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In this section we will provide you with the specifics of some advanced capabilities of Structured Query
Language. We will talk about assertions and Triggers, Views as well as standard Procedure as well as Cursors.
The notions for embedded, interactive SQL and SQLJ which are used together with JAVA are as well.
The SQL database has been introduced. A few of the most advanced capabilities of SQL are covered. We will be able to provide
Examples are scattered throughout the sections instead of putting together examples in a separate section. The examples
This information is in the SQL3 standard and will be applicable to any database management system for commercial use.
System that can support SQL3 standards Purchase Finland email lists.
The expression WHERE could be a predicate of a simple type as described above, or it could
be a query itself! The part that follows WHERE is referred to as a Subquery.
A subquery, which is a query, may be a subquery on its own and the method of defining
Subqueries are able to continue indefinitely! The process is over once the query is completed Finland quality email lists .
be fully expressed in a SQL statement.
Subqueries are possible to appear you use the compare predicate or the IN predicate, and when
Quantifiers are employed.
Subqueries are like SELECT chaining. When SELECT chaining is a combination of with SELECTs, subqueries are similar to
the same level of query, but subqueries permit SELECTs to be included in other queries.
They are able to perform many tasks:
1. They can be used to take the place of the constant.
2. They could replace an unchanging value, but they differ based on the row that is processed.
3. They can provide an assortment of values for use in a comparative.
Subqueries appear always inside the HAVING as well as the WHERE clause of queries. Subqueries are always present in the HAVING clause or the WHERE clause of
could contain a WHERE clause , and/or a HAVING clause and accordingly.
Example of a Example: AVG(salary)FROM employee WHICH the title is ‘Programmer’
This will give the median salary for all employees whose title equivalent to
The HAVING clause lets you to define conditions for the rows of each group . This is in addition to
Words, the rows to be chosen will depend on the criteria you have specified. The HAVINGclause must be used in conjunction with that of the GROUP BY clause, if you
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Select column 1, SUM(column2)
GROUP by “column-list”
HAVING is best explained in terms of an examples. Let’s imagine that the table of employees that contains the
Employee’s name department, name, salary and the employee’s age. If you’d like to determine the salary average for an employee, you can select the department’s name, salary, and age.
every employee in the department you work in, you could input:
SELECT dept, avg(salary)
GROUP BY dept;
Let’s suppose that you only want to calculate and display the average when their earnings are greater than 20000:
SELECT dept, avg(salary)
GROUP BY dept
HAVING avg(salary) > 20000;
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4. Advanced SQL
4.2 Nested Subqueries: Notes
A query within the query is known as a nested question. The inner query is referred to sub query. Sub-query is
typically found in WHERE or HAVING clauses.
Look at the following example.
(a): Search for department number 6. (a) Search for all the employees’ names employed in department 6.
From Employee E
Where E.eid IS (SELECT D.Dept_managerid
FROM Department D
Where is D.DNo is 6)
This query will return “David”
These SQL statements are interpreted as
Select the name of an employee from the table employee E so that E.eid is found in dept_managerid.
in which department number 6 is the number. Department number is 6. DBMS first tries to solve the sub query
and retrieves the managerids of all employees that work in department 6.
Then then, the DBMS is able to verify the existence of this ID in the the employee table. If the ID that is 122, then
it shows the result for the ID.
The result is: ename
The primary query which contains the sub-questions is known as an outer query.
As mentioned previously, IN can also be substituted for not in. In this case , it looks for tuples.
that are not that are not present in the specified connection. To find out employees who aren’t in the workplace
In de 6th number in de number 6, we simply need to replace IN with NOT IN , and the rest of the question is the same.
From Employee E
Where E.eid is NOT (SELECT D.Dept_managerid
Is it possible to determine D.DNo is 6)
Question (b) Find employee names involved in project C.
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Database Management Systems/Managing Database
Notes Solution Solution:
From Employee E
Where E.eid IS (SELECT D.Dept_managerid
FROM Department D
Where D.PNo IS (SELECT P.PNo
Where P.Pname = “C”)
This query is solved using the bottom-up method. The first sub-question is solved, and the rest of the
Project numbers are chosen that are named C. The output of this query is then fed into the one sub query.
In the department, the manager ID is chosen. In the end, the names of the employees are listed
The ID of the person is in the relationship.
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1. PNo. is selected if pname has “C” i.e. 33.
2. The first subquery determines whether this PNo exists in the department or not. If it is , then the department will be notified.
Then the dept_managerid for that is chosen, i.e. 120, for example.
3. The principal query determines whether this ID is present in the employee or not. If it’s present,
The corresponding ename is then obtained, i.e., Smith.
Task Discuss the need for the HAVING clause.
4.3 Complex Questions
Beyond the straightforward queries described in the preceding subsection, there is the ability to also create complicated queries.
which could contain which may include more than one SELECT query. At its highest level, it is a query
statement, that is composed of a query statement and the ORDER BY clause, which is optional. at
The next level is lower at which you can combine multiple blocks of queries into one query expression
The UNION operator. In the lower part, within each query block , there is an optional search term, that
may contain predicates that include subqueries. Subqueries are always an individual query block
(SELECT) which can include other subqueries, but not contain (SELECT) that contain a UNION. A query expression can
can contain up to 16 query blocks from every source which include UNION subqueries, subqueries, as well as the
outer query block.
It is possible to create a complex query using these steps:
1. UNION operator can be used to join the rows returned by multiple
block of queries within a single the SELECT statement.
2. Subqueries (also known as nested query) can be used to insert an entire query block
within the search conditions of an outer within the SELECT statement.
3. Particular predicates, such as ANY or ALL, SOME and IN, that let you evaluate
the significance of an expression based on the specific structure and subqueries
4. Advanced SQL
A SELECT statement could comprise several blocks of queries that are linked to each other by UNION as well as UNION ALL
statements. Each SELECT statement produces results from queries that are an array of rows
Selected from a particular table or selected from a specified table or. The combined results are displayed in an array of tables
which is comprised of every row appearing in any of the results of the query.
In the event that you only use one UNION statement is utilized then all duplicate rows will be eliminated from the final row set.
In this scenario the largest dimension of the tuple within the query result is determined in the formula below:
(SelectListItems +1)*2 + (SumListLengths) <= 4000
SelectListItems refers to the number of items that are in the list of selectables.
SumListLengths is lengths of all columns of the list of selects.
At the time of compilation, SumKeyLengths is computed assuming columns of NULL and VARCHAR
contain no data. When running the actual lengths of data are assumed to be the lengths of the data.
In the event that you use the UNION ALL operator is used the duplicates will not be eliminated. Candidates for removal of duplicates
They are evaluated by comparing complete Tuples rather than just one field. If at least two rows
completely identical are completely identical are removed. For UNION ALL, the duplicates are removed. UNION ALL operator, the maximum
The size of a tuple for the result of a query is 3996 bytes as it’s for Non-UNION-based query. The size of a tuple in the query result is 3996 bytes.
It is not possible to utilize LONG columns in the UNION statement.
Figure 4.1 The Variation of Complex Query Types
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Example: To locate every customer who has an account, a loan or both then we record their names.
from the depositor)
(from the borrower)
The union operation eliminates duplicates, in contrast to that of the choose clause. Therefore, in
prior question the preceding query, for instance, if a client-like Jones has multiple accounts or loans (or each) from the same bank
If Jones is not selected, Jones will be the only player to appear as a result.
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If we want to preserve all duplicates, we have to create union in lieu of union
from the depositor)
union of all (select customer-name
(from the borrower)
The amount of duplicates that are in the output is the same as all duplicates.
Both appear in the d and b. Therefore it is the case that Jones is a customer with three loans and has two accounts with the bank, then it will
There will be five tuples and the name Jones as the end.
The term “view” refers to a type of table that does not actually contain information. If it doesn’t hold any information the view is considered to be empty.
What exactly is it?
A view is actually an actual query, and therefore contains a SELECT FROM ….. clause that operates on
physical table that holds the information. The view, therefore, is an array of pertinent data for
a specific person or.
For example, a database of a student might contain one or more of the tables below:
STUDENT (name, enrolment-no, dateofbirth)
MARKS (enrolment-no, subjectcode, smarks)
For the database mentioned above, the view can be created for a teacher restricted to viewing only the database.
The performance level of the students in their area of study, let’s take MM-01 as an example.
CREATE VIEW SUBJECT-PERFORMANCE AS
(SELECT s.enrolment-no, name, subjectcode, smarks
From STUDENTS and MARKS M
WHERE s.enrolment-no = m.enrolment-no AND
subjectcode ‘MM-01’ ORDER BY s.enrolment-no;
A view can be removed by using the DROP statement in the following manner:
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DROP VIEW SUBJECT-PERFORMANCE;
The table that stores the information that is used to write statements of views are written can be
often referred to as the called the base table. It is possible to build views of two or more base tables by combining them.
data using joins. This means that an index hides the process of joining tables from the user. It is also possible to index
Views too. This could speed up the performance. Indexed views can be helpful in very specific situations.
large tables. After a view is made, it is searched exactly as an existing table.
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Fourth Unit: Advanced SQL
Example of Select *
ADVANCED STUDENT PERFORMANCE
Where are marks greater than 50
What is the way in which views are implemented?
There are two methods for applying the ideas. They are:
1. Modification to the query
2. View materialisation.
With the modification of query strategy every query made by the view is altered to include the
View defining expression.
Example: Take the view STUDENT-PERFORMANCE. A query for this view could be:
The instructor of the course MM-0a would like to determine the highest and the average mark for the course.
The query is in SQL would be
Select MAX(smarks), AVG(smarks)
ARISING FROM SUBJECT-PERFORMANCE
Since SUBJECT-PERFORMANCE is its own view, the query will be changed automatically in the following manner:
Select MAX (smarks) AVG (smarks)
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WHERE s.enrolment-no=m.enrolment-no AND subjectcode= “MM-01”;
But, this method has one major drawback. For a large database system, if complex
queries need to be repeated executed on a particular view. the modification of queries will need to be completed
Each time, resulting in an inefficient use of resources like space and time.
The view materialisation strategy addresses this issue by creating a temporary physical table
for a viewpoint, and thus creating it. This strategy, however, does not work when there is a lot of
Database updates are carried out on tables that are used to create views because it requires
an appropriate update for a table that is temporary every after the initial table has been refreshed.
Are views able to be used to Data Manipulations?
Views can be utilized for DML operations such as INSERT, UPDATE, and DELETE. If you
Perform DML operations, the modifications must be transmitted to the base table.
However, it isn’t allowed for all views. The view must meet certain conditions that permit data
A view can allow data updates in the event that it meets the following requirements:
1. If the view is constructed using a single table then:
(a) for the INSERT operation, the primary Key column(s) as well as all columns that are NOT NULL
It is essential to include this information within the scope of view.
(b) View must not be defined by any aggregate function, GROUP BY OR HAVING
or and. This is because any change in these or DISTINCT clauses is considered to be an update in the aggregate
Groups or attributes cannot be linked back to one tuple from the table base. For
For instance, think of an example of a view of avgmarks (coursecode, Avgmark) that is created using an existing table.
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Notes student(st_id, coursecode marks). The avgmarks table is altering the average of the class
marks for the coursecode “MA 03” up to 50 points from the calculated value of 40 cannot be counted
For a single instance of tuples within for a single tuple in the Student database, since for the marks average, they are calculated by comparing the marks of all Student tuples associated with that particular coursecode. This update will only be
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2. The views of SQL that are created using joins are typically not an updatable.
3. With CHECK OPTION clause in SQL examines the validity of views’ data, thus,
It is recommended to use views you intend to keep up-to-date.
Views and Security
Views are beneficial for the protection of data. A view permits a user to access the information accessible
by the view. Thus through the view; thus, the hidden data is not available. Access rights are granted via the view. The data is hidden
views. Let us discuss this using an illustration.
Consider the view that we have created for teacher-STUDENT-PERFORMANCE. We are able to give
rights to the teacher who’s name is ABC to mean:
GRANT SELECT, INERT, DELETE STUDENT-PERFORMANCE DATA TO ABC With GRANT
Notes The ABC teacher ABC was granted the right to query, insert , and erase the records from
the view you are viewing. Also, note that s/he has the authority to grant access rights (WITH
GRANT an option) to anyone who is a data entry user, so that they can fill in data on behalf of. The
access rights may be revoked by using the REVOKE statement to mean:
RELEASE ALL STUDENT-PERFORMANCE OFF ABC;
Task: Create a table that has five columns and create a view on this table.
4.5 Connected Relations
SQL joins can be utilized to access information from multiple tables using a relation between them.
Certain columns of the specific columns of these tables. A JOIN is an approach to join fields from two tables making use of
Common values to all.
SQL is a relational database query language, and one of its key characteristics is its
ability to retrieve data from various related databases. In the terms of relational databases,
This is known as this process is known as a join. The tables that are joined are identified by the clause From of Select
Each table’s name is separated by the use of a space separated by a. The tables’ relationships within a join
determined by the predicate in the”Where clause.
4.5.1 Notes for the Inner Join
This is by far the easiest of join operations.
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Inner joins return rows from multiple tables when the join conditions are met. It must be
A match in a field that is common to the table. An Inner Join can’t be nestled inside the Left
When you join or click Right Join creates an entirely new result table the column values of two tables.
on the join-predicate. The join condition decides if both records match or
not. If there isn’t a match found, there are no records returned.
the borrower’s loan is joined by the borrower’s loan.loan-no = borrower.loan-no
The expression calculates the theta join between the loan and the relationship between the borrower and the loan with the joining condition
being loan.loan_no = borrower.loan_no. The attributes of the final result are the characteristics of
the left-hand-side relationship then follows by the characteristics of the right-hand side of the relationship.
Notes: The attribute loan_no is found twice in the results. The first instance comes from loan.
and the other comes from a the borrower.
Results of loan exchange join the borrower to loan.loan_no = borrower.loan_no
We name the result relation of a join as well as the characteristics of the result relationship using as clauses, or as
Below are the images:
Loan inner join borrower on the oan.loan_no = borrower.loan_no
As lnbr (branch, loan_no, amount, cust, cust_loan_no)
The second loan_no occurrence is now known as cust_loan_no. The attributes are arranged in the order they appear.
as a result of the joining is crucial to rename.
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NOTES 4.5.2 Natural Join
Natural joins two tables with column common to both tables i.e. columns sharing the identical
name. This is why join conditions are hidden and is dependent on table structures during runtime. This
is a clear sign of future risk-as when table structure changes and the results can be
Unpredictable, yet syntactically correct, it provides a different specialization of the equi-joins. The join
The predicate is created by implicitly the comparison of all columns from both tables with the same columnnames for the table that is joined. The table that is created as a result of joining contains just one column per pair
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As mentioned previously in RELATIONAL ALGEBRA, the action when executed, creates an
equality on attributes that are common to the relationship that is specified. If we consider an organic connection of
the borrower and the loan, then equality is imposed by the property loan_no. natural join borrower and loan
The only thing that is common to both the borrower and the lender is loan_no. The result of this expression is
identical to that similar to the result of an inner join with the exception that the attribute loan_no is present only in the
The result from the naturally joining.
4.5.3 Left Outer Join
In the left outer join rows that meet the criteria for selection from both tables joined are also selected.
since all rows remaining from the table left jointed are maintained with Nulls, instead of the actual
Right-joint table values. It returns all the values of the left table, and matches
value from the table on the left (or NULL in the event of no join predicate that matches). If the right table is not found, the left table
Returns one row, and it returns more than one row table, and the values are in the
Right table for every particular row of the table on left.
The expression LEFT OUTER JOIN is written in the following manner:
Loan left outside join the borrower to loan.loan_no = borrower.loan_no
4.5.4 Complete Outer Join
A full-outer join can be described as a mixture of both the left and right outer-join types. Following the results of
the inner join is calculated by tuples that form the left-hand side relationship that was not compatible with any
from the right-hand side from the right-hand side are extended by nulls and in turn added to the end result. similarly as from the right-hand side, tuples
from the right-hand side relationship which did not correspond to any tuples of the left-hand side
Relationships are also extended by nulls, and then are included in the final result.
Full loan borrower with an outer join (loan_no)
SUPER PROFESSIONAL UNIVERSITY 70
Fourth Unit: Advanced SQL
The outcome of the formula is as follows Notes
Find all customers with one or the other
When account_no is not null or loan_no is not null
Natural join is a task-related role in DBMS.
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Lab Exercise: Create an 8 column table and write minimum five numbers into the table. Then, do
1. Choose the your top 40% from table
2. Select columns 1, 2, and 3 simultaneously from the table (joint all)
SQL also provides a powerful programming interface. SQL also has a good programming level interface. SQL provides a set of functions that allow access to databases. The functions can also be known as”the Application Programming Interface (API) of SQL. The benefit of the API is it gives flexibility when accessing databases.
within the same program, regardless of DBMS in the same program, whereas the drawback is that it is more
Full outer joins A full outer-join type can be described as a combination the right and left outer-join types.
Inner Joins: Internal joins return the entire rows from different tables where the join conditions are met.
Natural Joins: A natural joins two tables that are based on the columns they share i.e. columns
that has the identical with the same.
Nested query: A question within an existing query is referred to as a nested query.
Subqueries: Subqueries have a similarity in a way to SELECT chaining. In contrast, SELECT chaining blends
The same level of SELECTs can be found within a query, however subqueries allow SELECTs be embedded
within other inquiries.
Sometimes, a class type actually is a collection of distinct components. Even though this
Patterns can be described using the normal association. Its significance is much more clear when we employ
the notation used to describe an the notation for an. Database objects are able to map functionality between Java objects and
Relational Database (RDBMS) in an extremely flexible and standard method in order it is possible to use the object itself
It can be integrated into your application, eliminating the requirement to integrate SQL data directly within your Java
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5.1 Integrity Constraints
Integrity constraints ensure that any changes that are made to databases by users who have permission don’t have the effect of
which can result in result in a loss of consistency. Therefore, integrity constraints protect against damage that could be caused by an accident to the
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Unit 5 Integrity Constraints
Apart from the cell’s name, cell length , and the cell type Other parameters are available, i.e. Other data Notes
constraints that are able to be passed on to the DBA during the cell’s creation.
These data constraints are connected to cells by the DBA in the form of flags. If a user attempts
when loading a cell loaded with data to load data into a cell, to load a cell with data, DBA will validate the data transferred into the cell with the information
restrictions that were set at the time the cell was made. If the data that is being loaded does not meet any of the conditions defined at the time of creation
check for constraint that is performed by the DBA when a constraint check is fired by the DBA, the DBA cannot insert the information into the cell. It will and reject the data entered
record and flash an error message to the user.
These constraints are identified by an appropriate name for the constraint and the DBA records the constraints by their name.
and internal instructions, as well as and instructions internally within the cell.
The constraint could be put in the column or the level of tables.
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Constraints at the Column Level When you define the constraint in line to the definition of the column, it’s
Also known as a column level constraint at the column level. A column-level constraint can be applied to any column.
A time i.e. they are localized to a specific column. If the constraint is spread across multiple columns,
Users will need to utilize table-level constraints.
Table Level Constraints When the data constraint that is attached to one particular cell of the table is referencing the
the contents of a cell within the table. The user must make use of tables level restrictions. Table
Constraints at the level of constraints are recorded as part of the definition of the global table.
NULL Value Concepts
When creating tables, if one row does not have a value for a column, the value is considered to be
null. Columns of any type of data could contain null values, unless the column is defined as non
Null at the time that the table was made.
Null Values and Principles
1. The setting of a null value is acceptable when the value isn’t known or when there is a valid value
It would be meaningless.
2. Null values are not equivalent to a zero value.
3. A null value will translate into null any time in any form. e.g. Null multiplied 10 times is null.
4. If a column’s name is declared to be not null, the column becomes mandatory
column. It means that users will be required to input data into the column. It implies that the user is required to enter data into that.
Example Create table master client with a constraint of not null to columns no client
Name, address, address2.
Not NULL as a column constraint:
CREATE TABLE master client
(client_no varchar2(6) NOT NULL,
Name varchar2(20) NOT NULL
address address varchar2(30) not NULL
address2 varchar2(30) not NULL,
city varchar2(15) city varchar2(15), state varchar2(15) pin code( 6,),
remarks varchar2(60), bal_due number (10,2));
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Notes on Primary Essential Concepts
Primary keys are one or more columns within the table that are used to identify every row of the table.
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Primary key values cannot be null or invalid and should be distinct across columns.
A multicolumn primary key can be known as a composite primary key. The sole function of is a primary key performs is to
The function of key is to identify a row, and when a column is utilized, it will be as effective as when
Multiple columns are utilized. The use of multiple columns i.e. (composite key) are only utilized when it is necessary.
The system is designed to require the primary key to be contained in one column.
Example Primary Key in the form of a column constraint
Create a client master where client_no is the key that is used as the primary one.
CREATE TABLE master client
(client_no varchar2(6) PRIMARY KEY,
name varchar2(20)| Add| name varchar2(20), add}-essl varchar2(30) address2 varchar2(30),
city varcbar2(15) state varchar2(15) pincode number(6),
remarks varchar2(60), bal_due number (10,2));
Primary Key as Table Constraint
Create a sales purchase details table, in which
Column Name Type Data Type Size Attributes
S_order_no_varchar2 6 Primary Key
product_no_varchar2 6 Primary Key
Qty_ordered for Number 8
Quantity-dispersion number 8
product_rate number 8,2
CREATE TABLE sales order details
(s_order_no varchar2(6), product_no varchar2(6),
Qty – ordered number(8) Qty number – dis number(8),
PRIMARY KEY (s_order_no, product_no));
Unique Key Concepts
Unique keys are identical as a key that is primary but the primary reason behind an distinct key is to guarantee that
data that appears in each column is distinct like the telephone number or driver’s license
numbers. Tables can include a number of keys that are unique.
Example: Create a table client_master that has a an unique constraint on the column client_no
Unique for a Column Constrained
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CREATE TABLE master client
(client_no variable( (6) CONSTRAINT cnmn – Ukey Unique,
Name varchar2(20) address1 varchar2(30) address2 varchar2(30),
city varchar2(15) state varchar2(l5) pincode number(6),
remarks varch_2(60), bal_due number(lO,2), partpaY311 char(l));
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Unit 5 Integrity Constraints
Unique as a Table Contraint Notes
CREATE TABLE master client
(client_no varchar2(6), name varchar2(20),
addressl varchar2(30) address2 varchar2(30),
The city varchar2(15) city varchar2(15), state varchar2(15) pincode number(6),
remarks varchar2(60), bal_due number(lO,2),
CONSTRAINT cnmn_ukey unique (client_no));
Concepts of Default Value
At the time of cell’s creation, an initial value may get assigned. If you load the cell, a
If you record a value and leave this cell empty by default, the DBA will load this cell using the
default value that has been specified the default value. – The type of data that is used in the default value needs to be the same as the data type used by the
column. You can utilize this clause by default to define any default value that you would like.
Foreign Key Concepts
Foreign keys represent relationships between tables. A foreign key is column (or an array of
columns) which have values that are derived by the key of the table or another table.
The presence in a key that is foreign indicates that the table that has this key connected to the table that contains the
the primary key table that is from where the foreign key is from. A foreign key should be accompanied by a
The primary key value of the table of primary keys have a significance
This constraint on Foreign Key References works as in the following order:
1. Rejects an INERT or UPDATE of one’s values, if a similar value does not exist
in the key table that is the primary one.
2. Rejects a DEL_TE in the event that it invalidates an REFERENCES constraint
3. It must be a reference to a PRIMARY key or unique column(s) in the primary key table
4. Refers to the PRIMARYKEY in the key table primary, if there is no column is present or a group.
columns are specified in the constraint
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5. It must refer to a table that is not a view cluster;
6. It requires that you have your primary table of keys, hold REFERENCE privileges on it, or
REFERENCE privilege at the column level for the collwnns that are referenced on the table of primary keys
7. Doesn’t limit how other constraints can be referencing the same tables.
8. It is required that the FOREIGN key column(s) along with the CONSTRAINT column(s) have a matching
9. It could be a reference to the same table in the statement CREATE TABLE;
10. Do not refer to one column more than one time (in one limitation).
After installing PL/SQL developer, all users will be able to utilize all features of PL/SQL Developer.
the limitations of the privileges of the system as well as object privileges which are granted to the Oracle user.
The database is linked to it.
Example A: If the Oracle user is not granted the system privilege of creating users The Oracle user will not have the create user privilege.
Developer users are able to start the New User function in the Developer PL/SQL database, but eventually receive an
“ORA-01031 Insufficient rights” warning message sent by Oracle.
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Database Management Systems/Managing Database
You can expressly authorize the PL/SQL Developer functions that are relevant to a specific Oracle
Users and users and. For a database for development,, you give all developers full capabilities, while for instance, in the case of a
Test database, you’ll typically restrict users from being able to modify objects in the case of a production database, you will not allow users to alter objects.
You would normally turn off all functions for the majority of users who might modify the database or consume
excessive resources, which could impact performance.
By the granting of permission to PL/SQL Developer rights to roles, you can alter authorization for certain roles.
groups of groups of. It is possible to use roles that are already in use that connect to user groups (such
as DBA and as RESOURCE) or create specific roles for as PL/SQL Developer groups of users.
To stop the majority of PL/SQL Developer users from accessing the database you want to block the database, you simply cannot
Grant the System.Logon access to any role or user.
5.3 DCL Commands
The term “data control language” (DCL) is a subset consisting of SQL statements that control access to
Database items and information.
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This category that includes SQL statements is particularly relevant for database administrators handling
databases user groups and user IDs. DCL statements are utilized on the database level to manage who
users can run SQL statements, limit the types of SQL statements users are allowed to execute, and be able to assign
authority to users to allow them to run a pre-defined set SQL statements. While the users
access to the database may be managed on the operating system level or with the help of security
plugins DCL declarations provide the best and straightforward method of the granting and revocation of the user
rights and authority. Database administrators can grant or remove rights to users when a new user is added. When when a user is removed the user’s rights are to be restricted or relaxed in response to a change in
in security policies or in special circumstances that justify granting a user new rights in security policies, or when certain circumstances warrant it.
run the SQL statement.
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Unit 5 Integrity Constraints
The majority of DCL statements start with the one or more of the words GRANT, or REVOKE. Notes
There are a variety of sub-categorizes for DCL statements based on nature of the action that is to be carried out.
granted or cancelled. For instance, you can get DCL statements that relate to packages, statements and
utilities. They typically contain clauses that reference the name of the database
authorization or privilege and what is the name given to the database item that is associated with the privilege in the event that it is
one is the name of the user is to be changed. DCL statements are also used to delegate
the power to grant and the power to revoke certain privileges granted to users who do not have the authority.
DCL commands can be executed using many user interfaces and interactive interfaces however
They are typically run as scripts, or via DB2(R) instruments that can support SQL statement
Optimizing and Processing processing and optimization of queries using the Language (also known as SQL is among the primary reasons for the its success with RDBMS. The user only needs to
The query should be written to be used in SQL that is similar in English language, and doesn’t require a description of how.
The query needs to be assessed. It is important for a query to be properly evaluated through the DBMS. However, it is not necessary.
How do you evaluate a query effectively? This unit seeks to address this issue. This unit covers
the fundamental principles in query evaluation and the costs of evaluation the evaluation of the join
queries, etc. in detail. It also contains information about queries evaluation strategies as well as the importance of
storage for query evaluation and optimization.
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The first step of Scanning and Parsing is completed to translate the query to its internal
form. Then, it is transformed into an algebraic relation (an intermediate form of query). Parser
Verifies syntax and checks relationships. The query is then optimised by using a query planning tool, which is then
It is then compiled into a program which can be executed by the runtime processor for databases.
It is possible to define query evaluation as the query execution engine that executes the plan of evaluation for a query,
Implementing the plan and returning the answer for the question. The process of processing queries involves
Study of the following concepts:
1. How can I measure the cost of a query?
2. Algorithms to assess algebraic relations.
3. How do you evaluate an entire expression by using algorithms for specific operations?
Every relational algebraic operation is able to be assessed using one of many algorithms available.
The same is true for a relational-algebraic equation. is able to be assessed in various ways.
A term that specifies an elaborate evaluation strategy is called an evaluation-plan. This is for instance
you can search an index on salaries to find employees who earn below 5000. We can also provide complete
relationship scan and remove employees earning 5000 or more. The reason for selecting any one of the
Cost of the plan will be the main concern.
Query Optimization: In the list of comparable plans, select the plan with the lowest price. Cost is
Estimated using statistical data from the database catalogue, for instance the numbers of
Tuples in each relation the size of tuples etc.
In query optimization, we will find an evaluation strategy that has the lowest price. Cost estimation
It is based on the use of heuristic principles.
Cost is typically measured as the time it takes to complete the question. There are numerous
elements that influence the cost of time. They include the time spent on disks, CPU usage and even networking
Typically, access to disks is the primary cost since disk transfer is an extremely slow process and also
fairly easy to determine. It is calculated using the following aspects.
This is because it is necessary to read the information back following writing to verify that the writing was done correctly.
successful. But it is for simplicity, we’ll use the number of block transfer from
disk as the measure of cost. We also will not take into account the cost difference between the sequential and
randomly-generated I/O/CPU, and communication cost. The I/O cost is based on the requirements for the search i.e.,
A point/range query based on an order/other fields, and also the structure of the files such as heap, sorted and hashed. It’s
Also, the use of indices like primary and clustering, secondary B+ Tree, Multilevel
etc. Other cost elements that could be included, for example, buffering materialsisation, disk placement,
overflow/free space management etc
The process of selecting is possible to perform in many methods. Let’s look at the algorithm and
the cost associated with performing the associated cost of performing.
12.2.1 File Scan
They are the algorithms that find and retrieve the records that meet an eligibility requirement
within the form of a in a. These are the two most basic file scan algorithms to choose a scan operation:
1. Linear search method searches each block of data and examines each record to determine if they
Meet the selection criteria.
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Cost of the algorithm (in regards to block transfers) can be defined by follows:
Cost of searching records meeting an exigence = Number of blocks in the database = Nb.
Cost of searching for a key attribute value is the average number of blocks transfers to find
The number (on the average) (on about half of the file has to be traversed) thus it’s Nb/2.
Linear search can be used regardless of the selection conditions or order of records within the
File, or availability of indexes.
2. Binary search: It’s appropriate when the choice is an equality match to the attribute
upon which file is ordered. If the relation’s blocks are continuously stored,
Cost estimates can be made as follows:
Cost is the cost of finding the first tuple using searching in binary on the blocks and Sequence of the other
blocks that meet the requirement.
= [log2 (Nb)+ the average number of tuples having the same value
The blocking factors (Number of tuples within the block) of the relationship
These two numbers can be calculated using the data in the databases.
12.2.2 Index Scan
Search algorithms that make use of an index are limited because the selection criteria must be met.
The search-key in the index.
1. (a) Primarily index scan to determine equality: The search locates one record that meets the criteria.
the equivalent equality the corresponding equality. The cost can be calculated using:
Cost is the height traversed within index to find that block’s pointer +1(block that is the first
The key is transferred for access).
(b) Key to hash is a method of retrieving one record directly so, the cost associated with hash key could also be
could be described as the block transfer that is required to locate the hash target.
2. The primary index-scan used for comparison Assuming the relation is sorted based on the attribute(s)
which are being compared (that are being compared, (. ) Then we have to find the first one that is able to satisfy the
conditions in which records are scanned either forward or backward in accordance with the condition.
This would mean displaying all records. Therefore, the cost could be:
Cost = The number of blocks to transfer to find the index value + Transferring all blocks
of information that meets that requirement.
Equality in clustering indexes to find multiple records: The cost calculation in this instance
are somewhat similar to the algorithm of algorithm (primary index scan for comparison).
(a) Equality on secondary index search-key is a way to retrieve a single record in the event that the search-key
a candidate key.
Cost is the cost of accessing an index plus 1.
It can retrieve multiple records even if the search-key isn’t an acceptable key.
Cost = cost of accessing index + number of records that are retrieved (It is possible to be extremely expensive).
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Each record could be in an entirely different block, therefore it is necessary to have one access point per record.
The record was retrieved (this is the most expensive cost).
(b) (b) Secondary index for comparison: For types of queries that make use of comparison
A secondary index value that is not a value, the index could be used to locate the first index entry
that is greater than the value, you can scan the index sequentially from there until the end
Also, keep looking for the clues to the records.
For type queries, simply go through index leaf pages and keep looking for pointers to
documents, up to the point that the one entry is found to be in compliance with the requirement.
The implementation of complicated selections
Conjunction: It is an array of AND conditions.
1. Conjunctive choice using one index: In this situation, you can choose any algorithm that was previously described.
one or one or. If you are able, try different conditions with these tuples after the fetching
These are stored in memory buffers.
2. Conjunctive search using multiple-key index : Use the appropriate composite (multiple–
key) index if they’re indexes that are available.
3. Conjunctive selection using the intersection of identifiers needs indexes and record pointers.
Find the index that corresponds to each of the conditions, and then find an intersection between all of the results.
Sets of record pointers. After that, fetch records from the file in case certain circumstances do not allow
the appropriate indexes, and test them after removing the tuple out of the memory buffer.
A list of OR conditions.
Disjunctive selection through union of identifiers is valid in the event that all conditions are based on indexes.
Otherwise, utilize otherwise, use. Make sure to use the appropriate the index of each situation. Take the sum of all the
Record pointers were obtained to eliminate duplicates then retrieve information from the record.
Utilize linear scans on files. However, if only a few records are found in the scan and an index is needed,
that is applicable to attribute, and is negated, look for records that are in line with the attribute by using index and
Find them in the file.
It is time to examine the ways to sort that are suitable to calculate cost. There
There are a variety of methods that can be utilized in these methods:
1. Utilize an existing or a standardized index (e.g. B+ Tree) to view the relation in order of.
2. Create an index for the relation. Then, utilize the index to view the relation’s order of reading.
(Options 1 and 2 may result in one block of access per tuple).
3. To find relations that can fit into the memory, techniques such as quicksort can be employed.
4. If the relations do not meet the criteria for memory, external sort-merge can be an ideal choice.
Let’s go through the procedure for External Sort-Merge.
12.3.1 Create Partitions that are Sorted
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Repeat the steps (1) through (4) to the conclusion of the relationship:
1. Blocks of M read from the relationship into the memory. (Assumption that M represents the count of memory that is available).
buffers to aid in buffers for).
2. Find these blocks that are buffered with internal sorting.
1. Choose the record that is the first (in sorts) from the input buffer blocks
2. Record the data to the buffer for output;
3. If the buffer for output is full, then record it on disk, then empty it to make room for the next batch of data.
This action can be executed in a way that is automated by the Operating System;
4. Remove the record from its buffer of input;
5. If the buffer blocks become empty, then you can you should read in the following block (if there’s one) in the temporary file
to the buffer.
If N M, multiple merge passes are required for each merge pass groupings of contiguous M 1 partitions
The two files are combined, and a process decreases the amount of temporary files (i) in a number of M-1. To
For instance, if you have M=11, and the temporary file count is 90 one pass decreases the number of temporary files
Files to 9 each temporary file starts 10 times larger than the partitions before it.
The process continues until the partitions are merged into one.
Figure 12.2 provides an example of sort-merge for external sorting.
Cost Analysis may be conducted, based on the following actions:
1. Assume the file is Z blocks. Z blocks.
2. Z block input to buffers and Z block output for temporarily file generation.
3. If we assume that N M will be the total number of merge passes, then several merge passes are needed
4. The number of merge passes is (log M-1 (Z/M)[log M-1 (Z/M)]. It is important to note that of the M buffers 1, 1 is utilized to calculate
5. Thus, the there is a need for block transfers to merge passes is 2xZ ([log M-1 (Z/M)[log M-1 (Z/M)]) in addition to all
Blocks will be read and copied back into the buffer after each merge process.
6. Therefore that it is estimated that the number of times in the sort-merge algorithm equals 2Z + ( log M-1 (Z/M)[log M-1 (Z/M)] ) = 2Z
x ([log M-1 (Z/M)]+1).
1. In the loop that is nested, there is an outer relationship and an inner one.
2. It doesn’t make use of or require indexes. It is compatible with any type of join condition. However,
It is costly because it examines each pair of tuples that are in the two related relations.
3. If there is just enough memory to store one relational block The number of disks
accessibility can be calculated using:
For each STUDENT tuple, all MARKS Tuples (blocks) which need to be access.
However, if neither one of the relations are perfectly in the memory block transfer is required
only once, and there is only one transfer, in this case could be calculated as follows:
= The number of blocks of STUDENT + number of blocks of marks
= 100 + 500 = 600.
If the smaller of the two relations is completely into the memory, make use of that as the inner
The bound and relation is in effect.
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Costs for the worst-case scenario:
The number of tuples in the outer relation x the number of inner relations’ blocks + Blocks of
2000 1,000 x 500 + 100 is 1,000,100 using STUDENT as the outer relationship.
There’s a third potential problem in which MARKS is located on the outer loop and STUDENT on the inner loop.
loop. In this scenario block transfer will be: Block transfer is:
10000 1,000 x 1000 + 500 equals 1,000,500 using marks as the outer relationship.
This is a variation on the nested loop join where an entire block of the outer loop is joined by the
Block of inner loop
Indexed Nested-loop Join
Index scans are able to substitute file scans in the event that it is an equi-join , or natural join and the index is
accessible via the join attribute of the inner relation.
For each tuple in the STUDENT outer relation, you can use the index to find tuples in the MARKS that
meet the join condition using the tuple si.
In the event of a worst-case scenario the buffer can be used for just the size of a single page and for each tuple
In MARKS In MARKS, we must do an index lookup using MARKS index.
The worst case scenario The worst case: Block transfer of STUDENT + number of records from STUDENT cost of search
by searching the index and retrieving the matches for each Tuple of by searching index and retrieving all matching tuples for each student.
If a support index is not available, it could be built at any time.
If indexes are available for those join characteristics of STUDENT and MARKS, make use of the relationship
with fewer tuples than the outer relationship.
The merge-join applies to natural joins and equi-joins only. It works as follows:
1. Sort both relations based on the join attributes (if they have not been sorted already).
2. Join the separated relations for joining them. The joining step is the same as the merge phase of
Sort-merge algorithm. The only difference is in the way that duplicate values are handled are sorted
join attribute is treated, i.e., every pair with the same value in join attribute should be match.
Each block should be read just once (assuming that all tuples are equal to any particular value in the join
attributes can be stored to the memory). The number of block accesses for merge-join:
This only applies in the case of an equi-join or natural join, and the other relation is separated
and the other is another B+-tree index which is a secondary one to this join feature.
The algorithm is:
Combine the sorted relation with the leaf entries in the B+-tree. Sort the result based on the addresses of
the unsorted relations’ tuples. Sort the unsorted relation by physical address order, then merge
using the results from the previous ones using the previous results, replacing addresses by the actual with the actual. In these cases, a sequence scan is used
is more effective than random lookup methods.
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This applies to both equi-joins as well as natural joins. A hash function is employed to divide
Tuples of both relations in which h maps joining attributes (enroll not in our case) values to
Join attributes are hashed to join-hash partitions. In the case of Figure 12.4 we see
Mod 100 function was used to hashing using mod 100 function, and n =100
After the partition tables of the STUDENT and MARKS are created by the enrolment number then
Only the partitions with the same name only the partitions that are corresponding in the following manner:
A STUDENT tuple as well as an MARKS tuple which satisfy joining conditions will be of the same value.
the join the join attributes. Thus, they will be washed to an equivalent partitions and can thus be joined
Algorithm for Hash-join
The hash-join between two relationships that are r and s is calculated by following:
1. Partition the relation between r and s by using the the hashing algorithm called h. (When partitioning the relation, you can use one
A block of memory is used as an memory buffer that is used for outputs of each partition).
2. To each si partition in s, take the partition and load it into memory, and create an in-memory hash
index of joining attributes.
3. The tuples in the ri disk one at a time. For each tuple in the ri disk, find each
Si matches tuples by using the hash index in memory as output. The output is the concatenation
In this case the relation s can be referred to as the build relation, while the relation r is referred to as”the probe relationship.. The
value number (the value of the number of partitions) and the hash function h are determined in such a way that each
si must fit into the memory. Usually, n is used as the number of blocks that si/Number of Average size of the partition si will be smaller than M blocks according to this formula. described above. Hence,
making enough room in the case of the index. If the construct relation s is extremely massive, then it is it is n’s value is calculated by
the formula mentioned above may be higher than M” the formula above may be greater than M” i.e. that the amount of buckets in the formula is greater than the number of buffers
pages. In this case the relation can be partitioned in recursive ways instead of partitioning in n ways,
Use M-1 partitions to further divide the M – 1 partitions by using another hash function.
It is recommended to employ the same partitioning procedure for the r. This method isn’t often necessary, because it is recursive
partitioning is not necessary when the relation is 1GB or less. storage size up to 2MB with blocks of size
Calculation of cost for Simple Hash-join
1. Partitioning costs of r and S The elements of are read one time and are read after partitioning
returned, thus the cost of 1 is 2 (blocks of r plus block of s).
2. Costs of carrying out the hash-join with probe and build will require at minimum one block
Transfer to read the partitions
Cost 2 equals (blocks of r ) + Blocks of s)
3. There are additional blocks within the main memory that could be used for testing and testing.
can be written or read back. It is possible to write back or read. it is too low than the cost
1 and costs 2.
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So, the total price is equal to cost 1 plus cost 2
is 3 (blocks of r plus blocks of s)
Cost of Hash-Joining needing Recursive Partitioning
Partitioning costs in this scenario will be increased according to the how many recursions are required It could be
Calculated to be:
The number of times required to repeat the calculation (x) = ([log M-1) (blocks of s) 1)
Therefore Cost 1 would be changed in the following manner:
2. (blocks of r plus blocks of s) * ([log M-1 (blocks of s)[x1)
The price for steps (2) (3) and (3) in this case will be the same as described in the steps (2) (2) and (3) previously.
Therefore, total cost = 2(blocks of r plus blocks of s) ( [logM-1(blocks of s) + 1(blocks of s)] ) (blocks of r + blocks of s) (blocks of r ) + blocks
Since s is the inner word in this sentence, it’s advised to pick the smaller relation
the build relationship. If all build input can be stored within the memory of main it is possible to set n to 1 and
the algorithm is not required to split the relationships, but could nevertheless create an index in memory when it is needed.
In some cases, the cost estimate is downwards by (Number of Blocks r x the number of blocks in s).
Handling of overflows
Even if si is partitioned recursively, hash-table overflow could happen, i.e., some partition si might not be able to handle it.
to fit into memory. This can be the case if there are many Tuples in s having the same join value.
characteristics or a bad hash function.
Overflows and Handling
Even if si is partitioned recursively, hash-table overflow could be a possibility, i.e., some partition si might not be able to handle it.
to fit into memory. This could be the case if there are many Tuples in s having the same join value.
attributes or a poor hash function.
Partitioning can be skewed when some partitions contain significant more tuples than the others.
others. This is known as the overflow situation. The overflow is dealt with in various ways:
1. Resolution (during the building phase) Resolution (during the build phase) S is further divided by
Different hash functions. It is the equivalent of partition r. needs to be further divided similarly.
2. Avoidance (during the build phase) Build relations: Divide into several partitions, then
However, these strategies for dealing with overflows do not work when faced with huge numbers of duplicates. One
One way to avoid such issues is to utilize a block-nested-loop join on the overflowing partitions.
Let us look at the hash join and the cost to the normal join for STUDENT MARKS
Assume that a memory size of 25 blocks M=25.
The SELECT build is STUDENT in that it has a smaller than 100 blocks (100 blocks) and R probe for marks
The number of partitions that will be made to accommodate STUDENT (block that contains STUDENT / M)* Fudge percentage (1.2) =
(100/25) (100/25) 1.2 = 4.8
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So, STUDENT relation will be divided into 5 parts comprising 20 separate blocks. MARKS will be included.
There are five partitions with 100 each. The 25 buffers are used as -20 blocks to make one whole
Partition of STUDENT, plus four blocks for each block in the 4 other partitions. One
Blocks are used to provide input to MARKS partitions.
The total cost is 3(100+500) is 1800 because there is no need for recursive partitioning.
AWESOME PROFESSIONAL UNIVERSITY 209
Unit 12: Search Processing and Optimization
NotesPartitioning is believed to be biased if certain partitions contain substantially more tuples than others.
others. This is an overflow issue. The overflow condition can be dealt with in many ways:
1. Resolution (during the building phase) Overflow Partition is further divided by
Different hash functions. A similar partition to r needs to be further divided similarly.
2. Avoidance (during construction phase) Partition the build relationships into multiple partitions, and then
They are combined.
But, these methods to deal with overflows do not work when faced with the large number of duplicates. One
Another option to avoid these issues is to utilize a block-nested-loop joins on the partitions that are overflown.
Let us discuss the hash join as well as the price to the normal join for STUDENT MARKS
Assume a memory of 25 blocks M=25.
The SELECT build is STUDENT if it is less the number of blocks (100 blocks) and R probe for marks
The number of partitions that will be made in STUDENT (block that contains STUDENT/M)* Fudge percentage (1.2) =
(100/25) (100/25) 1.2 = 4.8
So, STUDENT relation will be divided into 5 parts with 20 block each. MARKS will also be partitioned.
The partitions are made up of five 100 each. The 25 buffers are used as -20 blocks in one whole
The STUDENT partition is augmented by four additional blocks to make each block in the 4 other partitions. One
Blocks will be used for input into MARKS partitions.
The total cost is 3(100+500) equals 1800 because there is no need for recursive partitioning.
This is helpful when the memory is quite large while the building inputs are bigger than
The memory. Hybrid hash joining preserves the initial partition of the construct relation in memory. The
The STUDENT first partition is kept in the initial twenty blocks and is not written to
the disk. First block MARKS is immediately used for probingpurposes, not being written on
Read back. This means that it has the cost that is 3(80 plus 400) + 20 + 100 = 1560 block transfer for hybrid hash-join
instead of 1800 , which had a the plain instead of 1800 with hash-join.
Hybrid hash-join can be very useful in the case of M being large so that we could have larger partitions.
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A join that has conjunctive properties can be dealt with through the block or nested loop.
A nested loop join, or, alternatively the result from one of the simpler joins (on certain circumstances) can
can be calculated and the result could be assessed by determining the tuples that meet the remaining requirements.
conditions that affect the outcome.
A join that is disjunctive conditions can be determined by using the loop or block
The loop can be joined in a nested fashion, or, it could be calculated by combining the records from separate joins.
This is an excellent internal representation, however, it could be beneficial to depict the
A relational algebraic expression for query trees, using algorithms for optimizing queries can
can be easily designed. A query tree can be easily designed nodes are the operators while relationships represent the leaf. The
Tree of queries for the expression relational above could be:
In the previous section, we’ve seen the algorithms used for each operation. Let’s take a examine the algorithms for individual operations.
Examine the ways to evaluate the entire expression. In general we use two methods:
Examine a relational algebraic equation from the bottom up by creating and explicitly generating the expression.
The results of each operation into the expression. As an example as shown in figure 12.5 compute and
save the results of the selection process on the STUDENT relation, and then join this result
using the MARKS relationship and finally create the projection with MARKS relation and finally compile the projection.
It is possible to evaluate a materialized evaluation however, the expense of transferring results from writing/reading to/from
The disk may be very high.
Examine operations in a multi-threaded fashion, (i.e., passes through tuples of output from one operation
for the operation to be executed by the parent (as to the next parent operation as input) while the initial operation is in progress. In the
Previous trees of expressions, but it doesn’t save (materialise) results but instead, it just passes tuples
to join. In the same way, it does not store the results of joining, but transmits tuples direct to the projector.
So, there’s no requirement to save the temporary relationship to a disk for every procedure. Pipelining could
Not always feasible or simple to sort or sorted. Hash-join.
The pipelining techniques might involve filling a buffer with the result tuples
lower level operations, whereas, records can be pulled from the buffer by higher level
operationComplex joins operationComplex
If an expression has three or more relations, then we’ve got multiple strategies to use
analysis of the definition. For instance the join of relationships
The creation of queries Evaluation Plans
Generating query-evaluation-related plans for an expression requires a number of steps:
1. Constructing logically equivalent expressions with the equivalence rules
2. Noting the resultant expressions in order to obtain different query plans
3. The choice of the best plan is basing your decision on the estimated costs.
The whole process is called cost-based optimisation.
The price difference between a sound and poor method of evaluating a question could be huge.
Thus, we will have to determine the costs of operations, as well as statistical information on
relations. For instance, a set of tuples or a set of distinct attributes, and so on.
Statistics aids in estimating intermediate results in order to calculate the cost of complicated expressions.
12.7 Changes to Relational Expressions
Two algebraic equations with relational roots are considered identical if they are found in all legal databases
The two expressions produce the identical number of tuples (order of tuples is not important).
The above-mentioned rules are not enough general, and a few heuristics or heuristics could be derived by these rules.
that help to modify the expression of a relation to make it more effective. These rules help in changing the relational expression.
1. Combining a series of choices in a conjunction and evaluating the validity of all the predicates
2. Combining a number of projections into a single projection:
3. The commuting of the projection and selection or vice versa can reduce costs
4. Utilizing rules that are associative or commutative for Cartesian joint or product to discover many
5. Moving the projection and selection (it might need to be altered) before joining. The selection
and projection leads to the decrease in the total number of Tuples in the system and consequently, could decrease
Cost of joining.
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6. Converting the projection and selection using Cartesian products or unions.
The selection condition could be changed to join operation. The selection condition is described in
The Figure 12.6 is the following: the Figure 12.6 is: DBMS and grade is A. Both of these conditions are part of different
tables, as they are referred to, are only available in the SUBJECTS table , and grade is only in the MARKS table. Thus,
The selection criteria are mapped as illustrated in Figure 12.7. So the
The equivalent expression is
Finding Alternative Expressions for Query
Query optimisers employ equivalence rules to generate a set of expressions that are equivalent to
the expression given. Conceptually speaking, they create every equivalent expression by repeating
applying the equivalence rules until there are no more expressions to be identified. For every expression
discovered, make use of all rules of equivalence applicable and then add the newly created expressions to the list of
expressions have been found. But, this method is extremely expensive in both time and space.
requirements. The heuristics principles mentioned above can be applied to cut costs and generate several
possible but good equivalent query expression.
The choice of Evaluation Plan
Let’s define the word Evaluation Plan.
A plan of evaluation defines precisely the algorithm to be applied to each operation and the method to use it.
that the implementation of an process is coordinated. As an example the figure 12.10 depicts the query tree
with an evaluation plan.
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Notes 12.9 Selection of Evaluation Plan
Let’s define the word Evaluation Plan.
An evaluation plan outlines exactly the algorithm to be applied to each operation and in what manner.
The execution process is coordinated. For instance the figure 12.10 illustrates the query tree
with an evaluation plan.
Option of Evaluation Plans
When choosing an evaluation method it is important to consider the interplay between evaluation methods.
It is important to note that selecting the most cost-effective algorithms for any operation separately might not be feasible.
provide the best overall results. For example, merge-join might be more expensive than hash-join however, it could
Provide sorted output, which lowers the cost of the higher-level aggregation. A nested-loop
joining could be an opportunity to pipeline. Practical query optimisers incorporate elements
of the two general of the following two broad
1. Explores all plans and selects the most effective plan based on cost.
2. Utilizes heuristic rules to select the best method to select a.
In this class you’ll be working on queries and their processing as well as evaluation.
A query within a DBMS is a crucial procedure, since it has to run efficiently.
Query processing involves query parsing, representing query in alternative forms, finding
the most effective method of evaluating of a question and conducting an actual evaluation.
The primary cost for query evaluation is the time to access disks.
In this article we’ve looked at the costs of various processes in detail.
But, an overall cost can’t be an easy addition of the expenses.
Search algorithms that utilize an index are limited by the selection conditions
It must appear on the search-key that is in the index.
Indexing Database Indexes an information structure that increases speeds of operation within the database
Join the Join operation as the main power that lies behind relational databases.
Cost of a query Cost is usually determined by the total time to answer the question.