RDBMS_day 7

RDBMS - Day6 OLTP basics, Concurrency, Data integrity, Security

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Agenda
Transactions
Data integrity & Security
Concurrency control

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In today’s session, we would be talking about the basic concepts of transactions, online transaction processing, database integrity, security features using DDL statements, the concept of serializability of transactions and about concurrent transactions.+

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Transaction

Logical unit of program execution that takes a database from one consistent state to another consistent state

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In other words, a transaction is a sequence of database actions which must either all be done, or none of them must be done. if only some of the actions of a transaction are carried out the database will be left in an inconsistent state Consider the example transaction of a transfer of funds between bank accounts Suppose there are two bank accounts, A and B The balance in A is Rs 1000 and that in Y is 5000 The transaction has to transfer rs100 from bank account A into bank account B. The sequence of actions would be: Read balance in A Calculate A – transfer amount

1000 900

Store new value of A

900

Read balance of B Calculate B + transfer amount Store new value of B

5000 5100 5100

If only the steps till 3 are done, then database would be in an inconsistent state

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ACID Properties





Atomicity Consistency Isolation Durability

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Atomicity- Transaction management •DBMS keeps track of the old value of a data on which the transaction acts so that, if there is a failure, the old value of the data is restored as though no transaction acted on it

` Consistency-Application programmer •To ensure that the database remains consistent after execution of the transaction Isolation-Concurrency control • To ensure concurrent execution of transactions results in a state equivalent to that obtained by sequential execution Durability-Recovery management •To ensure that after successful transaction completion, all updates persist irrespective of system failures

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State diagram of a transaction While executing

active al fin as e th t h d ter en te Af tem xecu sta en e be

When normal execution can’t proceed

partially completed

failed After rollback and restoration to prev state

A s u c fte r c com essf p le u l tion

aborted

committed

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Transaction state Active Partially committed Failed Aborted Committed

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SQL and Transactions
BEGIN TRANSACTION
COMMIT TRANSACTION;
ROLLBACK TRANSACTION

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A transaction is typically represented by a combination of the above three operations in SQL The COMMIT statement defines the end of a transaction The ROLLBACK statement undoes all of the actions of a transaction. Consider the following example Begin transaction is not standard. A new transaction begins immediately after a commit statement COMMIT; Update Account Set Balance = Balance - 100 Where AccountNo = ‘11111’; Update Account Set Balance = Balance + 100 Where AccountNo = ‘22222’; COMMIT; COMMIT; Update Account Set Balance = Balance - 1000 Where AccountNo = ‘11111’; Update Account Set Balance = Balance + 100 Where AccountNo = ‘22222’; ROLLBACK;

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On-line Transaction Processing Systems Handle
Several concurrent transactions from
Spatially Distributed M/cs
Execution of Instructions and Queries across LAN/WAN
Geographically distributed processors
Spatially Distributed Databases

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•Several concurrent transactions are initiated from spatially distributed terminals. •Each transaction has the need for executing several machine instructions, retrievals, updates, and sending or receiving messages. •Processors distributed geographically execute the programs initiated by these transactions. •There may be one or more databases which may be spatially distributed and used by the transaction. •Typical examples are: Railway reservation, Bank transactions etc.

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Data Integrity
Refers to :
Correctness and completeness of data
Validity of individual items
Preservation of interrelationships in the DB


Data integrity constraints:





Required data Domain integrity Entity integrity Referential integrity

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To preserve correctness of data, any RDBMS imposes data integrity constraints. These constraints restrict the data values that can be inserted / modified. The constraints commonly found in RDBMSs are as found in the slide.

We will discuss in detail about these constraints in the following slides..

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Required data
Requires a column to contain Non-NULL values
Indicated with the key word NOT NULL in create statement An example: CREATE TABLE EMPLOYEE ( EMP_NO EMP_NAME EMP_AGE ……. …………

integer NOT NULL, varchar (15) , integer,

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An insert into the table without a value for the column with NOT NULL constraint fails An update trying to set the value of the column to a NULL value fails

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Domain Integrity-Check constraint
Specify a range of values that a column can take.
Used to specify business rules.
Specified in the create statement An example: CREATE TABLE EMPLOYEE ( EMP_NO

integer NOT NULL,

…………… ………. EMP_LOC

varchar(15)

CHECK ( EMP_LOC in ( ‘BANGALORE’, ’BOMBAY’,’DELHI’)));

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Check constraint is like a search condition. This will produce a true/false value. The condition specified in the check constraint is verified by the RDBMS for every insert and update operations. If any violation is observed, the operation fails.

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Entity Integrity
The database models the outside world
A table’s primary key should have unique values so that it represents some real world entity
The requirement that the primary key be unique is called “Entity integrity constraint”

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When a primary key is defined on a table, DBMS automatically checks to see if any duplicates are entered, whenever an insert or update is attempted on the table. If any duplicate is found , the operation fails with an error message.

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Referential integrity  Ensures that the integrity of the parent-child relationship between tables created by primary and foreign keys is preserved

 Issues:





Inserting a new child row Updating foreign key in a child row Updating the primary key in a parent row Deleting a parent row

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Let us take the first issue: inserting a new child row: When we try to insert a row in the child table, its foreign key value must be the same as one of the primary key values in a parent table. For example, The deptno is a foreign key in the employee table which refers to the dept# in the department table. let us say, we try to insert a row in the employee table, with a department number which is not there in the department table, then it indicates the data is wrong. So , such a insert should not be allowed. Databases take care of this situation automatically A similar check is performed when we try to modify the foreign key field The last two issues are similar: Updating /deleting the parent row. Let us say, we are dissolving a department altogether, what will happen to the eployees who belong to the department? Ve to do one of the following: 1. Prevent the dept being deleted until all its employees are reallotted to some other dept 2. Automatically delete the employees who belong to the dept 3. Set the dept column for the employees who belong to the dept to NULL 4. Set the dept column of these employees to some default value which indicates they are currently not allotted to any dept To achieve this, 4 options can be set in CREATE table command: ON DELETE RESTRICT ON DELETE SET NULL ON DELETE SET DEFAULT ON DELETE SET CASCADE For ex: CREATE table ORDERS ( Order_Num Integer NOT NULL, ………………………… ……………………. Foreign key (Cust) REFERENCES Customers ON DELETE CASCADE, Foreign key (Rep) REFERENCES SalesReps ON DELETE SET NULL, ON UPDATE CASCADE, Foreign key (Mfr, Product) REFERENCES Products ON DELETE RESTRICT

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Security Protection of data against unauthorized disclosure, alteration or destruction. Access allowed to only authorized users User identification - Authorized users connect to the database using user id and password. Views, Synonyms,Roles Access Privileges

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Data Definition Language statements for Data security
GRANT & REVOKE

GRANT ….. TO … REVOKE ….. FROM ...

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Privileges on a specified database Privileges on specified tables or views System privileges

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1. GRANT …. database GRANT { [DBADM[, ]] - Database administrator authority [DBCTRL[,]] - Database control authority [DBMAINT[, ]] - Database maintenance authority [CREATETAB[,]] - Privilege to create table [DROP[, ]] - Privilege to DROP/ALTER [STARTDB[, ]] - Start database [STOPDB[, ]] } - Stop database ON DATABASE database-name[,...] TO [AuthID][,...] [PUBLIC] [WITH GRANT OPTION]

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2. GRANT …. Tables or views GRANT { [ALTER[, ]] [DELETE[, ]] [INDEX[, ]] [INSERT[, ]] [SELECT[, ]] [UPDATE [(column-name[,...])][, ]] [REFERENCES[, ]] | ALL [PRIVILEGES] } ON [TABLE] {table-name[,...] | view-name[,...]} TO [AuthID][,...] [PUBLIC [AT ALL LOCATIONS]] [WITH GRANT OPTION]

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Privileges on a specific table or a view created based on a table.

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3. GRANT .. System privileges

GRANT { [CREATEALIAS[, ]] - create alias [CREATEDBA[, ]] - create DB to get DBADM authority [CREATEDBC[, ]] - create DB to get DBCTRL authority [CREATESG[, ]] - to create new storage group [SYSADM[, ]] - to provide system ADM authority [SYSCTRL[, ]] - to provide system control authority } TO [AuthID][,...] [PUBLIC] [WITH GRANT OPTION] Copyright © 2004, 17 Infosys Technologies Ltd

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Grants the system level privileges using which the list of actions that can be performed by a particular user is defined

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GRANT …. TO ….





Used to grant access to new users; Permission can be granted for all DML commands; Permission is granted on a database/table/view; Permission for further grant.


E.g:
User1 is an owner of Customer table.
User1 wants User2 perform queries on it.
User1 issues following command:

GRANT SELECT ON Customer to User2;

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To allow insert permission, User1 issues the commandGRANT INSERT ON Customer to User2; GRANT SELECT, INSERT ON Customer to User2; GRANT INSERT ON Customer to User2, User3;

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Restricting Privileges


GRANT SELECT, UPDATE ON Customer to User2
GRANT UPDATE ( Comm) ON Customer to User2
GRANT UPDATE (CName,City) ON Customer to User2;

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By explicitly saying what kind of queries can be performed on a table/view, we can restrict the kind of changes that may be done to a table/view by a particular user. We can even specify what columns of a table can be updated as shown in the slide

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ALL & PUBLIC arguments


GRANT ALL PRIVILEGES ON Customer to User2
GRANT ALL ON Cusomer to PUBLIC;
GRANT SELECT ON Customer to PUBLIC;

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Granting with GRANT option


GRANT SELECT ON Customer To User2 WITH GRANT OPTION
GRANT SELECT ON User1.Customer To User3;
GRANT SELECT ON User1.Customer To user3 WITH GRANT OPTION;

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Taking PRIVILIGES away The syntax of REVOKE command is patterned after GRANT, but with a reverse meaning. REVOKE{ [ALTER[, ]] [DELETE[, ]] [INDEX[, ]] [INSERT[, ]] [SELECT[, ]] [UPDATE [(column-name[,...])][, ]] | ALL [PRIVILEGES] } ON [TABLE] {table-name[,...] | view-name [,...]} FROM AuthID[,...][PUBLIC [AT ALL LOCATIONS]] [BY {AuthID[,...] | ALL}]

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Examples of REVOKE REVOKE INSERT ON Customer FROM User2; REVOKE SELECT, INSERT ON Customer FROM User2, User3;

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Concurrency

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Concurrency
Two or more users access a database concurrently
DBMS ensures serializability
Problems associated with concurrent execution:





Lost update Dirty read Non repeatable read Phantom records


Concurrency techniques:
Locking
Time stamping

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Serializability: If two transactions T1 and T2 are executing concurrently, the DBMS would ensure that the final result will be the same as when these transactions are executed serially (one after the other). This is called serializability of transactions. The problems that may occur if this serializability is not ensured by the dbms are as given in the slide Lost update: This happens when one transaction updates a table and before it commits, another transaction reads the value (old value) and makes some updates based on that. Consider the example below: Let A=20 Read (A, a1) t1 a1 = a1 + 5 t2 t3 Read (A, b1) Write(A,a1) t4 Commit t5 t6 b1 = b1 * 2 t7 Write(A,b1) t8 Commit Here the update done by the first transaction is not taken into account at all. Dirty read A transaction A may read some data updated by another transaction B. B might not have yet committed. If B fails and gets aborted, the data as read by A would not exist (database would undo all changes done by B) and hence would be incorrect. Nonrepeatable read This happens when a transaction A reads a value from a table, another transaction B modifies that value and A gaian reads that value. Now A will find a different value than what it was before. Phantom records Let’s say a transaction A reads a set of rows from a table that satisfy a where condition. Now another transaction B inserts few more rows into the table which would also satisfy the where condition. Now if A executes the query again, it will read more records than what it fetched before

These problems are illustrated in the following few slides with examples

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Serial execution of two transactions A = 200 Trans T1 A = 200 A = 150 B = 100 B = 150

Time

Read(A,a1) a1=a1-50 Write(A,a1) Read(B,b1) b1=b1+50 Write(B,b1)

B = 100

A = 135 B = 165

t1 t2 t3 t4

Trans T2

t5

Read(A,a2) temp=a2*0.1 a2=a2-temp Write(A,a2) read(B,b2) b2=b2+temp Write(B,b2)

t6 t7 t8

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A = 150

A = 135 B = 150 B = 165

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Slide shows the serial execution of transactions T1 and T2. only after T1 finishes completely, T2 begins.

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A = 200 B = 100

Serialized execution Trans T1 A = 200 A = 150

B = 100 B = 150

Read(A,a1) a1=a1-50 Write(A,a1)

Read(B,b1) b1=b1+50 Write(B,b1) Commit

t1 t2 t3

t4 t5 t6 t7 t8

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A = 135 B = 165

Trans T2 Read(A,a2) temp=a2*0.1 a2=a2-temp Write(A,a2)

Read(B,b2) b2=b2+temp Write(B,b2) commit

A = 150

A = 135

B = 150 B = 165

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Serialized execution means, interleaving the execution of the two transactions T1 and T2 in such a way that, the effect on the database is the same as executing these two transactions serially.

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Lost update

A=200 A=100 Trans T1

A=100 A=100

Read (A, a1) a1 = a1 + 50

A=150

write(A,a1) Commit

Time t1 t2 t3 t4 t5 t6 t7 t8

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Trans T2

Read (A, b1) A=100 b1 = b1 * 2 A=200 Write(A,b1) A=200 Commit

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This example shows how two or more concurrent transactions which update a common record can introduce inconsistency in the database The Updation done by transaction T1 is totally lost even before it is seen

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Dirty Read

Trans T1 Time A=100 A=150 A=100

Read(A,a1) a1 = a1+50 Write(A,a1)

Rollback

t1 t2 t3 t4 t5 t6 t7 t8

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A=300 A=100 Trans T2

Read(A,b1) A=150 b1 = b1 * 2 Write(A,b1) A=300 Commit

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This eg shows how two or more concurrent transactions that see uncommitted data of any other transactions can introduce inconsistency in the db Transaction T1 Updates record A at time t3 and then it decides to rollback or undo But, Transaction T2 reads the updated data which is not the correct data, and does some Updation on the wrong data and commits.

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Incorrect summary Trans T1

Time

Trans T2

A = 100 B = 200 Sum=350

A=100

A=50 B=200 B=250

Read (A,a1) a1 = a1-50

t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12

Write(A,a1) Read(B,a2) a2 = a2 +50 Write(B,a2) Commit

Sum = 0 Read(A,b1)

A=100

Sum = Sum + b1

Sum=100

Read(B,b2) Sum = Sum + b2 Commit

B=250

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Sum=350

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This e.g. shows that in certain cases, interleaving of transactions some of which only retrieve data and others update the data is being retrieved by the other transactions, may result in inconsistent data being generated. Transaction T1 Updates record A and record B Transaction T2 which has to calculate the sum of updated record, has read record A before Updation and Record B after Updation, resulting in Incorrect Summary or The transaction T2 has seen the database in an inconsistent state and has therefore performed an INCONSISTENT ANALYSIS

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Phantom Record TRANS-T1

Time

TRANS-T2

T1

Sum = 0

Insert X

T2

Insert Y

T3 T4

Read (X, Bal_X)

T5

Sum=sum + Bal_X

T6

Read (Y, Bal_Y)

T7

Sum=sum + Bal_Y

Insert Z

T8 T9

COMMIT

T10

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COMMIT

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Until TRANS-T1 COMMITS, the TRANS-T2 cannot see the existence of Z Thus, Z is a PHANTOM RECORD as far as TRANS-B is concerned Unless TRANS-T2, prevents TRANS-T1 from inserting Z, the two transactions are not serializable

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Locking
A lock is a variable associated with each data item in a database.
When updated by a transaction, DBMS locks the data item
serializability could be maintained by this.
Lock could be Shared or Exclusive
An example ->

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Granularity of locks A database consists of several items that form a hierarchy. For example, the general hierarchy is: 1. A field 2. A data row or a tuple 3. A table 4. A tablespace 4. A database The position of a database item in the hierarchy is an indication of its granularity. Thus, a field has a finer granularity while a database has the coarsest granularity of all. Field level locking is not practically being used because of the high overhead involved.

Shared lock is used by the DBMS when a transaction wants to read some data from the database. Another transaction can also acquire lock on the same data item and concurrently perform a read operation. Exclusive lock is used when a transaction wants to update data. Once exclusive lock is acquired on a data item, another transaction can’t lock the same data item (for read or write) until the first transaction releases the lock. To summarize the compatibility: S

X

S

Y

N

X

N

N

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Locking (Example) T1

B=200

A=100 A=300

lock-S(B) Read(B,b1) unlock(B)

Lock-X(A) Read(A,a1) Temp=a1+b1 Write(A,Temp) unlock(A)

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T2 A = 100 B = 200 lock-S(A) Read(A,a2) unlock(A) lock-X(B) Read(B,b2) Temp=a2+b2 Write (B,Temp) Unlock(B)

A = 300 B = 300

A=100 B=200 B=300

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Intent Locking
Also called Preemptive lock
Used to tag (lock) all ancestors of a node to be locked in share or exclusive mode.
This tag signals to other requesting transactions that locking may take place at a finer level by the transaction that holds the intent lock
This prevents other transactions from obtaining shared or exclusive lock to the ancestors.
A variant of this is the SIX (Share-intention exclusive ) lock

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Intent locking: The lockable units in a generalized DBMS are:Database, Tablespace, Tables, Rows and Fields. Each node of this list can be locked. When a lock request is granted to a node at a particular level, the requester node as well as all its descendants would be implicitly granted the same level of lock. For example, if a transaction locks a table in exclusive mode, it means it has been implicitly given exclusive access to every row of this table. Thus, generalization can be made that the same lock is granted to the entire sub-tree starting at the requested node. Now, let us say transaction A wants to update some rows of a table, and doesn’t know how many apriori. If A locks only the row which it currently updates, then in the meantime, some other transaction B may acquire an exclusive lock on the entire table. After this, when A wants to update few more rows, it may not be possible because the exclusive lock that has been acquired on the entire table by B will lock all rows of the table also in exclusive mode. A may have to wait till B releases the lock. To avoid this, A can put an intention lock on the table (which means A has the intention to lock some nodes under the subtree ie some rows of the table in future) . This will prevent B from acquiring an exclusive lock on the table. After this, A can acquire individual locks on each row it may want to read or update and complete the task. The intention lock is of two types: intention share (IS) and intention exclusive (IX). When a transaction A puts an IS lock on a table, it means A has the intention to lock some node under this subtree, ie some row under the table in shared mode. This does not stop another transaction B from acquiring a similar lock on the table. After this lock is aqcuired, if A wants to read any row of the table, it has to explicitly acquire a shared lock on the row. A similar principle applies for the IX mode also. The difficulty with this scheme is that, after acquiring an intention lock on a top level node (say a table), for even reading any row of the table, the transaction has to acquire, individual shared lock on each such row. Please refer to notes page of the next slide for explanation on the SIX lock

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Lock Compatibility matrix

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S Gives share access to the requested node and to all descendants of the requested node. Any other transaction can get a S lock or IS lock only. X Gives exclusive access to the requested node and to all its descendants. No other locks are permitted in this mode. IS Gives intention share access to the requested node and allows the requester to explicitly lock the descendants of this node in S , IS, IX or SIX mode. Such explicit locking at granular level is possible only if compatibility at that finer level is supported. IX Gives intention exclusive access to the requested node and allows the requester to explicitly lock the descendants in IS or IX modes. This is again subject to compatibility with the other mode at the descendant's level. SIX The subtree rooted by the node under consideration is locked explicitly in a shared mode and a few nodes at lower levels are being locked in the exclusive mode. Only another IS lock is allowed in this mode. To understand the SIX mode, consider, there is an employee table which transaction T1 would be updating. As of now, it is not known as to which all rows may be required. If T1 locks the table in the IX mode, then some other transaction may acquire an IX lock on the same node and lock any descendant in the x mode. If T1 now comes to know that it has to update the same node that some other transaction has locked, it may have to wait. So, when T1 knows that it would be reading all the records of the table and updating some records, it can obtain a shared and intention-exclusive (SIX) lock on the table (root) so that, no other transaction can lock any child node of this table (row) in an exclusive (X) mode

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Two-Phase locking
Serializability of concurrently executing transactions can be guaranteed by two phase locking


Each transaction is divided into two phases:
Growing phase
Shrinking phase

locks can be acquired but not released locks can be released but not acquired

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The “Lost update” problem and the “Dirty Read” problem show that serializability requires that a transaction updating a record should not only lock the record but also hold the lock until COMMIT/ROLLBACK time. The “Incorrect summary” problem and the “Phantom Record” problem require that the table itself be locked even for read transaction until the transaction comes to an end. Thus, we can see that locks keep growing during a certain phase of the transaction and the locks start shrinking during the COMMIT/ROLLBACK time. Thus, there is a lock-growing phase and lock-shrinking phase. Such a scheme is called Two-phase locking (2PL). The 2PL theory can be summarized as: A transaction should not operate on any object unless the transaction has acquired an appropriate lock on the object. The transaction should not acquire any fresh locks after releasing a lock. We can say: If a transaction follows the 2PL protocol, then it is serializable. It is very important to notice that all 2PL transactions are serializable. But, not all serializable transactions follow 2PL protocol.Thus, 2PL protocol is a sufficient but not necessary condition for serialization. 2PL is a way of ensuring serializability in a simple way.

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Locking (Example)

T1 B=200

Lock-S(B) Read(B,b1) Lock-X(A) unlock(B)

T2

A = 100 B = 200

Lock-X(B) Read(B,b2) A=100 A=300

A = 300 B = 500

B=200

Read(A,a1) Temp=a1+b1 Write(A,Temp) unlock(A) Lock-S(A) Read(A,a2) unlock(A) Temp=a2+b2 Write (B,Temp) Unlock(B) Copyright © 2004, 37 Infosys Technologies Ltd

A=300 B=500

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Deadlock
Occurs when two or more separate processes compete for resources held by one another. T1

T2

Write_lock A … action(s) Read_lock B … action(s) Write_lock B WAIT

Read_lock A WAIT

T1 must wait for T2 to release lock T2 must wait for T1 to release lock

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•Deadlock occurs when one transaction is waiting on another to release a lock it needs, and vice versa - each then will wait forever for the other •If a deadlock occurs one of the offending transactions must be rolled back to allow the other to proceed •There are various methods for choosing which transaction to roll back when a deadlock is detected •Time (how long the transactions have been running) •Data updated •Data remaining to update •There are schemes for preventing deadlock. But, most DBMSs allow them to occur and resolve when they are detected Detection may be based on: •Timeout •Wait-for-graph (shows which transactions are waiting on which other transactions for a lock)

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Deadlock T1 Lock-X(A) update A

lock-X(B) update B

T2 t1 t2 t3 t4 t5 t4 t5

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lock-X(B) update B lock-X(A) update A

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Summary
Transaction is a logical unit of work which takes the database from one consistent state to the other
Atomicity, consistency, isolation and durability are the ACID properties of a transaction
Data integrity and Security are enforced using SQL DDL statements
Transactions should be able to concurrently execute without affecting the consistency of the database
Locking is a mechanism of achieving such controlled concurrency

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