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IMS Concepts

This article provides a high-level overview of IMS database concepts, terminology, and database design

considerations. It covers the following topics:

Overview

Hierarchical versus Relational Databases

Design Considerations

OverviewThe term database means a collection of related data organized in a way that can be processed by

application programs. A database management system (DBMS) consists of a set of licensed programs that

define and maintain the structure of the database and provide support for certain types of application

programs. The types of database structures are network, relational, and hierarchical. This manual presents

information on IMS, a hierarchical database management system from IBM*.

The IMS software environment can be divided into five main parts:

database Data Language I (DL/I) DL/I control blocks data communications component (IMS TM) application programs

Figure 1-1 shows the relationships of the IMS components. We discuss each of these components in

greater detail in this and subsequent chapters.

Figure 1-1: IMS environment components.IMS Database

Before the development of DBMSs, data was stored in individual files, or as flat files. With this system,

each file was stored in a separate data set in sequential or indexed format. To retrieve data from the file,

an application had to open the file and read through it to the location of the desired data. If the data was

scattered through a large number of files, data access required a lot of opening and closing of files,

creating additional I/O and processing overhead. To reduce the number of files accessed by an

application, programmers often stored the same data in many files. This practice created redundant data

and the related problems of ensuring update consistency across multiple files. To ensure data consistency,

special cross-file update programs had to be scheduled following the original file update.
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The concept of a database system resolved many data integrity and data duplication issues encountered

in a file system. A database stores the data only once in one place and makes it available to all application

programs and users. At the same time, databases provide security by limiting access to data. The user's

ability to read, write, update, insert, or delete data can be restricted. Data can also be backed up and

recovered more easily in a single database than in a collection of flat files.

Database structures offer multiple strategies for data retrieval. Application programs can retrieve datasequentially or (with certain access methods) go directly to the desired data, reducing I/O and speeding

data retrieval. Finally, an update performed on part of the database is immediately available to other

applications. Because the data exists in only one place, data integrity is more easily ensured.

The IMS database management system as it exists today represents the evolution of the hierarchical

database over many years of development and improvement. IMS is in use at a large number of business

and government installations throughout the world. IMS is recognized for providing excellent

performance for a wide variety of applications and for performing well with databases of moderate to

very large volumes of data and transactions.

DL/IBecause they are implemented and accessed through use of the Data Language I (DL/I), IMS databases are

sometimes referred to as DL/I databases. DL/I is a command-level language, not a database management

system. DL/I is used in batch and online programs to access data stored in databases. Application

programs use DL/I calls to request data. DL/I then uses system access methods, such as Virtual Storage

Access Method (VSAM), to handle the physical transfer of data to and from the database.

IMS databases are often referred to by the access method they are designed for, such as HDAM, PHDAM,

HISAM, HIDAM, and PHIDAM. IMS makes provisions for nine types of access methods, and you can design

a database for any one of them. We discuss each of them in greater detail in Chapter 2, "IMS Structures

and Functions." The point to remember is that they are all IMS databases, even though they are referred

to by access type.

Control Blocks

When you create an IMS database, you must define the database structure and how the data can be

accessed and used by application programs. These specifications are defined within the parameters

provided in two control blocks, also called DL/I control blocks:

database description (DBD) program specification block (PSB)

In general, the DBD describes the physical structure of the database, and the PSB describes the database

as it will be seen by a particular application program. The PSB tells the application which parts of the

database it can access and the functions it can perform on the data.

Information from the DBD and PSB is merged into a third control block, the application control block(ACB). The ACB is required for online processing but is optional for batch processing.

Data Communications

The IMS Transaction Manager (IMS TM) is a separate set of licensed programs that provide access to the

database in an online, real-time environment. Without the TM component, you would be able to process

data in the IMS database in a batch mode only. With the IMS TM component, you can access the data and

can perform update, delete, and insert functions online. As Figure 1-1 shows, the IMS TM component

provides the online communication between the user and DL/I, which, in turn, communicates with the

application programs and the operating system to access and process data stored in the database.

Application Programs


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The data in a database is of no practical use to you if it sits in the database untouched. Its value comes in

its use by application programs in the performance of business or organizational functions. With IMS

databases, application programs use DL/I calls embedded in the host language to access the database.

IMS supports batch and online application programs. IMS supports programs written in ADA, assembler,

C, COBOL, PL/I, VS PASCAL, and REXX.

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Hierarchical versus Relational Databases

There are several types of database management systems, categorized generally by how they logicallystore and retrieve data. The two most common types in use today are relational and hierarchical. Each

type has its advantages and disadvantages, and in many organizations both types are used. Whether you

choose a relational or hierarchical database management system depends largely on how you intend to

use the data being stored.

Relational Database

In a relational database, data is stored in a table made up of rows and columns. A separate table is

created for logically related data, and a relational database may consist of hundreds or thousands of

tables.

Within a table, each row is a unique entity (or record) and each column is an attribute common to the

entities being stored. In the example database described in Table 1-1 on page 1-9, Course No. has beenselected as the key for each row. It was chosen because each course number is unique and will be listed

only once in the table. Because it is unique for each row, it is chosen as the key field for that row. For

each row, a series of columns describe the attributes of each course. The columns include data on title,

description, instructor, and department, some of which may not be unique to the course. An instructor,

for instance, might teach more than one course, and a department may have any number of courses. It is

important early in design of a database to determine what will be the unique, or key, data element.

Hierarchical Databases

Now let's look at the same data stored in a hierarchical format. This time the data is arranged logically in a

top-down format. In a hierarchical database, data is grouped in records, which are subdivided into a series

of segments. In the example Department database on Figure 1-2 on page 1-8, a record consists of the

segments Dept, Course, and Enroll.

In a hierarchical database, the structure of the database is designed to reflect logical dependencies-

certain data is dependent on the existence of certain other data. Enrollment is dependent on the

existence of a course, and, in this case, a course is dependent on the existence of a department. In a

hierarchical database, the data relationships are defined. The rules for queries are highly structured. It is

these fixed relationships that give IMS extremely fast access to data when compared to a relational

database. Speed of access and query flexibility are factors to consider when selecting a DBMS.

Strengths and Weaknesses

Hierarchical and relational systems have their strengths and weaknesses. The relational structure makes it

relatively easy to code requests for data. For that reason, relational databases are frequently used fordata searches that may be run only once or a few times and then changed. But the query-like nature of

the data request often makes the relational database search through an entire table or series of tables

and perform logical comparisons before retrieving the data. This makes searches slower and more

processing-intensive. In addition, because the row and column structure must be maintained throughout

the database, an entry must be made under each column for every row in every table, even if the entry is

only a place holder-a null entry. This requirement places additional storage and processing burdens on the

relational system.
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With the hierarchical structure, data requests or segment search arguments (SSAs) may be more complex

to construct. Once written, however, they can be very efficient, allowing direct retrieval of the data

requested. The result is an extremely fast database system that can handle huge volumes of data

transactions and large numbers of simultaneous users. Likewise, there is no need to enter place holders

where data is not being stored. If a segment occurrence isn't needed, it isn't inserted.

The choice of which type of DBMS to use often revolves around how the data will be used and how quickly

it should be processed. In large databases containing millions of rows or segments and high rates of accessby users, the difference becomes important. A very active database, for example, may experience 50

million updates in a single day. For this reason, many organizations use relational and hierarchical DBMSs

to support their data management goals.

Sample Hierarchical Database

To illustrate how the hierarchical structure looks, we'll design two very simple databases to store

information for the courses and students in a college. One database will store information on each

department in the college, and the second will contain information on each college student.

In a hierarchical database, an attempt is made to group data in a one-to-many relationship. An attempt is

also made to design the database so that data that is logically dependent on other data is stored in

segments that are hierarchically dependent on the data. For that reason, we have designated Dept as thekey, or root, segment for our record, because the other data would not exist without the existence of a

department. We list each department only once. We provide data on each course in each department. We

have a segment type Course, with an occurrence of that type of segment for each course in the

department. Data on the course title, description, and instructor is stored as fields within the Course

segment. Finally, we have added another segment type, Enroll, which will include the student IDs of the

students enrolled in each course.

In Figure 1-2, we also created a second database called Student. This database contains information on all

the students enrolled in the college. This database duplicates some of the data stored in the Enroll

segment of the Department database. Later, we will construct a larger database that eliminates the

duplicated data. The design we choose for our database depends on a number of factors; in this case, we

will focus on which data we will need to access most frequently,

The two sample databases, Department and Student, are shown in Figure 1-2. The two databases are

shown as they might be structured in relational form in Table 1-1, Table 1-2, and Table 1-3 on page 1-9.

Figure 1-2: Sample hierarchical databases for department and student.

Department Database

The segments in the Department database are as follows:

DeptInformation on each department. This segment includes fields for the department ID (the key

field), department name, chairman's name, number of faculty, and number of students registered
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in departmental courses.

CourseThis segment includes fields for the course number (a unique identifier), course title, course

description, and instructor's name.

EnrollThe students enrolled in the course. This segment includes fields for student ID (the key field),

student name, and grade.

Student Database

The segments in the Student database are as follows:

StudentStudent information. It includes fields for student ID (key field), student name, address, major,

and courses completed.

BillingBilling information for courses taken. It includes fields for semester, tuition due, tuition paid, and

scholarship funds applied.

The dotted line between the root (Student) segment of the Student database and the Enroll segment of

the Department database represents a logical relationship based on data residing in one segment and

needed in the other. Logical relationships are explained in detail in "The Role of Logical Relationships" onpage 2-55.

Example Relational Structure

Tables 1-1, 1-2 and 1-3 show how the two hierarchical Department and Student databases might be

structured in a relational database management system. We have broken them down into three tables-

Course, Student, and Department. Notice that we have had to change the way some data is stored to

accommodate the relational format.

Course No. Course Title Description Instructor Dept ID

HI-445566 History 321 Survey course J. R. Jenkins HIST

MH-778899 Algebra 301 Freshman-level A.L. Watson MATH

BI-112233 Biology 340 Advanced course B.R. Sinclair BIOL

Table 1-1: Course database in relational table format.

Student ID Student Name Address Major

123456777 Jones, Bill 1212 N. Main History

123456888 Smith, Jill 225B Baker St Physics

123456999 Brown, Joe 77 Sunset St Zoology

Table 1-2: Student database in relational table format.

Dept ID Dept. Name Chairman Budget Code

HIST History J. B. Hunt L72

MATH Mathematics R. K. Turner A54

BIOL Biology E. M. Kale A25

Table 1-3: Department database in relational table format.

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Design Considerations

Before implementing a hierarchical structure for your database, you should analyze the end user's

processing requirements, because they will determine how you structure the database. To help you

understand the business processing needs of the user, you can construct a local view consisting of the

following:

list of required data elements controlling keys of the data elements data groupings for each process, reflecting how the data is used in business practice mapping of the data groups that shows their relationships

In particular, you must consider how the data elements are related and how they will be accessed. The

topics that follow should help you in that process.

Normalization of Data

Even though you have a collection of data that you want to store in a database, you may have a hard time

deciding how the data should be organized. Normalization of data refers to the process of breaking data

into affinity groups and defining the most logical, or normal, relationships between them. There are

accepted rules for the process of data normalization. Normalization usually is discussed in terms of form.

Although there are five levels of normalization form, it is usually considered sufficient to take data to the

third normalization form. For most uses, you can think of levels of normalization as the following: First normal form. The data in this form is grouped under a primary key-a unique identifier. In other

words, the data occurs only once for each key value.

Second normal form. In this form, you remove any data that was only dependent on part of the key. Forexample, in Table 1-1 on page 1-9, Dept ID could be part of the key, but the data is really only dependent

on the Course No.

Third normal form. In this form, you remove anything from the table that is not dependent on the primarykey. In Table 1-3, the Department table, if we included the name of the University President, it would

occur only once for each Dept ID, but it is in no way dependent on Dept ID. So that information is not

stored here. The other columns, Dept. Name, Chairman, and Budget Code, are totally dependent on the

Dept ID.Example Database Expanded

At this point we have learned enough about database design to expand our original example database.

We decide that we can make better use of our college data by combining the Department and Student

databases. Our new College database is shown in Figure 1-3.

Figure 1-3: College database (combining department and student databases).

The following segments are in the expanded College database:
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CollegeThe root segment. One record will exist for each college in the university. The key field is the

College ID, such as ARTS, ENGR, BUSADM, and FINEARTS.

Dept

Information on each department within the college. It includes fields for the department ID

(the key field), department name, chairman's name, number of faculty, and number of

students registered in departmental courses.

Course

Includes fields for the course number (the key field), course title, course description, and

instructor's name.

EnrollA list of students enrolled in the course. There are fields for student ID (key field), student

name, current grade, and number of absences.

Staff

A list of staff members, including professors, instructors, teaching assistants, and clerical

personnel. The key field is employee number. There are fields for name, address, phone

number, office number, and work schedule.

StudentStudent information. It includes fields for student ID (key field), student name, address, major,

and courses being taken currently.

BillingBilling and payment information. It includes fields for billing date (key field), semester, amount

billed, amount paid, scholarship funds applied, and scholarship funds available.

AcademicThe key field is a combination of the year and the semester. Fields include grade point averageper semester, cumulative GPA, and enough fields to list courses completed and grades per

semester.

Data Relationships

The process of data normalization helps you break data into naturally associated groupings that can be

stored collectively in segments in a hierarchical database. In designing your database, break the individual

data elements into groups based on the processing functions they will serve. At the same time, group

data based on inherent relationships between data elements.

For example, the College database (Figure 1-3) contains a segment called Student. Certain data is naturally

associated with a student, such as student ID number, student name, address, and courses taken, Other

data that we will want in our College database-such as a list of courses taught or administrative

information on faculty members-would not work well in the Student segment.

Two important data relationship concepts are one-to-many and many-to-many. In the College database,

there are many departments for each college (Figure 1-3 shows only one example), but only one college

for each department. Likewise, many courses are taught by each department, but a specific course (in this

case) can be offered by only one department. The relationship between courses and students is one of

many-to-many, as there are many students in any course and each student will take a number of courses.

A one-to-many relationship is structured as a dependent relationship in a hierarchical database: the many

are dependent upon the one. Without a department, there would be no courses taught: without a college,there would be no departments.

Parent and child relationships are based solely on the relative positions of the segments in the hierarchy,

and a segment can be a parent of other segments while serving as the child of a segment above it. In

Figure 1-3, Enroll is a child of Course, and Course, although the parent of Enroll, is also the child of Dept.

Billing and Academic are both children of Student, which is a child of College. (Technically, all of the

segments except College are dependents.)
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When you have analyzed the data elements, grouped them into segments, selected a key field for each

segment, and designed a database structure, you have completed most of your database design. You may

find, however, that the design you have chosen does not work well for every application program. Some

programs may need to access a segment by a field other than the one you have chosen as the key. Or

another application may need to associate segments that are located in two different databases or

hierarchies. IMS has provided two very useful tools that you can use to resolve these data requirements:

secondary indexes and logical relationships.

Secondary indexes let you create an index based on a field other than the root segment key field. That

field can be used as if it were the key to access segments based on a data element other than the root

key. Logical relationships let you relate segments in separate hierarchies and, in effect, create a hierarchic

structure that does not actually exist in storage. The logical structure can be processed as if it physically

exists, allowing you to create logical hierarchies without creating physical ones. We discuss both of these

concepts in greater detail in Chapter 2, "IMS Structures and Functions."

Hierarchical Sequence

Because segments are accessed according to their sequence in the hierarchy, it is important to understand

how the hierarchy is arranged. In IMS, segments are stored in a top-down, left-to-right sequence (see

Figure 1-4). The sequence flows from the top to the bottom of the leftmost path or leg. When the bottomof that path is reached, the sequence continues at the top of the next leg to the right.

Understanding the sequence of segments within a record is important to understanding movement and

position within the hierarchy. Movement can be forward or backward and always follows the hierarchical

sequence. Forward means from top to bottom, and backward means bottom to top. Position within the

database means the current location at a specific segment.

Hierarchical Data Paths

In Figure 1-4, the numbers inside the segments show the hierarchy as a search path would follow it. The

numbers to the left of each segment show the segment types as they would be numbered by type, not

occurrence. That is, there may be any number of occurrences of segment type 04, but there will be only

one type of segment 04. The segment type is referred to as the segment code.

To retrieve a segment, count every occurrence of every segment type in the path and proceed through

the hierarchy according to the rules of navigation:

top to bottom front to back (counting twins) left to right

For example, if an application program issues a GET-UNIQUE (GU) call for segment 6 in Figure 1-4, the

current position in the hierarchy is immediately following segment 6 (not 06). If the program then issued a

GET-NEXT (GN) call, IMS would return segment 7.

As shown in Figure 1-4, the College database can be separated into four search paths:

The first path includes segment types 01, 02, 03, and 04. The second path includes segment types 01, 02, and 05. The third path includes segment types 01, 06, and 07. The fourth path includes segment types 01, 06, and 08.

The search path always starts at 01, the root segment.
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Figure 1-4: Sequence and data paths in a hierarchy.

Database Records

Whereas a database consists of one or more database records, a database record consists of one or more

segments. In the College database, a record consists of the root segment College and its dependent

segments. It is possible to define a database record as only a root segment. A database can contain only

the record structure defined for it, and a database record can contain only the types of segments defined

for it.

The term record can also be used to refer to a data set record (or block), which is not the same thing as a

database record. IMS uses standard data system management methods to store its databases in data sets.

The smallest entity of a data set is also referred to as a record (or block). Two distinctions are important:

A database record may be stored in several data set blocks. A block may contain several whole records or pieces of several records.

In this article, we try to distinguish between database record and data set record where the meaning may

be ambiguous.

Segment Format

A segment is the smallest structure of the database in the sense that IMS cannot retrieve data in an

amount less than a segment. Segments can be broken down into smaller increments called fields, whichcan be addressed individually by application programs.

A database record can contain a maximum of 255 types of segments. The number of segment occurrences

of any type is limited only by the amount of space you allocate for the database. Segment types can be of

fixed length or variable length. You must define the size of each segment type.

It is important to distinguish the difference between segment types and segment occurrences. Course is a

type of segment defined in the DBD for the College database. There can be any number of occurrences for

the Course segment type. Each occurrence of the Course segment type will be exactly as defined in the

DBD. The only differences in occurrences of segment types is the data contained in them (and the length,if the segment is defined as variable length).

Segments consist of two major parts, a prefix and the data being stored. (SHSAM and SHISAM database

segments consist only of the data, and GSAM databases have no segments.) The prefix portion of a

segment is used to store information that IMS uses in managing the database.

Prefix Data

segment delete byte counters and size seq. data
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code

1 byte

1 byte

pointers

4 bytes per

element

field

2 bytes

(key)

field length varies, based

on a minimum and

maximum size

Figure 1-5: Format of a variable-length segment.

Figure 1-6 shows the format of a fixed length segment. In the fixed-length segment, there is no size field.

Prefix Data

segment

code

1 byte

delete byte

1 byte

counters and

pointers

4 bytes per

element

size

field

2 bytes

seq.

(key)

field

data

length is whatever

is specified for

the segment

Figure 1-6: Format of a fixed-length segment.

The fields contained in an IMS database segment are described below. In the data portion, you can define

the following types of fields: a sequence field, data fields.

Segment

Code

IMS uses the segment code field to identify each segment type stored in a database. A unique

identifier consisting of a number from 1 to 255 is assigned to each segment type when IMS

loads the database. Segment types are numbered in ascending sequence, beginning with the

root segment as 1 and continuing through all dependent segment types in hierarchic order.

Delete

Byte

IMS uses this byte to track the status of a deleted segment. The space it occupied may (or may

not) be available for use.

Counters and Pointers

This area exists in hierarchic direct access method (HDAM) and hierarchic indexed direct access method

(HIDAM) databases and, in some cases, hierarchic indexed sequential access method (HISAM) databases.

It can contain information on the following elements:

Counters - Counter information is used when logical relationships are defined. Logical relationships arediscussed in detail in "The Role of Logical Relationships" on page 2-55.

Pointers - Pointers consist of one or more addresses of segments pointed to by this segment. Pointers arediscussed in detail in "Pointer Types" on page 2-37.

Size Field

For variable-length segments, this field states the size of the segment, including the size field (2 bytes).

Sequence (Key) FieldThe sequence field is often referred to as the key field. It can be used to keep occurrences of a segment

type in sequence under a common parent, based on the data or value entered in this field. A key field can

be defined in the root segment of a HISAM, HDAM, or HIDAM database to give an application program

direct access to a specific root segment. A key field can be used in HISAM and HIDAM databases to allow

database records to be retrieved sequentially. Key fields are used for logical relationships and secondary

indexes.

The key field not only can contain data but also can be used in special ways that help you organize your

database. With the key field, you can keep occurrences of a segment type in some kind of key sequence,
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which you design. For instance, in our example database you might want to store the student records in

ascending sequence, based on student ID number. To do this, you define the student ID field as a unique

key field. IMS will store the records in ascending numerical order. You could also store them in

alphabetical order by defining the name field as a unique key field.

Three factors of key fields are important to remember:

The data or value in the key field is called the key of the segment. The key field can be defined as unique or non-unique. You do not have to define a key field in every segment type

Data

You define data fields to contain the actual data being stored in the database. (Remember that the

sequence field is a data field.) Data fields, including sequence fields, can be defined to IMS for use by

applications programs. Field names are used in SSAs to qualify calls. See "Segment Search Argument" on

page 3-22 for more information.

Segment Definitions

In IMS, segments are defined by the order in which they occur and by their relationship with other

segments:

Root segmentThe first, or highest segment in the record. There can be only one root segment for each

record. There can be many records in a database.

Dependent

segmentAll segments in a database record except the root segment.

Parent segment A segment that has one or more dependent segments beneath it in the hierarchy.

Child segment A segment that is a dependent of another segment above it in the hierarchy.

Twin segmentA segment occurrence that exists with one or more segments of the same type under a

single parent.

Segment Edit/Compression

IMS provides a Segment Edit/Compression Facility that lets you encode, edit, or compress the data

portion of a segment in full-function or Fast Path DEDB databases. You can use the Edit/Compression

Facility to perform the following tasks:

encode data-make data unreadable to programs that do not have the edit routine to see it in decodedform

edit data-allow an application program to receive data in a format or sequence other than that in which itis stored

compress data-use various compression routines, such as removing blanks or repeating characters, toreduce the amount of DASD required to store the data

The Segment Edit/Compression Facility allows two types of data compression:

data compression-compression that does not change the content or relative position of the key field. Forvariable-length segments, the size field must be updated to show the length of the compressed segment.

For segments defined to the application as fixed-length, a 2-byte field must be added at the beginning of

the data portion by the compression routine to allow IMS to determine storage requirements.

key compression-compression of data within a segment that can change the relative position, value, orlength of the key field and any other fields except the size field. In the case of a variable-length segment,

the segment size field must be updated by the compression routine to indicate the length of the

compressed segment.

Pointers
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IMS uses pointers to locate related segments in a database. Pointers are physically stored in the prefix

portion of a segment. Each pointer contains the relative byte address (RBA) of another segment. When

the database is loaded, IMS creates pointers according to the DBD you specified. During subsequent

processing, IMS uses pointers to traverse the database (navigate from segment to segment). IMS

automatically maintains the contents of pointers when segments are added, deleted, and updated.

Part 2 in a series (Part 1|Part 3)

This article discusses IMS database organization, access methods, secondary indexes, and logical

relationships. It covers the following topics:

Control Blocks

Access Methods

Hierarchic Sequential Databases

Hierarchic Direct Databases

Fast Path DatabasesThe Role of Secondary Indexes

The Role of Logical Relationships

Control Blocks

When you create an IMS database, you tell IMS what the physical structure of the database will be-the

segment names, segment lengths, the fields that each segment will contain, the segment's position in the

hierarchy, and so on. You also tell IMS what segments can be accessed, whether they can be updated,

deleted, or new ones inserted, and other access control specifications. You do this through a series of

specifications that will be contained in control blocks, also called DL/I control blocks, because the DL/I

command language is used perform the data manipulation functions. Control blocks do just what the

name implies-they control the way in which IMS will structure and access the data stored in the database.

The data structure and control specifications you write will be contained in three major control blocks:

DBD, which describes the database organization and access methods PSB, which describes an application program's view and use of the database ACB, which combines information from the DBD and PSB

Database Description

A database description (DBD) is a series of macro statements that define the type of database, all

segments and fields, and any logical relationships or indexing. DBD macro statements are submitted to

the DBDGEN utility, which generates a DBD control block and stores it in the IMS.DBDLIB library for use

when an application program accesses the database.

Figure 2-1 shows a sample DBD for an HDAM database. When the DBD is assembled and link-edited, aload module is created and stored in an IMS DBDLIB library. In the DBDGEN process, each segment is

assigned a segment code, a one-byte value in ascending sequence, that is used to identify the segment in

physical storage.

In the DBD statement, an IMS access method and a system access method are specified (HDAM, OSAM in

this example). The roles of the two access methods are discussed in greater detail in "Access Methods."

Fields within each segment can be defined as key fields or non-key search fields for use by application

programs in retrieving segments. A key field is used for searching and sequencing. Each segment

occurrence will be placed in a database record according to the sequence of the key fields. In Figure 2-1,
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the statement for field COLLID (college ID) is defined as a sequence field (SEQ) and as unique (U). Only

fields that will be used in SSAs or that are key fields must be defined in the DBD.

Figure 2-1: DBD for an HDAM database with secondary index.

The DBD contains the following statements:

DATASETDefines the DDname and block size of a data set. One DATASET statement is required for each

data set group.

SEGM

Defines a segment type, its position in the hierarchy, its physical characteristics, and its

relationship to other segments. Up to 15 hierarchic levels can be defined. The maximum number

of segment types for a single database is 255.

FIELDDefines a field within a segment. The maximum number of fields per segment is 255. The

maximum number of fields per database is 1,000.

LCHILDDefines a secondary index or logical relationship between two segments. It also is used to define

the relationship between a HIDAM index and the root segment of the database.

XDFLD

Used only when a secondary index exists. It is associated with the target segment and specifies

the name of the indexed field, the name of the source segment, and the field to be used to

create the secondary index. See "The Role of Secondary Indexes" for more information.

DBDGEN Indicates the end of statements defining the DBD.

END Indicates to the assembler that there are no more statements.

DBD Names the database being described and specifies its organization.

Program Specification BlockThe program specification block (PSB) is a series of macro statements that describe the data access

characteristics of an application program. Among other things, the PSB specifies:

all databases that the application program will access which segments in the database that the application program is sensitive to how the application program can use the segments (inquiry or update)

A PSB consists of one or more program communication blocks (PCBs). The PCB specifies the segments to

which the application program can have access and the processing authorization for each segment. You

define a PCB for each database (or each view of the database) accessed by the application program. In

the application program host code, you specify the PSB for that application.

For each PCB, you must code a corresponding block in the application program's linkage section. These

data communication I/O areas are used for communication between IMS and the application. (There are

actually two types of PCBs, a database PCB and a data communications PCB.)

PCBs contain SENSEG (sensitive segment) and SENFLD (sensitive field) statements. These statements allow

you to specify which segments and fields the application program will "see." If you define a segment as

sensitive, it will be accessible to the application. If you do not, it will be ignored by the application

program. This gives you great flexibility in creating the views that application programs will have of your

database.
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The PSB macros are used as input to the PSBGEN utility, a macro assembler that generates a PSB control

block. The PSB control block is stored in the IMS.PSBLIB library for use during database processing. There

can be many PSBs for one DBD.

Figure 2-2 shows the structure of PSB generation input.

Figure 2-2: Sample PSBGEN generation input.

The PSB statements include the following:

PCB

Defines the database to be accessed by the application program. The statement also defines the

type of operations allowed by the application program. Each database requires a separate PCBstatement. PSB generation allows for up to 255 database PCBs (less the number of alternate

PCBs defined).

SENSEG

Defines the segment types to which the application program will be sensitive. A separate

SENSEG statement is required for each segment type. If a segment is defined as sensitive, all the

segments in the path from the root to that segment must also be defined as sensitive. Specific

segments in the path can be exempted from sensitivity by coding PROCOPT=K in the SENSEG

statement.

SENFLD

Defines the fields in a segment type to which the application program is sensitive. Can be used

only in association with field-level sensitivity. The SENFLD statement must follow the SENSEG

statement to which it is related.

PROCOPTDefines the type of access to a database or segment. PROCOPTs can be used on the PCB or

SENSEG statements. Primary PROCOPT codes are as follows:

G read only

R replace, includes G

I insert

D delete, includes G

A get and update, includes G, R, I, D

K used on SENSEG statement; program will have key-only sensitivity to this segment

L load database

Secondary PROCOPT codes are as follows:

E exclusive use of hierarchy or segmentsO get only, does not lock data when in use

P must be used if program will issue path call using the D command code

S sequential (LS is required to load HISAM and HIDAM databases; GS gets in ascending

sequence)

Application Control Block

Application control blocks (ACBs) are created by merging information from PSBs and DBDs. For online

applications, ACBs must be prebuilt using the ACB maintenance utility. For batch applications, ACBs can be

built dynamically using DBDLIB and PSBLIB as input (PARM=DL/I) or the prebuilt ACB from ACBLIB can be

used (PARM=DBB). The ACBGEN process is shown in Figure 2-3.


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Figure 2-3: ACB generation.

Prebuilt ACBs require less time to schedule an application program and use less storage. The ACB

maintenance utility also provides some error-checking capability.

ACBs can be built for all PSBs, for particular PSBs, or for all PSBs that reference a particular DBD. Prebuilt

ACBs are stored in the IMS.ACBLIB library. During ACB generation, the ACB maintenance utility must have

exclusive control of the IMS.ACBLIB. Because of this, the utility must be executed using an IMS.ACBLIBthat is not currently allocated to an active IMS system. You can execute the ACB maintenance utility

against an inactive copy of ACBLIB, then use the IMS Online Change function to make the new members

available to an active IMS online system.

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Access Methods

IMS accesses data after it has been retrieved from DASD and places it in a buffer pool in memory. The task

of retrieving the data from DASD is performed by one of several system access methods. These should not

be confused with IMS access methods such as HSAM, HISAM, HDAM, HIDAM, and so on. IMS access

methods are actually types of database organizations. In IMS terminology, however, databases often are

referred to by their IMS access method. An IMS database definition must always specify an IMS accessmethod and a system access method. In some cases, you can choose the type of system access method

you want to use. In other cases, the system access method is dictated by the IMS access method. HISAM,

for instance, uses only VSAM.

Both the system and IMS access methods are used for IMS database retrieval and update. Application

programs specify the data to retrieve and make a DL/I call to the system access method. The system

access method returns a block of data to IMS. The IMS access method then locates the data within the

block and passes it to the application program. The IMS database types and their access methods are

shown in Table 2-1.

Table 2-1: IMS database and system access types.

VSAM

In the discussion on HISAM and HIDAM databases later in this chapter, you will find reference to VSAM,particularly in association with VSAM key-sequenced data sets (KSDSs) and entry-sequenced data sets

(ESDSs), because of the way in which certain databases use these data sets. Before discussing the various

IMS access methods, it is helpful to have an understanding of VSAM's role in the storage and retrieval of

data. VSAM performs the physical I/O of data for IMS. It retrieves the data from DASD and places it in the

main storage buffer pool for use by IMS. When processing has been completed, VSAM returns the data to

DASD, where it is stored until needed again. To perform these functions, VSAM uses its own set of data

storage and retrieval structures.
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A VSAM data set consists of a set of records. The records are grouped into control intervals (CIs), which in

turn are grouped into larger groupings called control areas (CAs). The layout of a control interval is shown

in Figure 2-4.

Figure 2-4: VSAM control interval layout.

A VSAM CI consists of records, free space, and control information. You can determine the size of a CI or

let VSAM do it for you. When you define the size of a CI for a data set, all CIs in the data set will be the

same size. When you define the CI, you also determine the percentage of free space to be designated.

You will attempt to create enough free space to avoid CI splits while not using so much free space that you

waste DASD. CI splits occur when there is no room to insert another record; consequently, VSAM moves

half of the records from the CI where the record was to be inserted to a new CI. CI splits are a costly

overhead, especially in high-activity systems. (You can correct CI splits by reorganizing the database.)

CIs are grouped inside a control area (CA). The goal is to have enough unused CIs to allow new data to be

added without causing a CA split. CA splits are more processing-intensive than CI splits. On the other

hand, you don't want to waste DASD by defining too many unused CIs. For information on calculating

space requirements, refer to the IBM manuals IMS/ESA Administration Guide: Database Manager and

IMS/ESA Administration Guide: System.

The control information portion of the CI contains two types of fields:

The record definition field (RDF) contains information on the records stored in the CI, their length, andwhether they are fixed or variable length.

The control interval definition field (CIDF) contains information on the CI itself. It keeps track of theamount of free space available and where the free space is located relative to the beginning of the CI. CIshave only one CIDF but may have a variable number of RDFs, depending on whether the CI contains fixed-

length or variable-length records or a combination of the two.

Sequence Sets and Indexes

For KSDSs, VSAM keeps track of all CAs and CIs through the use of two levels of indexing-the sequence set

and the index set.

VSAM maintains a sequence set record for each CA in the data set. The sequence set record contains an

entry for each CI in the CA. Each entry contains the key of the highest record in the CI and a pointer to the

CI. The index contains an entry for each sequence set record. This gives the index an entry for each CA,

since there is a sequence set for every CA. Each index entry contains the key of the highest record in its CA

and a pointer to the sequence set record for that CA.

By following the values of record keys from index to sequence set to CA to CI, VSAM can locate any record

in the data set. When VSAM reaches the CI, it can obtain record information from the CIDF and RDFs of

the CI. The example in Figure 2-5 illustrates this concept.


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Figure 2-5: Structure of VSAM index set and sequence set records.

Key-Sequenced Data Sets

The data sets we have described so far have been key-sequenced data sets (KSDSs). You can see that the

name derives from the way VSAM stores and retrieves records based on the record key.

VSAM can retrieve the records in a KSDS in a number of ways. The simplest and most obvious way is toread each record in the logical order (lowest key to highest key) in which they are stored. This is called

sequential retrieval. Obviously, this method has limitations if you want only some of the records or if you

want them in other than key sequence order.

VSAM can use the key sequence retrieval method to return only a portion of the records. This method is

called keyed sequential retrieval or skip sequential retrieval. With this method, you specify the keys of the

records you want retrieved, but they must be in ascending order. Another method, addressed sequential

retrieval, locates the records to be retrieved by their RBA (relative byte address-the number of bytes from

the beginning of the data set to the beginning of the record). You must supply the RBAs to VSAM.

Addressed sequential retrieval can be used with KSDSs but is primarily designed for ESDSs.

VSAM can also retrieve KSDS records directly. You provide the record key, and VSAM uses the index set

and sequence set to navigate its way to the correct CI and to the record you requested. With this method,

you can retrieve records in any order.

VSAM can retrieve a record directly by its RBA. This method, addressed direct retrieval, like addressed

sequential retrieval, is designed primarily for ESDSs.

Entry-Sequenced Data Sets

Entry-sequenced data sets (ESDSs) are stored in the order in which they are loaded, rather than by key

sequence. With ESDSs, VSAM does not create an index and does not reserve free space. No index is


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needed because there are no record keys to track. Likewise, free space is not needed because the next

record added to the data set is stored at the end of the existing set of records. If a record is too large to fit

in the CI being loaded, VSAM creates a new CI and puts the record there. VSAM does not attempt to use

space that may be left at the end of each CI.

ESDSs are retrieved only by RBA using either addressed sequential retrieval or addressed direct sequential

retrieval. With addressed sequential retrieval, you give VSAM the RBA of the first record. It retrieves the

succeeding records by computing their RBA based on the record length field of each record's RDF. Withthe addressed direct method, you must supply VSAM with the RBA of each record you want.

Because of their storage and retrieval mechanisms, ESDSs have certain limitations that make them less

attractive for many applications. Although updating is relatively simple, adding and deleting records

proves more difficult. With updating, you read the record, enter changes, and rewrite it, without

changing the record length. To delete, you read the record and mark it for deletion, but VSAM does not

physically delete the record or reclaim the unused space. To add a record, you must add it at the end of

the data set.

QSAM

The queued sequential access method (QSAM) processes records sequentially from the beginning of thedata set. QSAM groups logical records into physical blocks before writing them to storage and handles the

blocking and deblocking of records for you. QSAM is typically used by application programs that retrieve

or create a single member at a time within a partitioned data set (PDS). The characteristics of a member

of a PDS-which is a collection of sequentially organized members-are the same as those of a sequential

data set.

BSAM

The basic sequential access method (BSAM) allows you to read and write physical records only. It does not

perform blocking or deblocking of records. With BSAM, you can begin processing a data set at any point

BDAM

The basic direct access method (BDAM) allows you to write or retrieve records directly by address, using

the physical track, relative track, or relative record number.

OSAM

The overflow sequential access method (OSAM) was developed for use with DL/I databases. It combines

many features of sequential access methods and of BDAM. To the operating system, an OSAM data set

appears the same as a sequential data set. An OSAM data set can be read with BSAM or QSAM. OSAM

allows direct access to records.

Database Organizations

The nine types of databases supported by IMS can be grouped by their IMS access method. Hierarchic

sequentially accessed databases include

HSAM SHSAM HISAM SHISAM GSAM

Hierarchic direct databases include

HDAM HIDAM

Fast Path databases provide fast access with limited functionality

DEDB MSDB


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Hierarchic Sequential Databases

The earliest IMS database organization types were based on sequential storage and access of database

segments. Hierarchic sequential databases share certain characteristics. Compared to hierarchic direct

databases, which we will discuss later, hierarchic sequential databases are of simpler organization. The

root and dependent segments of a record are related by physical adjacency. Access to dependent

segments is always sequential. Deleted dependent segments are not physically removed but are marked

as deleted. Hierarchic sequential databases can be stored on tape or DASD.HSAM

In a hierarchic sequential access method (HSAM) database, the segments in each record are stored

physically adjacent. Records are loaded sequentially with root segments in ascending key sequence.

Dependent segments are stored in hierarchic sequence. The record format is fixed-length and unblocked.

An HSAM database is updated by rewriting the entire database. Although HSAM databases can be stored

on DASD or tape, HSAM is basically a tape-based format. Figure 2-6 shows an HSAM database record and

segment format.

Figure 2-6: HSAM database segment structure.

IMS identifies HSAM segments by creating a two-byte prefix consisting of a segment code and a delete

byte at the beginning of each segment. HSAM segments are accessed through two operating system

access methods:

basic sequential access method (BSAM) queued sequential access method (QSAM)

QSAM is always used as the access method when the system is processing online, but you can specifyeither method for batch processing through the PROCOPT parameter in the PCB.

Entry to an HSAM database is through GET UNIQUE (GU) or GET NEXT (GN) calls. The first call starts at the

beginning of the database and searches sequentially through the records until it locates the requested

segment. Subsequent calls use that position as the starting point for calls that process forward in the

database.

HSAM databases are limited by the strictly sequential nature of the access method. DELETE (DLET) and

REPLACE (REPL) calls are not allowed, and INSERT (ISRT) calls are allowed only during the database load.

Field-level sensitivity is provided, but HSAM databases are limited in the number of IMS options they canuse.

Because of the numerous limitations, HSAM databases see limited use and are reserved primarily for

applications that require sequential processing only.

SHSAM

A simple HSAM (SHSAM) database contains only one type of segment-a fixed-length root segment.

Because there is no need for a segment code and deletes are not allowed, there is no need for a prefix

portion of a SHSAM database segment. Because they contain only user data, SHSAM databases can be
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accessed by BSAM and QSAM. The only DL/I calls used with SHSAM databases are the GET calls. Like

HSAM, SHSAM database segments can be deleted or inserted only during a reload.

HISAM

The hierarchic indexed sequential access method (HISAM) database organization adds some badly needed

capabilities not provided by HSAM. Like HSAM, HISAM databases store segments within each record in

physically adjacent sequential order. Unlike HSAM, each HISAM record is indexed, allowing direct access

to each record. This eliminates the need to read sequentially through each record until the desired record

is found. As a result, random data access is considerably faster than with HSAM. HISAM databases alsoprovide a method for sequential access when that is needed.

A HISAM database is stored in a combination of two data sets. The database index and all segments in a

database record that fit into one logical record are stored in a primary data set that is a VSAM KSDS.

Remaining segments are stored in the overflow data set, which is a VSAM ESDS. The index points to the CI

containing the root segment, and the logical record in the KSDS points to the logical record in the ESDS, if

necessary.

If segments remain to be loaded after the KSDS record and the ESDS record have been filled, IMS uses

another ESDS record, stores the additional segments there, and links the second ESDS record with apointer in the first record. You determine the record length for the KSDS and the ESDS when you create

the DBD for the database.

If segments are deleted from the database, they are still physically present in the correct position within

the hierarchy, but a delete byte is set to show that the record has been deleted. Although the segment is

no longer visible to the application program, it remains physically present and the space it occupies is

unavailable until the database is reorganized. The only exception to this is the deletion of a root segment

where the logical record in the VSAM KSDS is physically deleted and the index entry is removed; any VSAM

ESDS logical records in the overflow data set are not be deleted or updated in any way.

Inserting segments into a HISAM database often entails a significant amount of I/O activity. Because IMS

must enforce the requirement for segments to be physically adjacent and in hierarchic order, it will move

existing segments within the record or across records to make room for the insertion; however, any

dependent segments are not flagged as deleted. To facilitate indexing, HISAM databases must be defined

with a unique sequence field in each root segment. The sequence fields are used to construct the index.

HISAM databases are stored on DASD, and data access can be much faster than with HSAM databases. All

DL/I calls can be used against a HISAM database. Additionally, HISAM databases are supported by a

greater number of IMS and MVS options.

HISAM databases work well for data that requires direct access to records and sequential processing of

segments within each record.

Figure 2-7 shows the database structure for HISAM. Notice that four ESDS records have been used in

loading one logical record. The arrows represent pointers.


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Figure 2-7: HISAM database structure.

HISAM Segment Structure

Figure 2-8 shows the HISAM segment structure.

Figure 2-8: HISAM segment structure.

A HISAM segment contains the following fields:

Segment

Code

1 byte. The segment code byte contains a one-byte unsigned binary number that is unique to

the segment type within the database. The segments are numbered in hierarchic order,

starting at 1 and ending with 255 (X'01' through X'FF').

Delete

Byte1 byte. The delete byte contains a set of flags.

Counters and Pointers

The appearance of this area depends on the logical relationship status of the segment:

o If the segment is not a logical child or logical parent, this area is omitted.o If the segment is a logical child, and if a direct pointer (see "Pointer Types") is specified (the logical parent

must be in an HD database), the four-byte RBA of the logical parent will be present.

o If the segment is a logical parent and has a logical relationship that is unidirectional or bidirectional withphysical pairing, a four-byte counter will exist.

If the segment is a logical parent and has one or more logical relationships that are bidirectional with

virtual pairing, then for each relationship there is a four-byte RBA pointer to the first logical child segment

(a logical child first pointer) and, optionally, a four-byte RBA pointer to the last logical child segment (a

logical child last pointer), depending on whether you specified LCHILD=SNGL or LCHILD=DBLE in the DBD.
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There is only one counter in a segment, but there can be multiple logical child first (LCF) and logical child

last (LCL) pointers. The counter precedes the pointers. The pointers are in the order that the logical

relationships are defined in the DBD, with a logical child first pointer before a logical child last pointer.

Figure 2-9 shows a segment with multiple logical child pointers.

Figure 2-9: Multiple logical child pointers in a segment.

Data

The length of the data area (which is specified in the DBD) can be a fixed length or a variable length. For a

logical child segment with symbolic keys (PARENT=PHYSICAL on the SEGM statement), the concatenated

key of the logical parent will be at the start of the segment.

If the segment is variable length, the first two bytes of the data area are a hexadecimal number that

represents the length of the data area, including the two-byte length field.

SHISAM

As is the case with SHSAM, a simple HISAM (SHISAM) database contains only a root segment, and its

segment has no prefix portion. SHISAM databases can use only VSAM as their access method. The data

must be stored in a KSDS. All DL/I calls can be used with SHISAM databases, and their segments can beaccessed by DL/I calls and VSAM macros.

GSAM

Generalized sequential access method (GSAM) databases are designed to be compatible with MVS data

sets. They are used primarily when converting from an existing MVS-based application to IMS because

they allow access to both during the conversion process. To be compatible with MVS data sets, GSAM

databases have no hierarchy, database records, segments, or keys. GSAM databases can be based on the

VSAM or QSAM/BSAM MVS access methods. They can have fixed-length or variable-length records when

used with VSAM or fixed-length, variable-length, or undefined-length records when used with

QSAM/BSAM.

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