sonali dbms notes

1.2 Advantages of Database Systems

As shown in the figure, the DBMS is a central system which provides a common interface between the data and the various front-end programs in the application. It also provides a central location for the whole data in the application to reside.

Due to its centralized nature, the database system can overcome the disadvantages of the file-based system as discussed below.

Minimal Data Redundancy

Since the whole data resides in one central database, the various programs in the application can access data in different data files. Hence data present in one file need not be duplicated in another. This reduces data redundancy. However, this does not mean all redundancy can be eliminated. There could be business or technical reasons for having some amount of redundancy. Any such redundancy should be carefully controlled and the DBMS should be aware of it.

Data Consistency

Reduced data redundancy leads to better data consistency.

Data Integration

Since related data is stored in one single database, enforcing data integrity is much easier. Moreover, the functions in the DBMS can be used to enforce the integrity rules with minimum programming in the application programs.

Data Sharing

Related data can be shared across programs since the data is stored in a centralized manner. Even new applications can be developed to operate against the same data.

Enforcement of Standards

Enforcing standards in the organization and structure of data files is required and also easy in a Database System, since it is one single set of programs which is always interacting with the data files.

Application Development Ease

The application programmer need not build the functions for handling issues like concurrent access, security, data integrity, etc. The programmer only needs to implement the application business rules. This brings in application development ease. Adding additional functional modules is also easier than in file-based systems.

Better Controls

Better controls can be achieved due to the centralized nature of the system.

Data Independence

The architecture of the DBMS can be viewed as a 3-level system comprising the following:

- The internal or the physical level where the data resides.

- The conceptual level which is the level of the DBMS functions

- The external level which is the level of the application programs or the end user.

Data Independence is isolating an upper level from the changes in the organization or structure of a lower level. For example, if changes in the file organization of a data file do not demand for changes in the functions in the DBMS or in the application programs, data independence is achieved. Thus Data Independence can be defined as immunity of applications to change in physical representation and access technique. The provision of data independence is a major objective for database systems.

Reduced Maintenance

Maintenance is less and easy, again, due to the centralized nature of the system.

1.3 Functions of a DBMS

The functions performed by a typical DBMS are the following:

Data Definition

The DBMS provides functions to define the structure of the data in the application. These include defining and modifying the record structure, the type and size of fields and the various constraints/conditions to be satisfied by the data in each field.

Data Manipulation

Once the data structure is defined, data needs to be inserted, modified or deleted. The functions which perform these operations are also part of the DBMS. These function can handle planned and unplanned data manipulation needs. Planned queries are those which form part of the application. Unplanned queries are ad-hoc queries which are performed on a need basis.

Data Security & Integrity

The DBMS contains functions which handle the security and integrity of data in the application. These can be easily invoked by the application and hence the application programmer need not code these functions in his/her programs.

Data Recovery & Concurrency

Recovery of data after a system failure and concurrent access of records by multiple users are also handled by the DBMS.

Data Dictionary Maintenance

Maintaining the Data Dictionary which contains the data definition of the application is also one of the functions of a DBMS.

Performance

Optimizing the performance of the queries is one of the important functions of a DBMS. Hence the DBMS has a set of programs forming the Query Optimizer which evaluates the different implementations of a query and chooses the best among them.

Thus the DBMS provides an environment that is both convenient and efficient to use when there is a large volume of data and many transactions to be processed.

1.4 Role of the Database Administrator

Typically there are three types of users for a DBMS. They are :

1. The End User who uses the application. Ultimately, this is the user who actually puts the data in the system into use in business. This user need not know anything about the organization of data in the physical level. She also need not be aware of the complete data in the system. She needs to have access and knowledge of only the data she is using.

2. The Application Programmer who develops the application programs. She has more knowledge about the data and its structure since she has manipulate

the data using her programs. She also need not have access and knowledge of the complete data in the system.

3. The Database Administrator (DBA) who is like the super-user of the system. The role of the DBA is very important and is defined by the following functions.

Defining the Schema

The DBA defines the schema which contains the structure of the data in the application. The DBA determines what data needs to be present in the system ad how this data has to be represented and organized.

Liaising with Users

The DBA needs to interact continuously with the users to understand the data in the system and its use.

Defining Security & Integrity Checks

The DBA finds about the access restrictions to be defined and defines security checks accordingly. Data Integrity checks are also defined by the DBA.

Defining Backup / Recovery Procedures

The DBA also defines procedures for backup and recovery. Defining backup procedures includes specifying what data is to backed up, the periodicity of taking backups and also the medium and storage place for the backup data.

Monitoring Performance

The DBA has to continuously monitor the performance of the queries and take measures to optimize all the queries in the application.

1.5 Types of Database Systems

Database Systems can be catagorised according to the data structures and operators they present to the user. The oldest systems fall into inverted list, hierarchic and network systems. These are the pre-relational models.

In the Hierarchical Model, different records are inter-related through hierarchical or tree-like structures. A parent record can have several children, but a child can have only one parent. In the figure, there are two hierarchies shown - the first storing the relations between CUSTOMER, ORDERS, CONTACTS and ORDER_PARTS and the second showing the relation between PARTS, ORDER_PARTS and SALES_HISTORY. The many-to-many relationship is implemented through the ORDER_PARTS segment which occurs in both the hierarchies. In practice, only one tree stores the ORDER_PARTS segment, while the other has a logical pointer to this segment. IMS (Information Management System) of IBM is an example of a Hierarchical DBMS.

In the Network Model, a parent can have several children and a child can also have many parent records. Records are physically linked through linked-lists. IDMS from Computer Associates International Inc. is an example of a Network DBMS.

In the Relational Model, unlike the Hierarchical and Network models, there are no physical links. All data is maintained in the form of tables consisting of rows and columns. Data in two tables is related through common columns and not physical links or pointers. Operators are provided for operating on rows in tables. Unlike the other two type of DBMS, there is no need to traverse pointers in the Relational DBMS. This makes querying much more easier in a Relational DBMS than in the the Hierarchical or Network DBMS. This, in fact, is a major reason for the relational model to become more programmer friendly and much more dominant and popular in both industrial and academic scenarios. Oracle, Sybase, DB2, Ingres, Informix, MS-SQL Server are few of the popular Relational DBMSs.

CUSTOMER

CUST. NO. CUSTOMER NAME ADDRESS CITY

15371 Nanubhai & Sons L. J. Road Mumbai

... ... ... ...

... ... ... ...

... ... ... ...

CONTACTS

ORDERS

CUST.NO. CONTACT DESIGNATION ORDER NO.ORDER DATE CUSTOMER NO.

15371 Nanubhai Owner 3216 24-June-1997 15371

15371 Rajesh Munim Accountant ... ... ...

... ... ... ... ... ...

... ... ... ... ... ...

PARTS ORDERS-PARTS

PARTS NO.PARTS DESC PART PRICE ORDER NO.PART NO. QUANTITY

S3 Amkette 3.5" Floppies

400.00 3216 C1 300

... ... ... 3216 S3 120

... ... ... ... ... ...

... ... ... ... ... ...

SALES-HISTORY

PART NO. REGION YEAR UNITS

S3 East 1996 2000

S3 North 1996 5500

S3 South 1996 12000

S3 West 1996 20000

The recent developments in the area have shown up in the form of certain object and object/relational DBMS products. Examples of such systems are GemStone and Versant ODBMS. Research has also proceeded on to a variety of other schemes including the multi-dimensional approach and the logic-based approach.

3-Level Database System Architecture

The External Level represents the collection of views available to different end-users.

The Conceptual level is the representation of the entre information content of the database.

The Internal level is the physical level which shows how the data data is stored, what are the representation of the fields etc.

2. The Internal Level

This chapter discusses the issues related to how the data is physically stored on the disk and some of the access mechanisms commonly used for retrieving this data.

The Internal Level is the level which deals with the physical storage of data. While designing this layer, the main objective is to optimize performance by minimizing the number of disk accesses during the various database operations.

The figure shows the process of database access in general. The DBMS views the database as a collection of records. The File Manager of the underlying Operating System views it as a set of pages and the Disk Manager views it as a collection of physical locations on the disk.

When the DBMS makes a request for a specific record to the File Manager, the latter maps the record to a page containing it and requests the Disk Manager for the specific page. The Disk Manager determines the physical location on the disk and retrieves the required page.

2.1 Clustering

In the above process, if the page containing the requested record is already in the memory, retrieval from the disk is not necessary. In such a situation, time taken for the whole operation will be less. Thus, if records which are frequently used together are placed physically together, more records will be in the same page. Hence the number of pages to be retrieved will be less and this reduces the number of disk accesses which in turn gives a better performance.

This method of storing logically related records, physically together is called clustering.

Eg: Consider CUSTOMER table as shown below.

Cust ID Cust Name Cust City ...

10001 Raj Delhi ...

10002 ... ... ...

10003 ... ... ...

10004 ... ... ...

... ... ... ...

... ... ... ...

If queries retrieving Customers with consecutive Cust_IDs frequently occur in the application, clustering based on Cust_ID will help improving the performance of these queries. This can be explained as follows.

Assume that the Customer record size is 128 bytes and the typical size of a page retrieved by the File Manager is 1 Kb (1024 bytes).

If there is no clustering, it can be assumed that the Customer records are stored at random physical locations. In the worst-case scenario, each record may be placed in a different page. Hence a query to retrieve 100 records with consecutive Cust_Ids (say, 10001 to 10100), will require 100 pages to be accessed which in turn translates to 100 disk accesses.

But, if the records are clustered, a page can contain 8 records. Hence the number of pages to be accessed for retrieving the 100 consecutive records will be ceil(100/8) = 13. i.e., only 13 disk accesses will be required to obtain the query results. Thus, in the given example, clustering improves the speed by a factor of 7.7

Q: For what record size will clustering be of no benefit to improve performance ?

A: When the record size and page size are such that a page can contain only one record.

Q: Can a table have clustering on multiple fields simultaneously ?

A: No

•Intra-file Clustering – Clustered records belong to the same file (table) as in the above example.

•Inter-file Clustering – Clustered records belong to different files (tables). This type of clustering may be required to enhance the speed of queries retrieving related records from more than one tables. Here interleaving of records is used.

2.2 Indexing

Indexing is another common method for making retrievals faster.

Consider the example of CUSTOMER table used above. The following query is based on Customer's city.

“Retrieve the records of all customers who reside in Delhi”

Here a sequential search on the CUSTOMER table has to be carried out and all records with the value 'Delhi' in the Cust_City field have to be retrieved. The time taken for this operation depends on the number of pages to be accessed. If the records are randomly stored, the page accesses depends on the volume of data. If the records are stored physically together, the number of pages depends on the size of each record also.

If such queries based on Cust_City field are very frequent in the application, steps can be taken to improve the performance of these queries. Creating an Index on Cust_City is one such method. This results in the scenario as shown below.

A new index file is created. The number of records in the index file is same as that of the data file. The index file has two fields in each record. One field contains the value of the Cust_City field and the second contains a pointer to the actual data record in the CUSTOMER table.

Whenever a query based on Cust_City field occurs, a search is carried out on the Index file. Here, it is to be noted that this search will be much faster than a sequential search in the CUSTOMER table, if the records are stored physically together. This is because of the much smaller size of the index record due to which each page will be able to contain more number of records.

When the records with value 'Delhi' in the Cust_City field in the index file are located, the pointer in the second field of the records can be followed to directly retrieve the corresponding CUSTOMER records.

Thus the access involves a Sequential access on the index file and a Direct access on the actual data file.

Retrieval Speed v/s Update Speed : Though indexes help making retrievals faster, they slow down updates on the table since updates on the base table demand update on the index field as well.

It is possible to create an index with multiple fields i.e., index on field combinations. Multiple indexes can also be created on the same table simultaneously though there may be a limit on the maximum number of indexes that can be created on a table.

Q: In which of the following situations will indexes be ineffective ?

a) When the percentage of rows being retrieved is large

b) When the data table is small and the index record is of almost the same size as of the actual data record.

c) In queries involving NULL / Not NULL in the indexed field.

d)All of the above

A: d) All of the above

Q: Can a clustering based on one field and indexing on another field exist on the same table simultaneously ?

A: Yes

2.3 Hashing

Hashing is yet another method used for making retrievals faster. This method provides direct access to record on the basis of the value of a specific field called the hash_field. Here, when a new record is inserted, it is physically stored at an address which is computed by applying a mathematical function (hash function) to the value of the hash field. Thus for every new record,

hash_address = f (hash_field), where f is the hash function.

Later, when a record is to be retrieved, the same hash function is used to compute the address where the record is stored. Retrievals are faster since a direct access is provided and there is no search involved in the process.

An example of a typical hash function is given by a numeric hash field, say an id, modulus a very large prime number.

Q: Can there be more than one hash fields on a file ?

A: No

As hashing relates the field value to the address of the record, multiple hash fields will map a record to multiple addresses at the same time. Hence there can be only one hash field per file.

Collisions : Consider the example of the CUSTOMER table given earlier while discussing clustering. Let CUST_ID be the hash field and the hash function be defined as ((CUST_ID mod 10000)*64 + 1025). The records with CUST_ID 10001, 10002, 10003 etc. will be stored at addresses 1089, 1153, 1217 etc. respectively.

It is possible that two records hash to the same address leading to a collision. In the above example, records with CUST_ID values 20001, 20002, 20003 etc. will also map on to the addresses 1089, 1153, 1217 etc. respectively. And same is the case with CUST_ID values 30001, 30002, 30003 etc.

The methods to resolve a collision are by using :

1. Linear Search:

While inserting a new record, if it is found that the location at the hash address is already occupied by a previously inserted record, search for the next free location available in the disk and store the new record at this location. A pointer from the first record at the original hash address to the new record will also be stored. During retrieval, the hash address is computed to locate the record. When it is seen that the record is not available at the hash address, the pointer from the record at that address is followed to locate the required record.

In this method, the over head incurred is the time taken for the linear search to locate the next free location while inserting a record.

2. Collision Chain:

Here, the hash address location contains the head of a list of pointers linking together all records which hash to that address.

In this method, an overflow area needs to be used if the number of records mapping on to the same hash address exceeds the number of locations linked to it.

3.1 The Relational Model

Relational Databases: Terminology

Databases: Case Example

Ord_Aug

Ord # OrdDate Cust#

101 02-08-94 002

102 11-08-94 003

103 21-08-94 003

104 28-08-94 002

105 30-08-94 005

Ord_Items

Ord # Item # Qty

101 HW1 100

101 HW3 50

101 SW1 150

102 HW2 10

103 HW3 50

104 HW2 25

104 HW3 100

105 SW1 100

Items

Item # Descr Price

HW1 Power Supply 4000

HW2 101-Keyboard 2000

Customers

Ord # OrdDate Cust#

101 02-08-94 002

102 11-08-94 003

HW3 Mouse 800

SW1 MS-DOS 6.0 5000

SW2 MS-Word 6.0 8000

103 21-08-94 003

104 28-08-94 002

105 30-08-94 005

Term Meaning Eg. from the given Case Example

Relation A table Ord_Aug, Customers, Items etc.

Tuple A row or a record in a relation.

A row from Customers relation is a Customer tuple.

Attribute A field or a column in a relation.

Ord_Date, Item#, CustName etc.

Cardinality of a relation

The number of tuples in a relation.

Cardinality of Ord_Items relation is 8

Degree of a relation

The number of attributes in a relation.

Degree of Customers relation is 3.

Domain of an attribute

The set of all values that can be taken by the attribute.

Domain of Qty in Ord_Items is the set of all values which can represent quantity of an ordered item.

Primary Key of a relation

An attribute or a combination of attributes that uniquely defines each tuple in a relation.

Primary Key of Customers relation is Cust#.

Ord# and Item# combination forms the primary Key of Ord_Items

Foreign Key

An attribute or a combination of attributes in one relation R1 which indicates the relationship of R1 with another relation R2.

The foreign key attributes in R1 must contain values matching with those of the values in R2

Cust# in Ord_Aug relation is a foreign key creating reference from Ord_Aug to Customers. This is required to indicate the relationship between Orders in Ord_Aug and Customers.

Ord# and Item# in Ord_Items are foreign keys creating references from Ord_Items to Ord_Aug and Items respectively.

3.2 Properties of Relations

• No Duplicate Tuples – A relation cannot contain two or more tuples which have the same values for all the attributes. i.e., In any relation, every row is unique.• Tuples are unordered – The order of rows in a relation is immaterial.• Attributes are unordered – The order of columns in a relation is immaterial.

• Attribute Values are Atomic – Each tuple contains exactly one value for each attribute.

It may be noted that many of the properties of relations follow the fact that the body of a relation is a mathematical set.

3.3 Integrity Rules

The following are the integrity rules to be satisfied by any relation.

• No Component of the Primary Key can be null.

• The Database must not contain any unmatched Foreign Key values. This is called the referential integrity rule.

Q: Can the Foreign Key accept nulls?A: Yes, if the application business rule allows this.

How do we explain this ?

Unlike the case of Primary Keys, there is no integrity rule saying that no component of the foreign key can be null. This can be logically explained with the help of the following example:

Consider the relations Employee and Account as given below.

Employee

Emp# EmpName EmpCity EmpAcc#

X101 Shekhar Bombay 120001

X102 Raj Pune 120002

X103 Sharma Nagpur Null

X104 Vani Bhopal 120003

Account

ACC# OpenDate BalAmt

120001 30-Aug-1998 5000

120002 29-Oct-1998 1200

120003 01-Jan-1999 3000

120004 04-Mar-1999 500

EmpAcc# in Employee relation is a foreign key creating reference from Employee to Account. Here, a Null value in EmpAcc# attribute is logically possible if an Employee does not have a bank account. If the business rules allow an employee to exist in the system without opening an account, a Null value can be allowed for EmpAcc# in Employee relation.

In the case example given, Cust# in Ord_Aug cannot accept Null if the business rule insists that the Customer No. needs to be stored for every order placed.

The next issue related to foreign key reference is handling deletes / updates of parent?

In the case example, can we delete the record with Cust# value 002, 003 or 005 ?

The default answer is NO, as long as there is a foreign key reference to these records from some other table. Here, the records are referenced from the order records in Ord_Aug relation. Hence Restrict the deletion of the parent record.

Deletion can still be carried if we use the Cascade or Nullify strategies.

Cascade: Delete/Update all the references successively or in a cascaded fashion and finally delete/update the parent record. In the case example, Customer record with Cust#002 can be deleted after deleting order records with Ord# 101 and 104. But these order records, in turn, can be deleted only after deleting those records with Ord# 101 and 104 from Ord_Items relation.

Nullify: Update the referencing to Null and then delete/update the parent record. In the above example of Employee and Account relations, an account record may have to be deleted if the account is to be closed. For example, if Employee Raj decides to close his account, Account record with Acc# 120002 has to be deleted. But this deletion is not possible as long as the Employee record of Raj references it. Hence the strategy can be to update the EmpAcc# field in the employee record of Raj to Null and then delete the Account parent record of 120002. After the deletion the data in the tables will be as follows:

Employee

Emp# EmpName EmpCity EmpAcc#


X102 Raj Pune 120002 Null



Account

ACC# OpenDate BalAmt

120001 30-Aug-1998 5000

120002 29-Oct-1998 1200

120003 01-Jan-1999 3000

120004 04-Mar-1999 500

3.4 Relational Algebra Operators

The eight relational algebra operators are

1. SELECT – To retrieve specific tuples/rows from a relation.

2. PROJECT – To retrieve specific attributes/columns from a relation.

Ord# OrdDate Cust#

101 02-08-94 002

104 18-09-94 002

Descr Price

Power Supply 4000

101-Keyboard 2000

Mouse 800

MS-DOS 6.0 5000

MS-Word 6.0 8000

3. PRODUCT – To obtain all possible combination of tuples from two relations.

Ord# OrdDate O.Cust# C.Cust# CustName City

101 02-08-94 002 001 Shah Bombay

101 02-08-94 002 002 Srinivasan Madras

101 02-08-94 002 003 Gupta Delhi

101 02-08-94 002 004 Banerjee Calcutta

101 02-08-94 002 005 Apte Bombay

102 11-08-94 003 001 Shah Bombay

102 11-08-94 003 002 Srinivasan Madras

4. UNION – To retrieve tuples appearing in either or both the relations participating in the UNION.

Eg: Consider the relation Ord_Jul as follows(Table: Ord_Jul)

Note: The union operation shown above logically implies retrieval of records of Orders placed in July or in August

5. INTERSECT- To retrieve tuples appearing in both the relations participating in the INTERSECT.

Ord# OrdDate Cust#

101 03-07-94 001

102 27-07-94 003

101 02-08-94 002

102 11-08-94 003

103 21-08-94 003

104 28-08-94 002

105 30-08-94 005

Eg: To retrieve Cust# of Customers who've placed orders in July and in August

Cust#

003

6. DIFFERENCE – To retrieve tuples appearing in the first relation participating in the DIFFERENCE but not the second.

Eg: To retrieve Cust# of Customers who've placed orders in July but not in August

Cust#

001

7. JOIN – To retrieve combinations of tuples in two relations based on a common field in both the relations.

Eg:

ORD_AUG join CUSTOMERS (here, the common column is Cust#)

Note: The above join operation logically implies retrieval of details of all orders and the details of the corresponding customers who placed the orders.

Such a join operation where only those rows having corresponding rows in the both the relations are retrieved is called the natural join or inner join. This is the most common join operation.

Consider the example of EMPLOYEE and ACCOUNT relations.

EMPLOYEE

EMP # EmpName EmpCity Acc#


X102 Raj Pune 120002


Ord# OrdDate Cust# CustNames City

101 02-08-94 002 Srinivasan Madras

102 11-08-94 003 Gupta Delhi

103 21-08-94 003 Gupta Delhi

104 28-08-94 002 Srinivasan Madras

105 30-08-94 005 Apte Bombay


ACCOUNT

Acc# OpenDate BalAmt

120001 30. Aug. 1998 5000

120002 29. Oct. 1998 1200

120003 1. Jan. 1999 3000

120004 4. Mar. 1999 500

A join can be formed between the two relations based on the common column Acc#. The result of the (inner) join is :

Emp# EmpName EmpCity Acc# OpenDate BalAmt

X101 Shekhar Bombay 120001 30. Aug. 1998 5000

X102 Raj Pune 120002 29. Oct. 1998 1200

X104 Vani Bhopal 120003 1. Jan 1999 3000

Note that, from each table, only those records which have corresponding records in the other table appear in the result set. This means that result of the inner join shows the details of those employees who hold an account along with the account details.

The other type of join is the outer join which has three variations – the left outer join, the right outer join and the full outer join. These three joins are explained as follows:

The left outer join retrieves all rows from the left-side (of the join operator) table. If there are corresponding or related rows in the right-side table, the correspondence will be shown. Otherwise, columns of the right-side table will take null values.

EMPLOYEE left outer join ACCOUNT gives:



X102 Raj Pune 120002 29. Oct. 1998 1200

X103 Sharma Nagpur NULL NULL NULL

X104 Vani Bhopal 120003 1. Jan 1999 3000

The right outer join retrieves all rows from the right-side (of the join operator) table. If there are corresponding or related rows in the left-side table, the correspondence will be shown. Otherwise, columns of the left-side table will take null values.

EMPLOYEE right outer join ACCOUNT gives:



X102 Raj Pune 120002 29. Oct. 1998 1200

X104 Vani Bhopal 120003 1. Jan 1999 3000

NULL NULL NULL 120004 4. Mar. 1999 500

(Assume that Acc# 120004 belongs to someone who is not an employee and hence the details of the Account holder are not available here)

The full outer join retrieves all rows from both the tables. If there is a correspondence or relation between rows from the tables of either side, the correspondence will be shown. Otherwise, related columns will take null values.

EMPLOYEE full outer join ACCOUNT gives:



X102 Raj Pune 120002 29. Oct. 1998 1200

X103 Sharma Nagpur NULL NULL NULL

X104 Vani Bhopal 120003 1. Jan 1999 3000

NULL NULL NULL 120004 4. Mar. 1999 500

Q: What will the result of a natural join operation between R1 and R2 ? A:

8. DIVIDE

Consider the following three relations:

a1 b1 c1

a2 b2 c2

a3 b3 c3

R1 divide by R2 per R3 gives:

a

Thus the result contains those values from R1 whose corresponding R2 values in R3 include all R2 values.

4. Structured Query Language (SQL)

4.1 SQL : An Overview

The components of SQL are

a. Data Manipulation Language – Consists of SQL statements for operating on the data (Inserting, Modifying, Deleting and Retrieving Data) in tables which already exist.

b. Data Definition Language – Consists of SQL statements for defining the schema (Creating, Modifying and Dropping tables, indexes, views etc.)

c. Data Control Language – Consists of SQL statements for providing and revoking access permissions to users

Tables used:

Ord_Aug

Ord# OrdDate Cust#

101 02-AUG-94 002

102 11-AUG-94 003

103 21-AUG-94 003

104 28-AUG-94 002

Ord_Items

Ord# Item# Qty

101 HW1 100

101 HW3 50

101 SW1 150

102 HW2 10

105 30-AUG-94 005

Items

Item# Descr Price

HW1 Power Supply 4000

HW2 101- Keyboard 2000

HW3 Mouse 800

SW1 MS-DOS 6.0 5000

SW2 MS-Word 6.0 8000

Customers

Cust# CustName City

001 Shah Bombay

002 Srinivasan Madras

003 Gupta Delhi

004 Banerjee Calcutta

005 Apte Bombay

103 HW3 50

104 HW2 25

104 HW3 100

105 SW1 100

4.2 DML – SELECT, INSERT, UPDATE and DELETE statements.

The SELECT statement

Retrieves rows from one or more tables according to given conditions.

General form:

SELECT [ ALL | DISTINCT ] <attribute (comma)list>

FROM <table (comma)list>

[ WHERE <conditional expression>]

[ ORDER BY [DESC] <attribute list>

[ GROUP BY <attribute (comma)list>]

[ HAVING <conditional expression>]

Query 1:

Some SELECT statements on the Case Example

SELECT * <-----------------

FROM items;

* -denotes all attributes in the table

Result

Query 2:

SELECT cust#,custname

FROM customers;

Result

Query 3:

SELECT DISTINCT item#

FROM ord_items;

Result

Query 4:

SELECT ord# "Order ", orddate "Ordered On" <----

FROM ord_aug;

In the result set the column headings will appear as “Order” and “Ordered On” instead of ord# and orddate.

Result

Query 5:

SELECT item#, descr

FROM items

http://elearning.tvm.tcs.co.in/dbms/dbms/QueryResults.htm#4




WHERE price>2000;

Result

Query 6:

SELECT custname

FROM customers

WHERE city<>'Bombay';

Result

Query 7:

SELECT custname

FROM customers

WHERE UPPER(city)<>'BOMBAY';

Result

Query 8:

SELECT *

FROM ord_aug

WHERE orddate > '15-AUG-94'; <-----------

Illustrates the use of 'date' fields. In SQL, a separate datatype (eg: date, datetime etc.) is available to store data which is of type date.

Result

Query 9:

SELECT *

FROM ord_items

WHERE qty BETWEEN 100 AND 200;

Result

Query 10:

SELECT custname






FROM customers

WHERE city IN ('Bombay', 'Madras'); <-------

The conditional expression evaluates to TRUE for those records for which the value of city field is in the list ('Bombay, 'Madras')

Result

Query 11:

SELECT custname

FROM customers

WHERE custname LIKE 'S%' ; <------------

LIKE 'S%' - 'S' followed by zero or more characters

Result

Query 12:

SELECT *

FROM ord_items

WHERE qty>100 AND item# LIKE 'SW%';

Result

Query 13:

SELECT custname

FROM customers

WHERE city='Bombay' OR city='Madras';

Result





Query 14:

SELECT *

FROM customers

WHERE city='Bombay'

ORDER BY custname; <--------------------Records in the result set is displayed in the ascending order of custname

Result

Query 15:

SELECT *

FROM ord_items

ORDER BY item#, qty DESC; <-------------

Display the result set in the ascending order of item#. If there are more than one records with the same item# , they will be displayed in the descending order of qty

Result

Query 16:

SELECT descr, price

ORDER BY 2



FROM items

ORDER BY 2; <----------------------------

ORDER BY the 2nd attribute (price) in the attribute list of the SELECT clause

Result

Query 17:

SELECT ord#, ord_aug.cust#, custname <----------------

FROM ord_aug, customers

WHERE city='Delhi'

AND ord_aug.cust# = customers.cust#; <----------------

SELECT statement implementing JOIN operation.

JOIN condition

Result

Query 18:

SELECT ord#, customers.cust#, city


WHERE ord_aug.cust# = customers.cust#;

Result

Query 19:

SELECT ord#, customers.cust#, city


WHERE ord_aug.cust# = customers.cust# (+); <-----------

(+) indicates outer join. Here it is a right outer join as indicated by the (+) after the right side field.

Result

Nested SELECT statements

SQL allows nesting of SELECT statements. In a nested SELECT statement the inner SELECT is evaluated first and is replaced by its result to evaluate the outer SELECT statement.





Query 20:

SELECT item#, descr, price <---------------------------

FROM items

WHERE price > (SELECT AVG(price) FROM items); <------

Outer SELECT statement

Inner SELECT statement

Result

Query 21:

SELECT cust#, custname <------------------

FROM customers

WHERE city = ( SELECT city FROM customers

WHERE custname='Shah');

Here the outer SELECT is evaluated asSELECT cust#, custnameFROM customers WHERE city = "BOMBAY"

Result

Arithmetic Expressions

+

-

*

/

( )

Arithmetic functions are allowed in SELECT and WHERE clauses.

Query 22:

SELECT descr, price, price*0.1 "discount"

FROM items

WHERE price >= 4000

ORDER BY 3;

Result




Query 23:

SELECT descr

FROM items, ord_items

WHERE price*qty > 250000

and items.item# = ord_items.item#;

Result

Numeric Functions

Query 24:

SELECT qty, ROUND(qty/2,0) "qty supplied"

FROM ord_items

WHERE item#='HW2';

Result

Query 25:

SELECT qty, TRUNC(qty/2,0) "qty supplied"

FROM ord_items

WHERE item#='HW2';

Result

Examples of Numeric Functions

MOD(n,m)

SQRT(n)

ROUND(n,m)

TRUNC(n,m)

'm' indicates the number of digits after decimal points in the result.

Date Arithemetic





Date + No. of days

Date - No. of days

Date – Date

Query 26:

SELECT ord#, orddate+15 "Supply by"

FROM ord_aug;

Result

Date Functions

MONTHS_BETWEEN(date1, date2)

ADD_MONTHS(date, no. of months)

SYSDATE

Returns system date.

Query 27:

SELECT ord#,

MONTHS_BETWEEN(SYSDATE,orddate)

FROM ord_aug;

Result

Query 28:

SELECT TO_CHAR(orddate,' DD/MM/YYYY') <---

FROM ord_aug;

Converts the value of the date field orddate to character string of the format DD/MM/YYYY

Result

Note:

DD - day of month (1-31)




D - day of week (1-7)

DAY - name of day

MM - month (01-12)

MONTH - name of month

MON - abbreviated name of month

HH:MI:SS - hours:minutes:seconds

fm - fill mode : suppress blank padding

Character Expressions & Functions

|| - Concatenate operator

Query 29:

SELECT custname || ' - ' || city

FROM customers;

Result

Examples of Character Functions:

INITCAP(string)

UPPER(string)

LOWER(string)

SUBSTR(string,start,no. of characters)

Group Functions

Group functions are functions which act on the entire column of selected rows.

Query 30:

SELECT SUM(qty), AVG(qty) <---------------

FROM ord_items

SUM and AVG are examples of Group Functions. They compute the sum/average of qty values of all rows where item#='SW1'.


WHERE item#='SW1';

Result

Examples of Group Functions:

SUM

AVG

COUNT

MAX

MIN

Query 31:

SELECT item#, SUM(qty)

FROM ord_items

GROUP BY item#; <-------------------------

GROUP BY clause used to group rows according to the value of item# in the result. SUM function acts individually on each group of rows.

Result

Query 32:


FROM ord_items

GROUP BY item#

HAVING SUM(qty)>100; <------------------

HAVING clause used to apply the condition to be applied on the grouped rows and display the final result.

Result

Query 33:


FROM ord_items

GROUP BY item#




HAVING COUNT(*)>2;

Result

The INSERT statement

Inserts one or more tuples in a table.

General forms:

To insert a single tuple

INSERT INTO <table-name> [<attribute (comma)list>]

VALUES <value list>;

To insert multiple tuples

INSERT INTO <table-name> [<attribute (comma)list>]

SELECT [ ALL | DISTINCT ] <attribute (comma)list>

FROM <table (comma)list>*

[ WHERE <conditional expression>];

* - list of existing tables

Sample INSERT statements from the Case Example

Query 34: Insert all values for a new row

INSERT INTO customers <-------------------

VALUES (006, 'Krishnan', 'Madras');

Inserts a single row in Customers Table. Attribute list need not be mentioned if values are given for all attributes in the tuple.

Query 35: Insert values of item# & descr columns for a new row

INSERT INTO items (item#, descr) <----------

VALUES ('HW4', '132-DMPrinter');

Attribute list mentioned since values are not given for all attributes in the tuple. Here Price column for the newly inserted tuple takes NULL value.

Query 36: Inserts a new row which includes a date field

INSERT INTO ord_aug


VALUES(106, '31-AUG-94', 005);

Query 37: Inserts a new row with the date field being specified in non DD-MON-YY format

INSERT INTO ord_aug

VALUES (106, TO_DATE('310894','DDMMYY'), 005);

The UPDATE statement

Updates values of one or more attributes of one or more tuples in a table.

General form:

UPDATE <table-name>

SET <attribute-1 = value-1[, attribute-2 = value-2,...attribute-n = value-n]


Sample UPDATE statements from the Case Example

Query 38: changes price of itmem SW1 to 6000

UPDATE items

SET price = 6000

WHERE item# ='SW1';

Query 39: Changes a wrongly entered item# from HW2 to SW2

UPDATE ord_items

SET item# = 'SW2'

WHERE ord#=104 AND item# = 'HW2';

The DELETE statement

Deletes one or more tuples in a table according to given conditions

General form:

DELETE FROM <table-name>


Sample DELETE statements from the Case Example

Query 40: Deletes Customer record with Customer Number 004

DELETE FROM customers

WHERE cust# = 004;

DELETE FROM Ord_Items; <-------------------Deletes all rows in Ord_Items Table. The table remains empty after the DELETE operation.

4.3 DDL – CREATE, ALTER, and DROP statements.

DDL statements are those which are used to create, modify and drop the definitions or structures of various tables, views, indexes and other elements of the DBMS.

The CREATE TABLE statement

Creates a new table.

General form:

CREATE TABLE <table-name>

(<table-element (comma)list>*);

* - table element may be attribute with its data-type and size or any integrity constraint on attributes.

Some CREATE TABLE statements on the Case Example

Query:

CREATE TABLE customers

( cust# NUMBER(6) NOT NULL,

custname CHAR(30) ,

city CHAR(20));

- This query Creates a table CUSTOMERS with 3 fields - cust#, custname and city. Cust# cannot be null

Query:

CREATE TABLE ord_sep <------------------- Creates a new table ord_sep, which has the same structure of

AS SELECT * from ord_aug; ord_aug. The data in ord_aug is copied to the new table ord_sep.

- This query Creates table ORD_SEP as a cpy of ORD-AUG. Copies structure as well as data.

Query:

CREATE TABLE ord_sep <------------------

AS SELECT * from ord_aug

WHERE 1 = 2;

Creates a new table ord_sep, which has the same structure of ord_aug. No data in ord_aug is copied to the new table since there is no row which satisfies the 'always false' condition 1 = 2.

- This query Creates table ORD_SEP as a copy of ORD_AUG, but does not copy any data as the WHERE clause is never satisfied.

The ALTER TABLE statement

Alters the structure of an existing table.

General form:

ALTER TABLE <table-name>

ADD | MODIFY (<table-element (comma)list);

Examples of ALTER TABLE statement.

Query:

ALTER TABLE customers

MODIFY custname CHAR(35); <-------------

Modifies the data type/size of an attribute in the table

- This query changes the custname field to a character field of length 35. Used for modifying field lengths and attributes.

Query:

ALTER TABLE customers

ADD (phone number(8), <------------------

credit_rating char(1));

Adds two new attributes to the Customers table. Here, for existing tuples (if any), the new attribute will take NULL values since no DEFAULT value is mentioned for the attribute.

- This query adds two new fields - phone & credit_rating to the customers table.

The DROP TABLE statement

DROPS an existing table.

General form:

DROP TABLE <table-name>;

Example:

Query:

DROP TABLE ord_sep;

- The above query drops table ORD_SEP from the database

Creating & Dropping Views

A view is a virtual relation created with attributes from one or more base tables.

SELECT * FROM myview1; at any given time will evaluate the view-defining query in the CREATE VIEW statement and display the result.

Query:

CREATE VIEW myview1

AS SELECT

ord#, orddate, ord_aug.cust#, custname


WHERE ord_aug.cust# = customers.cust#;

- This query defines a view consisting of ord#, cust#, and custname using a join of ORD_AUG and CUSTOMERS tables.

Query:

CREATE VIEW myview2 (ItemNo, Quantity)

AS SELECT item#, qty

FROM ord_items;

- This query defines a view with columns item# and qty from the ORD_ITEMS table, and renames these columns as ItemNo. and Quantity respectively.

Query:

CREATE VIEW myview3

AS SELECT item#, descr, price

FROM items

WHERE price < 1000

WITH CHECK OPTION; <-------------------

WITH CHECK OPTION in a CREATE VIEW statement indicates that INSERTs or UPDATEs on the view will be rejected if they violate any integrity constraint implied by the view-defining query.

- This query defines the view as defined. WITH CHECK OPTION ensures that if this view is used for updation, the updated values do not cause the row to fall outside the view.

Query:

DROP VIEW myview1; <---- To drop a view

- this query drops the view MYVIEW1

Creating & Dropping Indexes

Query:

CREATE INDEX i_city <--------------------

ON customers (city);

Creates a new index named i_city. The new index file(table) will have the values of city column of Customers table

Query:

CREATE UNIQUE INDEX i_custname <------

ON customers (custname);

Creates an index which allows only unique values for custnames

Query:

CREATE INDEX i_city_custname <---------

ON customers (city, custname);

Creates an index based on two fields : city and custname

Query:

DROP INDEX i_city; <-------------------- Drops index i_city

4.4 DCL – GRANT and REVOKE statements.

DCL statements are those which are used to control access permissions on the tables, indexes, views and other elements of the DBMS.

Granting & Revoking Privileges

Query:

GRANT ALL <------------------

ON customers

TO ashraf;

Grants all permissions on the table customers to the user who logs in as 'ashraf'.

Query:

GRANT SELECT <--------------

ON customers

TO sunil;

Grants SELECT permission on the table customers to the user 'sunil'. User 'sunil' does not have permission to insert, update, delete or perform any other operation on customers table.

Query:

GRANT SELECT

ON customers

TO sunil

WITH GRANT OPTION; <---------

Enables user 'sunil' to give SELECT permission on customers table to other users.

Query:

REVOKE DELETE <-------------

ON customers

FROM ashraf;

Takes away DELETE permission on customers table from user 'ashraf'.

5. Recovery and Concurrency

Recovery and Concurrency in a DBMS are part of the general topic of transaction management. Hence we shall begin the discussion by examining the fundamental notion of a transaction.

5.1 Transaction

A transaction is a logical unit of work.

Consider the following example:

The procedure for transferring an amount of Rs. 100/- from the account of one customer to another is given.

EXEC SQL WHENEVER SQLERROR GOTO UNDO EXEC SQL UPDATE DEPOSIT

SET BALANCE=BALANCE-100WHERE CUSTID=from_cust;

EXEC SQL UPDATE DEPOSIT SET BALANCE=BALANCE+100

WHERE CUSTID=to_cust: EXEC SQL COMMIT;

GOTO FINISHUNDO: EXEC SQL ROLLBACK;FINISH: RETURN;

Here, it has to be noted that the single operation “amount transfer” involves two database updates – updating the record of from_cust and updating the record of to_cust. In between these two updates the database is in an inconsistent (or incorrect in this example) state. i.e., if only one of the updates is performed, one cannot say by seeing the database contents whether the amount transfer operation has been done or not. Hence to guarantee database consistency it has to be ensured that either both updates are performed or none are performed. If, after one update and before the next update, something goes wrong due to problems like a system crash, an overflow error, or a violation of an integrity constraint etc., then the first update needs to be undone.

This is true with all transactions. Any transaction takes the database from one consistent state to another. It need not necessarily preserve consistency of database at all intermediate points. Hence it is important to ensure that either a transaction executes in its entirety or is totally cancelled. The set of programs which handles this forms the transaction manager in the DBMS. The transaction manager uses COMMIT and ROLLBACK operations for ensuring atomicity of transactions.

COMMIT – The COMMIT operation indicates successful completion of a transaction which means that the database is in a consistent state and all updates made by the transaction can now be made permanent. If a transaction successfully commits, then the system will guarantee that its updates will be permanently installed in the database even if the system crashes immediately after the COMMIT.

ROLLBACK – The ROLLBACK operation indicates that the transaction has been unsuccessful which means that all updates done by the transaction till then need to be undone to bring the database back to a consistent state. To help undoing the updates once done, a system log or journal is maintained by the transaction manager. The before- and after-images of the updated tuples are recorded in the log.

The properties of transaction can be summarised as ACID properties - ACID standing for atomicity, consistency, isolation and durability.

Atomicity: A transaction is atomic. Either all operations in the transaction have to be performed or none should be performed.

Consistency: Transactions preserve database consistency. i.e., A transaction transforms a consistent state of the database into another without necessarily preserving consistency at all intermediate points.

Isolation: Transactions are isolated from one another. i.e., A transaction's updates are concealed from all others until it commits (or rolls back).

Durability: Once a transaction commits, its updates survive in the database even if there is a subsequent system crash.

5.2 Recovery from System Failures

System failures (also called soft crashes) are those failures like power outage which affect all transactions in progress, but do not physically damage the database.

During a system failure, the contents of the main memory are lost. Thus the contents of the database buffers which contain the updates of transactions are lost. (Note: Transactions do not directly write on to the database. The updates are written to database buffers and, at regular intervals, transferred to the database.) At restart, the system has to ensure that the ACID properties of transactions are maintained and the database remains in a consistent state. To attain this, the strategy to be followed for recovery at restart is as follows:

Transactions which were in progress at the time of failure have to be undone at the time of restart. This is needed because the precise state of such a transaction which was active at the time of failure is no longer known and hence cannot be successfully completed.

Transactions which had completed prior to the crash but could not get all their updates transferred from the database buffers to the physical database have to redone at the time of restart.

This recovery procedure is carried out with the help of

• An online logfile or journal – The logfile maintains the before- and after-images of the tuples updated during a transaction. This helps in carrying out the UNDO and REDO operations as required. Typical entries made in the logfile are :

Start of Transaction Marker Transaction Identifier Record Identifier Operations Performed

Previous Values of Modified Data (Before-image or Undo Log) Updated Values of Modified Records (After-image or Redo Log) Commit / Rollback Transaction Marker

• Taking a checkpoint at specific intervals – This involves the following two operations:a) physically writing the contents of the database buffers out to the physical database. Thus during a checkpoint the updates of all transactions, including both active and committed transactions, will be written to the physical database. b)physically writing a special checkpoint record to the physical log. The checkpoint record has a list of all active transactions at the time of taking the checkpoint.

5.3 Recovery : An Example

At the time of restart, T3 and T5 must be undone and T2 and T4 must be redone.

T1 does not enter the recovery procedure at all since it updates were all written to the database at time tc as part of the checkpoint proces

5.4 Concurrency

Concurrency refers to multiple transactions accessing the same database at the same time. In a system which allows concurrency, some kind of control mechanism has to be in place to ensure that concurrent transactions do not interfere with each other.

Three typical problems which can occur due to concurrency are explained here.

a) Lost Update Problem

(To understand the above situation, assume that

there is a record R, with a field, say Amt, having value 1000 before time t1. o Both transactions A & B fetch this value at t1 and t2 respectively. o Transaction A updates the Amt field in R to 800 at time t3. o Transaction B updates the Amt field in R to 1200 at time t4.

Thus after time t4, the Amt value in record R has value 1200. Update by Transaction A at time t3 is over-written by the Transaction B at time t4.)

b) Uncommitted Dependency Problem

(To understand the above situation, assume that

there is a record R, with a field, say Amt, having value 1000 before time t1. o Transaction B fetches this value and updates it to 800 at time t1. o Transaction A fetches R with Amt field value 800 at time t2. o Transaction B rolls back and its update is undone at time t3. The Amt

field takes the initial value 1000 during rollback.

Transaction A continues processing with Amt field value 800 without knowing about B's rollback.)

c) Inconsistent Analysis Problem

5.5 Locking

Locking: A solution to problems arising due to concurrency.

Locking of records can be used as a concurrency control technique to prevent the above mentioned problems. A transaction acquires a lock on a record if it does not want the record values to be changed by some other

transaction during a period of time. The transaction releases the lock after this time.

Locks are of two types

1. shared (S lock) 2. and exclusive (X Lock).

A transaction acquires a shared (read) lock on a record when it wishes to retrieve or fetch the record.

An exclusive (write) lock is acquired on a record when a transaction wishes to update the record. (Here update means INSERT, UPDATE or DELETE.)

The following figure shows the Lock Compatibility matrix.

Normally, locks are implicit. A FETCH request is an implicit request for a shared lock whereas an UPDATE request is an implicit request for an exclusive lock. Explicit lock requests need to be issued if a different kind of lock is required during an operation. For example, if an X lock is to acquired before a FETCH it has to be explicitly requested for.

5.6 Deadlocks

Locking can be used to solve the problems of concurrency. However, locking can also introduce the problem of deadlock as shown in the example below.

Deadlock is a situation in which two or more transactions are in a simultaneous wait state, each of them waiting for one of the others to release a lock before it can proceed.

If a deadlock occurs, the system may detect it and break it. Detecting involves detecting a cycle in the “Wait-For Graph” (a graph which shows 'who is waiting for whom'). Breaking a deadlock implies choosing one of the deadlocked transactions as the victim and rolling it back, thereby releasing all its locks. This may allow some other transaction(s) to proceed.

Deadlock prevention can be done by not allowing any cyclic-waits.

6. Query Optimization

6.1 Overview

When compared to other database systems, query optimization is a strength of the relational systems. It can be said so since relational systems by themselves do optimization to a large extent unlike the other systems which leave optimization to the programmer. Automatic optimization done by the relational systems will be much more efficient than manual optimization due to several reasons like :

uniformity in optimization across programs irrespective of the programmer's expertise in optimizing the programs.

system's ability to make use of the knowledge of internal conditions (eg: volume of data at the time of querying) for optimization. For the same query, such conditions may be different at different times of querying. (In a manual system, this knowledge can be utilised only if the query is re-written each time, which is not practically possible.)

system's ability to evaluate large number of alternatives to find the most efficient query evaluation method.

In this chapter we shall look into the process of automatic query optimization done by the relational systems.

6.2 An Example of Query Optimization

Let us look at a query being evaluated in two different ways to see the dramatic effect of query optimization.

Consider the following query.

Select ORDDATE, ITEM#, QTYfrom ORDTBL, ORD_ITEMSwhere ORDTBL.ORD# = ORD_ITEMS.ORD#and ITEM# = 'HW3';

Assumptions:

There are 100 records in ORDTBL There are 10,000 records in ORD_ITEMS There are 50 order items with item# 'HW3'

Query Evaluation – Method 1

T1 = ORDTBL X ORD_ITEMS (Perform the Product operation as the first step towards joining the two tables)

- 10000 X 100 tuple reads (1000000 tuple reads -> generates 1000000 tuples as intermediate result) - 1000000 tuples written to disk (Assuming that 1000000 tuples in the intermediate result cannot be held in the memory. 1000000 tuple writes to a temporary space in the disk.)

T2 = ORDTBL.ORD# = ORD_ITEMS.ORD# & ITEM# 'HW3'(T1) (Apply the two conditions in the query on the intermediate result obtained after the first step)

- 1000000 tuples read into memory (1000000 tuple reads)- 50 selected (those tuples satisfying both the conditions. 50 held in the memory itself)

T3 = ORDDATE,ITEM#,QTY (T2) (Projection performed as the final step. No more tuple i/o s)

- 50 tuples (final result)

Total no. of tuple i/o s = 1000000 reads + 1000000 writes + 1000000 reads = 3000000 tuple i/o s

Query Evaluation – Method 2

T1 = ITEM#='HW3' (ORD_ITEMS) (Perform the Select operation on ORD_ITEMS as the first step)

- 10000 tuple reads (10000 tuple reads from ORD_ITEMS)- 50 tuples selected; no disk writes (50 tuples satisfy the condition in Select. No disk writes assuming that the 50 tuples forming the intermediate result can be held in the memory)

T2 = ORDTBL JOIN T1

- 100 tuple reads (100 tuple reads from ORDTBL)- resulting relation with 50 tuples

T3 = ORDDATE, ITEM#, QTY(T2) (Projection performed as the final step. No more tuple i/o s)

- 50 tuples (final result)

Total no. of tuple i/o s = 10000 reads + 100 reads = 10100 tuple i/o's

Comparison of the two Query Evaluation Methods

10,100 tuple I/O's (of Method 2) v/s 3,000,000 tuple I/O's (of Method 1) !

Thus by sequencing the operations differently a dramatic difference can be made in the performance of queries.

Here it needs to be noted that in the Method 2 of evaluation, the first operation to be performed was a 'Select' which filters out 50 tuples from the 10,000 tuples in the ORD_ITEMS table. Thus this operation causes elimination of 9950 tuples. Thus elimination in the initial steps would help optimization.

Some more examples:

1.select CITY, COUNT(*) from CUSTTBLwhere CITY != 'BOMBAY'group by CITY;

v/sselect CITY, COUNT(*) from CUSTTBLgroup by CITYhaving CITY != 'BOMBAY';

2. select * from ORDTBL where to_char(ORDDATE,'dd-mm-yy') = '11-08-94';

v/sselect * from ORDTBL where ORDDATE = to_date('11-08-94','dd-mm-yy');

Here the second version is faster. In the first form of the query, a function to_char is applied on an attribute and hence needs to be evaluated for each tuple in the table. The time for this evaluation will be thus proportional to the cardinality of the relation. In the second form, a function to_date is applied on a constant and hence needs to be evaluated just once, irrespective of the cardinality of the relation. Moreover, if the

attribute ORDDATE is indexed, the index will not be used in the first case, since the attribute appears in an expression and its value is not directly used.

6.3 The Query Optimization Process

The steps of query optimization are explained below.

a) Cast into some Internal Representation – This step involves representing each SQL query into some internal representation which is more suitable for machine manipulation. The internal form typically chosen is a query tree as shown below.

Query Tree for the SELECT statement discussed above:

b)Convert to Canonical Form – In this second step, the optimizer makes use of some transformation laws or rules for sequencing the internal operations involved. Some examples are given below. (Note: In all these examples the second form will be more efficient irrespective of the actual data values and physical access paths that exist in the stored database. )

Rule 1:

(A JOIN B) WHERE restriction_A AND restriction_B

(A WHERE restriction_A) JOIN (B WHERE restriction_B)Restrictions when applied first, cause eliminations and hence better performance.

Rule 2:

(A WHERE restriction_1) WHERE restriction_2

A WHERE restriction_1 AND restriction_2Two restrictions applied as a single compound one instead applying the two individual restrictions separately.

Rule 3:

(A[projection_1])[projection_2]

A[projection_2]

If there is a sequence of successive projections applied on the same relation, all but the last one can be ignored. i.e., The entire operation is equivalent to applying the last projection alone.

Rule 4:

(A[projection]) WHERE restriction

(A WHERE restriction)[projection]Restrictions when applied first, cause eliminations and hence better performance.

Reference [1] gives more such general transformation laws. c)Choose Candidate Low-level Procedures – In this step, the optimizer decides how to execute the transformed query. At this stage factors such as existence of indexes or other access paths, physical clustering of records, distribution of data values etc. are considered.

The basic strategy here is to consider the query expression as a set of low-level implementation procedures predefined for each operation. For eg., there will be a set of procedures for implementing the restriction operation: one (say, procedure 'a') for the case where the restriction attribute is indexed, one (say, procedure 'b') where the restriction attribute is hashed and so on.

Each such procedure has and associated cost measure indicating the cost, typically in terms of disk I/Os.

The optimizer chooses one or more candidate procedures for each low-level operations in the query. The information about the current state of the database (existence of indexes, current cardinalities etc.) which is available from the system catalog will be used to make this choice of candidate procedures.

d)Generate Query Plans and Choose the Cheapest – In this last step, query plans are generated by combining a set of candidate implementation procedures. This can be explained with the following example(A trivial one but illustrative enough).

Assume that there is a query expression comprising a restriction, a join and a projection. Some examples, of implementation procedures available for each of these operations can be assumed as given in the table below.

Operation Condition Existing Implementation Procedure

Restriction Restriction attribute is indexed a

Restriction Restriction attribute is hashed b

Restriction Restriction attribute is neither indexed nor hashed

c

Join d

Join e

Projection f

Projection g

Now the various query plans for the original query expression can be generated by making permutations of implementation procedures available for different operations. Thus the query plans can be

– adf- adg– aef– aeg– bdf ......

It has to be noted that in reality, the number of such query plans possible can be too many and hence generating all such plans and then choosing the cheapest will be expensive by itself. Hence a heuristic reduction of search space rather than exhaustive search needs to be done. Considering the above example, one such heuristic method can be as follows:

If the system knows that the restriction attribute is neither indexed nor hashed, then the query plans involving implementation procedure 'c ' alone (and not 'a' and 'b') need to be considered and the cheapest plan can be chosen from the reduced set of query plans.

6.4 Query Optimization in Oracle

Some of the query optimization measures used in Oracle are the following:

–Indexes unnecessary for small tables. i.e., if the size of the actual data record is not much larger than the index record, the search time in the index table and the data table will be comparable. Hence indexes will not make much difference in the performance of queries.

–Indexes/clusters when retrieving less than 25% of rows. The overhead of searching in the index file will be more when retrieving more rows.

–Multiple column WHERE clauses

–evaluations causing largest number of eliminations performed first

–JOIN-columns should be indexed. JOIN columns or Foreign Key columns may be indexed since queries based on these columns can be expected to be very frequent.

–Index not used in queries containing NULL / NOT NULL. Index tables will not have NULL / NOT NULL entries. Hence need not search for these in the index table.

sonali dbms notes

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