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Database, Database Objects and SQL
11 April 2011
© 2011 IBM Corporation
Agenda
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IDS SQL Architecture
Database (DB), DB variants
Tables
Data types
User-Defined Types (UDT)
Isolation Levels
Constraints
Stored Procedures/User-Defined Routines (UDR)
Summary
Resources
© 2011 IBM Corporation
Tables (Cont.)
 Temporary:
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Not registered in system catalogs.
Per-session or connection.
Reside in Temporary DBSpace if available.
Not logged by default.
Automatically cleaned up at end of session or connection.
Complete indexing supported.
Data statistics supported.
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IDS SQL Architecture
External Space
Client Requests
Access Methods
SQL Engine
Tables
Index
Type System
System catalogs
API for UDRs
C
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Language Managers
Java
SPL
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Database (DB), DB variants
 A database is a logical storage unit that contains tables and
indexes.
 Contains system catalog that tracks information about
database objects like:
• Tables and indexes.
• SPL routines.
• integrity constraints.
 Database names are always lower case.
Tip: Command to create a database in dbaccess
CREATE DATABASE mydb;
Command to connect to and close database in dbaccess
DATABASE mydb;
CLOSE DATABASE;
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Database (DB), DB variants (Cont.)
 Databases come in three flavors
– Non-logged:
• No transactions.
• No logging.
• Data consistency not guaranteed.
– Logged:
• Transactions are logged.
• Logging can be buffered or un-buffered.
• Owner name not used for object resolution.
– Ansi:
• Statements always in transaction.
• Un-buffered logging.
• Owner name used for object resolution.
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Create DB Commands
-- Command to create a un-logged database in dbaccess
CREATE DATABASE mydb;
-- Command to create a logged database in dbaccess
CREATE DATABASE mydb WITH LOG;
CREATE DATABASE mydb WITH BUFFERED LOG;
-- Command to create a ANSI database in dbaccess
CREATE DATABASE mydb WITH LOG MODE ANSI;
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Tables
 Permanent
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Registered in system catalogs.
Available across sessions or connections.
Can be created in a specific DBSpace or root DBSpace.
Two modes:
• Raw – un-logged.
• Standard – logged.
 Temporary
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Not registered in system catalogs.
Per-session or connection.
Reside in Temporary DBSpace if available.
Not logged by default.
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Tables (Cont.)
 Virtual Tables (outside the scope of this presentation):
– Registered in system catalogs.
– Storage is not managed by Informix.
– Used to access external data as a relational table.
 Pseudo Tables:
– Registered in system catalogs.
– No storage, data generated from in memory data structures.
– Created internally by Informix to expose administrative data.
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Tables (Cont.)
-- Command to create a table in a database
CREATE TABLE customer
(
customer_num
SERIAL (101) NOT NULL,
fname
CHAR(15),
lname
CHAR(15),
company
CHAR(20),
PRIMARY KEY (customer_num)
);
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Tables (Cont.)
-- Command to create a temporary table in a database
CREATE TABLE mytemptab
(
stock_no
SMALLINT,
mfg_code
CHAR(5),
mfg_name
CHAR(20),
phone
CHAR(18),
descript
VARCHAR(255)
);
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Tables (Cont.)
-- Command to query pseudo table in sysmaster database
-- Returns all table names from all databases in an
-- instance. systabnames is a pseudo table.
SELECT tabname FROM systabnames;
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Table Fragmentation: Why ?
Parallelism
Table scan threads *
Fragments are accessed in
parallel, decreasing scan or insert
time.
fragments
Fragment Elimination
Table scan threads *
X
X
fragments
X
X
Fragment Elimination &
Parallelism
Table scan threads *
fragments
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Unneeded fragments are
eliminated, decreasing scan or
insert time, and reducing disk
contention.
X
X
Both goals are achieved.
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Table Fragmentation: OLTP
OLTP characteristics:
For this environment:
 High volume of short transactions.  Fragment the data by round robin or
 Each transaction accesses a few
expression.
rows.
 For large tables that receive a large
 Index access method is used.
percentage of transaction activity,
fragment the indexes using an
expression-based fragmentation
strategy.
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Table Fragmentation: Data Warehousing
For this environment:
DW characteristics:
 Low volume of long running queries.
 Fragment elimination.
 Queries access most of the rows in each
 Parallel scan the needed
table.
fragments.
 Very few indexes are generally required.
 Preferred access method is sequential
scan.
 Preferred join method is the hash join.
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Table Fragmentation: Expression based
dbspaces
tab_adbs1
tab_adbs2
tab_adbs3
CREATE TABLE table_a
(x INTEGER, y INTEGER, z CHAR (25))
FRAGMENT BY EXPRESSION
x <= 10 and x >= 1 in tab_adbs1,
x <= 20 and x > 10 in tab_adbs2,
x <= 30 and x > 20 in tab_adbs3;
Data is split across fragments
based on the range given for
expression
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Table Fragmentation: Expression based
dbspaces
tab_adbs1
tab_adbs2
tab_adbs3
CREATE TABLE table_a
(x INTEGER, y INTEGER, z CHAR (25))
FRAGMENT BY ROUND ROBIN
in tab_adbs1, tab_adbs2, tab_adbs3;
Data is split across fragments
evenly based on round robin
strategy
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System Catalog Tables
 System Catalog Tables:
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Each database has its own system catalog tables/views.
These store meta data about the database objects.
You can query these tables just like any other tables.
Tables with tabid < 100 in “informix”.systables.
Reside under the owner name “informix”.
Examples:
• “informix”.systables
• “informix”.sysindices
• “informix”.syscolumns
-- query to retrieve system catalog tables/views in the current
-- database
SELECT tabname FROM “informix”.systables WHERE tabid < 100
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Views
 Virtual table based on a specified SELECT statement.
 Registered in system catalogs.
 Data is not stored on disk.
 View query dynamically loaded and evaluated as needed.
 Different views of underlying tables for different users.
 Access privilege restrictions can be enforced.
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© 2011 IBM Corporation
Views (Cont.)
-- create a view on customer table in stores_demo database
CREATE VIEW
custview
(firstname, lastname, company, city)
AS
SELECT fname, lname, company, city
FROM customer
WHERE city = ‘San Jose'
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© 2011 IBM Corporation
Indexes
 Index Characteristics
– Unique
– Clustered
– Composite key
• Index over multiple columns of a table.
– Functional index
• Index over a set of keys derived from the results of a function.
-- Create index cidx on fname column of customer table
CREATE INDEX cidx ON customer(fname)
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Indexes
 Built-in Index Types
– BTREE (Default)
– RTREE
• Tree structure to index Spatial data.
 User defined Indexing schemes
– GIST
• Generalized Search Tree – to index any content.
– VII
• Virtual Index Interface based access method.
• Index Structure maintained by routines registered with Informix.
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SQL Datatypes
Informix Datatypes
Common SQL Types
Extended Datatypes
Complex Datatypes
Collection
User Defined Datatypes
Row Types
Distinct
Opaque
Numeric
Character
Large Objects
Time
SET
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Multi-Set
List
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Built-in Data types
 Numeric
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INTEGER
SERIAL
SERIAL8
SMALLINT
BIGINT
BIGSERIAL
DECIMAL or NUMERIC(p,s)
REAL or SMALLFLOAT
FLOAT
DOUBLE PRECISION
MONEY(p,s)
 Time
– DATE
– DATETIME
– INTERVAL
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 Character
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CHAR(n)
CHARACTER VARYING(n,r)
VARCHAR(n,r)
LVARCHAR(m)
NCHAR(n)
NVARCHAR(m,r)
 Large Objects
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BLOB
CLOB
BYTE
TEXT
 Misc
– BOOLEAN
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Auto Generated Data types
 SERIAL, SERIAL8 and BIGSERIAL are auto generated.
 Store a sequential integer.
 Starting value for sequence can be optionally provided.
 Automatically generated by the server.
 SERIAL type takes 4 bytes of storage.
 SERIAL8 and BIGSERIAL take 8 bytes of storage each.
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UDT (Cont.)
 Distinct data type
– Based on existing built-in or extended data type.
– Same internal structure as the source type.
CREATE DISTINCT TYPE pound AS FLOAT;
CREATE DISTINCT TYPE kilos AS FLOAT;
CREATE DISTINCT TYPE my_pound AS pound;
CREATE TABLE my_weight(name VARCHAR(24), weight pound);
CREATE TABLE my_kweight(name VARCHAR(24), weight kilos);
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© 2011 IBM Corporation
UDT (Cont.)
 Opaque Types
– Fully encapsulated.
– Internal structure is unknown to the database server.
– Type designer needs to provide routines that describe its characteristics
(storage, input, output, arithmetic and relational operators, functions…).
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UDT (Cont.)
 Complex Types
– Built with combination of other built-in or extended data types.
– Row data type:
• Has group of elements of any data type known to the server.
– Collection data type:
• SET, LIST and MULTISET types.
• Has groups of elements of the same data type.
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UDT (Cont.)
-- create a ROW Type
CREATE ROW TYPE prx
(
patient VARCHAR(28),
pid INT,
addr VARCHAR(128)
);
-- create a table with SET data type
CREATE TABLE employee
(
name CHAR(30),
address CHAR (40),
salary INTEGER,
dependents SET(VARCHAR(30) NOT NULL)
);
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© 2011 IBM Corporation
UDT (Cont.)
-- create a table with MULTISET data type
CREATE TABLE employee
(
name CHAR(30),
address CHAR (40),
salary INTEGER,
bonus MULTISET(MONEY NOT NULL)
);
-- create a table with LIST data type
CREATE TABLE employee
(
name CHAR(30),
address CHAR (40),
salary INTEGER,
dependents LIST(VARCHAR(30) NOT NULL)
);
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Sequences
 A database object that generates a sequence of whole
numbers within a defined range.
 Unlike SERIAL Auto generated type, sequences are not tied to
any table.
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SEQUENCES Example
CREATE SEQUENCE seq_2
INCREMENT BY 1
START WITH 1
MAXVALUE 30 MINVALUE 0
NOCYCLE CACHE 10 ORDER;
CREATE TABLE tab1 (col1 int, col2 int);
INSERT INTO tab1 VALUES (0, 0);
INSERT INTO tab1 (col1, col2) VALUES
(
seq_2.NEXTVAL,
seq_2.NEXTVAL
);
SELECT * FROM tab1;
col1
col2
0
0
1
1
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ISOLATION Levels
 DIRTY READ:
– No locks and it respects none.
 COMMITTED READ:
– Guarantees that every retrieved row is committed in the table at the
time that the row is retrieved.
 COMMITTED READ LAST COMMITTED:
– Same as COMMITTED READ, but readers avoid locking errors as they
work on last committed data.
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© 2011 IBM Corporation
ISOLATION Levels (Cont.)
 CURSOR STABILITY:
– Holds locks on row until next row is fetched or cursor is closed.
 REPEATABLE READ:
– Places a shared lock on every row that is selected during the
transaction.
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Constraints
 Primary Key Constraint:
– Defined over a column or group of columns.
– Enforces uniqueness in column values.
– NULL values are not allowed in primary key columns.
CREATE TABLE customer
(
customer_num
SERIAL (101) NOT NULL,
fname
CHAR(15),
lname
CHAR(15),
company
CHAR(20),
PRIMARY KEY (customer_num)
);
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© 2011 IBM Corporation
Constraints (Cont.)
 Referential Constraint
– Relationship between
tables or same table.
– Primary key in one table
can be referenced in the
same or another table.
– ON DELETE CASCADE
option to delete referenced
table rows on delete.
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© 2011 IBM Corporation
Constraints (Cont.)
-- Referential constraint
CREATE TABLE customer
(
customer_num
SERIAL (101) NOT NULL,
fname
CHAR(15),
lname
CHAR(15),
company
CHAR(20),
PRIMARY KEY (customer_num)
);
CREATE TABLE orders
(
order_num SERIAL(1001),
order_date DATE,
customer_num INTEGER NOT NULL,
ship_instruct CHAR(40),
PRIMARY KEY (order_num), FOREIGN KEY (customer_num)
REFERENCES customer (customer_num)
);
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© 2011 IBM Corporation
Constraints (Cont.)
 Check Constraint:
– Condition or requirement on a data value before data can be assigned
to a column during an INSERT or UPDATE.
-- Check that acct1 and acct2 are between 0 and 99999
CREATE TABLE my_accounts
(
chk_id SERIAL PRIMARY KEY,
acct1 MONEY CHECK (acct1 BETWEEN 0 AND 99999),
acct2 MONEY CHECK (acct2 BETWEEN 0 AND 99999)
);
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Stored Procedure/UDR
 Stored Procedure:
– Written in Stored Procedure Language (SPL).
– Compiled and stored natively in server.
– Extremely powerful constructs to implement business logic.
CREATE PROCEDURE raise_prices ( per_cent INT )
UPDATE stock SET unit_price =
unit_price + (unit_price * (per_cent/100));
END PROCEDURE;
EXECUTE PROCEDURE raise_prices (10);
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© 2011 IBM Corporation
Stored Procedure/UDR
 UDR
– C Language UDRs:
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Written in C language.
Compiled into a shared library and registered with the server.
Server dynamically loads and executes code in shared library.
Used heavily for UDT and VTI/VII implementations.
DataBlade APIs provided to perform advanced tasks .
– JAVA Language UDRs:
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Written in Java Language.
Compiled into class or jar files and registered with the server.
UDRs can contain JDBC code to execute advanced tasks.
Server contains embedded JVM to execute the code.
© 2011 IBM Corporation
XML, XPATH, XSLT functions
 Informix server has built-in support for:
– Publishing relational data as XML.
– Performing XPATH operations.
– Transforming generated XML using XSLT.
 XML Publishing Functions:
– genxml() and genxmlClob().
– genxmlqueryhdr() and genxmlqueryhdrClob().
– genxmlelem() and genxmlelemClob().
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XML, XPATH, XSLT functions
(Cont.)
 XPath functions for pattern matching and extraction within
XML document:
– extract() and extractClob()
– extractValue()
– existsNode()
 XSLT functions:
– XSLTransform()
– XSLTransformAsCLOB()
– XSLTransformAsBLOB()
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XML functions example
SELECT genxml(mycustrpt, 'mycustomers')
FROM (SELECT c.customer_num, c.customer_name.last, o.order_num
FROM customer c, orders o
WHERE c.customer_num = o.customer_num
and order_date = '05/20/1998‘
) AS mycustrpt;
Output:
genxml <mycustomers customer_num="104" expression="Kilmore"
order_num="1001"/>
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© 2011 IBM Corporation
XML functions example (Cont.)
SELECT genxmlelem(mycustrpt, 'mycustomers')
FROM ( SELECT c.customer_num, c.customer_name.last, o.order_num
FROM customer c, orders o
WHERE c.customer_num = o.customer_num
AND order_date = '05/20/1998‘
) AS mycustrpt;
genxmlelem <mycustomers>
<row>
<customer_num>104</customer_num>
<expression>Kilmore</expression>
<order_num>1001</order_num>
</row>
</mycustomers>
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© 2011 IBM Corporation
XML functions example (Cont.)
SELECT XSLTransform(s.style_doc, x.xml_doc)
FROM style_sheets s, xml_docs x
WHERE s.style_title = 'ecommerce_ROOT.xsl'
AND x.xml_transaction_id = 12345;
-- Transforming dynamically generated XML documents
SELECT XSLTransform(s.style_doc,
genxml ( “transaction”,
row(o.trnid, o.custid, o.addr)
)
)
FROM style_sheets s, orders o
WHERE s.style_title = 'ecommerce_ROOT.xsl'
AND o.trnid = 12345;
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© 2011 IBM Corporation
Summary
 We have learned the following:
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Types of databases: Ansi, Logged and non-logged.
Database objects: tables, indexes, views, sequences.
SQL Datatypes, UDT
Isolation levels
Constraints
Stored Procedures and UDR
XML functions
© 2011 IBM Corporation
Resources
 The Online IDS Information Center
– http://publib.boulder.ibm.com/infocenter/idshelp/v115/index.jsp
– One-stop shop for IDS product documentation
– Supports book marking favorite topics, narrowing the scope to refine
searches, printing subsets of topics
 IBM Informix DeveloperWorks Technical Articles
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http://www.ibm.com/developerworks/db2/products/informix/index.html
Premium technical resource site for DBAs and developers
Features explained with examples/sample code
Contributions from IBM experts as well as customers
 IBM DeveloperWorks IDS Blogs
– http://www-128.ibm.com/developerworks/blogs/page/roundrep (IDS
Replication)
– http://www-128.ibm.com/developerworks/blogs/page/gbowerman (IDS
Application Development)
– http://www-128.ibm.com/developerworks/blogs/page/idsteam (IDS Experts
Blog)
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