Database overview

At a Glance
• Terminology and Characteristics of Database
• Introduction to RDBMS
• Types of DBMS
• Database Security and Recovery
• Data Mining, Data Warehousing & Data Marts
• SQL Overview
• JDBC
• Indexes
• Clustered & Non-Clustered Indexes
• Failure Management with DB2 Cluster Services
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Database Terminology (1/7)
• Data vs. Information
– Data are chunks of information about a particular system.
• E.g. Normal temperature of human body is 98.4°F and No. of
subjects studied by a student are data.
– Data by itself does not have any meaning (Raw data).
– Information is the interpretation and understanding of data.
• Data Handling
– Process to obtain facts include:
• Data Collection + Data Organization + Data Analysis + Depiction
of facts (graphs, charts, etc.)
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• Data Organization
– Method of classifying and organizing data sets to make them
more useful.
• Need of Organization – Case Study
– Consider the marks scored by 25 students out of 50 in a
Computer exam are arranged as follows:
26, 15, 40, 18, 26, 24, 48, 40, 39, 26, 23, 37, 38, 40, 45, 48.
– Discussion:
• The above form of data representation becomes confusing if the
number of students increases to 1000.
• If the above data can be organized in form of a table, then this
form of representation is easy to interpret and to analyze the
data (E.g. Finding students with >90% marks).
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• Need of Organization (contd.)
– Volume of data increases with time.
– Data organization may initially take some time. But once data
is organized, in future it would help us to search and retrieve
the required information in minimal time.
– Data organization can be of various types, depending on the
requirement of the user. For example:
• The repeated values in the data are collected together to know
the mode of the data.
• The data is organized in increasing or decreasing order, to find
the median of the given set of data.
– Organizing data would reduce data loss and errors.
– Helps us understand the purpose of data collection and
provides insight about the proper use of it.
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• Methods of Data Organization (1/3)
– Characters
• Basic elements/symbols (Digit, Letter, special character) of data.
– Fields
• Data Items (Attributes of an Entity) composed with characters or
related words.
• Natural and Artificial (fact-less) Attributes
– Record
• Collection of fields (tuple) related to an entity.
– Database File or Table (relations)
• Collection of related records.
• Classification
– Based on purpose or intended application. E.g. Inventory File.
– Based on Degree of permanence (Temporary/Permanent).
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– Key
• Unique Identifier to a particular record on each table. Such keys
are called primary keys.
• Primary key should always have a constant value. Number types
are preferred over text type.
– It is better to have artificial attribute serve as primary key (surrogate
key) rather than natural attribute.
• A composite primary key is a key made up of two or more
attributes
• Primary keys within a database are used to define relationships
among the tables. Rows in a table can be linked to rows in other
tables by adding a column for the unique key of the linked row.
Such columns are known as foreign keys (A field in a relational
table that matches the primary key column of another table).
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• “Person” table with primary key “PersonID”
• “Order” table with primary key “OrderID” and foreign key
“PersonID"
PersonID LastName FirstName Age
1 Hansen Ola 30
2 Svendson Tod 23
3 Pettersen Kat 20
OrderID OrderNumber PersonID
1 77895 3
2 44678 3
3 22456 2
4 24562 1
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– Database
• Collection of related files/tables that are consolidated, organized,
linked or cross-referenced to one another and stored together.
• Databases are usually customized to suit a particular application.
No two customized applications are alike, and hence, no two
database are alike.
• Computerized databases are more useful than manila folders and
filing cabinets.
– Database Management System (DBMS)
• Specialized database management software or Database
manager used to organize and retrieve data and records
contained in database files.
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Characteristics of Databases (1/2)
• Small vs. Large Databases
– Small Databases are stored in a desktop computer.
• DBMS used:
– Microsoft Access, Paradox, dBASE 5
– Large Databases are specialized public databases stored on
network servers or powerful mainframe computers.
• Provides online access (Information Utilities or Online services)
for a fee
• DBMS used:
– Oracle RDBMS, Microsoft SQL Server, IBM DB2, Teradata (VLDB),
SAP ASE (Sybase), PostgreSQL (ORDBMS), MariaDB Enterprise,
MySQL, MongoDB (NoSQL)
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Characteristics of Databases (2/2)
• Database Access
• Database Attributes for Effective use
– Keep some tables separate?
• More efficient & effective search/retrieval from smaller data sets.
• Only the required data sets can be accessible to certain groups of
people to maintain some degree of security and personal privacy.
• Multiple Sources
– Minimal redundancy between the files in a database.
– Linking together data from multiple sources to accomplish a
specific task.
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Fundamentals (1/3)
• Computerized File Management systems (File Managers) are
not considered as true DBMS.
– Files are independent & cannot be linked.
– Used for storing information in files.
– E.g. Mailing List, Address Book
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Fundamentals (2/3)
• Advantages
– Improved availability to different users
– Minimized redundancy and more concise
• Contrast: Data fields
– Accuracy
• Integrity => Accurate + Consistent + Up-to-date
– Program and File Consistency
• Easier to maintain multiple types of data
• Easier to manage data, even with multiple programmers
– User-friendly
• Ease to access & manipulate
– Improved security
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Fundamentals (2/3)
• Disadvantages
– Cost
• Expensive & Time consuming – Includes implementation &
training requirements.
– Security
• Degree if breach & Threat to privacy
• Need of Backups
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Database Models (1/7)
• Hierarchical Database Model (1/3)
– Developed by IBM in 1960 as a Tree-like structure commonly
used on mainframe computers.
– Schema has single root & hence functions top to bottom.
– Each record has one parent record and many children
– Links (from child) are used to connect the Records & the
relationships between Records should be defined prior
• Parent-Child Relationships are of type 1:N. For relationships of
type M:N duplication of child record is required.
• Higher & Lower level records, Parent/Root & Child records
– Examples
• IBM Information Management System (IMS), MUMPS
– Applications
• File Systems, GIS, Banking and Telecommunication
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• Hierarchical Database Model (2/3)
– Example for Hierarchical Data represented as Relational tables
– Advantages: High performance.
– Disadvantages:
• Relationships/linkages between children are not permitted
• Rigid/Inflexible Design
• Designed in advance
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Hierarchical Schema
Hierarchical Occurrence Tree
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• Network Database Model (1/2)
– Invented by Charles Bachman, and it was developed into a
standard specification published in 1969 by the Conference on
Data Systems Languages (CODASYL) Consortium
– Has Hierarchical generalized graph structure with few
variations used on mainframe computers
• Depicted as interconnected network of records.
• Each record can have multiple parents (owners) and child records
(Members)
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• Network Database Model (2/2)
– Requires user to pass down through a hierarchy in order to
access data.
– Example
• Integrated Data Store (IDS), Integrated DBMS, TurboIMAGE
– Advantage: Flexible
– Disadvantages
• Limited no. of connections between records.
• Designed in advance.
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• Relational Database Model
– 1970, Invented by Edgar Frank Codd (of IBM).
– Consists of cross-linked (relational) files/tables.
• Use of common data elements or key field.
– Example
• IBM DB2, IBM Informix, Oracle, Microsoft SQL Server.
– Advantage
• More Flexible thane Network and Hierarchical Models.
• Can be used with little/no training.
• Database entries can be modified without redefining the entire
structures.
– Disadvantage: Searching can take more time comparatively.
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• Object Oriented Database Model
– Combines database capabilities with OOP language capability
by representing information as Objects, which consist of data
& instructions to handle data (methods).
– Used to handle structured data and new multimedia data
types (Audio, Video, Pictures, etc.).
– Disadvantages
• Costly to develop, Migration to OODBMS is mostly debated.
– Application: Healthcare
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Database Security & Recovery
• Database Security
– Protection from unauthorized use and sabotage.
– Degree of Access
• Privileges to view, retrieve, and modify.
– Physical Protection
– Threats to data security
• Human error, hardware/software/DB failure, theft and fraud, loss
of personal/corporate data, loss of data/corporate integrity, loss
of availability, loss of backups, etc.
• Data Recovery
– Backup & Recovery includes
• Journalizing facility, DB change logs, Transactions logs,
checkpoint facility, etc.
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Strategies of Data Recovery
• Mirroring
– Making frequent simultaneous copies of a DB in different
locations.
– Expensive
– Used by Ticketing systems
• Reprocessing
– Reverting back to a known point of DB activity prior to
failure/problem
– Time consuming & incur delay till DB is made available.
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Data Warehousing (1/5)
• Conceptualized by Bill Inmon, 1990
• Data repository which mostly includes historical data and
data from several diverse sources.
• Organizations mostly have Evaluational & Operational
databases.
– OLAP tools can be used to provide business intelligence
(analyze business) and OLTP tools provide data entry and
retrieval transaction processing (run business).
• Features of Data warehouses
– Constructed by integrating data from heterogenous sources
– Provides information of a particular time period.
– Non-volatile and separated from operational DB.
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• Like a database a data warehouse also requires to maintain a
logical description (Schema)
– Operational databases is based on ER Model
– Data warehouse (Evaluational database) is based on Schema
– Types of Schema
• Star Schema
• Snowflake Schema
• Fact constellation or Galaxy Schema
– Data warehouse Schema can be defined using DMQL.
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• Example: Star Schema
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• Example: Snowflake Schema
– Redundancy is reduced by splitting the dimension tables.
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• Example: Fact Constellation or Galaxy Schema
– Consists of more number of Fact tables.
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Data Mining
• Patented by HNC Software Inc., San Diago in 1980s
• Knowledge Discovery from a Data warehouse
– Finding patterns, trends & correlations ease understanding and
decision making.
– Knowledge can be used for prediction (as in Businesses).
• Applications (Discussion)
– Online and Retail store
– Telecommunication
– Hospitals
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Data Marts (1/5)
• Groups related data sets for specific sets of customers.
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Data Marts (2/5)
• Why Data Mart?
– Maintain temporary copy of data from few fact tables which
does not change frequently for analysis. It is discarded after
analysis.
– Decrease load on a Data warehouse by making a copy of fact
tables in separate server. Refreshed periodically.
– Simulation of new scenarios by changing data.
• Data in a Data warehouse cannot be changed without affecting
performance.
• Star schema offers ease of design
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Data Marts (3/5)
• Type-1: Dependent data mart
– Data from Data warehouse are
aggregated, restructured &
summarized with Enterprise context.
– Built to achieve improved
performance and availability, better
control, & lower telecommunication
costs resulting from local access of
data relevant to a specific
department.
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Data Marts (4/5)
• Type-2: Independent data mart
– No centralized data warehouse.
– Number of sources are few.
– All aspects of the ETT process
(Extract, transform, transfer) should
be dealt.
– Preferred when a solution is required
within a shorter time.
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Data Marts (5/5)
• Type-3: Hybrid data mart
– Combine input from sources
other than a data warehouse
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Introduction (1/5)
• Developed by Donald D. Chamberlin & Raymond Boyce in
IBM, early 1970s.
• SQL (earlier SEQUEL) is a high level language which provides
practical implementation of Codd’s relational model.
– i.e. Store, manipulate and retrieval of data stored in a
relational database.
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Introduction (2/5)
• ANSI standard (1986) & ISO standard (1987).
• Since standardization SQL standards have been updated 6
times. Version 5.1 is the latest update in Nov. 2008.
• Note:
– 1979, Larry Ellison, Bob Miner and Ed Oates developed SQL-
based RDBMS named Oracle in Relational Software Inc. (now
Oracle Corp.).
• Versions: Oracle 12c/11g/10g/9i/8i, etc.
– Versions and Editions of MS SQL Server.
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Introduction (3/5)
• SQL Architecture
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Introduction (4/5)
• SQL Commands can be classified into 4 groups based on
functionality
– DDL: Deals with schemas and descriptions of how data should
reside in a DB.
• E.g. Create, Alter, Drop, Truncate, Rename, etc.
– DML: Deals with reading and modifying of data.
• E.g. Select, Insert, Update, Delete, etc.
– DCL: Concerned with rights, permissions and other controls.
• E.g. Grant, Revoke
– DCL: Deals with transactions within a database.
• E.g. Commit, Rollback, Savepoint, etc.
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Introduction (5/5)
• Database Normalization
– Efficiently organizing data in DB without redundancy and with
required data dependencies.
– Consists of series of guidelines (known as forms). i.e. 1NF, 2NF,
etc.
• SQL-RDMS Concepts
– Table
– Field, Column and Records.
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SQL Data Types (1/7)
• Specifies the type of data of any object.
• Categories
– Exact and Approximate Numeric
– Date and Time
– Unicode and Non-Unicode Character Strings
– Binary
– Miscellaneous
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• Exact Numeric Data Types
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• Approximate Numeric Data Type
• Date and Time Data Type
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• Character String Data Type
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• Unicode Character String Data Type
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• Binary Data Type
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• Miscellaneous Data Type
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SQL Operators (1/5)
• Operators
– Reserved word/character that specify conditions or serve as
conjunctions to multiple conditions in a SQL statement's
WHERE clause to perform operation(s).
– Categories
• Arithmetic Operators & Comparison Operators
• Logical Operators: ALL, AND, ANY, BETWEEN, EXISTS, IN, LIKE,
NOT, OR, IS NULL, UNIQUE, Negation
• String Manipulation: CONCATENATE, LENGTH, SUBSTRING
• Grouping: COUNT
– Can be used with GROUP BY statement.
– Performs on 2/more rows and provides a summarized result set.
• Aggregate: COUNT, SUM, AVERAGE, MINIMUM, MAXIMUM.
– Can be used with GROUP BY statement.
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SQL Operators (2/5)
• Wild Card operators
– Percentage (%) - 0/1/more characters
– Underscore ( _ ) represents single number/character.
• Miscellaneous operators: DISTINCT
• SQL Expressions
– Combination of one or more values, operators or SQL
functions that evaluate to a value.
– Types: Boolean (Single value matching), Numeric, Date
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SQL Operators (3/5)
• Union Operator
– Join two data sets having same column definitions in the same
order. Removes duplicate rows from resulting data set.
– UNION ALL can be used to return all data sets with duplicates
in the result set.
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SQL Operators (4/5)
• Intersection (INTERSECT) Operator
– Returns a result set common to both data sets. Common data
sets are listed only once even if there are multiple duplicate
rows in either of the table.
– INTERSECT ALL can be used to return all data sets with
duplicates in the result set.
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• Difference (EXCEPT) Operator
– Returns the result set that exists only in the left table but not
in right table. Common data sets are listed only once even if
there are multiple duplicate rows in left table.
– EXCEPT ALL can be used to return all data sets with duplicates
in the result set.
SQL Operators (5/5)
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SQL Syntax
• SQL statements
– Start with keyword and End with semi-colon.
– Not case-sensitive.
• Clauses
– Distinct, where, and/or, in, between, like, order by, group by,
count, having
• Refer the Word Doc for the Discussion on SQL commands
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SQL Constraints (1/5)
• Constraints enforce certain rules to limit the type of data
inserted into a table so as to ensure accuracy and reliability
of data in DB.
– Column level Constraints & Table level Constraints.
• Constraints can be specified during table creation and can be
altered after creation.
• DEFAULT constraint
– Auto-repeat the same specified default value for all records in
a column.
– Used to insert system values, by using functions like GETDATE()
• Auto-increment fields
– IDENTITY property auto generates values for all records in a
column. These values are not guaranteed to be unique.
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• NOT NULL constraint
– A field which can be left blank during record creation is
considered to take a NULL value (default).
– In certain cases tables are created with mandatory (NOT NULL)
fields which should take a value while record creation or
updation.
• CHECK constraint
– Ensures that the concerned field can take only those values
which satisfy the rules specified.
– New constraint can be defined to evaluate values of multiple
columns.
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• UNIQUE constraint
– Ensures that all values in a column(s) are unique/different (no
repetition).
– NULL is also a part of UNIQUE data values domain.
• PRIMARY KEY constraint
– Can take unique values and cannot take NULL value.
– Has an index associated with it, using which, it uniquely
identifies each record in a table.
– A new Primary key constraint can be defined using one or
multiple columns.
– Automatically has a UNIQUE constraint.
• Note: A table can have multiple UNIQUE constraints, but
only one PRIMARY KEY constraint.
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• FOREIGN Key constraint
– Foreign key is a field(s) in one table (i.e. child) that refers to
the primary key in another table (i.e parent).
– Used to link two tables together.
– Handling deletion and updation on linked tables.
• Options
– Delete Action: Cascade, Set null, No Action, Restrict
– Update Action: No Action, Restrict
• CASCADE
– Operation is cascaded to all rows in the child tables that reference
the row/value to be modified/deleted in parent table.
• SET NULL
– All referring cells in child tables are set to NULL.
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• INDEX constraint
– Column(s) of a table can be indexed for fast data retrieval and
speedy searches.
• Create indexes on column(s) which are frequently searched as
updating a table with indexes takes more time than updating a
table without indexes (because the indexes also need an update).
– Indexes can be made unique (with UNIQUE constraint).
– Indexes are invisible to the users.
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Sub-Queries (1/2)
• Queries can be nested.
– Outer query is referred as Main/Parent Query & the inner
query is referred as Sub-Query.
• Sub-Queries are executed first.
• Correlated sub-query
– Sub-Query is executed for each row of parent table instead of
once.
• Sub-Query can also be used in FROM clause.
• Sub-Queries may return Scalar value
– Returns single value of any attribute.
– Example
• SQL> SELECT name FROM student_enrollment WHERE age =
(SELECT min(age) FROM students);
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Sub-Queries (2/2)
• Sub-Queries may return Vector values
– Returns data set representing multiple values for a column or
array of values for multiple columns.
– Example
• SQL> SELECT name FROM students WHERE course_enrolled IN
(SELECT distinct course_name FROM courses WHERE
department_name = ‘CSE’);
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Views & Cursors
• Views
– A view is a virtual table (contains rows and columns) based on
the result-set of an SQL statement.
• The fields in a view are fields from one/more real tables in DB.
• Cursors
– It is a result set holding the result of a SELECT statement
– Widely used for fetching multiple rows from a table and
processing them inside applications.
– Syntax
– DECLARE <cursor_name> CURSOR [WITH RETURN <return target>]
<SELECT statement>;
– OPEN <cursor_name>;
– FETCH <cursor_name> INTO <variables>;
– CLOSE <cursor_name>;
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Table Joins (1/6)
• JOIN clause combines rows from 2 or more tables based on a
related column between them.
• Types of Join
– (INNER) JOIN: Returns records that have matching values in
both tables
– LEFT (OUTER) JOIN: Return all records from the left table, and
the matched records from the right table
– RIGHT (OUTER) JOIN: Return all records from the right table,
and the matched records from the left table
– FULL (OUTER) JOIN: Return all records when there is a match in
either left or right table
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Table Joins (2/6)
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Table Joins (3/6)
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Table Joins (4/6)
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Table Joins (5/6)
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Table Joins (6/6)
• Types of Inner Join
– Equi-join
• Based on equality of specified columns.
– Natural join
• Joining column does not require specification. System
automatically selects the column with same name in tables and
applies equality operation on it.
• Removes all duplicate attributes
– Cross join
• Cartesian product of tables to be joined.
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Mapping of OO & Relational concepts
• Examples
– Hibernate
• Provides ORM (Object-Relational Mapping) libraries for Java.
– IBM pureQuery
• Eclipse IDE based, pluggable component that provide the design
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What is JDBC?
• Std. Java API for DB independent connectivity between Java
based Executables & wide range of Databases.
– Executables include Applications, Applets, Servlets, JSPs, EJBs
• JDBC library includes APIs for the tasks associated with DB
usage
– DB connectivity
– Creating SQL/MySQL statements
– Execution of Queries in DB
– View & Modify resulting records
• Provides same capability as ODBC allowing Java programs to
contain DB-independent code.
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JDBC Architecture
• Supports both 2-tier & 3-tier processing
models. In general has 2 layers
– JDBC API
– JDBC Driver API
• Supports multiple concurrent drivers
connected to heterogeneous DBs.
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JDBC Components
• JDBC API provides following interfaces and classes
– DriverManager (Class)
• Manages list of DB drivers & provides DB connection
– Driver (Interface)
• Handles communication with DB Server. Driver objects are rarely
used directly. Instead DriverManager objects are used.
– Connection (Interface)
• Consists of methods for using the DB. These are accessed using
Connection object.
– Statement (Interface)
• Statement objects are used to submit Queries to DB.
– ResultSet (Object)
• Object holds the data retrieved from DB
– SQLException (Class)
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Database APIs (1/4)
• Set of APIs that gives application developers a mechanism to
interact with DB from within the application dynamically.
• DB vendors provide different APIs wrt Languages.
• APIs are developed independent of DB and do not require
pre-compilation.
– Applications can be developed independently of target DBs
without need of DB connection at compilation phase.
• DB Connectivity Driver
– Serves as intermediate layer between Application & DB server.
– Driver libraries are linked with source code libraries, code is
compiled and later executed.
– E.g. ODBC & IBM Data Server CLI Driver
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Database APIs (2/4)
• Callable SQL Interface
– DB vendors could provide connectivity drivers which are not
compatible with other DBs.
– Some level of standardization is required for increased
application portability.
– Few Standard Callable SQL Interface
• X/Open CLI, ODBC, IBM Data Server CLI Driver
• X/Open CLI (Call Level Interface)
– Developed by X/Open Company & SQL Access Group
– ISO CLI International Standard (ISO/IEC 9075-3: 1995 SQL/CLI)
• ODBC
– Developed by Microsoft based on preliminary draft of X/Open
CLI.
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Database APIs (3/4)
• IBM Data Server CLI Driver
– DB2 CLI based on Microsoft ODBC spec. & SQL/CLI International
standard which includes DB2 specific extensions to exploit DB2
features.
– Conforms to ODBC 3.51 standard.
• i.e. Works equivalent to ODBC driver when loaded by an ODBC
Driver Manager.
– DB2 CLI is a C/C++ API for Relational DB access which uses fn.
call to pass dynamic SQL statements as function arguments.
• E.g. Callable Interfaces include SQLPrepare(), SQLConnect(),
SQLFetch(), SQLExecute()
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Database APIs (4/4)
• Environments of Microsoft ODBC & IBM DB2 CLI
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What is Index? (1/2)
• An Index is a set of pointers logically ordered by the value of
one/more keys.
– Columns in an index are known as keys.
– Keys can be unique or non-unique. But a table should have at
least one unique key.
• Why to Index?
– Improve Performance with quick data access.
– Ensure Uniqueness in identifying rows (records).
– Sequencing rows according to the values in column.
• Index can refers to rows in a table, block in a Cluster table,
XML data in an XML storage object
– Cluster table indexes & XML indexes are system generated.
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What is Index? (2/2)
• Relationship between an index and a table
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Database partitioning (1/3)
• Relationships among selected database objects
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• DB partition group
– Set of one/more DB partitions that belong to a database.
• Table spaces (1/2)
– Specifies where data in a DB is physically stored on a system.
• Data includes tables, Indexes, Large objects (LOB: 8-128TB)
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• Table spaces (2/2)
– Defines how data for a table is distributed across DB partitions
in a DB partition group.
– Single table can span several containers (disk).
– DB manager balances the data load across containers.
• Extent Size: No. of pages that DB manager writes to a container
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Types of Indexes (1/3)
• Indexes on Partitioned Tables within a database partition
– Indexes are non-partitioned & exists in a single table space.
– Indexes are partitioned & exists across one/more table spaces.
– Combination of the above.
• Commands to handle Indexes (Create/Modify/Remove)
– CREATE INDEX statement, ALTER INDEX command, DROP
statement, IBM db2advis Design Advisor commands.
• Classification of Index Architecture
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Index
Architecture
Clustered
Index
Architecture
Non-Clustered
Index
Architecture
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• Clustered Indexes
– Defines the order in which data is physically stored in a table.
– There can be only one clustered index per table.
• In SQL Server, the primary key constraint automatically creates a
clustered index on that particular column in ascending order.
• Only one index in table has high degree of clustering.
– Data Manager keeps the data in data pages in the same order
as the corresponding keys in index page.
– Reorganizing a table with a chosen index will re-cluster data.
– Clustered index allows better sequential access of data as rows
with similar index key values are stored together.
– Statements
• CREATE INDEX statement with CLUSTER option specified.
• ALTER TABLE statement – add primary key that corresponds to
clustering index.
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• Non-Clustered Indexes
– Resembles the words index at the back of a book. i.e.
Structured in a way that does not correspond to the order of
the actual data records.
– Many non-clustered indexes can exist per table.
– Data Manager does not maintain any order of the data when
compared to the order of the corresponding keys.
– Non-clustered indexes are slower than clustered indexes.
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DB2® cluster services
• Provides auto heartbeat failure detection & auto initiation of
recovery options after a failure is detected.
• Provides cluster file system that provides each host in DB2
pureScale® instance access to a common file system.
• Includes technology from following IBM® softwares:
– Tivoli® System Automation for Multi-platforms (Tivoli SA MP)
– Reliable Scalable Clustering Technology (RSCT)
– General Parallel File System (GPFS ™) Software
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Failure Management
• Automated cluster caching facility (CF) failover
– When Primary cluster CF fails, DB2 cluster services
automatically attempts to restart it while Secondary cluster CF
takes over.
• Automated restart
– DB2 cluster services detects Software and Hardware failure,
and initiates a member/group restart depending on the type
of failure.
• Manual intervention in failure situations
– DB2 cluster services automatically initiates the required type
of restart or crash recovery. However, human intervention is
required at times to restart or crash recovery and take steps to
complete group crash recovery.
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Peep into the next Module
• Storage Overview
– Storage Networking Technology
– Types of Storage System
– Fiber Channel Arbitrated Loop
– Fabric
– SANs
– Zoning
– Storage Virtualization
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Database overview

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