Deleting a GDG base is fairly straightforward. First, you must delete each of the generations that make up the base, starting with the oldest generation. This can be done using the DELETE command and specifying the volume serial and the generation number.
Once all generations have been deleted, the macro that defined the GDG base must be deleted, using the DELETE command with the syntax T{dsname}. If any resource-action measures, such as system-wide alerts or reports, were set up to run when generating a new generation, they must be removed using the CLEAR command.
After the above steps have been taken, the GDG base will have been deleted.
What is purge in GDG?
Purge in GDG (Generation Data Groups) is a method of managing and keeping track of data sets in the mainframe environment. It is a feature of the IBM mainframe operating system that allows for the time-phased data sets in the system to be managed more easily.
A GDG typically consists of a base data set and several successive generations of that data set. The purge process allows administrators to specify which generations of the GDG should be available for use, thereby preventing unnecessary data set generations from accumulating on the mainframe.
The purging process is a critical tool used by system administrators to manage the total number of data sets and conserve disk space on the mainframe. Purge operations can be scheduled on a regular basis to ensure that disk space remains available for storing other types of data sets.
What is scratch and no scratch in GDG?
Scratch and No Scratch are terms used in Google Developer Groups (GDGs) to reference the diversity of members. Scratch members are typically new to the GDG and their interests may lie in coding, digital marketing, UX/UI design, data science, and more.
On the other hand, No Scratch members are more experienced, often having been in the group for a longer period of time. They may have a more advanced understanding of programming languages, digital marketing techniques, and other key elements of development.
These members are often seen as the mentors of the group, providing support and guidance to the Scratch members.
What is catalog and Uncatalog in JCL?
Catalog and Uncatalog are two commands used in Job Control Language (JCL) to manage cataloged and uncataloged tape and disk and libraries. A cataloged file or library is one defined to the operating system, allowing it to be accessed by its name.
Cataloged libraries are saved in a catalog maintained by the operating system, and an uncataloged library is not saved in the system catalog.
With the CATALOG command, users can catalog tape and disk libraries and assign a name to them. This means that data sets, files and libraries can be referred to and accessed by their user-defined name instead of a device name or volume serial.
The CATALOG command also allows users to build a link between two libraries or to a data set, and allows users to replace other cataloged files, or to add new files.
The UNCATALOG command removes the cataloged entry of a library or file and removes it from the system catalog. After the UNCATALOG command the file or library is considered uncataloged, and the user must refer to it using its device name or volume serial.
The UNCATALOG command is often used when a user is finished using a program and does not intend to use it again. It is also used when a library needs to be changed so that is no longer accessibly.
How do you skip steps in JCL?
Skipping steps in a JCL (Job Control Language) job is one of the most effective ways of avoiding unnecessary processing and saving resources. To skip a step in the JCL job, the “COND parameter” is used.
This parameter is used to specify the conditions under which a step should be skipped. It can be used to skip a step based on the return code of the previous step, or the date, time and volume. Some of the most common uses of the COND parameter are to skip steps that are only needed during certain times of the day, weeks or months, steps that are only needed when certain datasets are present or missing, or steps that are only needed if certain tasks have failed.
The COND parameter can also be used to specify ‘go to’ conditions, to jump over one or more steps and change the flow of the JCL job. In addition to using the COND parameter, some utilities and tools can also be used to control the operation flow of the job.
For example, the DFSMSdss utility has the ability to skip following steps upon successful completion of certain steps, and the IBM ISPF editor can be used to alter and comment out selected sections of the job.
What is the maximum limit of GDG?
The maximum limit of GDG, or Google Developer Groups, is determined by Google. Generally speaking, the maximum limit for each GDG location is 150 members. However, Google does sometimes grant exceptions for larger GDG groups.
Google requires that all GDG groups are active and engaged in order to be able to administer the group effectively. As a result, if the group falls inactive or fails to adhere to the GDG Code of Conduct, then the group may be asked to limit its membership size or reformed.
In addition, GDG members must also agree to the GDG Code of Conduct before they can join.
In short, the maximum limit of a GDG group depends largely on Google’s discretion and a group’s adherence to the GDG Code of Conduct.
What is scratch in mainframe?
Scratch in mainframe refers to a section of memory in the mainframe system used as a temporary workspace. This memory is called a “scratch pad” and used by applications, utilities and other programs to store intermediate data while they are running.
The scratch space is typically not assigned to any specific use and is essentially an empty space that applications can take advantage of to carry out their tasks. Scratch memory on the mainframe can range in size from several hundred bytes to several kilobytes depending on the system configuration.
It can also be used by system personnel for diagnostics, running utilities and other tasks to aid in getting the system to work correctly. It is important to understand that data stored in the scratch space is usually not persistent and is usually wiped from the system each time the mainframe system is restarted.
How can we retain two versions of a single GDG generation?
Retaining two versions of a single GDG (General Data Group) generation can be achieved by using the labels referred to as a version-identifying qualifier. This is done by specifying a version-identifying qualifier when defining the GDG base.
Thus, when a dataset receives a new generation, the dataset can be assigned with a label. This label will help to identify the dataset as either being current or with a previous version.
The process of retaining two different versions of the same GDG generation involves creating two distinct base definitions, one for the current version and another for the older version. It is important to note that the two file base definitions needs to have different names, hence they will be referred to differently while defining members.
For example, if the current version is V1 and the old version is V2, two distinct base definitions needs to be created such as V1 and V2 respectively. This can be achieved by using the DEFINE command.
The DEFINE command can also used to specify the number of generations each version can contain. For example, for V1 the DEFINE command will include an SYSIN statement such as DYNALLOC=((V1,5)). This indicates that only up to 5 generations will be retained under the version V1.
Similarly, the DEFINE command for V2 can include an SYSIN statement such as DYNALLOC=((V2,2)). This indicates that only up to 2 generations will be retained under the version V2.
Once the base definitions are in place for each versions, the GDG process can go ahead with its usual process (ie. creating new generations as and when required). However, the new generations will be automatically assigned with the appropriate label depending on the printer’s base definition, thus retaining two versions of the same generation.
What is Iebgener utility in JCL?
The IEFGENER utility is used in IBM Job Control Language (JCL) to create resource information in a shared pool. It produces executable Resource Definition (RDE) modules that contain information about a shared resource (such as an input/output dataset), making it easier for other job steps to access the shared resource.
The generated RDE modules are created for a specific Jobs operating system (and specific operating system version), and can be used for all other jobs that use the same operating system. The IEFGENER utility also allows the user to identify different resource types and allows the user to combine resource definitions into a single pool, which can be used across multiple jobs.
By using IEFGENER to create the resource information, the user can speed up the process of accessing the shared resource, as well as reduce IO contention.
How many GDG versions can be created?
Depending on the data that needs to be stored, as well as the time period in which the data will be stored, the number of GDG versions created can range from one to hundreds. It is important to note that for each GDG version that is created, the associated logical dataset names can only contain 16 characters, which will affect the number of versions that can be stored by a given system.
In addition, the number of generations of GDG versions can be limited to a defined number. For example, if a storage system allows for 8 versions, then it would only be able to store 8 version generations of a given GDG.
Ultimately, most systems will have a maximum of 100 GDG versions that are available for use.
What are the parameters used for creating GDG?
GDG stands for Generation Data Groups, which are a vital part of IBM’s z/OS operating system. GDGs are used to store and organize datasets that have a large number of versions, or generations, that exist across multiple computing platforms.
These datasets can include data files, program libraries, or output files. To create a GDG, there are five parameters that need to be specified.
The first parameter is the main GDG base name, which is the common name of all the generations and must be eight characters or fewer. The second parameter is the GDG base, which is the number that specifies how many generations of the GDG will be retained.
Usually, 0 or a negative number is used for this value. The third parameter is the GDG limit, which is the maximum number of generations allowed for the GDG. The fourth parameter is the GDG data set size, which is the size in KB of the datasets.
Finally, the fifth parameter is the data set organization, which is the type of data in the dataset such as PS.
Once these five parameters have been specified, the GDG can be created. It is important to note that once created, the first generation of a GDG will have a generation number of zero, and each successive generation incrementally increases the generation number by one.
GDGs can be an invaluable asset for organizations that need to manage large amounts of data, as the GDG allows for dataset versions to be maintained, organized, and accessed quickly.
Can we alter GDG limit?
Yes, you can alter GDG limit. GDG is a type of dataset that is dynamically allocated as more data is added. Thus, unlike conventional datasets, GDG limits can be adjusted as needed. To alter the GDG limit, you need to use the IDCAMS REPRO command with the LIMIT parameter.
The LIMIT parameter allows you to specify the maximum size of the GDG base. You can also use the MAXG number parameter, which specifies the maximum number of generations that the GDG will maintain. Keep in mind that the changes you make with the REPRO command will not be reflected until the GDG dataset is closed and reopened.
How check if file is empty in JCL?
To check if a file is empty in JCL, a COND parameter in your JCL can be used. This COND parameter allows you to specify a condition that must be met before the file is allowed to be processed. The syntax used to determine if a file is empty is COND=(0,EQ).
This condition checks if the dataset is empty by comparing the file size to zero (0), and if the file size equals 0, then the file is empty. If the file is not empty, then the COND parameter will evaluate to false and the job step processing the file will be bypassed.
It is important to note that this method will only check for the size of the file and not the actual content of the file, so any loop holes in how the empty file is written will not be caught.