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| Generation Data Group Overview. |
Generation Data Group (GDG) is a concept that has been widely adopted in mainframe computing, where managing large volumes of data is a critical task. A GDG is a collection of two or more chronologically related versions of the same data set, stored as separate generations. This allows organizations to efficiently manage data while retaining access to historical versions for disaster recovery, auditing, and other purposes.
In this article, we will take a closer look at what GDGs are, how they work, and why GDGs are essential for mainframe data management. From their definition, and evolution to their use in modern mainframe environments, this article will provide a comprehensive overview of the world of Generation Data Groups (GDG) in Mainframe.
What is Generation Data Group?
A Generation Data Group (GDG) is a collection of two or more chronologically related versions of the same data set, stored as separate generations. This allows for efficient management of data and the ability to access historical versions of the data set. GDGs are commonly used in mainframe computing, where data is frequently processed in large batch jobs and where keeping multiple generations of the data set can be important for disaster recovery and auditing purposes.
What are the benefits of using GDGs in the Mainframe?
The following are some benefits of using Mainframe Generation Data Group (GDG):
- Efficient Data Management: GDGs allows for the efficient management of large volumes of data by storing multiple generations of the same data set, reducing the need for manual organization and management.
- Access to Historical Data: With GDGs, organizations have access to historical versions of their data sets, which can be important for disaster recovery, auditing, and compliance purposes.
- Improved Data Retention: GDGs can help organizations retain important data, such as backups and archives, for longer periods of time, reducing the risk of data loss.
- Increased Data Security: By storing multiple generations of data sets, GDGs can improve data security and reduce the risk of data breaches or unauthorized access to sensitive information.
- Streamlined Backup and Recovery: GDGs can simplify the process of backing up and recovering data, allowing organizations to quickly recover critical data in the event of a disaster.
- Better Data Auditing: By keeping multiple generations of data sets, GDGs can improve data auditing and make it easier to track changes and maintain compliance with regulatory requirements.
- Improved Resource Utilization: GDGs can help organizations optimize resource utilization by reducing the need for manual data management and allowing for more efficient use of storage and computing resources.
How do you define a Mainframe GDG (Generation Data Group)?
To define a Generation Data Group (GDG), you must create a catalog entry for the GDG, and create a model data set control block on the same volume as the catalog entry for the GDG. Additionally, you must create a GDG catalog entry, by invoking the VSAM utility program, AMS, and issue a DEFINE GDG command.
DEFINE Generation Data Group Syntax.
DEFINE GDG|GENERATIONDATAGROUP( NAME(entry-name)
LIMIT(limit)
[ EMPTY | NOEMPTY ]
[ SCRATCH | NOSCRATCH ]
[ OWNER(owner-id) ]
[ TO(yyyyddd) | FOR(nnnn) ] )
| Name | Specifies the name of the generation data group. This name follows normal data set naming conventions but is limited to 35 characters. |
| LIMIT | Specifies how many generation data sets are to be maintained in the group. The maximum limit is 255. |
| EMPTY | NOEMPTY | Specifies what action should be taken when the LIMIT value is reached. EMPTY means that all generations should be removed from the group. NOEMPTY means that just the oldest generation should be removed. |
| SCRATCH | NOSCRATCH | Specifies whether the data sets that are removed from the group should be scratched (deleted) or just un-cataloged. |
| TO | FOR | Specifies an expiration date or a retention period for the GDG. |
Sample JCL to define a generation data group (GDG)
//JOB123 JOB (12345),'RC MAINFRAME', CLASS=0,
// MSGCLASS=X,MSGLEVEL=(1,1),NOTIFY=&SYSUID
//STEP1 EXEC PGM=IDCAMS
//MODEL DD DSNAME=RCTM.EMPPAYRL.MNTH.GDG,
// DISP=(NEW,KEEP,DELETE),UNIT=DISK,
// SPACE=(CYL,(10,10),RLSE),
// DCB(DSORG=PS,RECFM=FB,LRECL=80)
//SYSIN DD *
DEFINE GDG (NAME(PRRT.PAYROLL.MONTH.GDG) -
LIMIT(30) -
SCRACTH -
EMPTY)
/*
//
Note: In above mention example, the job would define the new Generation Data Group (GDG) base (i.e. RCTM.EMPPAYRL.MNTH.GDG) with a limit of 30. We have used IDCAM's utility to define, however same can be achieved FILE-AID / FILE-MANAGER or using ISPF utility.
DELETE Generation Data Group (GDG) Syntax.
DELETE entry-nameGDG|GENERATIONDATAGROUP
[ PURGE|NOPURGE ]
[ FORCE|NOFORCE ]
| entry-name | Provide the name of the GDG catalog entry that is to be deleted. |
| GDG | Specifies that the entry to be deleted is a generation data group. |
| PURGE | Deletes the catalog entry even if the expiration date hasn’t been reached. When used with FORCE, it deletes the catalog entry and the GDG members even if any of their expiration dates haven’t been reached. |
| FORCE | Forces the GDG catalog entry and all of its members to be deleted, assuming that the expiration dates have passed or that PURGE is coded. |
Sample JCL to delete a generation data group (GDG)
//JOB123 JOB (12345),'RC MAINFRAME', CLASS=0,// MSGCLASS=X,MSGLEVEL=(1,1),NOTIFY=&SYSUID
//STEP1 EXEC PGM=IDCAMS
//SYSIN DD *
PURGE –
FORCE
/*
Youtube: Generation Data Group (GDG) Tutorial.
Conclusion.
In conclusion, the Mainframe Generation Data Group (GDG) is an essential tool for managing and organizing data in mainframe environments. With its ability to efficiently manage large volumes of data, provide access to historical data, simplify backup and recovery processes, improve data auditing, and optimize resource utilization, GDGs provide a comprehensive solution for organizations that rely on mainframes for their data processing needs.
As data management continues to evolve and become more complex, GDGs will remain a crucial component of mainframe data management, ensuring that organizations can effectively manage their data and meet the demands of their growing business.
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