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Relational Database Management System


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Presentation is about the Basic RDBMS Concepts

Presentation is about the Basic RDBMS Concepts

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  • 1. Relational Database Management System
  • 2. Surender Singh Sr. Programmer
  • 3. Relational Database Management System DATA DATABASE DBMS/RDBMS Information
  • 4. File Processing System
  • 5. File Processing System Application Programs File System (Programs Database Written in C (Data Structure (Information in Pascal etc.) File Handling) Files Format)
  • 6. File System Database
  • 7. Disadvantages of FPS  Data Redundancy and Inconsistency  Difficulty in accessing data  Data isolation  Integrity Problems  Atomicity Problems  Concurrent-access anomalies  Security Problems
  • 8. Data Redundancy and Inconsistency Name Address AccNo Name Address ABC Bhiwani 1002 ABC Bhiwani DEF Delhi 1005 DEF Jaipur Customer Information Saving Account
  • 9. Difficulty in accessing data Manager Requirement Application Programs File System (Programs Database Written in C (Data Structure (Information Storage Pascal etc.) File Handling) in Files Format)
  • 10. Data Isolation and Integrity Problems Program in C Program in COBOL #include <stdio.h> 01 Reserve-rec. Main() 03 saving { 05 accno PIC A(2) ----- -------- } New Document
  • 11. Atomicity Problems Bank Data Transmission USER USER
  • 12. Concurrent-access anomalies
  • 13. Security Problems Employee Information
  • 14. Database the Piece of mind
  • 15. Requirements of a DBMS • 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).
  • 16. 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. D B File Secondary M Manager Storage S
  • 17. View of DATA View Level (External Level) View 1 View 2 View n Logical Level Conceptual View Physical Level Internal View
  • 18. 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), -------------- );
  • 19. Data Models A Data Model is a mechanism for describing the data, their interrelationships and the constraints. Object-based Conceptual models. Entity-Relationship model Record-based models. Relational Model Network Model Hierarchical Model Physical data models.
  • 20. The E-R Model Entities : An entity is a distinct clearly identifiable object of the database e.g Book 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. Relationships : A relationship is a mapping between entity sets. Acc_No Card_No Name Acc_No Title BOOK Borrowed_By USERS Author YearofPub Card_No DOI Address
  • 21. The Relational Model 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. Attribute Book Table/Relation AccNo Title Author YearofPub Tuple
  • 22. Network Model Data in the network model are represented by collection of records and relationships among data are represented by links, which can be viewed as Pointers. User Card_No Name Address Link Pointer Next Book Acc_No Author ----- Link
  • 23. Hierarchical Model This is special kind of a network model where the relationship is essentially a tree-like structure. Hospital Wards Units Patient Doctors Nurses Cardiology Skin
  • 24. 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 models In use. Two of the widely known ones are the Unifing model and frame-Memory model.
  • 25. Database Languages Database Languages Data-Definition Data-Manipulation Data-Control Create Table Test ( Update Title Varchar2(20), Insert GRANT Connect, -------- Delete Resource TO xUser ); Query
  • 26. Database Management System Structure Naïve Users Application Sophisticated Database Users (tellers, agents, etc.) Programmers Users Administrators Application Application Database Interfaces Programs Query Scheme Embedded DML DDL Application DML Compiler Interpreter Programs Precompiler Object Code Query Query Processor Evaluation Engine Database Management System Transaction Buffer Manager Storage Manager Manager File Manager Indices Statistical Data Disk Storage Data Files Data Dictionary
  • 27.
  • 28. Oracle Storage System Structure
  • 29. 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
  • 30. Terms Simple and Composite Attributes Single-valued and Multivalued Attributes Null Attributes Derived Attributes Existence Dependencies Weak Entity Set and Strong Entity Set
  • 31. Weak Entity Set
  • 32. Attributes
  • 33. Keys Keys Candidate Key Secondary Key Foreign Key Primary Key Alternate Key Composite Key
  • 34. Candidate Keys Primary Alternate Keys 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
  • 35. Primary Secondary Key Key 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
  • 36. Composite Primary Key Name Branch City Deepak Computers Bhiwani Mukesh Electronics Rohtak Teena Computers Bhiwani Deepak Electronics Rohtak Monika Computers Delhi
  • 37. P# Part P_Name Colour Quantity P1 Nut Red 200 P2 Bolt Green 250 P3 Screw Blue 300
  • 38. S# 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
  • 39. SP# 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
  • 40. 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 mapping Cardinality must be one of the following A B A B One to One One to Many
  • 41. A B A B Many to One Many to Many
  • 42. More on E-R Diagrams Company Owns Multiple Relationship between Leased Same entity set Vehicle Manager Staff Reports to Subordinate Circular Relationship
  • 43. Ternary E-R Diagram Instructors Teaches Students Courses Book Borrowed_By User N 1 Constraints
  • 44. E-R Diagram Components Entity Sets Attributes Relationship Sets Connectors/Constraints Multivalued Attributes Derived Attributes Total Participation of an entity in a relationship set
  • 45. Existence Dependencies
  • 46. Generalization and Specialization
  • 47. Generalization and Specialization The abstraction mechanisms Emp_No Name Date_of_hire Generalization Employee Specialization IS_A IS_A Full_time Part_time Type Employee Salary Employee IS_A IS_A IS_A IS_A Faculty Staff Teaching Casual Degree Interest Stipend Hour_Rate
  • 48. Aggregation The Process of compiling information on an object Teaches Teacher Uses Course Book Teacher-Teaches Teacher Teaches Course Uses Book
  • 49. Represent ER model using tables
  • 50. 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.
  • 51. The Relational Algebra The relational algebra is a procedural query language. The Borrow and Branch relations
  • 52. 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.
  • 53. Formal Definition of Relational Algebra
  • 54. The Select Operation
  • 55. The Project Operation
  • 56. The Cartesian Product Operation
  • 57. Output of Cartesian Product Relation A Relation B AXB A B A B 1 1 X X 2 1 Y Y 2 X 3 2 Y 3 X 3 Y
  • 58. The Rename Operation
  • 59. The Union Operation
  • 60. The Set Difference Operation
  • 61. Additional Operations The Set Intersection Operation
  • 62. The Natural Join Operation
  • 63. The Division Operation
  • 64. Example of Division Operation Relation R Relation S ÷S R A B B A P A A P Q A P B B Q Q T M A Q B
  • 65. The Assignment Operation
  • 66. Relational Calculus Relational Calculus is a nonprocedural Query language  Tuple Relational Calculus Uses Tuple variables which take values of an entire tuple  Domain Relational Calculus Uses Domain variables which takes values from an attribute
  • 67. Tuple Relational Calculus
  • 68. Example Queries
  • 69. Some More Examples
  • 70. Domain Relational Calculus
  • 71. SQL
  • 72. Integrity Constraints Integrity and Consistency is of primary concern to any database design At any instance a database must be correct according to a set of rules. Rules are checked during any database operation. Insertion Deletion Updation Recovery from Failure Concurrent Operations Types of Constraints Domain Constraints Referential Integrity Constraint Functional Dependencies
  • 73. Domain Constraints Includes Type Width Null or Not Null Checks/Conditions Specify at the time of designing Checked at the time of insertion, deletion or modification e.g Bname char(20) Amount number(7,2) DOL date check (date>=29/09/2004 City char(10) not null TotalAmt = amount + interest
  • 74. Referential Integrity Foreign Key Referential integrity states that all values of the foreign key of one Relation must be present in another relation where the same attribute Is declared as the primary key Checks during Database Modification Insert Delete Update
  • 75. Assertions and Triggers An assertion is a general predicate, expressed in relational algebra Or 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 executed Automatically by the system when an event (i.e., insertion, updation Or deletion) takes place on a table Define trigger insert_record on delete of emp e (insert into emp_history values e.empno,, e.deptno)
  • 76. 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
  • 77. 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 have The 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 R 2. for all tuples t1 and t2 in r, if t1 [α ] = t2 [α ] then t1 [β ] = t2 [β ]
  • 78. Closure of a Set of Functional Dependencies
  • 79. Armstrong’s Axioms
  • 80. Closure of a Set of F+
  • 81. Closure of Attribute Sets
  • 82. Canonical Cover To minimize the number of functional dependencies that need to be Tested 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 logically Implies all dependencies in Fc. A canonical cover Fc of a set of FDs F is a minimal cover of F in the Sense that there is no subset of Fc which also covers F.
  • 83. Example of Cannonical Cover Consider a relation r ( X, Y, Z ) with the FDs F. 1. X YZ 2. Y Z 3. X Y 4. 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 YZ 2. Y Z Which is a cover of F. Here again since X Y and Y Z holds, by Transitivity X Z holds. So it is redundant. Deleting this we get the FDs as X Y Y Z Which is a cannonical cover of F.
  • 84. Relational Database Design
  • 85. Database Decomposition – 1 Representation of Information
  • 86. Database Decomposition – 2
  • 87. Database Decomposition – 3
  • 88. Database Decomposition – 4
  • 89. Lossless-join Decomposition
  • 90. Example of lossy decomposition S_by s_name s_addr Item Price A1 B1 C1 D1 A1 B1 C2 D1 p1 A2 B2 C1 D2 p2 S_addr Item price S_name Item A2 B2 C3 D3 B1 C1 D1 A1 C1 A3 B1 C2 D2 B1 C2 D1 A1 C2 A2 C1 Natural Join of P1 and p2 B2 C1 D2 S_name S_addr Item Price B2 C3 D3 A2 C3 A1 B1 C1 D1 B1 C2 D2 A3 C2 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
  • 91. Dependency Preservation
  • 92. 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)
  • 93. First Normal Form (1NF) 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. Name C_addr F_name L_name City State Zip
  • 94. Second Normal Form (2NF) A relation is said to be in 2NF if it is in 1NF and All non-prime attributes are fully functionally dependent on candidate key Consider a relation savings_deposit having the following structure:- Saving_deposit (name, addr, acc_no, amt ) With the following FDs : name addr name, acc_no amt 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 same Address causing redundancy. This redundancy arises because a non-prime attribute like address is dependent on an attribute Which is not a candidate key.
  • 95. 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 and Acc_no. that this decomposition is also lossless join and dependency preserving Courses ( Course_no, title, loc, time ) And FD’s are – Course_no title Course_no, time loc
  • 96. Third Normal Form (3NF) A relation is said to be in 3NF and non-prime attributes are not dependent On each other. Consider the relation – s_by ( s_name, item, price, gift_item ) With FDs s_name, item price price gift_item Here all prime attributes are fully functional dependent on candidate keys, the Non-prime attribute gift-item is also fully functional dependent on the non-prime Attribute price. This create redundancy because every price value there is a fixed Gift item. We shall have to impose the additional restriction that no non-prime attribute can Be functionally dependent on another non-prime attributes.
  • 97. Solution We decompose the relation s_by (s_name, item, price, gift_item ) Into s_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 )
  • 98. Boyce-Codd Normal Form (BCNF)
  • 99. More on BCNF
  • 100. Comparison of BCNF and 3NF
  • 101. Comparison of BCNF and 3NF - 2
  • 102. Normalization using Multivalued Dependencies
  • 103. Multivalued Dependencies -2
  • 104. Rules
  • 105. More Rules
  • 106. Fourth Normal Form (4NF)
  • 107. Example
  • 108. 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 is A lossless-join decomposition of R. Formally, if R = R1∪ R2 ∪ …… ∪ Rn, we say that a relation r( R ) satisfies the join dependency.
  • 109. 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 and Join 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 4NF Thus, in general, we may not be able to find a dependency-preserving decomposition Into PJNF for a given schema.
  • 110. Storage and File Structure Hierarchy of Storage
  • 111. Description
  • 112. Description - 2
  • 113. File Organization
  • 114. Fixed Length Record -1
  • 115. Fixed Length Record -2
  • 116. Variable-length Records
  • 117. Fixed-length representation
  • 118. Organization of Records in files
  • 119. Concurrency Control and Recovery
  • 120. 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 various Data 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:
  • 121. States of Transactions Partially Committed Active Aborted Failed
  • 122. 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.
  • 123. Example  Consider two transactions (Xacts): T1: BEGIN A=A+100, B=B-100 END T2: BEGIN A=1.06*A, B=1.06*B END y 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. y 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.
  • 124. Example (Contd.)  Consider a possible interleaving (schedule): T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B y This is OK. But what about: T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B y 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)
  • 125. 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) A T1 T2 Dependency graph B y Dependency graph: One node per Xact; edge from Ti to Tj if Tj reads or writes an object last written by Ti. y The cycle in the graph reveals the problem. The output of T1 depends on T2, and vice-versa.
  • 126. 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).
  • 127. Detection of Serializability One of the techniques of concurrency control is to detect whether a schedule is valid or not Prior to execution. The task of understanding a schedule is simplified by considering only the sequence of read and 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
  • 128. Serializable Concurrency T1 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 two Consecutive 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.
  • 129. Deadlock Condition T1 T2 UPDATE account UPDATE account SET balance = balance * 0.1 SET balance = balance * 0.1 WHERE acc_no = ‘FC821’ WHERE acc_no = ‘FC523’ UPDATE account UPDATE account SET age = 30 SET age = 38 WHERE acc_no = ‘FC523’ WHERE acc_no = ‘FC821’
  • 130. Lock-Based Techniques In this technique the system does not participate in detection of inconsistency nor does it take any Corrective 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.
  • 131. Example T1 T2 Lock-X(P) Read (P,p) P=p-1 Write(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+1 Write(Q,q) Unlock(Q)
  • 132. 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 schedule are automatically conflict serialzable.
  • 133. 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.
  • 134. 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.
  • 135. 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.
  • 136. 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.
  • 137. The Log - 2 Log file e.g. X=1000, Y= 2000 T: Read (X, xi) Transaction Name xi  xi – 500 Data item Name Write (X,xi) Old Value New Value Read ( Y, yi) yi  yi + 500 <T starts> Write (Y, yi) <T, X, 1000, 500> <T, Y, 2000, 2500> <T, commits>
  • 138. Checkpoints At the time of recovery the entire log needs to be searched to know which transaction need to Be 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>
  • 139. 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 available To 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 committed Since 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>.
  • 140. 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.
  • 141. Query Processing/Optimization
  • 142. Rules Optimization using algebraic Manipulation Any algebraic manipulation approach to query optimization uses a set of rules, which may Be 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.
  • 143. Example
  • 144. Example contd.
  • 145. Projection Operation
  • 146. Natural Join Operation
  • 147. Natural Join Operation - 2