data. Each table has multiple Attributes and similar kind of tuples.
Tuple
Attribute
Book Table/Relation
Network Model
Data in the network model are represented by collection of records and relationships among data are represented by links, which can be viewedas Pointers.
User
Card_No Name Address Link
Pointer Next
Acc_No Author ----- LinkBook
Hierarchical Model
This is special kind of a network model where the relationship is essentially a tree-like structure.
Hospital
Wards Units
Cardiology SkinPatient Doctors Nurses
Physical Data Models
Physical data models are used to describe data at the lowest level.In contrast to logical data models, there are few physical data modelsIn use. Two of the widely known ones are the Unifing model and frame-Memory model.
Database Languages
Data-Definition Data-Manipulation Data-Control
Create Table Test(
Title Varchar2(20),--------
);
UpdateInsertDeleteQuery
GRANT Connect,Resource TO xUser
Database Languages
Database Management System Structure
ApplicationInterfaces
DatabaseScheme
ApplicationPrograms Query
ApplicationPrograms
Object Code
EmbeddedDML
Precompiler
QueryEvaluation
Engine
DML Compiler
DDLInterpreter
TransactionManager
BufferManager
FileManager
Indices
Data Files
Statistical Data
Data Dictionary
Naïve Users Application Sophisticated Database(tellers, agents, etc.) Programmers Users Administrators
Users
QueryProcessor
StorageManager
DatabaseManagementSystem
Disk Storage
Database Administrator
Roles of DBA
• Schema Definition• Storage structure and access-method definition• Schema and Physical-organization modification• Granting of authorization for data access• Integrity-constraint specification
Terms Simple and Composite Attributes Single-valued and Multivalued Attributes Null Attributes Derived Attributes Existence Dependencies Weak Entity Set and Strong Entity Set
Keys
Keys
Candidate Key Secondary Key Foreign Key
Primary Key Alternate Key
Composite Key
Roll_No Name Branch City
01 Deepak Computers Bhiwani
02 Mukesh Electronics Rohtak
03 Teena Mechanical Bhiwani
04 Deepti Chemical Rohtak
05 Monika Civil Delhi
Candidate Keys
Primary key
Alternate Keys
Roll_No Name Branch City
01 Deepak Computers Bhiwani
02 Mukesh Electronics Rohtak
03 Teena Computers Bhiwani
04 Deepak Electronics Rohtak
05 Monika Computers Delhi
Primary Key
Secondary Key
Name Branch City
Deepak Computers Bhiwani
Mukesh Electronics Rohtak
Teena Computers Bhiwani
Deepak Electronics Rohtak
Monika Computers Delhi
Composite Primary Key
Supplier S_Name City Quantity
S1 John Delhi 200
S2 Smith Kolkata 250
S3 James Delhi 300
S4 David Chennai 400
S5 John Chennai 300
S#
P# S# Quantity
P1 S1 200
P2 S1 300
P3 S1 400
P1 S2 250
P2 S3 250
P3 S4 200
P2 S4 300
P3 S5 400
SP#
Mapping Cardinalities
Mapping cardinalities, or cardinality ratios, express the number of entities to which another entity can be associated via a relationship set.
For a binary relationship set R between entity sets A and B, the mappingCardinality must be one of the following
A AB B
One to One One to Many
Company
Vehicle
Owns Leased
More on E-R Diagrams
Multiple Relationship between Same entity set
Staff Reports toManager
Subordinate
Circular Relationship
Instructors Students
Courses
Teaches
Ternary E-R Diagram
Book UserBorrowed_ByN 1
Constraints
E-R Diagram Components
Entity Sets
Attributes
Relationship Sets
Connectors/Constraints
Multivalued Attributes
Derived Attributes
Total Participation of an entity in a relationship set
Generalization and SpecializationThe abstraction mechanisms
Employee
Emp_No Name Date_of_hire
IS_A IS_A
Full_timeEmployee Salary
IS_AIS_A
Faculty Staff
Interest
Part_timeEmployee
Type
IS_A IS_A
Teaching Casual
Hour_RateStipendDegree
Generalization Specialization
Aggregation
The Process of compiling information on an object
Teacher
Teaches
Course
Book
Uses
Teacher Teaches Course
Uses
Book
Teacher-Teaches
Query Languages
A query language is a language in which a user requests information from a database. These are typically higher-level than programming languages.They may be one of:
Procedural, where the user instructs the system to perform a sequence of operations on the database. This will compute the desired information.
Nonprocedural, where the user species the information desired without giving a procedure for ob-taining the information.
A complete query language also contains facilities to insert and delete tuples as well as to modify parts of existing tuples.
The Relational Algebra
The Borrow and Branch relations
The relational algebra is a procedural query language.
Fundamental Operations
select (unary) project (unary) rename (unary) cartesian product (binary) union (binary) set-difference (binary)
Several other operations, dened in terms of the fundamental operations: set-intersection natural join division assignment Operations produce a new relation as a result.
Output of Cartesian Product
A
1
2
3
B
X
Y
A B
1 X
1 Y
2 X
2 Y
3 X
3 Y
Relation A Relation B A X B
Example of Division Operation
A B
P A
Q A
P B
Q T
M A
Q B
B
A
B
A
P
Q
Relation R Relation S R S÷
Relational Calculus
Tuple Relational Calculus Uses Tuple variables which take values of an entire tuple
Domain Relational CalculusUses Domain variables which takes values from an attribute
Relational Calculus is a nonprocedural Query language
Integrity Constraints
Types of Constraints
Domain ConstraintsReferential Integrity ConstraintFunctional Dependencies
Integrity and Consistency is of primary concern to any database designAt any instance a database must be correct according to a set of rules. Rules are checked during any database operation.
InsertionDeletionUpdationRecovery from FailureConcurrent Operations
Domain Constraints
Includes
TypeWidthNull or Not NullChecks/Conditions
Specify at the time of designingChecked at the time of insertion, deletion or modification
e.g Bname char(20)Amount number(7,2)DOL date check (date>=29/09/2004City char(10) not nullTotalAmt = amount + interest
Referential Integrity
Foreign Key
Referential integrity states that all values of the foreign key of oneRelation must be present in another relation where the same attributeIs declared as the primary key
Checks during Database ModificationInsertDeleteUpdate
Assertions and Triggers
An assertion is a general predicate, expressed in relational algebraOr calculus or any language like SQL which must always hold in a Database
Assert salary-constraint on emp salary >= 1000
A trigger is a statement or a block of statements which are executedAutomatically by the system when an event (i.e., insertion, updationOr deletion) takes place on a table
Define trigger insert_recordon delete of emp e
(insert into emp_historyvalues e.empno, e.name, e.deptno)
Functional Dependencies
Functional Dependencies provide a formal mechanism to express Constraints between attributes.
It is a mean of identifying how values of certain attributes are Determined by values of other attributes.
A functional dependency (FD) generalizes the concept of a key.
Book (acc_no, yr_pub, title)
Acc_no is Primary Key
Formal representation of Constraints acc_no yr_pub acc_no title
Formal Notation of FD
In general if there are two attributes A and B and the FD
A B
Holds then, it means that there can be no two tuple which haveThe same value of attributes A and different values in attribute B.
If α and β are two sets of attributes then the FD α β holds on a Relation r(R), if –
1. α , β ⊆ R, i.e. α , β subset of R2. for all tuples t1 and t2 in r, if t1 [α ] = t2 [α ] then
t1 [β ] = t2 [β ]
Canonical Cover
To minimize the number of functional dependencies that need to beTested in case of an update we may restrict F to a canonical cover Fc .
A canonical cover for F is a set of dependencies such that F logicallyImplies all dependencies in Fc .
A canonical cover Fc of a set of FDs F is a minimal cover of F in theSense that there is no subset of Fc which also covers F.
Example of Cannonical Cover
Consider a relation r ( X, Y, Z ) with the FDs F.
1. X Y Z2. Y Z3. X Y4. XY Z
Here 4 is redundant because (1) states that X Y and X Z holds.Thus (4) can be derived from (1). Also (3) is redundant because (1) contains (3).Deleting these two we get
1. X YZ2. Y Z
Which is a cover of F. Here again since X Y and Y Z holds, byTransitivity X Z holds. So it is redundant. Deleting this we get the FDs as
X YY Z
Which is a cannonical cover of F.
Example of lossy decomposition
s_name s_addr Item Price
A1 B1 C1 D1
A1 B1 C2 D1
A2 B2 C1 D2
A2 B2 C3 D3
A3 B1 C2 D2
S_name Item
A1 C1
A1 C2
A2 C1
A2 C3
A3 C2
S_name S_addr Item Price
A1 B1 C1 D1
A1 B2 C1 D2
A1 B1 C2 D1
A1 B1 C2 D2
A2 B1 C1 D1
A2 B2 C1 D2
A2 B2 C3 D3
A3 B1 C2 D1
A3 B1 C2 D1
S_addr Item price
B1 C1 D1
B1 C2 D1
B2 C1 D2
B2 C3 D3
B1 C2 D2
S_by
p1 p2
Natural Join of P1 and p2
Normalization
Normalization is a process of removing redundancy using functional Dependencies.
To reduce redundancy it is necessary to decompose a relation into a number of smaller relations.
There are several normal Forms.
-First Normal Form (1 NF)-Second Normal Form (2 NF)-Third Normal Form(3 NF)-Boyce-Codd Normal Form (BCNF)
First Normal Form (1NF)
Name
F_name L_name
This normal form says that all attributes are simple.
An attribute is said to be simple if it does not contain any subparts.An attributes which contains subparts is called complex attributes.
C_addr
City State Zip
Second Normal Form (2NF)
Consider a relation savings_deposit having the following structure:- Saving_deposit (name, addr, acc_no, amt )
With the following FDs :name addrname, acc_no amt
A relation is said to be in 2NF if it is in 1NF andAll non-prime attributes are fully functionally dependent on candidate key
Here [name, acc_no ] is the candidate key and addr and amt are the non prime attributes.Among the non-prime attributes amt depends on [name, acc_no ] whereas addr depends on name only.
Note that due to FD name addr every tuple with the same name will contain the sameAddress causing redundancy.
This redundancy arises because a non-prime attribute like address is dependent on an attributeWhich is not a candidate key.
Solution
We can remove this redundancy by splitting the original relation into following two relations
Sav_sch1 (name, addr)Sav_sch2(name, acc_no,amt)
Both the relations are now 2NF. In the first relation name is Primary Key and the onlyNon-prime attribute is addr which is dependent on name
In the second relation the only non-prime attribute amt depend on both name andAcc_no. that this decomposition is also lossless join and dependency preserving
Courses ( Course_no, title, loc, time )
And FD’s are –
Course_no titleCourse_no, time loc
Third Normal Form (3NF)
A relation is said to be in 3NF and non-prime attributes are not dependentOn each other.
Consider the relation –s_by ( s_name, item, price, gift_item )
With FDss_name, item priceprice gift_item
Here all prime attributes are fully functional dependent on candidate keys, theNon-prime attribute gift-item is also fully functional dependent on the non-primeAttribute price. This create redundancy because every price value there is a fixedGift item.
We shall have to impose the additional restriction that no non-prime attribute canBe functionally dependent on another non-prime attributes.
Solution
We decompose the relations_by (s_name, item, price, gift_item )
Intos_by_1 (s_name, item, price )s_by_2 (price, gift_item)
Now we have a lossless join and dependency preserving decomposition.
An alternative yet equivalent definition for 3NF is :
For every FD α β on R at least one of the following conditions hold –
• α ⊆ β (trivial dependency)• α R (α is a super key )
Normalization using Join Dependencies
Let R be a relation schema and R1, R2,….Rn be a decomposition of R. The join dependency*(R1, R2,….Rn) is used to restrict the set of legal relations to those for which R1, R2,….Rn isA lossless-join decomposition of R.
Formally, if R = R1∪R2 ∪ …… ∪ Rn, we say that a relation r( R ) satisfies the join dependency.
Fifth Normal Form (5NF)Project-Join Normal Form
Project-join normal form (PJNF) is defined in a manner similar to BCNF and 4NF, Except that join dependencies are used.
A relation schema R is in PJNF with respect to a set D of functional multivalued andJoin dependencies if, for all join depencdencies in D+ of the form *(R1, R2,…. Rn).Where each Ri ⊆ R and R = R1 ∪ R2 ∪…… ∪ Rn, at least one of the following holds:
• *(R1, R2…..Rn) is a trival join dependency.• Every Ri is a superkey for R.
It’s seems that every PJNF is also in 4NFThus, in general, we may not be able to find a dependency-preserving decompositionInto PJNF for a given schema.
Concurrency Control and Recovery
Transactions
Concurrent execution of user programs is essential for good DBMS performance. Because disk accesses are frequent, and relatively slow, it is important to keep the cpu humming by
working on several user programs concurrently. A user’s program may carry out many operations on the data retrieved from the database, but the
DBMS is only concerned about what data is read/written from/to the database. A transaction is the DBMS’s abstract view of a user program: a sequence of reads and writes.
A Tracnsaction is a unit of program execution That accesses and possibly updates variousData items.
Collection of operations that form a single logical unit of work are called tracsactions. A database system must ensure proper execution of transaction despite failures.
To ensure integrity of the data, database system must maintain the following properties of the transactions:
Concurrency in a DBMS
Users submit transactions, and can think of each transaction as executing by itself. Concurrency is achieved by the DBMS, which interleaves actions (reads/writes of DB objects) of
various transactions. Each transaction must leave the database in a consistent state if the DB is consistent when the
transaction begins. DBMS will enforce some ICs, depending on the ICs declared in CREATE TABLE statements. Beyond this, the DBMS does not really understand the semantics of the data. (e.g., it does not
understand how the interest on a bank account is computed). Issues: Effect of interleaving transactions, and crashes.
Example
Consider two transactions (Xacts):
T1: BEGIN A=A+100, B=B-100 ENDT2: BEGIN A=1.06*A, B=1.06*B END
❖ Intuitively, the first transaction is transferring $100 from B’s account to A’s account. The second is crediting both accounts with a 6% interest payment.
❖ There is no guarantee that T1 will execute before T2 or vice-versa, if both are submitted together. However, the net effect must be equivalent to these two transactions running serially in some order.
Example (Contd.)
Consider a possible interleaving (schedule):T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B
❖ This is OK. But what about:T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B
❖ The DBMS’s view of the second schedule:
T1: R(A), W(A), R(B), W(B)T2: R(A), W(A), R(B), W(B)
Example (Contd.)
The DBMS must not allow schedules like this!
T1: R(A), W(A), R(B), W(B)T2: R(A), W(A), R(B), W(B)
T1 T2A
BDependency graph
❖ Dependency graph: One node per Xact; edge from Ti to Tj if Tj reads or writes an object last written by Ti.
❖ The cycle in the graph reveals the problem. The output of T1 depends on T2, and vice-versa.
Scheduling Transactions
Equivalent schedules: For any database state, the effect (on the set of objects in the database) of executing the first schedule is identical to the effect of executing the second schedule.
Serializable schedule: A schedule that is equivalent to some serial execution of the transactions. If the dependency graph of a schedule is acyclic, the schedule is called conflict serializable. Such a
schedule is equivalent to a serial schedule. This is the condition that is typically enforced in a DBMS (although it is not necessary for
serializability).
Detection of SerializabilityOne of the techniques of concurrency control is to detect whether a schedule is valid or notPrior to execution.
The task of understanding a schedule is simplified by considering only the sequence of readand write operation in a transaction
T1 T2
Read(X)Read(X)Write(X)
Write(X)Read(Y)Write(Y)
Read(Y)Write(Y)
Read-Write sequence of a non-serializable schedule
Serializable ConcurrencyT1 T2
Read(X)Write(X)
Read(X)Write(X)
Read(Y)Write(Y)
Read(Y)Write(Y)
A serializable concurrent schedule
Generalize the idea of conflict. Consider the four possibilities which can arise between twoConsecutive instructions T1 and T2 in a schedule ( T1 and T2 belong to two different transactions)
1. T1 : Read(X) followed by T2 : Write(X)2. T1 : Read(X) followed by T2 : Read(X)3. T1 : Write(X) followed by T2 : Read(X)4. T1 : Write(X) followed by T2 : Write(X)
T1 and T2 are said to be conflict if they cannot be swapped without fear of loss of consistency.In above 3 cases all pairs except case 2 are said to be in conflict.
Deadlock Condition
T1 T2
UPDATE account UPDATE accountSET balance = balance * 0.1 SET balance = balance * 0.1WHERE acc_no = ‘FC821’ WHERE acc_no = ‘FC523’
UPDATE account UPDATE accountSET age = 30 SET age = 38WHERE acc_no = ‘FC523’ WHERE acc_no = ‘FC821’
Lock-Based TechniquesIn this technique the system does not participate in detection of inconsistency nor does it take anyCorrective action.
The DBMS however, provides the user with a set of operations which when used properly can ensure that concurrent execution will not violate consistency.
In this techniques functions are provided to lock and unlock data items by transactions,
In the simplest case a data item X can be locked by a transaction T1 in two modes :
Shared Mode : if T1 locks X in shared mode then before T1 unlocks X, no other transaction T2 can write into X. But a transaction T2 can read the value of X even if T1 has locked locked X in shared mode.
Exclusive Mode : If T1 locks X in exclusive mode then before T1 unlocks X, no other transaction T2 can read or write into X.
Example
T1 T2
Lock-X(P)Read (P,p)P=p-1Write(P,p)Unlock(P)
Lock-S(Q)Read(Q,q)unlock(Q)Lock-S(P)Read(P,p)unlock(P)display(p)display(p)
Lock-X(Q)Read(Q,q)q = q + 1Write(Q,q)Unlock(Q)
Two-Phase locking
Phase I – Acquiring Phase : During this phase a transaction may lock a data item but not unlock any data item.
Phase II – Releasing Phase : During this phase a transaction may unlock data items locked earlier but no new locks may be acquired.
In two phase locking phase I must always precede phase II. This will ensure that all scheduleare automatically conflict serialzable.
Enforcing (Conflict) Serializability
Two-phase Locking (2PL) Protocol: Each Xact must obtain a S (shared) lock on object before reading, and an X (exclusive) lock on object
before writing. Once an Xact releases any lock, it cannot obtain new locks. If an Xact holds an X lock on an object, no other Xact can get a lock (S or X) on that object.
2PL allows only conflict-serializable schedules. Potential problem of deadlocks: we could have a cycle of Xacts, T1, T2, ... , Tn, with each Ti waiting for its
predecessor to release some lock that it needs. Dealt with by killing one of them and releasing its locks.
Atomicity of Transactions
A transaction might commit after completing all its actions, or it could abort (or be aborted by the DBMS) after executing some actions.
A very important property guaranteed by the DBMS for all transactions is that they are atomic. That is, a user can think of a Xact as always executing all its actions in one step, or not executing any actions at all. DBMS logs all actions so that it can undo the actions of aborted transactions.
This ensures that if each Xact preserves consistency, every serializable schedule preserves consistency.
Aborting a Transaction
If a transaction Ti is aborted, all its actions have to be undone. Not only that, if Tj reads an object last written by Ti, Tj must be aborted as well!
Most systems avoid such cascading aborts by releasing a transaction’s locks only at commit time. If Ti writes an object, Tj can read this only after Ti commits.
In order to undo the actions of an aborted transaction, the DBMS maintains a log in which every write is recorded. This mechanism is also used to recover from system crashes: all active Xacts at the time of the crash are aborted when the system comes back up.
The Log
The following actions are recorded in the log: Ti writes an object: the old value and the new value.
Log record must go to disk before the changed page! Ti commits/aborts: a log record indicating this action.
Log records are chained together by Xact id, so it’s easy to undo a specific Xact. Log is often duplexed and archived on stable storage. All log related activities (and in fact, all activities such as lock/unlock, dealing with deadlocks etc.) are
handled transparently by the DBMS.
The Log - 2
T: Read (X, xi)xi xi – 500Write (X,xi)
Read ( Y, yi)yi yi + 500Write (Y, yi)
Log file e.g. X=1000, Y= 2000
<T starts><T, X, 1000, 500><T, Y, 2000, 2500><T, commits>
Transaction NameData item NameOld ValueNew Value
Checkpoints
At the time of recovery the entire log needs to be searched to know which transaction need toBe redone and which transactions needs to be undone. The problem with this approach is:
1. It will take a reasonable amount of time.2. Most of the transactions that need to be redone have already modified the database.
To solve this problem the concept of checkpoint is used here at different points. Checkpoints are introduced to indicate that the data before this point has already been Updated to the database. Before writing checkpoints the following sequence of actions shuld to take place –
- Output all log records currently residing in the main store to a stable storage- Output all modified buffer blocks to secondary storage.- Output a log record <checkpoint>
Recovering From a Crash
There are 3 phases in the Aries recovery algorithm: Analysis: Scan the log forward (from the most recent checkpoint) to identify all Xacts that were active,
and all dirty pages in the buffer pool at the time of the crash. Redo: Redoes all updates to dirty pages in the buffer pool, as needed, to ensure that all logged
updates are in fact carried out and written to disk. Undo: The writes of all Xacts that were active at the crash are undone (by restoring the before value
of the update, which is in the log record for the update), working backwards in the log. (Some care must be taken to handle the case of a crash occurring during the recovery process!)
Data can be lost due to the failure of the nonvolatile storage like the disk. The scheme which is availableTo protect the data from disk failure is to periodically dump the entire contents of the database to any backup(or even stable) storage like a magnetic tape. When a failure occurs the most recent dump is used to restoring The datbase to a previous consistent state. Then the log is used to redo all the transactions that have committedSince the last dump occurred. The following steps are performed for this purpose :
• Output all log records currently residing in the main memory onto stable store.• Output all buffer blocks onto the disk.• Copy the contents of the database to stable store.• Output a log record <dump>.
Summary
Concurrency control and recovery are among the most important functions provided by a DBMS. Users need not worry about concurrency.
System automatically inserts lock/unlock requests and schedules actions of different Xacts in such a way as to ensure that the resulting execution is equivalent to executing the Xacts one after the other in some order.
Write-ahead logging (WAL) is used to undo the actions of aborted transactions and to restore the system to a consistent state after a crash.
Consistent state: Only the effects of commited Xacts seen.
Optimization using algebraic ManipulationAny algebraic manipulation approach to query optimization uses a set of rules, which mayBe enumerated as follows.
Perform selection as early as possible, in order to reduce the number of tuples to be processed subsequently. Projections of projections should be combined, if possible, in order to avoid repeated scanning of tuples. Projection over indexed attributes should be done earlier and That over non-indexed attributes should be done later. Intermediate relations produced in separate processing sequences must be shared as as and when possible. If possible, attributes which are controlling a join operation should be sorted earlier.
Rules
