ch5 - Copy.pptx advanced structure query language

Database System Concepts, 7th
Ed.
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Chapter 5: Advanced SQL

5.2
Database System Concepts - 7th
Edition
Outline
 Accessing SQL From a Programming Language
 Functions and Procedures
 Triggers
 Recursive Queries
 Advanced Aggregation Features

5.3
Edition
Accessing SQL from a Programming Language
 Not all queries can be expressed in SQL, since SQL does not provide
the full expressive power of a general-purpose language.
 Non-declarative actions -- such as printing a report, interacting with a
user, or sending the results of a query to a graphical user interface --
cannot be done from within SQL.
A database programmer must have access to a general-purpose programming
language for at least two reasons

5.4
Edition
Accessing SQL from a Programming Language (Cont.)
 A general-purpose program -- can connect to and communicate with
a database server using a collection of functions
 Embedded SQL -- provides a means by which a program can interact
with a database server.
 The SQL statements are translated at compile time into function
calls.
 At runtime, these function calls connect to the database using an
API that provides dynamic SQL facilities.
There are two approaches to accessing SQL from a general-purpose
programming language

5.5
Edition
JDBC

5.6
Edition
JDBC
 JDBC is a Java API for communicating with database systems supporting
SQL.
 JDBC supports a variety of features for querying and updating data, and
for retrieving query results.
 JDBC also supports metadata retrieval, such as querying about relations
present in the database and the names and types of relation attributes.
 Model for communicating with the database:
 Open a connection
 Create a “statement” object
 Execute queries using the statement object to send queries and fetch
results
 Exception mechanism to handle errors

5.7
Edition
JDBC Code
public static void JDBCexample(String dbid, String userid, String passwd)
{
try (Connection conn = DriverManager.getConnection(
"jdbc:oracle:thin:@db.yale.edu:2000:univdb", userid, passwd);
Statement stmt = conn.createStatement();
)
{
… Do Actual Work ….
}
catch (SQLException sqle) {
System.out.println("SQLException : " + sqle);
}
}
NOTE: Above syntax works with Java 7, and JDBC 4 onwards.
Resources opened in “try (….)” syntax (“try with resources”) are
automatically closed at the end of the try block

5.8
Edition
JDBC Code for Older Versions of Java/JDBC
public static void JDBCexample(String dbid, String userid, String passwd)
{
try {
Class.forName ("oracle.jdbc.driver.OracleDriver");
Connection conn = DriverManager.getConnection(
"jdbc:oracle:thin:@db.yale.edu:2000:univdb", userid, passwd);
Statement stmt = conn.createStatement();
… Do Actual Work ….
stmt.close();
conn.close();
}
catch (SQLException sqle) {
System.out.println("SQLException : " + sqle);
}
}
NOTE: Class.forName is not required from JDBC 4 onwards. The try with
resources syntax in prev slide is preferred for Java 7 onwards.

5.9
Edition
JDBC Code (Cont.)
 Update to database
try {
stmt.executeUpdate(
"insert into instructor values('77987', 'Kim', 'Physics', 98000)");
} catch (SQLException sqle)
{
System.out.println("Could not insert tuple. " + sqle);
}
 Execute query and fetch and print results
ResultSet rset = stmt.executeQuery(
"select dept_name, avg (salary)
from instructor
group by dept_name");
while (rset.next()) {
System.out.println(rset.getString("dept_name") + " " +
rset.getFloat(2));
}

5.10
Edition
JDBC SUBSECTIONS
 Connecting to the Database
 Shipping SQL Statements to the Database System
 Exceptions and Resource Management
 Retrieving the Result of a Query
 Prepared Statements
 Callable Statements
 Metadata Features
 Other Features
 Database Access from Python

5.11
Edition
JDBC Code Details
 Getting result fields:
 rs.getString(“dept_name”) and rs.getString(1) equivalent if
dept_name is the first argument of select result.
 Dealing with Null values
int a = rs.getInt(“a”);
if (rs.wasNull()) Systems.out.println(“Got null value”);

5.12
Edition
Prepared Statement
 PreparedStatement pStmt = conn.prepareStatement(
"insert into instructor values(?,?,?,?)");
pStmt.setString(1, "88877");
pStmt.setString(2, "Perry");
pStmt.setString(3, "Finance");
pStmt.setInt(4, 125000);
pStmt.executeUpdate();
pStmt.setString(1, "88878");
pStmt.executeUpdate();
 WARNING: always use prepared statements when taking an input from the
user and adding it to a query
 NEVER create a query by concatenating strings
 "insert into instructor values(' " + ID + " ', ' " + name + " ', " + " ' + dept
name + " ', " ' balance + ')“
 What if name is “D'Souza”?

5.13
Edition
SQL Injection
 Suppose query is constructed using
 "select * from instructor where name = '" + name + "'"
 Suppose the user, instead of entering a name, enters:
 X' or 'Y' = 'Y
 then the resulting statement becomes:
 "select * from instructor where name = '" + "X' or 'Y' = 'Y" + "'"
 which is:
 select * from instructor where name = 'X' or 'Y' = 'Y'
 User could have even used
 X'; update instructor set salary = salary + 10000; --
 Prepared stament internally uses:
"select * from instructor where name = 'X' or 'Y' = 'Y'
 Always use prepared statements, with user inputs as parameters

5.14
Edition
Metadata Features
 ResultSet metadata
 E.g.after executing query to get a ResultSet rs:
 ResultSetMetaData rsmd = rs.getMetaData();
for(int i = 1; i <= rsmd.getColumnCount(); i++) {
System.out.println(rsmd.getColumnName(i));
System.out.println(rsmd.getColumnTypeName(i));
}
 How is this useful?

5.15
Edition
Metadata (Cont)
 Database metadata
 DatabaseMetaData dbmd = conn.getMetaData();
// Arguments to getColumns: Catalog, Schema-pattern, Table-pattern,
// and Column-Pattern
// Returns: One row for each column; row has a number of attributes
// such as COLUMN_NAME, TYPE_NAME
// The value null indicates all Catalogs/Schemas.
// The value “” indicates current catalog/schema
// The value “%” has the same meaning as SQL like clause
ResultSet rs = dbmd.getColumns(null, "univdb", "department", "%");
while( rs.next()) {
System.out.println(rs.getString("COLUMN_NAME"),
rs.getString("TYPE_NAME");
}
 And where is this useful?

5.16
Edition
Metadata (Cont)
 Database metadata
 DatabaseMetaData dbmd = conn.getMetaData();
// Arguments to getTables: Catalog, Schema-pattern, Table-pattern,
// and Table-Type
// Returns: One row for each table; row has a number of attributes
// such as TABLE_NAME, TABLE_CAT, TABLE_TYPE, ..
// The value null indicates all Catalogs/Schemas.
// The value “” indicates current catalog/schema
// The value “%” has the same meaning as SQL like clause
// The last attribute is an array of types of tables to return.
// TABLE means only regular tables
ResultSet rs = dbmd.getTables (“”, "", “%", new String[] {“TABLES”});
while( rs.next()) {
System.out.println(rs.getString(“TABLE_NAME“));
}
 And where is this useful?

5.17
Edition
Finding Primary Keys
 DatabaseMetaData dmd = connection.getMetaData();
// Arguments below are: Catalog, Schema, and Table
// The value “” for Catalog/Schema indicates current catalog/schema
// The value null indicates all catalogs/schemas
ResultSet rs = dmd.getPrimaryKeys(“”, “”, tableName);
while(rs.next()){
// KEY_SEQ indicates the position of the attribute in
// the primary key, which is required if a primary key has multiple
// attributes
System.out.println(rs.getString(“KEY_SEQ”),
rs.getString("COLUMN_NAME");
}

5.18
Edition
Transaction Control in JDBC
 By default, each SQL statement is treated as a separate transaction that
is committed automatically
 bad idea for transactions with multiple updates
 Can turn off automatic commit on a connection
 conn.setAutoCommit(false);
 Transactions must then be committed or rolled back explicitly
 conn.commit(); or
 conn.rollback();
 conn.setAutoCommit(true) turns on automatic commit.

5.19
Edition
Other JDBC Features
 Calling functions and procedures
 CallableStatement cStmt1 = conn.prepareCall("{? = call some
function(?)}");
 CallableStatement cStmt2 = conn.prepareCall("{call some
procedure(?,?)}");
 Handling large object types
 getBlob() and getClob() that are similar to the getString() method, but
return objects of type Blob and Clob, respectively
 get data from these objects by getBytes()
 associate an open stream with Java Blob or Clob object to update large
objects
 blob.setBlob(int parameterIndex, InputStream inputStream).

5.20
Edition
JDBC Resources
 JDBC Basics Tutorial
• https://docs.oracle.com/javase/tutorial/jdbc/index.html

5.21
Edition
SQLJ
 JDBC is overly dynamic, errors cannot be caught by compiler
 SQLJ: embedded SQL in Java
 #sql iterator deptInfoIter ( String dept name, int avgSal);
deptInfoIter iter = null;
#sql iter = { select dept_name, avg(salary) from instructor
group by dept name };
while (iter.next()) {
String deptName = iter.dept_name();
int avgSal = iter.avgSal();
System.out.println(deptName + " " + avgSal);
}
iter.close();

5.22
Edition
ODBC

5.23
Edition
ODBC
 Open DataBase Connectivity (ODBC) standard
 standard for application program to communicate with a database
server.
 application program interface (API) to
 open a connection with a database,
 send queries and updates,
 get back results.
 Applications such as GUI, spreadsheets, etc. can use ODBC

5.24
Edition
Embedded SQL
 The SQL standard defines embeddings of SQL in a variety of programming
languages such as C, C++, Java, Fortran, and PL/1,
 A language to which SQL queries are embedded is referred to as a host
language, and the SQL structures permitted in the host language comprise
embedded SQL.
 The basic form of these languages follows that of the System R embedding of
SQL into PL/1.
 EXEC SQL statement is used in the host language to identify embedded SQL
request to the preprocessor
EXEC SQL <embedded SQL statement >;
Note: this varies by language:
 In some languages, like COBOL, the semicolon is replaced with END-
EXEC
 In Java embedding uses # SQL { …. };

5.25
Edition
Embedded SQL (Cont.)
 Before executing any SQL statements, the program must first connect to
the database. This is done using:
EXEC-SQL connect to server user user-name using password;
Here, server identifies the server to which a connection is to be
established.
 Variables of the host language can be used within embedded SQL
statements. They are preceded by a colon (:) to distinguish from SQL
variables (e.g., :credit_amount )
 Variables used as above must be declared within DECLARE section, as
illustrated below. The syntax for declaring the variables, however, follows
the usual host language syntax.
EXEC-SQL BEGIN DECLARE SECTION}
int credit-amount ;
EXEC-SQL END DECLARE SECTION;

5.26
Edition
 To write an embedded SQL query, we use the
declare c cursor for <SQL query>
statement. The variable c is used to identify the query
 Example:
 From within a host language, find the ID and name of students who
have completed more than the number of credits stored in variable
credit_amount in the host langue
 Specify the query in SQL as follows:
EXEC SQL
declare c cursor for
select ID, name
from student
where tot_cred > :credit_amount
END_EXEC

5.27
Edition
 The open statement for our example is as follows:
EXEC SQL open c ;
This statement causes the database system to execute the query and to
save the results within a temporary relation. The query uses the value of
the host-language variable credit-amount at the time the open statement
is executed.
 The fetch statement causes the values of one tuple in the query result to
be placed on host language variables.
EXEC SQL fetch c into :si, :sn END_EXEC
Repeated calls to fetch get successive tuples in the query result

5.28
Edition
 A variable called SQLSTATE in the SQL communication area (SQLCA)
gets set to '02000' to indicate no more data is available
 The close statement causes the database system to delete the
temporary relation that holds the result of the query.
EXEC SQL close c ;
Note: above details vary with language. For example, the Java
embedding defines Java iterators to step through result tuples.

5.29
Edition
Updates Through Embedded SQL
 Embedded SQL expressions for database modification (update, insert,
and delete)
 Can update tuples fetched by cursor by declaring that the cursor is for
update
EXEC SQL
declare c cursor for
select *
from instructor
where dept_name = 'Music'
for update
 We then iterate through the tuples by performing fetch operations on the
cursor (as illustrated earlier), and after fetching each tuple we execute the
following code:
update instructor
set salary = salary + 1000
where current of c

5.30
Edition
Functions and Procedures

5.31
Edition
Functions and Procedures
 Functions and procedures allow “business logic” to be stored in the
database and executed from SQL statements.
 These can be defined either by the procedural component of SQL or by an
external programming language such as Java, C, or C++.
 The syntax we present here is defined by the SQL standard.
 Most databases implement nonstandard versions of this syntax.

5.32
Edition
Declaring SQL Functions
 Define a function that, given the name of a department, returns the count of
the number of instructors in that department.
create function dept_count (dept_name varchar(20))
returns integer
begin
declare d_count integer;
select count (* ) into d_count
from instructor
where instructor.dept_name = dept_name
return d_count;
end
 The function dept_count can be used to find the department names and
budget of all departments with more that 12 instructors.
select dept_name, budget
from department
where dept_count (dept_name ) > 12

5.33
Edition
Table Functions
 The SQL standard supports functions that can return tables as results; such
functions are called table functions
 Example: Return all instructors in a given department
create function instructor_of (dept_name char(20))
returns table (
ID varchar(5),
name varchar(20),
dept_name varchar(20),
salary numeric(8,2))
return table
(select ID, name, dept_name, salary
from instructor
where instructor.dept_name = instructor_of.dept_name)
 Usage
select *
from table (instructor_of ('Music'))

5.37
Edition
Language Constructs (Cont.)
 For loop
 Permits iteration over all results of a query
 Example: Find the budget of all departments
declare n integer default 0;
for r as
select budget from department
where dept_name = 'Music'
do
set n = n + r.budget
end for

5.40
Edition
External Language Routines
 SQL allows us to define functions in a programming language such as
Java, C#, C or C++.
 Can be more efficient than functions defined in SQL, and
computations that cannot be carried out in SQLcan be executed by
these functions.
 Declaring external language procedures and functions
create procedure dept_count_proc(in dept_name varchar(20),
out count integer)
language C
external name '/usr/avi/bin/dept_count_proc'
create function dept_count(dept_name varchar(20))
returns integer
language C
external name '/usr/avi/bin/dept_count'

5.42
Edition
Security with External Language Routines
 To deal with security problems, we can do on of the following:
 Use sandbox techniques
 That is, use a safe language like Java, which cannot be used to
access/damage other parts of the database code.
 Run external language functions/procedures in a separate process,
with no access to the database process’ memory.
 Parameters and results communicated via inter-process
communication
 Both have performance overheads
 Many database systems support both above approaches as well as direct
executing in database system address space.

5.43
Edition
Triggers

5.44
Edition
Triggers
 A trigger is a statement that is executed automatically by the system as a
side effect of a modification to the database.
 To design a trigger mechanism, we must:
 Specify the conditions under which the trigger is to be executed.
 Specify the actions to be taken when the trigger executes.
 Triggers introduced to SQL standard in SQL:1999, but supported even
earlier using non-standard syntax by most databases.
 Syntax illustrated here may not work exactly on your database system;
check the system manuals

5.46
Edition
Trigger to Maintain credits_earned value
 create trigger credits_earned after update of takes on (grade)
referencing new row as nrow
referencing old row as orow
for each row
when nrow.grade <> 'F' and nrow.grade is not null
and (orow.grade = 'F' or orow.grade is null)
begin atomic
update student
set tot_cred= tot_cred +
(select credits
from course
where course.course_id= nrow.course_id)
where student.id = nrow.id;
end;

5.47
Edition
Statement Level Triggers
 Instead of executing a separate action for each affected row, a single
action can be executed for all rows affected by a transaction
 Use for each statement instead of for each row
 Use referencing old table or referencing new table to refer to
temporary tables (called transition tables) containing the affected
rows
 Can be more efficient when dealing with SQL statements that update a
large number of rows

5.48
Edition
When Not To Use Triggers
 Triggers were used earlier for tasks such as
 Maintaining summary data (e.g., total salary of each department)
 Replicating databases by recording changes to special relations
(called change or delta relations) and having a separate process that
applies the changes over to a replica
 There are better ways of doing these now:
 Databases today provide built in materialized view facilities to maintain
summary data
 Databases provide built-in support for replication
 Encapsulation facilities can be used instead of triggers in many cases
 Define methods to update fields
 Carry out actions as part of the update methods instead of
through a trigger

5.49
Edition
When Not To Use Triggers (Cont.)
 Risk of unintended execution of triggers, for example, when
 Loading data from a backup copy
 Replicating updates at a remote site
 Trigger execution can be disabled before such actions.
 Other risks with triggers:
 Error leading to failure of critical transactions that set off the trigger
 Cascading execution

5.50
Edition
Recursive Queries

5.51
Edition
Recursion in SQL
 SQL:1999 permits recursive view definition
 Example: find which courses are a prerequisite, whether directly or
indirectly, for a specific course
with recursive rec_prereq(course_id, prereq_id) as (
select course_id, prereq_id
from prereq
union
select rec_prereq.course_id, prereq.prereq_id,
from rec_rereq, prereq
where rec_prereq.prereq_id = prereq.course_id
)
select ∗
from rec_prereq;
This example view, rec_prereq, is called the transitive closure of the prereq
relation

5.52
Edition
The Power of Recursion
 Recursive views make it possible to write queries, such as transitive
closure queries, that cannot be written without recursion or iteration.
 Intuition: Without recursion, a non-recursive non-iterative program
can perform only a fixed number of joins of prereq with itself
 This can give only a fixed number of levels of managers
 Given a fixed non-recursive query, we can construct a database
with a greater number of levels of prerequisites on which the query
will not work
 Alternative: write a procedure to iterate as many times as required
– See procedure findAllPrereqs in book

5.54
Edition
Example of Fixed-Point Computation

5.55
Edition
Advanced Aggregation Features

5.56
Edition
Ranking
 Ranking is done in conjunction with an order by specification.
 Suppose we are given a relation
student_grades(ID, GPA)
giving the grade-point average of each student
 Find the rank of each student.
 select ID, rank() over (order by GPA desc) as s_rank
from student_grades
 An extra order by clause is needed to get them in sorted order
select ID, rank() over (order by GPA desc) as s_rank
from student_grades
order by s_rank
 Ranking may leave gaps: e.g. if 2 students have the same top GPA, both have
rank 1, and the next rank is 3
• dense_rank does not leave gaps, so next dense rank would be 2

5.57
Edition
Ranking
 Ranking can be done using basic SQL aggregation, but resultant query is
very inefficient
select ID, (1 + (select count(*)
from student_grades B
where B.GPA > A.GPA)) as s_rank
from student_grades A
order by s_rank;

5.58
Edition
Ranking (Cont.)
 Ranking can be done within partition of the data.
 “Find the rank of students within each department.”
select ID, dept_name,
rank () over (partition by dept_name order by GPA desc)
as dept_rank
from dept_grades
order by dept_name, dept_rank;
 Multiple rank clauses can occur in a single select clause.
 Ranking is done after applying group by clause/aggregation
 Can be used to find top-n results
 More general than the limit n clause supported by many databases,
since it allows top-n within each partition

5.59
Edition
Ranking (Cont.)
 Other ranking functions:
• percent_rank (within partition, if partitioning is done)
• cume_dist (cumulative distribution)
 fraction of tuples with preceding values
• row_number (non-deterministic in presence of duplicates)
 SQL:1999 permits the user to specify nulls first or nulls last
select ID,
rank ( ) over (order by GPA desc nulls last) as s_rank
from student_grades

5.60
Edition
Ranking (Cont.)
 For a given constant n, the ranking the function ntile(n) takes the tuples in
each partition in the specified order, and divides them into n buckets with
equal numbers of tuples.
 E.g.,
select ID, ntile(4) over (order by GPA desc) as quartile
from student_grades;

5.61
Edition
Windowing
 Used to smooth out random variations.
 E.g., moving average: “Given sales values for each date, calculate for
each date the average of the sales on that day, the previous day, and the
next day”
 Window specification in SQL:
• Given relation sales(date, value)
select date, sum(value) over
(order by date between rows 1 preceding and 1 following)
from sales

5.62
Edition
Windowing
 Examples of other window specifications:
• between rows unbounded preceding and current
• rows unbounded preceding
• range between 10 preceding and current row
 All rows with values between current row value –10 to current value
• range interval 10 day preceding
 Not including current row

5.63
Edition
Windowing (Cont.)
 Can do windowing within partitions
 E.g., Given a relation transaction (account_number, date_time, value),
where value is positive for a deposit and negative for a withdrawal
• “Find total balance of each account after each transaction on the
account”
select account_number, date_time,
sum (value) over
(partition by account_number
order by date_time
rows unbounded preceding)
as balance
from transaction
order by account_number, date_time

5.64
Edition
OLAP

5.65
Edition
Data Analysis and OLAP
 Online Analytical Processing (OLAP)
• Interactive analysis of data, allowing data to be summarized and
viewed in different ways in an online fashion (with negligible delay)
 Data that can be modeled as dimension attributes and measure attributes
are called multidimensional data.
• Measure attributes
 measure some value
 can be aggregated upon
 e.g., the attribute number of the sales relation
• Dimension attributes
 define the dimensions on which measure attributes (or aggregates
thereof) are viewed
 e.g., attributes item_name, color, and size of the sales relation

5.66
Edition
Example sales relation
...
...
...
...
...
...
...
...

5.67
Edition
Cross Tabulation of sales by item_name and color
 The table above is an example of a cross-tabulation (cross-tab), also
referred to as a pivot-table.
• Values for one of the dimension attributes form the row headers
• Values for another dimension attribute form the column headers
• Other dimension attributes are listed on top
• Values in individual cells are (aggregates of) the values of the
dimension attributes that specify the cell.

5.68
Edition
Data Cube
 A data cube is a multidimensional generalization of a cross-tab
 Can have n dimensions; we show 3 below
 Cross-tabs can be used as views on a data cube

5.70
Edition
Cross Tabulation With Hierarchy
 Cross-tabs can be easily extended to deal with hierarchies
• Can drill down or roll up on a hierarchy

5.71
Edition
Relational Representation of Cross-tabs
 Cross-tabs can be represented
as relations
• We use the value all is used
to represent aggregates.
• The SQL standard actually
uses null values in place of
all despite confusion with
regular null values.

5.72
Edition
Extended Aggregation to Support OLAP
 The cube operation computes union of group by’s on every subset of the
specified attributes
 Example relation for this section
sales(item_name, color, clothes_size, quantity)
 E.g., consider the query
select item_name, color, size, sum(number)
from sales
group by cube(item_name, color, size)
This computes the union of eight different groupings of the sales relation:
{ (item_name, color, size), (item_name, color),
(item_name, size), (color, size),
(item_name), (color),
(size), ( ) }
where ( ) denotes an empty group by list.
 For each grouping, the result contains the null value
for attributes not present in the grouping.

5.73
Edition
Online Analytical Processing Operations
 Relational representation of cross-tab that we saw earlier, but with null in
place of all, can be computed by
 select item_name, color, sum(number)
from sales
group by cube(item_name, color)
 The function grouping() can be applied on an attribute
• Returns 1 if the value is a null value representing all, and returns 0 in all
other cases.
select item_name, color, size, sum(number),
grouping(item_name) as item_name_flag,
grouping(color) as color_flag,
grouping(size) as size_flag,
from sales
group by cube(item_name, color, size)

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 Can use the function decode() in the select clause to replace
such nulls by a value such as all
• E.g., replace item_name in first query by
decode( grouping(item_name), 1, ‘all’, item_name)

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Extended Aggregation (Cont.)
 The rollup construct generates union on every prefix of specified list of
attributes
 E.g.,
from sales
group by rollup(item_name, color, size)
• Generates union of four groupings:
{ (item_name, color, size), (item_name, color), (item_name), ( ) }
 Rollup can be used to generate aggregates at multiple levels of a
hierarchy.
 E.g., suppose table itemcategory(item_name, category) gives the category of
each item. Then
select category, item_name, sum(number)
from sales, itemcategory
where sales.item_name = itemcategory.item_name
group by rollup(category, item_name)
would give a hierarchical summary by item_name and by category.

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Extended Aggregation (Cont.)
 Multiple rollups and cubes can be used in a single group by clause
• Each generates set of group by lists, cross product of sets gives overall
set of group by lists
 E.g.,
from sales
group by rollup(item_name), rollup(color, size)
generates the groupings
{item_name, ()} X {(color, size), (color), ()}
= { (item_name, color, size), (item_name, color), (item_name),
(color, size), (color), ( ) }

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 Pivoting: changing the dimensions used in a cross-tab is called
 Slicing: creating a cross-tab for fixed values only
• Sometimes called dicing, particularly when values for multiple
dimensions are fixed.
 Rollup: moving from finer-granularity data to a coarser granularity
 Drill down: The opposite operation - that of moving from coarser-
granularity data to finer-granularity data

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OLAP Implementation
 The earliest OLAP systems used multidimensional arrays in memory to
store data cubes, and are referred to as multidimensional OLAP (MOLAP)
systems.
 OLAP implementations using only relational database features are called
relational OLAP (ROLAP) systems
 Hybrid systems, which store some summaries in memory and store the
base data and other summaries in a relational database, are called hybrid
OLAP (HOLAP) systems.

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OLAP Implementation (Cont.)
 Early OLAP systems precomputed all possible aggregates in order to
provide online response
 Space and time requirements for doing so can be very high
 2n
combinations of group by
 It suffices to precompute some aggregates, and compute others on
demand from one of the precomputed aggregates
 Can compute aggregate on (item_name, color) from an aggregate
on (item_name, color, size)
– For all but a few “non-decomposable” aggregates such as
median
– is cheaper than computing it from scratch
 Several optimizations available for computing multiple aggregates
 Can compute aggregate on (item_name, color) from an aggregate on
(item_name, color, size)
 Can compute aggregates on (item_name, color, size),
(item_name, color) and (item_name) using a single sorting
of the base data

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End of Chapter 5

ch5 - Copy.pptx advanced structure query language

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ch5 - Copy.pptx advanced structure query language