ADABAS and NATURAL Presentation

RBC Induction - Proprietary and Confidential

ADABAS Adaptable DAta BAse System


CONTENTS

Day-1 Database Concepts ADABAS Overview ADABAS Architecture

Day-2 Fields & Descriptors Accessing ADABAS Data Protection/Security

Day-3 ADBAS Nucleus ADAPREP commands

Day-4– ADABAS programming

Day-5– Test the knowledge


MODULE - 1

Database Concepts


Database Concepts

A data base is not really anything new. It is just a concept, or a way of organizing data and software, to solve some of the problems found in conventional file processing.

The data base concept solves the problems of– Duplication of data and effort.– Programmers needs to know the exact file structure.– People looking at files to which they should not have

access.– Programs needs to be changed when the data is

organized differently.– Dynamic Reporting structure


Database Goals

Data bases are used to meet 4 main data- related goals

– Increase Data Independence– Reduce Data Redundancy– Increase Data Security– Maintain Data Integrity


Database Goals

INCREASE DATA INPENDENCE

INDEPENDENT FILE SYSEM

PROGRAM - A

PROGRAM - B

In database processing, the data and the programs are INDEPENDENT. A change in the data organization will NOT USUALLY affect the user programs. If the format of the data is changed or re-organized, the programs dependant on that data must be changed. For example, if you need three lines for an address instead of two, the data would have to be changed as well as the programs. Bottom line is users should not worry how the data is actually stored. Instead, they can focus on the particular part of data they want to access with an application program.


Database Goals

REDUCE DATA REDUNDANCY In a conventional file system, the same data may be stored in many different files to

meet the requirements of different applications. Thus some of the data is unnecessarily repeated (redundant).

For example in conventional File processing system Customer address is repeatedly found in

CUSTOMER RECORDS SYSTEM (CRS) PURCHASE ORDER SYSTEM (POS) ACCOUNTS RECEIVABLE SYSTEM (ARS)

This redundancy could cause problem when customer address changes. The address must be changed in each of the files causing duplication of effort and more opportunities of errors.

So it is better for different application systems to share same centralized database thus avoiding

the data redundancy. POS

ARS

CENTRALIZEDDATA BASECRS


Database Goals

INCREASE DATA SECURITY Most databases have security features which authorize access to the data

and place restrictions on the operations that may be performed on the data.

– Eg: You may be allowed to read particular data, but not update it.– Eg: Only certain people may be allowed to look at salary data.


Database Goals

MAINTAIN DATA INTEGRITY

Maintaining accuracy (integrity) of the data is very important in a database system because the database is shared by many users.

Without integrity, the data could end up to be invalid or unreliable. Everyone who uses the database, from programmer to DBA, is responsible

for integrity of data.


Database Goals

As per understandings the four main goals of Database system are: INCREASE INDEPENDENCE REDUCE REDUNDANCY MAINTAIN DATA INTEGRITY INCREASE DATA SECURITY


Types of Databases

There are three types of data base management systems

– HIERARCHICAL– NETWORK– RELATIONAL


Database Types

HIERARCHICAL Database

HIGH SCHOOL

STUDENT

REPORTS

REGISTARTION

COMMENTSCOURSES

DEGREES

Each Segment is directly dependent on one segment only


Database Types

HIERARCHICAL Database In a Hierarchical database…

– There is always a basic or "root" segment.– A segment may relate downwards to one or more subordinate segments; these

subordinates are referred to as "child" segments.– A subordinate (child) segment may relate upward to only one superior or

"parent" segment.


Database Types

NETWORK Database

PERSON VEHICLE

COMPANY

BUILDING

CLUB

Here data is organized so that the PERSON segment is directly dependent on both COMPANY and CLUB.Segments are equivalent to records in flat file structures.


Database Types

NETWORK Database

In a network database structure . . .– segments are related to one another as superiors and subordinates.– A segment may relate downwards to one or more subordinates.– A segment may relate upwards to one or more superiors. This is what

distinguishes a network structure from a hierarchical structure.– A network database structure can be considered an expansion of the

hierarchical structure


Database Types

RELATIONAL Database

SSN # DOB DEPT SexEmployee Record

DEPT Dept Name

Designation

TimingsKey Field

In a Relational Database, fields in one record type are used to point to other record types

DEPT Record


Database Types

ADABAS Database Which type of data base management system do you think ADABAS is

closest to?

RELATIONAL


MODULE - 2

ADABAS Overview


ADABAS - Overview

Adabas is a high-performance database for large, mission-critical applications. It is specifically designed to support thousands of users in parallel while giving sub-second response times.

Its legendary reliability in combination with its low cost of ownership makes it a first-choice database for any mission-critical business application

Some Key Features– Relational and nested data structures – Native and SQL access – Built-in transaction concept – Minimal space consumption – Built-in utilities – Automated DBA tasks– Add-on products for special requirements, such as Text retrieval – Distributed computing – Web support – Administration


ADABAS - Overview

Adabas is available for a wide range of platforms, including Windows NT, UNIX, Open VMS and mainframe.

Access to Adabas– Adabas can be accessed from Natural, Software AG’s 4GL development

environment,– Bolero, Software AG’s application factory, and from every 3GL and 4GL that is

equipped with a call interface. – In addition, Adabas can be accessed via standard interfaces such as ODBC or

JDBC and, of course, embedded SQL can be used


ADABAS Overview


ADABAS Overview

The Size of an Adabas Database– On the mainframe there is no size limit at all– On open-systems platforms the theoretical limit for Adabas databases is 128 TB -

a figure limited in practice only by the amount of hard disk space that can be made available

– Mainframe ADABAS supports around 65535 databases. Each Database in turn contains FILES (maximum 5000). A FILE can contain around 4 Trillion records and each record is collection of fields. Field is the last undivided level where data is stored. Each record can have maximum 926 fields


Features of ADABAS

Inverted lists are used to define relationships in the data Adabas is very flexible database management system Can produce many different views for various users In ADABAS, information can be logically interconnected (or related) through

the use of INVERTED LISTS The inverted lists are lists of addresses for each record, and there is one

inverted list for every key field in every ADABAS dataset. (A file is referred to as a dataset.)

Because relationships are defined in the keys, ADABAS is very FLEXIBLE or ADAPTABLE

The name ADABAS reflects it’s flexible structure ADAPTABLE DATA BASE SYSTEM ADABAS


Database Overview

What is Inverted List ? The inverted lists are lists of addresses or ISN (Internal Sequence Number)

numbers for each record, and there is one inverted list for every key field in every ADABAS dataset.

In Adabas , information can be logically inter connected through the use of Inverted lists.

Example Of Automobile Database Let's see an example of how inverted lists provide a flexible system for

keeping data.LICENSE TYPE MODEL COLORBDX 205 FORD TAURUS BLACKCHS 499 OLDS CUTLASS REDAMC 372 CHEVY MALIBU REDKEN 007 PORSHE 928 BLACKBPS 076 OLDS SUPREME BLUEMPM 644 FORD TEMPO RED


Database Overview

How ISN is supplied by Adabas There are no key fields defined in the data. ADABAS assigns an Internal

Sequence Number (ISN) to each record for searching purpose

ISN LICENSE TYPE MODEL COLOR1 BDX 205 FORD TAURUS BLACK2 CHS 499 OLDS CUTLASS RED3 AMC 372 CHEVY MALIBU RED4 KEN 007 PORSHE 928 BLACK5 BPS 076 OLDS SUPREME BLUE6 MPM 644 FORD TEMPO RED7 .. .. .. ..8 .. .. .. ..


Database Overview

What is a Descriptor When Key is defined, nothing is changed in the data, Instead an inverted

list is built for that key (Descriptor). Which field you want to describe as Descriptor LICENSE TYPE MODEL COLOR Inverted List Based on LICENSE

DESCRIPTOR COUNT ISN’sAMC 372 1 3BDX 205 1 1BPS 076 1 5CHS 499 1 2KEN 007 1 4MPM 644 1 6.. .. .... .. ..


Database Overview

Inverted List based on TYPEDESCRIPTOR COUNT ISN’sCHEVY 17 3,8,14,34..FORD 29 1,6,17,23,..OLDS 42 2,5,19,20,..PORSHE 2 4,57,..... .. .... .. ..

DESCRIPTOR COUNT ISN’s928 2 4,57,...CUTLASS 4 2,20,46,47,..MALIBU 17 3,8,34,63,..SUPREME 6 5,19,38,42TAURUS 13 1,17,32,40..TEMPO 10 6,23,28,37.... .. .... .. ..

Inverted List based on MODEL


Database Overview

Features of ADABAS Another feature of ADABAS is the ability to produce many different views

of the data (using PREDICT / NATURAL) Advantages of User Views are -

– Provides security for confidential data– Provides simple views to the users– Eliminates unnecessary information being presented– Data Compression Allows data to be stored efficiently– Security Facilities Provided for confidentiality of data


Database Overview

Data Compression ADABAS has an option to store data on the disk in a compressed form. This

option saves disk space and reduces I/O.

– leading zeros are removed from numeric fields– trailing spaces are removed from alphanumeric fields– null value fields are compressed to a single byte– numeric data is packed

Compression reduces data storage almost by 50% Secure Data ADABAS maintains data security in four ways: Dataset Level:

• An entire dataset can be password protected to allow no access, or read-only access, or update access for different security levels.

Field Level:• An individual field can be protected from unauthorized access or

unauthorized updating. Up to 15 security levels can be set for a field


Database Overview

– Security by Value• Access to a file can be restricted by the value of the field. For example,

managers could be restricted to personal information of employees in only their own department.

– Ciphered Data• Records can be ciphered or encrypted when information is highly

confidential. A ciphering key is entered by the user when records are stored. It must be entered again to access the data.


ADABAS Environment

ADABAS can operate in one of two environment

– Multi-User– Singe-User

Multi-User• This is the normal operation of a data base where many people in an

organization are reading, updating, adding and deleting data simultaneously.

ADABAS DATABASE

Operating System

ADABAS Nucleus---------------------------

MPM

Application - 2Application - 1

Multi Processing Module

Batch program

Online program


ADABAS Environment

Single UserThis mode can be used when there is huge amount of processing (like

rearranging theentire database). This could be done at night when no one else wants to use

Adabas.

ADABAS DATABASE

Operating System

ADABAS Nucleus

Application - 1Application - 2


Learning Objective

In this module you have learnt:

_ Advantages of a data base system _ Three types of data bases _ ADABAS features _ ADABAS environment


MODULE - 3

ADABAS Architecture


Main components of ADABAS

Associator

Work

Data

ADABAS Database


Main components of ADABAS

Associator

Work

Data

ADABAS Database

Operating System

ADABAS NucleusMPM

Application 1 Application 2


ASSOCIATOR

This dataset contains

– A list of all ADABAS datasets in the database– Physical location of datasets (by block number)– Descriptions of all fields of each ADABAS dataset in the Field Description Table– Inverted lists for each descriptor– ADDRESS CONVERTER for relative record locations

A second important component of the Associator is the Address Converter

– The Address Converter is an index to the physical storage location of the records, giving the physical block in which each record is stored.

– There is one Address Converter for every dataset in the database.


Address Converter

To understand how the Address Converter works, we need to know how the data records are organized

– The records are not necessarily stored sequentially by ISN– The records are grouped and stored in blocks on the disk and indexed by

Relative ADABAS Block Numbers (RABNs - pronounced like “Robin”)


Inverted List & Address Converter

ISN LICENSE MAKE COLOR2 MAP 199 FORD ORANGE9 BLY 409 DODGE MAROON6 MBP 526 HONDA MAROON: : : :11 XOX 000 PONTIAC ORANGE3 LNC 128 CHEVY BLUE1 CHS 710 FORD BLUE: : : :4 BDX 205 MERCURY GREEN7 FMT 931 HONDA BLUE5 SRA 101 OLDS GREEN: : : :

BLOCK 1

BLOCK 2

BLOCK 3


Inverted List & Address Converter

DESCRIPTOR COUNT ISNsBLUE 17 1,3,7,12,..GREEN 14 4,5,14,18,..MAROON 19 6,8,9,13,..ORANGE 2 2,11,..

ADDRESSCONVERTER

ISNs

212331341724

123456789101112

The Address Converter is ordered sequentially by ISN in this example: ISN#1 is in RABN 2 ISN#2 is in RABN 1 ISN#3 is in RABN 2 etc..

For an efficient search for the ISNs, the Address Converter is used


WORK Dataset

This dataset is used to keep data protection information that is required for automatic restarts and backouts after a system failure

It acts as a type of working scratch-pad area for ADABAS

Associator

Work

Data

Data Protection Information

-----------------------------------Temporary ISN list storage

-----------------------------------

Resulting ISN lists

– Besides storing DATA PROTECTION INFORMATION, the WORK dataset acts as temporary storage during a SEARCH command. The WORK dataset also acts as temporary storage for ISN lists

– When ADABAS is finding records for you, it uses the WORK dataset for intermediate results

– Only partial or incomplete transactions are kept track of here. A log of the commands is not kept in the WORK dataset


DATA Dataset

Data records are stored in the DATA dataset– In random order– compressed to save disk space– organized into fixed length blocks on disk

The DATA dataset contains the physical data stored in FIXED SIZE BLOCKS

DATA

WORK

Inverted List Address Converter Block 1

ASSOCIATOR

Block 2

Block 3


ADABAS - Logs

After knowing the datasets let’s see the two optional logs of ADABAS These are

– COMMAND LOG– PROTECTION LOG

The Command Log keeps a record of every command that goes through the ADABAS nucleus

– This log gives a basis for billing those who use the system– The log gives the valuable information for performance tuning– This log is also used for application program debugging

The Protection Log contains before and after images of any records that are updated, deleted, or added between checkpoints

– It is used for regeneration of the database• (eg. After a disk crash)

– It is also used for backing out of a job– It is used for keeping an audit trail


ADABAS Database Organization

After knowing the ADABAS environment , let’s see the organization of data in the database

We will look at – Datasets– Records– Fields

An ADABAS database is made up of up to 255 data datasets (files) An ADABAS file can have up to 16.7 million records An ADABAS record can have up to 926 fields Some special grouping fields in ADABAS are

– MU fields (Multiple Value fields)– PE groups (Periodic group of fields)

These are like arrays within the records– MU fields are 1- dimensional arrays– PE fields are 2- dimensional arrays


MU/PE fields example

MU fields This is a single dimensional array where ADABAS allows us to store multiple

values in that particular field. For example let’s look at the following typical employee record

If now we need Rajeev’s record of positions before his attaining the current Consultant designation we may use an MU Field to store this informationRajeev Sharma Consultant

Assc Const

Asst Const

M.E 87%

This designation field constitutes an MU Field. The maximum numbers of occurrences supported are 191.


MU/PE Fields

Rajeev

ConsultantAssc ConsAsst Cons

M.E DL3CM9998

DL3C4115

2005

1999

ALTO

Zen

04/11/200804/11/200704/11/200604/11/2005

09/09/200109/01/200009/01/1999

87%

PE FieldsPE is a group of fields that repeat together in a record. A PE field can consist of elementary fields as well as Multiple Value fields. For example I want to store the following details about car-Car Number-Model-Type-Servicing Dates All this information can be stored in a PE field consisting of elementary fields as Number, Model and Type and a Multiple Value field Service Date. A PE field can store up to 191 occurrences. Maximum of 99 PE group occurrences per PE


MU/PE Fields

When should PE groups or MU fields be used?– When you have user views that relate directly to one of the other user views in a

one-to-many relationship Databases with PE groups are more inflexible, acting more like a

hierarchical than relational One should have a good reason for using PE groups (I.e. If you want all

occurrences of the PE group that relate to a record every time you access


Learning Objective


_ The three types of ADABAS datasets _ Two optional logs in ADABAS _ Data file architecture _ MU and PE repeating fields


MODULE - 4

Fields &

Descriptors


Data Organization

ADABAS Data Base

1 - 255 Datasets

0 - 16.7 million records

1 - 926 fields


ADABAS Fields

ADABAS keeps track of field-related information in the Field Definition Tables (FDT) Here is some of the information that is kept: _ ADABAS field name _ Format _ Level _ Length _ Type of compression _ Type of field Data Format of fields Data in an ADABAS is in one of the 6 following formats, each with a one

letter code

F Fixed point binary 4 bytesP Packed numeric 15 bytesU Unpacked numeric 29 bytesA Alphanumeric 253 bytesB Binary 126 bytesG Floating point 8 bytes


Data Format of fields

The information about each field in the database is kept in the FDT (Field Definition Table)

Sample information of FDT looks like ...

External Field Name--------------------------This is the name that would be used for a field in an application program

EMPLOYEE-NONAME LAST-NAME FIRST-NAME DEPARTMENT

ADABAS Field Name--------------------------This is a two byte alphanumeric code that ADABAS will use to lookup the field

A1 A2 A3 A4 BB


FDT Information

Level--------------------------The level of the fields as ina PL/I declaration or a COBOL data division

01 01 02 02 01

Format-----------------------This indicates the typeof data in the field

P

A A P

Length--------------------------The standard length of thefield in bytes

4

16 16 10


DATA Compression

ADABAS uses special compression techniques to• Conserve disk space• Reduce Input/Output• Increase performance

Field compression can be set in one of three different waysFI -- Fixed field with no compression Default -- Compression of individual fieldsNU -- Null suppression uses special

techniques to make compressioneven greater than the default


Data Compression

Special Length bytes are added to the record when compressed

================================================================================= ¦ ¦ FI ¦ ¦ Default ¦ ¦ NU ¦

================================================================

¦ ¦ ¦ No length bytes ¦ ¦ A length byte is ¦ ¦ One byte is use¦

¦ ¦ ¦ are added. ¦ ¦ added at the ¦ ¦ to count empty ¦

¦ ¦ ¦ ¦ ¦ start of every ¦ ¦ fields. ¦¦ ¦ ¦ ¦ ¦ field. ¦ ¦ ¦¦ ¦ ¦ ¦ ¦ ¦ ¦ Fields with data ¦¦ ¦ ¦ ¦ ¦ ¦ ¦ in them have a ¦¦ ¦ ¦ ¦ ¦ ¦ ¦ length byte ¦¦ ¦ ¦ ¦ ¦ ¦ ¦ added. ¦================================================================================


DATA Compression

+-----------------------------------------------+ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ B ¦ A ¦ R ¦ B ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ +-----------------------------------------------+

This is a fixed alphanumeric field for "First Name".

Default or NU compression would add a length byte and remove trailing spaces.

+-------------------+ ¦ The length of ¦ ¦ ¦ ¦ ¦ ¦ ¦ '5' counts the ¦ 5 ¦ B ¦ A ¦ R ¦ B ¦ ¦ characters and ¦ ¦ ¦ ¦ ¦ ¦ ¦ includes itself. +-------------------+

Compression of this field saved 7 bytes from a 12 byte field!


DATA Compression

3 2 S A M S

This is a 16 byte alphanumeric field for an address. 9 3 2 S A M S The compressed field look like above


DATA Compression

The following is an example of a compressed numeric field.

+---------------------------+ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ 0 ¦ 0 ¦ 0 ¦ 2 ¦ 8 ¦ 4 ¦ 6 ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ +---------------------------+

This is a standard unpacked numeric field for "Employee Number".

+-------------------------------------------------------+ ¦ Default or NU compression would store this as packed, ¦ ¦ remove the leading zeroes and add a length byte. ¦ +-------------------------------------------------------+ ________________________________________________________ +---------------+ ¦ ¦ 0 ¦ 8 ¦ 6 ¦ ¦ 4 ¦ 2 ¦ 4 ¦ C ¦ +---------------+ This 7 byte field is compressed into only 4 bytes. The 'C' on the end indicates the sign

of the number (positive).


DATA Compression

J E R R Y 0 0 0 0 0 0 0 0 0 2 8 4 6 1 2 3 4 This record has 4 empty fields After compression it will look like 6 J E R R Y 5 0 4 4 0 3

4 02 84 6C

There is still wasted space that could be eliminated using NU compression


DATA Compression

Under NU compression it looks like

6 J E R R Y C4 4

02846C

The count indicates 4 empty fieldsDefault compression saves 50%NU compression saves 75%


DESCRIPTORS

In ADABAS, a key field is called a DESCRIPTOR , abbreviated to DE An ADABAS file may have up to 200 descriptors. These descriptors may be

defined when the dataset is initially created, or at a later time using the ADAINV utility

ADABAS has 5 types of DESCRIPTORS– Regular Descriptors– Super Descriptors– Sub Descriptors– Phonetic Descriptors– Hyper Descriptors

Regular Descriptor: A regular descriptor is a regular field that can be used for search criteria. Example:

– In a file of employee records, the employee number would likely be defined as a regular descriptor

A special feature of regular descriptors is the UNIQUE DESCRIPTOR option. This ensures that there will be only one occurrence of each value in the data base

dataset. Example:

– If all employee numbers are to be unique, the unique descriptor option could be used for the employee number field.


Super Descriptor

A super-descriptor is a concatenated or compound key, consisting of from 2 to 5 elements. These elements can come from other fields, from parts of other fields, or from combinations of whole and partial fields.

Super-descriptors may not exceed 253 bytes in length Example:

– In an employee data base, a search using "LAST NAME" will not always find a unique record

By using a super-descriptor combination of "LAST NAME", "FIRST NAME", and "BIRTH DATE", a search should always find a unique record

If the users of the data base often search by two factors (for example, price of FORD cars with SUPREME model), a super-descriptor combination of "MAKE" and "MODEL" will make this combination search efficient

Super descriptors can be made up of any combination of descriptors or non-descriptors

Up to 5 whole fields or parts of fields can be made as Super descriptor


Sub Descriptor

A Sub descriptor is a partial field that is used as a descriptor Example:

– An automobile repair shop may stock parts. The part numbers may be contained in one field yet contain more than one piece of information.

Part Number G85404003871Car manufacturer (GM) Year of car (85)Model of car (Delta 88)Part type (Hubcap)

Phonetic descriptors make use of a phonetic algorithm built into ADABAS. You can look up alphanumeric fields with "sounds-like" searching criteria.

– Example: • If sales personnel wanted to look up their customer records without knowing

how to spell the customer's name, they could use a phonetic descriptor.


Phonetic Descriptor

– The telephone companies use a phonetic descriptor for looking up names and phone numbers in directory assistance.

Hyper Descriptors Hyper-descriptors enable descriptor values to be generated based on a

user-supplied algorithm. – Example:

• If sales personnel wanted to look up customer records in reverse collating sequence in terms of latest sales date, they could use a hyper-descriptor.


Compression effects

Compression of records has an effect on the inverted lists built for each descriptor.

For regular descriptors, null suppression will not find records when a search is conducted for a zero value or a blank value.

No entry is made in the inverted list for a null value field under NU compression

The effects of NU compression for regular descriptors also applies to sub-descriptors and phonetic descriptors.

Further, the NU compression also affects super-descriptors. If any component of a super-descriptor is a zero or a blank under NU

compression, the ISN for that record will not be listed in the inverted list Here is how to handle this:

– If zero or blank values may be meaningful in a descriptor field, use default compression.

– If zero or blank values are definitely not meaningful, it is fine to use NU compression


Learning Objective


_ Contents of the Field Descriptor Table _ Compression effects on fields

_ Five types of descriptors _ Compression effects on descriptors


MODULE - 5

AccessingADABAS


ADABAS Constraints

– Maximum of 255 files per data base.– Maximum of 16.7 million records per file.– 4816 byte maximum compressed record length.– Up to 926 fields per record.– 253 byte maximum field length (alpha).– Maximum of 99 PE group occurrences per PE.– Maximum of 191 MU values per MU field.– Maximum of 200 descriptors per file.– Up to 5 fields or parts of fields in a Super Descriptor


ADABAS Accessing

You may access data in an ADABAS database using one of two approaches

Sequential Approach– Physical Order– ISN Order– Logical Order

Random Approach– Simple Find– Sorted Find– Complex Find– Getting by ISN


SEQUENTIAL Approach

Sequential access is done by using READ commands

READ PHYSICAL– In this method sequential access is to read in the physical order that the records

are stored Physical Order - READ PHYSICAL

ISN EMPNO LASTNAME

FIRSTNAME

DEPT

1 01037 HARRIS JILL PROD7 15429 BULWARK KEN ACCT5 66725 GUNNER DOUG PROD3 41180 CONCH TERRY ADMIN6 31622 WU MATT MIS4 09331 HOYTE BRIAN PROD

Employee Dataset

VALUE COUNT ISN’sACCT 30 2,7,17,19,.ADMIN 14 3,8,11,14,.MIS 7 6,10,18,34,.PROD 52 1,4,5,9,.

Inverted List for Dept


Physical Order - READ PHYSICAL

A READ PHYSICAL command will read the records in the order in which they are stored in the dataset.

So the records will be read in this order: 1 7 5 3 6 4 . . .

READ Physical

The first method for sequential access is to read in the physical order that the records are stored. A READ PHYSICAL command will read the records in the order in which they are stored in the dataset.

Example :EMPLOYEE Dataset+-------------------------------¦ ¦ ¦ LAST ¦FIRST¦¦ISN¦EMP. NO¦ NAME ¦NAME ¦ DEPT+---+-------+-------+-----+-----¦ 1 01037 ¦HARRIS ¦JILL ¦ PROD¦ 7 ¦ 15429 ¦BULWARK¦KEN ¦ ACCT¦ 5 ¦ 66725 ¦GUNNER ¦DOUG ¦ PROD¦---+-------+-------+-----+-----¦ 3 ¦ 41180 ¦CONCH ¦TERRY¦ ADMI¦ 6 ¦ 31622 ¦WU ¦MATT ¦ MIS| 4 ¦ 09331 ¦HOYTE ¦BRIAN¦ PROD¦---+-------+-------+-----+-----¦ : ¦ : ¦ : ¦ : ¦ :

So the records will be read in this order:1 7 5 3 6 4


ISN Order - READ by ISN

A second method for sequential access is to read in the order of the ISN. A READ by ISN will retrieve the records in ISN order So the records will be read in this order:

1 2 3 4 5 6 7 . . READ BY ISN :A second method for sequential access is to read in the

order of the ISN A READ by ISN will retrieve the records in ISN order The records will be read in this order: 1 2 3 4 5 6 . . .


Logical Order - READ LOGICAL

The third method for sequential access is to read in the order of ISNs of the Inverted Lists

A READ LOGICAL will retrieve records in the order of listing in one of the Inverted Lists

A READ LOGICAL by DEPT will be in this order 2 7 17 19 . . . 3 8 11 14 . . The third method for sequential access is to read in the order of ISNs listed in one of the Inverted Lists. A READ LOGICAL will retrieve

records in the order of listing in one of the Inverted Lists. A READ LOGICAL by DEPT will be in this order: 2 7 17 19 . . . 3 8 11 14 . .


RANDOM Approach

Random access is done by using the following commands– FIND

• Simple• Complex• Sorted

– GET• Using a specific ISN

FIND - SimpleEmployee Dataset +------------------------------- Inverted List for DEPT. ¦ ¦ ¦ LAST ¦FIRST¦ +------------------------- ¦ISN¦EMP. NO¦ NAME ¦NAME ¦ DEPT ¦VALUE ¦COUNT¦ ISNs +---+-------+-------+-----+----- +------+-----+------------ ¦ 1 ¦ 01037 ¦HARRIS ¦JILL ¦ PROD ¦ACCT ¦ 30 ¦2,7,17,19... ¦ 7 ¦ 15429 ¦BULWARK¦KEN ¦ ACCT ¦ADMIN ¦ 14 ¦3,8,11,14... ¦ 5 ¦ 66725 ¦GUNNER ¦DOUG ¦ PROD ¦MIS ¦ 7 ¦6,10,18,34.. ¦---+-------+-------+-----+----- ¦PROD ¦ 52 ¦1,4,5,9..... ¦ 3 ¦ 41180 ¦CONCH ¦TERRY¦ ADMI +------------------------- ¦ 6 ¦ 31622 ¦WU ¦MATT ¦ MIS ¦ 4 ¦ 09331 ¦HOYTE ¦BRIAN¦ PROD If a simple FIND command is given ¦---+-------+-------+-----+----- ¦ : ¦ : ¦ : ¦ : ¦ : FIND ALL RECORDS WITH DEPT = ADMIN the following ISNs would be selected: 3 8 11 14 . . .


FIND - Complex

Complex FINDs involve searching based on more than one descriptor. Complex FINDs involve more processing.

– EXAMPLE:• FIND ALL RECORDS WITH EMPNO < 50000 AND DEPT = PROD

ISNs where ISNs where EMPNO < 50000 DEPT = PROD

::::::::::::::::::

::::::::::::::::::

::::::::::::::::::

A list of the selected ISNs is produced for each search parameter given. The lists are then merged into one list where both conditions are true.


FIND – Complex

Inverted List for DEPT. Employee Dataset+------------------------- +-------------------------------¦VALUE ¦COUNT¦ ISN's ¦ ¦ ¦ LAST ¦FIRST¦+------+-----+------------ ¦ISN¦EMP. NO¦ NAME ¦NAME ¦ DEPT¦ACCT ¦ 30 ¦2,7,17,19... +---+-------+-------+-----+-----¦ADMIN ¦ 14 ¦3,8,11,14... ¦ 1 ¦ 01037 ¦HARRIS ¦JILL ¦ PROD¦MIS ¦ 7 ¦6,10,18,34.. ¦ 7 ¦ 15429 ¦BULWARK¦KEN ¦ ACCO¦PROD ¦ 52 ¦1,4,5,9..... ¦ 5 ¦ 66725 ¦GUNNER ¦DOUG ¦ PROD+------------------------- ¦---+-------+-------+-----+----- ¦ 3 ¦ 41180 ¦CONCH ¦TERRY¦ ADMI Inverted List for EMPNO. ¦ 6 ¦ 31622 ¦WU ¦MATT ¦ MIS+------------------------- ¦ 4 ¦ 09331 ¦HOYTE ¦BRIAN¦ PROD¦VALUE ¦COUNT¦ ISN's ¦---+-------+-------+-----+-----+------+-----+------------ ¦ : ¦ : ¦ : ¦ : ¦ :¦01037 ¦ 1 ¦ 1¦09331 ¦ 1 ¦ 4 Which ISN's would be selected by the command:¦15429 ¦ 1 ¦ 7 FIND ALL RECORDS WITH EMPNO < 50000 AND DEPT=PROD¦31622 ¦ 1 ¦ 6

¦41189 ¦ 1 ¦ 3 1,4¦66752 ¦ 1 ¦ 5+-------------------------


Let's see the ISN's selected by the command ...

FIND ALL RECORDS WITH EMPNO < 50000 AND DEPT = PROD.

ISN's with ISN's with DEPT = PROD EMPNO < 50000 +---------+ +-----------+ ¦ 1 ¦ ¦ 1 ¦ These 2 lists ¦ 4 ¦ ¦ 4 ¦ will be ¦ 5 ¦ ¦ 7 ¦ produced as ¦ 9 ¦ ¦ 6 ¦ intermediate ¦ : ¦ ¦ 3 ¦ results. ¦ : ¦ +-----------+ +---------+ This final list is a combination of the above two intermediate ISN lists. +-----+ ¦ 1 ¦ ¦ 4 ¦ +-----+


Inverted List for Color Automobile Dataset +------------------------------ +------------------------------ ¦ VALUE ¦ COUNT ¦ ISN's ¦ISN ¦LICENCE ¦ MAKE ¦COLOR ... +--------+-------+------------- +----+-------+-------+--------- ¦ BLUE ¦ 17 ¦ 1,3,7,12 ... ¦ 2 ¦MAP 199 ¦FORD ¦ORANGE ¦ GREEN ¦ 14 ¦ 4,5,14,18 .. ¦ 9 ¦BLY 409 ¦DODGE ¦MAROON ¦ MAROON ¦ 19 ¦ 6,8,9,13 ... ¦ 6 ¦MBP 526 ¦HONDA ¦MAROON ¦ ORANGE ¦ 2 ¦ 2,11 ¦ : ¦ : ¦ : ¦ : ¦----+-------+-------+--------- Inverted List for Make ¦ 11 ¦XOX 000 ¦PONTIAC ¦ORANGE +------------------------------ ¦ 3 ¦LNC 128 ¦CHEVY ¦BLUE ¦ VALUE ¦ COUNT ¦ ISN's ¦ 1 ¦CHS 710 ¦FORD ¦BLUE +--------+-------+------------- ¦ : ¦ : ¦ : ¦ : ¦ CHEVY ¦ 24 ¦ 3,5,11,13... ¦----+-------+-------+--------- ¦ DODGE ¦ 10 ¦ 9,17,23,28.. ¦ 4 ¦BDX 205 ¦MERCURY ¦GREEN ¦ FORD ¦ 32 ¦ 1,2,4,12.... ¦ 7 ¦FMT 931 ¦HONDA ¦BLUE ¦ HONDA ¦ 8 ¦ 6,7,8,29.... ¦ 5 ¦SRA 101 ¦OLDS ¦GREEN ¦ : ¦ : ¦ : ¦ : Which ISN's would be selected by the command:

FIND ALL RECORDS WITH MAKE=FORD AND COLOR=BLUE


Inverted List for Color +------------------------------ +------------------------+ ¦ VALUE ¦ COUNT ¦ ISN's ¦ To determine the +--------+-------+------------- ¦ selected ISN's, pick ¦ ¦ BLUE ¦ 17 ¦ 1,3,7,12 ... ¦ the ISN's where ¦ ¦ GREEN ¦ 14 ¦ 4,5,14,18 .. ¦ COLOR = BLUE then ¦ ¦ MAROON ¦ 19 ¦ 6,8,9,13 ... ¦ pick the ISN's where ¦ ¦ ORANGE ¦ 2 ¦ 2,11 ¦ MAKE = FORD . ¦ +------------------------+ Inverted List for Make +------------------------------ --------------------------- ¦ VALUE ¦ COUNT ¦ ISN's The ISN’s that match in +--------+-------+------------- these 2 lists are 1 and 12 ¦ CHEVY ¦ 24 ¦ 3,5,11,13... --------------------------- ¦ DODGE ¦ 10 ¦ 9,17,23,28.. ¦ FORD ¦ 32 ¦ 1,2,4,12.... ¦ HONDA ¦ 8 ¦ 6,7,8,29....

Which ISN's would be selected by the command: FIND ALL RECORDS WITH MAKE=FORD AND COLOR=BLUE


RANDOM Approach

A simple FIND involves little processing or I/O A complex FIND takes much more processing and I/O since intermediate steps are involved A FIND with SORT is very expensive command using even more processing and I/O

– Eg:• FIND ALL RECORDS WITH COLOR > GREEN SORTED BY LICENSE

FIND - with SORT Inverted List for Color

+------------------------------ ¦ VALUE ¦ COUNT ¦ ISNs FIND ALL RECORDS WITH COLOR = MAROON +--------+-------+------------- SORTED BY MAKE in desc. ¦ BLUE ¦ 17 ¦ 1,3,7,12 ... ¦ GREEN ¦ 14 ¦ 4,5,14,18 .. will first select all the ISNs where ¦ MAROON ¦ 19 ¦ 6,8,9,13 ... COLOR = MAROON. ¦ ORANGE ¦ 2 ¦ 2,11

Inverted List for Make +--------------------------------+ +------------------------------ ¦ Then a search through the MAKE ¦ ¦ VALUE ¦ COUNT ¦ ISNs ¦ inverted list will find any ¦ +--------+-------+------------- ¦ matches. ¦ ¦ CHEVY ¦ 24 ¦ 3,5,11,13... +--------------------------------+ ¦ DODGE ¦ 10 ¦ 9,17,23,28.. _________________________________ ¦ FORD ¦ 32 ¦ 1,2,4,12.... ¦ HONDA ¦ 8 ¦ 6,7,8,29.... The final list of ISNs produced would be:

13 9 6 8


GET - Using a specific ISN

GET using specific ISN is the last method for accessing ADABAS records in random order

This can be used to access specific records where the ISN is already known In some ADABAS data bases the ISNs are given extra meaning.

– For example: The ISNs could be used as the 5-digit employee number in a Personnel data base. Then instead of using a command like

• FIND ALL RECORDS WITH EMPNO = 23845 you could use a command like

• GET ISN 23845 . By setting up the data base this way, disk space is saved. (One field is

saved in the dataset) By using the GET command instead of a FIND, processing time and I/O will

be saved


ADABAS Accessing

Apart from the above ADABAS has other access command like– UPDATE– DELETE– ADD

ADABAS can be accessed with the following access facilities– DIRECT CALLS– ADABAS SQL– NATURAL– ADAPREP


ADABAS Accessing

DIRECT CALLS The Direct Calls from application programs use 2 character codes for commands Here are some commands used in direct calls

S1 - FINDL2 - READ PHYSICAL L3 - READ LOGICAL

N1 - ADD A NEW RECORDE1 - DELETE A RECORD

Using direct calls is like programming in a low-level language. With each command, control blocks and buffers must all be identified Because of the detail involved, direct calls are rarely used. Occasionally a

Systems Programmer may use direct calls for some special purposeADABAS SQL ADABAS SQL contains a subset of standard SQL, as well as extensions which are

not available within standard SQL. ADABAS SQL relieves the user from having to define the data base's data areas

manually, and takes over data transfer to the program data areas automatically.


ADABAS Accessing

NATURAL NATURAL is a powerful application programming language that is designed

for accessing ADABAS data bases. NATURAL commands are easy to understand and use. Here are some

examplesFIND UPDATEGET DELETEREAD

NATURAL programs can be either batch or on-line. NATURAL can also be used for simple ad-hoc inquiries, for reports, and for

complex programs updating, deleting and adding records. Programmers can save many lines of code using NATURAL instead of PL/I or

COBOL with ADABAS SQL or direct calls. NATURAL has automatic error handling built-in, and includes a user-friendly

development environment. A command to find ISNs that match certain criteria can be done in NATURAL,

ADABAS SQL, or by direct calls. NATURAL takes about 1 line of code. ADABAS SQL takes about 10 lines of code. Direct Calls take about 25 lines of code.


Learning Objective

In this module you have learnt: _ Sequential access logic _ Random access logic _ Direct Calls _ ADBAS SQL _ NATURAL


MODULE - 6

Data ProtectionSecurity


Data Protection

Data protection must be built into a Data Base Management System to ensure that the data base retains its integrity.

– Let's examine a situation where data integrity could easily be lost.• When preparing a packing slip on-line for items to be sent to a customer, the

customer record is accessed and many item records are accessed and updated

Packing Slip Transaction ____________________________ The packing slip transaction is only meaningful when all 1. Read customer record four physical transactions take place. 2. Access item A record, update inventory level 3. Access item B record, This group of transactions is update inventory level referred to as one logical transaction. 4. Access item C record, update inventory level __________________________________

Data Integrity loses If this logical transaction is not updated in database then


ET - END Transaction

An "End-Transaction" or "ET" statement is used with ADABAS to indicate the end of a logical transaction.

One application program may involve several logical transactionsET - How it takes place

+------------------------------------------------------+ ¦ As a logical transaction proceeds, changes are being ¦ ¦ made in the data base and in the work dataset. ¦ +------------------------------------------------------+ +-------------- +--------+ ¦ Logical ¦ FIND ¦ ¦ Transaction ¦ ¦ +-------------- ¦ UPDATE ¦ --------- Change in data base ¦ ¦ ¦ ADD ¦ --------- Change in data base ¦ ¦ ¦ DELETE ¦ --------- Change in data base ¦ ¦ ¦ FIND ¦ ¦ ¦ ¦ UPDATE ¦ --------- Change in data base ¦ ¦ ¦ ET ¦ --------- Make all changes +--------+ permanent.


ET - How it takes place

What would the effect be on the transaction if an ET did NOT occur? This situation may occur when:

– An operator does not complete entering a transaction.– A machine failure occurs.

BT - Back-out Transaction If an operator leaves part-way through a transaction, ADABAS will back-out

the transaction (BT)----------------------------------------------------------------- +-------------- ¦ ¦ ¦ Logical ¦ ¦ ¦ Transaction ¦ ¦ +------------------+ +-------------- ¦ FIND ¦ ¦ Back to original ¦ ______________ ¦ ¦ ¦ state of the ¦ ¦ UPDATE ¦ ¦ data base. ¦ ¦ ¦ +------------------+ ¦ ADD ¦ ¦ ¦ +---------------------+ ¦ - Operator leaves - ¦ +---------------------+ +------------------+ ______________ _______ ¦ BT ¦ +--------+ +------------------+ When the operator returns, the operator is informed that he/she was

backed-out


BT - Back-out Transaction

If a machine failure occurs, the back-out (BT) occurs automatically when the database is brought up again

When records are being updated the record is "held". This means that no other program can access that record for updating.(Use of this "hold" logic ensures accurate data integrity)

The third situation is when the transaction time limit is exceeded. If two programs request records that are being held by each other, a deadlock situation will occur.

When the transaction time limit is reached, the transaction will be backed out

A transaction may be backed out for one of three reasons we've discussed. – Machine failure – Non-activity time limit – Transaction time limit


PREDICT - Data Dictionary

ADABAS has a data dictionary called ....

PREDICT +--------------------------------------------------------------+ ¦ The dictionary maintains information about ¦ ¦ the data base, its datasets and fields. ¦ +--------------------------------------------------------------+ +--------------------------------------------------------+ ¦ Details kept about the fields include: ¦ ¦ _ name ¦ ¦ _ format ¦ ¦ _ length ¦ ¦ _ descriptor status ¦ ¦ _ repeating group information ¦ ¦ _ compression ¦ +--------------------------------------------------------+


What is the use of Data Dictionary

When a data base is first loaded, the elements of the datasets must be defined indicating the characteristics of each field.

– COBOL programs use the WORKING-STORAGE SECTION of the COBOL program to define variables used in the program.

– PL/I programs have DCL (declare) statements to define variables used in the programs.

– NATURAL programs use Data Definition Modules (DDMs) to define their variables. The data dictionary is used to store the definitions for the variables that

will be used in the COBOL, PL/I or NATURAL programs PREDICT can also be used to create reports cross-referencing fields,

datasets and programs


Data Security

ADABAS can provide security at many levels:– Dataset Level– Field Level– Security by Value in Field– Ciphered Datasets

All security is kept by a password system. Passwords are assigned to individuals or groups, and the password is associated with a certain security level.


The DBA sets the security level of each group of users of the data base. The levels of access and update privileges range from 0 (low) to 14 (high).

+---------------------------------------------------------------------------+ ¦ ACCESS/UPDATE LEVELS ¦ ¦--------------------------------------------------------¦ ¦ ¦ ¦ EMPLOYEE ¦ PROJECT ¦ AUTOMOBILE ¦ ¦ NAME ¦ PASSWORD ¦ DATASET ¦ DATASET ¦ DATASET ¦ ¦-----------+----------+----------+---------+---------------------------------------------------¦ ¦ J. BROWN ¦ HAT ¦ 14/8 ¦ 14/8 ¦ 10/5 ¦ ----+ ¦ S. STEELE ¦ STOL ¦ 10/6 ¦ 10/5 ¦ 14/9 ¦ ¦

| ¦ ¦ ¦ ¦ ¦ ¦ ¦

| +--------------------------------------------------------------

+ +----------------------------------------------+ ¦ ¦ +----------------------------------------------+ ¦ Sam Steele has access level ¦ ¦ ¦ From this table, what are Joan ¦ ¦ 10 for the employee dataset. He +--+ ¦ Brown's access and update levels ¦ ¦ has level 6 update privileges ¦ ¦ for the automobile dataset? ¦ ¦ on this file. ¦ ¦ ¦ +----------------------------------------------+ ¦ Access ¦ ¦ Update ¦ +---------------------------------------------------+


Learning Objective


_ Data Protection procedures _ Data Dictionary features _ Security features


MODULE - 7

ADABAS NUCLEUS


ADABAS Nucleus

For each ADABAS database there an ADABAS nucleus which resides in its own partition and responsible for all database requests by application programs

It handles control of concurrent access and update (online transactions), buffering of database records, back out and recovery in case of system crashes

ADABAS Buffers ADABAS maintains following buffers while accessing the database

CONTROL BLOCKSEARCH-BUFFERVALUE-BUFFER

FORMAT-BUFFER RECORD-BUFFERISN-BUFFER


ADABAS Buffers

CONTROL BLOCK– Contains Command-ID– Database and File-ID’s– Return codes

SEARCH BUFFER– Contains the names of the descriptors by which the search is made along with

the search criteria VALUE BUFFER

– Contains descriptor names used for search criteria– Value supplied dynamically for complex search

FORMAT BUFFER– List of the fields one wants to retrieve– Can be specified in any order

• (independent of physical location) RECORD BUFFER

– After retrieving the record the data values of the requested fields are kept here– Arranged in the order as specified in FORMAT buffer

ISN BUFFER– List of the ISN’s resulting from a successful FIND operation


ADABAS UTILITES

ADASAV A utility to dump database datasets onto tape or disk and retrieve them again. ADARES A utility to reapply or remove updates to the database using the protection log

facility. ADACMP A utility to compress and decompress the datasets. ADALOD A utility to load compressed data into database. ADAULD A utility to unload data from database to a dataset. ADAORD This utility has many options: Reorder the Associator for 1 or more datasets Reorder data storage for 1 or more datasets Unload 1 or more datasets for relocation to another device.


ADABAS UTILITIES

ADADBS To modify the characteristics of an ADABAS dataset, ADADBS may be used. Here are some: Delete a dataset Recover unused space in the data base Increase the size of a data base Rename or renumber datasets Change the length of a field Add new fields to the dataset Release a field from being a descriptor ADAINV The INVERT function of ADAINV creates a descriptor, sub descriptor, super descriptor, hyper descriptor, phonetic descriptor. ADAREP Which datasets are in database The size of each dataset Physical space usage Field definition table information


NATURAL

• NATURAL is a proprietary programming language developed and marketed by Software AG

• Applications developed using NATURAL are modular, which contributes to its success as a tool for Rapid Application Development

• A NATURAL program can execute in either batch or on-line mode, with full access to database files.

• In online mode, NATURAL programs can process user input in real-time.• In batch mode, NATURAL programs can read and write sequential (i.e. non-

database) files.

101


DESCRIPTION OF NATURAL STATEMENTS

• Data Definition• Data Access• Data Manipulation• Data Modification• Data Reporting• Loop Execution Statements• Screen Statements• Logical Check Statements• Routine Invoking Statements• File Control Statements

102


NATURAL STATEMENTS

DEFINE DATA ….. END-DEFINE

This statement along with its closing structure END-DEFINE explicitly defines the data area. It must be the first statement in a program. Only comment lines can be placed before DEFINE DATA statement. All fields (Database fields as well as user-defined variables) are defined in this statement The fields can be defined either inside the defined data segment or outside. When it is defined out side it is defined in 1.Local data area LDA 2.Global data area GDA 3.Paramater data area PDA

103


NATURAL STATEMENTS

1.Local data area LDA A local data area (LDA) defines data that becomes “local” to any module that uses the LDA.

2.Global data area GDA The term “global data” describes data that occupies space outside of

any executable module. Programs and external subroutines (including any in-line subroutines within them) have direct access to global data, whereas subprograms and help routines (including any in-line subroutines within them) do not.

3. Parameter data area PDA. The term “parameter data” describes data that is passed between a

subprogram and the “calling” module.

104


USER-DEFINED VARIABLES

Format Maximum length A = Alphanumeric 1-253

N = Numeric 1-29 (default = 7)

P = Packed Numeric 1-29 (default = 7)

I = Integer 1 or 2 or 4

B = Binary 1-126

C = Attribute Control 2

D = Date 6 (stored in 4 bytes)

T = Time 12 (stored in 7 bytes)

F = Floating point 4 or 8

L = Logical 1

105


USER-DEFINED VARIABLES

Variables Initialization:The default initial value for various formats is B,F,I,N,P 0A BlankL F(alse)D D''T T'00:00:00'C (AD=D)Used within the DEFINE DATA statement to indicate the structure and grouping of the definitions.–Relevant to view definitions, redefinition's and field groups.–Range from 1 to 9.–Generally, variable definitions are on level 1.–The level numbering in view definitions, redefinitions, and groups must be sequential. No level numbers may be skipped.–If you redefine a field, the REDEFINE option must be on the same level as the original field, and the fields resulting from the redefinition must be one level lower.

106


Group Fields

DEFINE DATA LOCAL1 #FIELDA (N2.2)1 #FIELDB (I4)1 #GEMP 2 #EMPNO (N5) 2 #BIRTH (D) 2 #EMPADDR (A10)1 #FIELDF (A2)….END-DEFINEUsed within the DEFINE DATA statement to indicate the structure and grouping of the

definitions.Relevant to view definitions, redefinition's and field groups.Range from 1 to 9.Generally, variable definitions are on level 1.The level numbering in view definitions, redefinitions, and groups must be sequential.

No level numbers may be skipped.If you redefine a field, the REDEFINE option must be on the same level as the original

field, and the fields resulting from the redefinition must be one level lower.

107


Group Fields

The fields #EMPNO, #BIRTH, EMPADDR are defined under the common group name #GEMP. #GEMP only serves as a group name and is not a field in its own and therefore does not have a format/length definition

ARRAYS Single dimensional array DEFINE DATA LOCAL 1 #ARRAY (A10/1:3) END-DEFINE

Multi dimensional array DEFINE DATA LOCAL 1 #ARRAY (A10/1:3,1:4) END-DEFINE

108


DATABASE VIEWS

DDM (Data Definition Module). For Natural to be able to access a database file, a logical definition of the

physical database file is required. Such a logical file definition is called a DDM (data definition module). A DDM contains the database-internal field name (which is a two letter mnemonic) as well as the "external" field name (English like names).

109


DATA ACCESS / MODIFICATION STATEMENTS

Various Statements used with Natural: READ FIND GET HISTOGRAM STORE UPDATE DELETEREAD READ is used to retrieve records from a database sequentially. READ & END-READ defines the

top & bottom of loop respectively Read physical In the order in which they are physically stored in the database Read logical==> preferred In the order of the values of a descriptor field Read by ISN In the order of Adabas Internal Sequence Numbers

110


READ

** Example 'READX01': READ ***********************************************************************

DEFINE DATA LOCAL 1 MYVIEW VIEW OF EMPLOYEES 2 NAME 2 PERSONNEL-ID 2 JOB-TITLE END-DEFINE * READ (6) MYVIEW BY NAME DISPLAY NAME PERSONNEL-ID JOB-TITLE END-READ END

111


FIND

The FIND statement is used to select a set of records from the database based on a search criterion consisting of fields defined as descriptors (keys). Various relational operators like (EQ, GT, LT, LE, GE, NE) or (=, >, >=, <, <=, =) are used along with clause to filter the records

************************************************************************ DEFINE DATA LOCAL 1 MYVIEW VIEW OF EMPLOYEES 2 PERSONNEL-ID 2 NAME 2 JOB-TITLE 2 CITY END-DEFINE * FIND MYVIEW WITH CITY = 'PARIS' WHERE JOB-TITLE = 'INGENIEUR COMMERCIAL' DISPLAY NOTITLE CITY JOB-TITLE PERSONNEL-ID NAME END-FIND END

112


GET

A GET statement presents a single record for processing when the ISN is known. Unlike READ and FIND, there is no associated processing loop

DEFINE DATA LOCAL 1 EMPLOY-VIEW VIEW OF EMPLOYEES 2 NAME 2 SALARY (1) END-DEFINE * RD. READ EMPLOY-VIEW BY NAME DISPLAY EMPLOY-VIEW IF SALARY (1) > 1500000 /* GE. GET EMPLOY-VIEW *ISN (RD.) /* WRITE '=' (50) 'RECORD IN HOLD:' *ISN(RD.) COMPUTE SALARY (1) = SALARY (1) * 1.15 UPDATE (GE.)

113


STORE

STORE inserts/adds one record to a database file. One has to populate the data fields with relevant values of the record before STORing it with the file.

DEFINE DATALOCAL 1 #CLIENT-ID (N8) 1 #CNAME (A20) 1 RECEVIEW VIEW OF RECEIVABLE 2 CLIENT-ID 2 CNAME END-DEFINE INPUT #CLIENT-ID #CNAME RECEVIEW .CLIENT-ID := #CLIENT-ID RECEVIEW .CNAME := #CNAME STORE RECEVIEW END TRANSACTION

114


UPDATE

UPDATE modifies one existing record with a database file. One has to select the particular record by using READ/FIND/GET statement before updating the record. If we READ/FIND a number of records before UPDATing one particular record, then all the records selected by READ/FIND get locked. To avoid this, GET can be used to select the record we want to update so that, only that record gets locked.

ExampleDEFINE DATA1 RECEVIEW VIEW OF RECEIVABLE 2 SALARYEND-DEFINERD1.READ RECEVIEW WHERE SALARY GT 10000IF SALARY EQ 14900GET-REC.GET *ISN(RD1.)ADD 100 TO RECEVIEW .SALARYUPDATE (GET-REC.)END-IFEND TRANSACTIONEND

115


DELETE

DELETE physically removes records from a database file. Before deleting the particular record, one has to select the record by using READ/FIND/GET statement.

DEFINE DATA 1 RECEVIEW VIEW OF RECEIVABLE 2 ACTIVE END-DEFINE GET-REC. READ RECEVIEW WHERE ACTIVE=FALSE DELETE (GET-REC.) END TRANSACTION END

116


HISTOGRAM

The HISTOGRAM statement is used to either read only the values of one database field, or determine the number of records which meet a specified search criterion.

DEFINE DATA LOCAL 1 MYVIEW VIEW OF EMPLOYEES 2 CITY END-DEFINE * LIMIT 8 HISTOGRAM MYVIEW CITY STARTING FROM 'M' DISPLAY NOTITLE CITY 'NUMBER OF/PERSONS' *NUMBER *COUNTER END-HISTOGRAM ENDRESULT: CITY NUMBER OF PERSONS CNT -------------------- ----------- ----------- MADISON 3 1 MADRID 41 2 MAILLY LE CAMP 1 3 MAMERS 1 4 MANSFIELD 4 5

117


Data Definition Module

09:05:06 ***** NATURAL LIST COMMAND ***** 2001-11-08 User M6919 - List DDM SHIPMENT - Library SYSTEM

DDM DBID 0 DDM FNR 66 VSAM Name Default Sequence Page 1

T L DB Name F Leng S D Remarks - - -- -------------------------------- - ---- - - ------------------------ G 1 JK SH-SHIPMENT-KEYS CONTAINS THE 4 BASIC 2 BK SH-BOOKING-NO A 9 N D S/L ASSIGNED RESERVA 2 ZP SH-FILLER-31 A 1 N 2 BL SH-BILL-NO-SHIPMENT-COUNT N 12.0 N D CONTAINS BILL OF LAD 2 CN SH-CONTAINER-NO A 10 N D IDENTIFIER OF A CONT 2 JG SH-SHIP-NO A 12 N D UNIQUE IDENTIFIER OF 2 OU EQP-CHK-DGT A 1 N ONE BYTE DIGIT DERIV P 1 QE SH-VVD-DEPENDENT-INFO CONTAINS INFO SPECIF 2 OP SH-OP-VVD-POL-POD A 68 N COMPOSITE FIELD ON S G 1 JL SH-SELECTION-GROUP DENOTES FIELDS THAT 2 RQ REQ-EQUIP-LTL-CANC A 19 N COMPOSITE FIELD ON S 2 ZQ SH-FILLER-32 A 1 N 2 DS SH-DEST-ORIGIN-AROT A 55 N COMPOSITIE FIELD ON Top of List. Command ===> Enter-PF1---PF2---PF3---PF4---PF5---PF6---PF7---PF8---PF9---PF10--PF11--PF12--- Help Print Exit Long -- - + ++ Canc


Data Definition Module

T L DB Name F Leng S D Remarks

T - Type (Group, MU,PE ) L - Level DB - 2 byte ADABAS field name Name - Field name F - Data format Leng - Length of the field S - Storage compression type D - Descriptor usage Remarks - Field comments


NATURAL SCREEN PRINTS SAMPLE

120

Natural screen prints sample


THE END

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