SlideShare a Scribd company logo
Datasets and Catalogs
Datasets
• Collection of data records which are logically connected.
• Every Dataset has an unique name which can be of 44 characters.
• Maximum 22 name segments separated by ‘.’ can be used. Overall
length is inclusive of the ‘.’.
• First segment of the name is called High Level Qualifier (HLQ),
Last segment is called Low Level Qualifier (LLQ) and the remaining
are called Middle Level Qualifiers.
Types of Datasets
• VSAM and Non-VSAM.
• VSAM Datasets – ESDS, KSDS, RRDS, and LDS
• Non-VSAM Datasets – PS, PDS, and PDSE
Non-VSAM Datasets
• PS – Physical Sequential – Contains Data stored inside it.
• PDS and PDSE – Partitioned Dataset and PDS(Extended) – Contains
sequential datasets in which the data is stored. These datasets
inside the PDS or PDSE are called PDS Members.
• PDS and PDSE are also called Libraries.
Non-VSAM Datasets (Contd..) – PS vs PDS
• PS – The records are grouped into BLOCKS. A Block is a basic unit that can
be read by the system into memory while processing the dataset. It is a
good idea to group as more records as possible for efficient processing of
data.
• PDS – The members are grouped into Directory Blocks. This determines
the number of members that can be created as each directory block can
have approximately 6 members.
Non-VSAM Datasets (Contd..) – PDS vs PDSE
PDS PDSE
Partitioned Dataset Partitioned Dataset Extended
Dataset Organisation – PO Dataset Organisation – LIBRARY
Can be stored on Tape and DASD Can be stored only on DASD
Slower Directory searches Faster Directory searches
Free space is not reused automatically,
manual compression required
Free space is automatically reused, no
manual compression required
Space is allocated in 16 extents Space is allocated in 123 extents
Non –VSAM Datasets (Contd..)
• Datasets are allocated space in extents of units (Cylinders, Tracks
etc.).
• Extents refer to the number of chunks of contiguous storage space
allocated at a time.
• If a dataset is allocated a space of 20 primary and 10 secondary
quantities of Cylinders, then there will be 1*20 + 15*10 = 170 Cylinders
of space allocated.
VSAM Datasets
• VSAM – Virtual Storage Access Method
• There are 4 types of VSAM Datasets – KSDS, ESDS, RRDS, and LDS
• Stored only on DASD for faster access.
• VSAM datasets have components like Cluster, Control Interval and Control Area.
• Cluster further has Data and Index components based on the type.
• VSAM records are grouped into Control Intervals (CI).
• CI is a smallest unit of data that can be swapped between DASD and memory for
processing.
• Cis are further grouped into Control Areas (CA).
• IDCAMS is the IBM utility used to Define, Delete, Rename, Copy etc the VSAM
datasets
VSAM vs Non-VSAM
Non-VSAM VSAM
PS KSDS, ESDS, RRDS, LDS
IEBGENER, IEFBR14 etc. IDCAMS
Stored on DASD and Tape Stored only on DASD
Records are grouped into Blocks Records are grouped into CI and CA
VSAM Datasets - KSDS
• KSDS – Key-Sequenced Dataset
• It has data and index components.
• Records in KSDS are uniquely identified by a key, arranged in key
sequential order.
• Data component contains records, where as the Index component
is used for faster access of the data in Data component.
• Records can be accessed sequentially or dynamically by supplying
the key value.
• Keyword ‘INDEXED’ is sued to create KSDS Datasets.
VSAM Datasets – ESDS
• Entry Sequential Dataset.
• It has only Data component.
• Records are arranged in the order they are inserted, identified by their
physical address – Relative Byte Address (RBA). If record length is 20,
then 0 is the RBA for first record(0-19), 20 is the RBA for second
record(20-39).
• Records can be accessed sequentially or dynamically by supplying the
RBA value.
• Keyword ‘NONINDEXED’ is used to create ESDS datasets.
• Records in the ESDS cannot be deleted but can be marked inactive.
VSAM Datasets - RRDS
• Relative Record Dataset.
• It has only Data component.
• Space allocated is divided into slots of fixed length, records of
fixed length or variable length can be inserted into the slots.
• Each record is identified by RRN – Relative Record Number which is
similar to serial numbers.
• Records can be accessed sequentially or dynamically by supplying
the RRN number.
• Keyword ‘NUMBERED’ is sued to create RRDS datasets.
Creation of VSAM through a JCL
//EXAMPLE JOB(VSAMJCL,XXXXXX),CLASS=A,MSGCLASS=A
//STEP1 EXEC PGM=IDCAMS
//SYSPRINT DD SYSOUT=*
//SYSIN DD *
DEFINE CLUSTER (NAME(EXT1RRV.VSAM.DATASET) -
INDEXED/NONINDEXED/NUMBERED - --> Specific to VSAM type
<<<Other parameters specific to type of VSAM>>>
DATA (NAME(EXT1RRV.VSAM.DATASET.DATA)) - --> For all types of VSAMs
INDEX (NAME(EXT1RRV.VSAM.DATASET.INDEX)) --> Only for KSDS VSAM
/*
Catalogs
• There are system critical datasets and user datasets in any environment.
• Catalogs provide the facility of isolating system datasets from user
datasets.
• A Catalog is a VSAM dataset that maintains records of all the other
datasets and the volumes in which they are stored.
• VTOC - Every Volume has a Table Of Contents that tells the system about
the physical address of the datasets stored inside it.
• In order to access a dataset, system reads the catalog to find the volume
in which it is stored and then reads the VTOC of that volume.
Types of Catalogs
• There are two types of catalogs – Master and User
• Master catalog maintains all the system datasets. Master Catalog is
created during the system generation and is stored in system
volume.
• User/Application datasets are separated from system datasets by
creating User catalogs. Aliases for user datasets are mapped to a
user catalog.
• Alias is nothing but the HLQ of the datasets but can be multilevel
as well.
• LISTCAT in front of a user dataset displays the user catalog.
Thank You!

More Related Content

What's hot

ALL ABOUT DB2 DSNZPARM
ALL ABOUT DB2 DSNZPARMALL ABOUT DB2 DSNZPARM
ALL ABOUT DB2 DSNZPARM
IBM
 
DB2 and storage management
DB2 and storage managementDB2 and storage management
DB2 and storage management
Craig Mullins
 
Vsam
VsamVsam
DB2 utilities
DB2 utilitiesDB2 utilities
DB2 utilities
Udayakumar Suseendran
 
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the mythsDB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
Florence Dubois
 
A First Look at the DB2 10 DSNZPARM Changes
A First Look at the DB2 10 DSNZPARM ChangesA First Look at the DB2 10 DSNZPARM Changes
A First Look at the DB2 10 DSNZPARM ChangesWillie Favero
 
MVS ABEND CODES
MVS ABEND CODESMVS ABEND CODES
MVS ABEND CODES
Nirmal Pati
 
Basic VSAM
Basic VSAMBasic VSAM
Basic VSAM
Dan O'Dea
 
Best practices for DB2 for z/OS log based recovery
Best practices for DB2 for z/OS log based recoveryBest practices for DB2 for z/OS log based recovery
Best practices for DB2 for z/OS log based recovery
Florence Dubois
 
Mainframe JCL Part - 1
Mainframe JCL Part - 1Mainframe JCL Part - 1
Mainframe JCL Part - 1
janaki ram
 
Db2
Db2Db2
Db2
yboren
 
Skillwise JCL
Skillwise JCLSkillwise JCL
Skillwise JCL
Skillwise Group
 
DB2 on Mainframe
DB2 on MainframeDB2 on Mainframe
DB2 on Mainframe
Skillwise Group
 
Jcl tutor
Jcl tutorJcl tutor
Jcl tutorshivas
 
Introduction of ISPF
Introduction of ISPFIntroduction of ISPF
Introduction of ISPF
Anil Bharti
 
Job Control Language
Job Control LanguageJob Control Language
Job Control Language
kapa rohit
 
IBM DB2 for z/OS Administration Basics
IBM DB2 for z/OS Administration BasicsIBM DB2 for z/OS Administration Basics
IBM DB2 for z/OS Administration Basics
IBM
 
Dbms Introduction and Basics
Dbms Introduction and BasicsDbms Introduction and Basics
Dbms Introduction and Basics
SHIKHA GAUTAM
 
GDPS and System Complex
GDPS and System ComplexGDPS and System Complex
GDPS and System Complex
Najmi Mansoor Ahmed
 

What's hot (20)

ALL ABOUT DB2 DSNZPARM
ALL ABOUT DB2 DSNZPARMALL ABOUT DB2 DSNZPARM
ALL ABOUT DB2 DSNZPARM
 
DB2 and storage management
DB2 and storage managementDB2 and storage management
DB2 and storage management
 
Vsam
VsamVsam
Vsam
 
DB2 utilities
DB2 utilitiesDB2 utilities
DB2 utilities
 
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the mythsDB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
DB2 for z/OS and DASD-based Disaster Recovery - Blowing away the myths
 
A First Look at the DB2 10 DSNZPARM Changes
A First Look at the DB2 10 DSNZPARM ChangesA First Look at the DB2 10 DSNZPARM Changes
A First Look at the DB2 10 DSNZPARM Changes
 
MVS ABEND CODES
MVS ABEND CODESMVS ABEND CODES
MVS ABEND CODES
 
Basic VSAM
Basic VSAMBasic VSAM
Basic VSAM
 
Best practices for DB2 for z/OS log based recovery
Best practices for DB2 for z/OS log based recoveryBest practices for DB2 for z/OS log based recovery
Best practices for DB2 for z/OS log based recovery
 
Mainframe JCL Part - 1
Mainframe JCL Part - 1Mainframe JCL Part - 1
Mainframe JCL Part - 1
 
Db2
Db2Db2
Db2
 
Skillwise JCL
Skillwise JCLSkillwise JCL
Skillwise JCL
 
DB2 on Mainframe
DB2 on MainframeDB2 on Mainframe
DB2 on Mainframe
 
Tso and ispf
Tso and ispfTso and ispf
Tso and ispf
 
Jcl tutor
Jcl tutorJcl tutor
Jcl tutor
 
Introduction of ISPF
Introduction of ISPFIntroduction of ISPF
Introduction of ISPF
 
Job Control Language
Job Control LanguageJob Control Language
Job Control Language
 
IBM DB2 for z/OS Administration Basics
IBM DB2 for z/OS Administration BasicsIBM DB2 for z/OS Administration Basics
IBM DB2 for z/OS Administration Basics
 
Dbms Introduction and Basics
Dbms Introduction and BasicsDbms Introduction and Basics
Dbms Introduction and Basics
 
GDPS and System Complex
GDPS and System ComplexGDPS and System Complex
GDPS and System Complex
 

Similar to Datasets and catalogs

16119 - Get to Know Your Data Sets (1).pdf
16119 - Get to Know Your Data Sets (1).pdf16119 - Get to Know Your Data Sets (1).pdf
16119 - Get to Know Your Data Sets (1).pdf
3operatordcslipiPeng
 
Database Administrator interview questions and answers
Database Administrator interview questions and answersDatabase Administrator interview questions and answers
Database Administrator interview questions and answers
MLR Institute of Technology
 
1650607.ppt
1650607.ppt1650607.ppt
1650607.ppt
KalsoomTahir2
 
Vsam presentation PPT
Vsam presentation PPTVsam presentation PPT
Vsam presentation PPTAnil Polsani
 
Storage Networking and Overview ppt.pdf
Storage Networking and Overview ppt.pdfStorage Networking and Overview ppt.pdf
Storage Networking and Overview ppt.pdf
Dr. Sajal Saha
 
Ch 2-introduction to dbms
Ch 2-introduction to dbmsCh 2-introduction to dbms
Ch 2-introduction to dbms
Rupali Rana
 
storage techniques_overview-1.pptx
storage techniques_overview-1.pptxstorage techniques_overview-1.pptx
storage techniques_overview-1.pptx
20CS102RAMMPRASHATHK
 
Cassandra tutorial
Cassandra tutorialCassandra tutorial
Cassandra tutorial
Ramakrishna kapa
 
Data Indexing Presentation-My.pptppt.ppt
Data Indexing Presentation-My.pptppt.pptData Indexing Presentation-My.pptppt.ppt
Data Indexing Presentation-My.pptppt.ppt
sdsm2
 
Queryproc2
Queryproc2Queryproc2
Introduction to Databases by Dr. Kamal Gulati
Introduction to Databases by Dr. Kamal GulatiIntroduction to Databases by Dr. Kamal Gulati
Cassandra - A Distributed Database System
Cassandra - A Distributed Database System Cassandra - A Distributed Database System
Cassandra - A Distributed Database System
Md. Shohel Rana
 
6.2 software
6.2 software6.2 software
12. oracle database architecture
12. oracle database architecture12. oracle database architecture
12. oracle database architecture
Amrit Kaur
 
Building your data warehouse with Redshift
Building your data warehouse with RedshiftBuilding your data warehouse with Redshift
Building your data warehouse with Redshift
Amazon Web Services
 
Rdbms
RdbmsRdbms
Dbms ii mca-ch1-ch2-intro-datamodel-2013
Dbms ii mca-ch1-ch2-intro-datamodel-2013Dbms ii mca-ch1-ch2-intro-datamodel-2013
Dbms ii mca-ch1-ch2-intro-datamodel-2013
Prosanta Ghosh
 
Unit 4 data storage and querying
Unit 4   data storage and queryingUnit 4   data storage and querying
Unit 4 data storage and querying
Ravindran Kannan
 
Deep Dive on Amazon Redshift
Deep Dive on Amazon RedshiftDeep Dive on Amazon Redshift
Deep Dive on Amazon Redshift
Amazon Web Services
 
AWS June 2016 Webinar Series - Amazon Redshift or Big Data Analytics
AWS June 2016 Webinar Series - Amazon Redshift or Big Data AnalyticsAWS June 2016 Webinar Series - Amazon Redshift or Big Data Analytics
AWS June 2016 Webinar Series - Amazon Redshift or Big Data Analytics
Amazon Web Services
 

Similar to Datasets and catalogs (20)

16119 - Get to Know Your Data Sets (1).pdf
16119 - Get to Know Your Data Sets (1).pdf16119 - Get to Know Your Data Sets (1).pdf
16119 - Get to Know Your Data Sets (1).pdf
 
Database Administrator interview questions and answers
Database Administrator interview questions and answersDatabase Administrator interview questions and answers
Database Administrator interview questions and answers
 
1650607.ppt
1650607.ppt1650607.ppt
1650607.ppt
 
Vsam presentation PPT
Vsam presentation PPTVsam presentation PPT
Vsam presentation PPT
 
Storage Networking and Overview ppt.pdf
Storage Networking and Overview ppt.pdfStorage Networking and Overview ppt.pdf
Storage Networking and Overview ppt.pdf
 
Ch 2-introduction to dbms
Ch 2-introduction to dbmsCh 2-introduction to dbms
Ch 2-introduction to dbms
 
storage techniques_overview-1.pptx
storage techniques_overview-1.pptxstorage techniques_overview-1.pptx
storage techniques_overview-1.pptx
 
Cassandra tutorial
Cassandra tutorialCassandra tutorial
Cassandra tutorial
 
Data Indexing Presentation-My.pptppt.ppt
Data Indexing Presentation-My.pptppt.pptData Indexing Presentation-My.pptppt.ppt
Data Indexing Presentation-My.pptppt.ppt
 
Queryproc2
Queryproc2Queryproc2
Queryproc2
 
Introduction to Databases by Dr. Kamal Gulati
Introduction to Databases by Dr. Kamal GulatiIntroduction to Databases by Dr. Kamal Gulati
Introduction to Databases by Dr. Kamal Gulati
 
Cassandra - A Distributed Database System
Cassandra - A Distributed Database System Cassandra - A Distributed Database System
Cassandra - A Distributed Database System
 
6.2 software
6.2 software6.2 software
6.2 software
 
12. oracle database architecture
12. oracle database architecture12. oracle database architecture
12. oracle database architecture
 
Building your data warehouse with Redshift
Building your data warehouse with RedshiftBuilding your data warehouse with Redshift
Building your data warehouse with Redshift
 
Rdbms
RdbmsRdbms
Rdbms
 
Dbms ii mca-ch1-ch2-intro-datamodel-2013
Dbms ii mca-ch1-ch2-intro-datamodel-2013Dbms ii mca-ch1-ch2-intro-datamodel-2013
Dbms ii mca-ch1-ch2-intro-datamodel-2013
 
Unit 4 data storage and querying
Unit 4   data storage and queryingUnit 4   data storage and querying
Unit 4 data storage and querying
 
Deep Dive on Amazon Redshift
Deep Dive on Amazon RedshiftDeep Dive on Amazon Redshift
Deep Dive on Amazon Redshift
 
AWS June 2016 Webinar Series - Amazon Redshift or Big Data Analytics
AWS June 2016 Webinar Series - Amazon Redshift or Big Data AnalyticsAWS June 2016 Webinar Series - Amazon Redshift or Big Data Analytics
AWS June 2016 Webinar Series - Amazon Redshift or Big Data Analytics
 

Recently uploaded

OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoam
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamOpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoam
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoam
takuyayamamoto1800
 
Cracking the code review at SpringIO 2024
Cracking the code review at SpringIO 2024Cracking the code review at SpringIO 2024
Cracking the code review at SpringIO 2024
Paco van Beckhoven
 
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...
Juraj Vysvader
 
First Steps with Globus Compute Multi-User Endpoints
First Steps with Globus Compute Multi-User EndpointsFirst Steps with Globus Compute Multi-User Endpoints
First Steps with Globus Compute Multi-User Endpoints
Globus
 
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...
Anthony Dahanne
 
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...
Globus
 
SOCRadar Research Team: Latest Activities of IntelBroker
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar Research Team: Latest Activities of IntelBroker
SOCRadar Research Team: Latest Activities of IntelBroker
SOCRadar
 
Into the Box 2024 - Keynote Day 2 Slides.pdf
Into the Box 2024 - Keynote Day 2 Slides.pdfInto the Box 2024 - Keynote Day 2 Slides.pdf
Into the Box 2024 - Keynote Day 2 Slides.pdf
Ortus Solutions, Corp
 
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERROR
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERROR
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERROR
Tier1 app
 
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...
Globus
 
May Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdfMay Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdf
Adele Miller
 
Globus Compute Introduction - GlobusWorld 2024
Globus Compute Introduction - GlobusWorld 2024Globus Compute Introduction - GlobusWorld 2024
Globus Compute Introduction - GlobusWorld 2024
Globus
 
Lecture 1 Introduction to games development
Lecture 1 Introduction to games developmentLecture 1 Introduction to games development
Lecture 1 Introduction to games development
abdulrafaychaudhry
 
Globus Compute wth IRI Workflows - GlobusWorld 2024
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus Compute wth IRI Workflows - GlobusWorld 2024
Globus Compute wth IRI Workflows - GlobusWorld 2024
Globus
 
2024 RoOUG Security model for the cloud.pptx
2024 RoOUG Security model for the cloud.pptx2024 RoOUG Security model for the cloud.pptx
2024 RoOUG Security model for the cloud.pptx
Georgi Kodinov
 
How Recreation Management Software Can Streamline Your Operations.pptx
How Recreation Management Software Can Streamline Your Operations.pptxHow Recreation Management Software Can Streamline Your Operations.pptx
How Recreation Management Software Can Streamline Your Operations.pptx
wottaspaceseo
 
How to Position Your Globus Data Portal for Success Ten Good Practices
How to Position Your Globus Data Portal for Success Ten Good PracticesHow to Position Your Globus Data Portal for Success Ten Good Practices
How to Position Your Globus Data Portal for Success Ten Good Practices
Globus
 
top nidhi software solution freedownload
top nidhi software solution freedownloadtop nidhi software solution freedownload
top nidhi software solution freedownload
vrstrong314
 
A Sighting of filterA in Typelevel Rite of Passage
A Sighting of filterA in Typelevel Rite of PassageA Sighting of filterA in Typelevel Rite of Passage
A Sighting of filterA in Typelevel Rite of Passage
Philip Schwarz
 
Prosigns: Transforming Business with Tailored Technology Solutions
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns: Transforming Business with Tailored Technology Solutions
Prosigns: Transforming Business with Tailored Technology Solutions
Prosigns
 

Recently uploaded (20)

OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoam
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamOpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoam
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoam
 
Cracking the code review at SpringIO 2024
Cracking the code review at SpringIO 2024Cracking the code review at SpringIO 2024
Cracking the code review at SpringIO 2024
 
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...
 
First Steps with Globus Compute Multi-User Endpoints
First Steps with Globus Compute Multi-User EndpointsFirst Steps with Globus Compute Multi-User Endpoints
First Steps with Globus Compute Multi-User Endpoints
 
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...
 
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...
 
SOCRadar Research Team: Latest Activities of IntelBroker
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar Research Team: Latest Activities of IntelBroker
SOCRadar Research Team: Latest Activities of IntelBroker
 
Into the Box 2024 - Keynote Day 2 Slides.pdf
Into the Box 2024 - Keynote Day 2 Slides.pdfInto the Box 2024 - Keynote Day 2 Slides.pdf
Into the Box 2024 - Keynote Day 2 Slides.pdf
 
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERROR
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERROR
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERROR
 
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...
 
May Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdfMay Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdf
 
Globus Compute Introduction - GlobusWorld 2024
Globus Compute Introduction - GlobusWorld 2024Globus Compute Introduction - GlobusWorld 2024
Globus Compute Introduction - GlobusWorld 2024
 
Lecture 1 Introduction to games development
Lecture 1 Introduction to games developmentLecture 1 Introduction to games development
Lecture 1 Introduction to games development
 
Globus Compute wth IRI Workflows - GlobusWorld 2024
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus Compute wth IRI Workflows - GlobusWorld 2024
Globus Compute wth IRI Workflows - GlobusWorld 2024
 
2024 RoOUG Security model for the cloud.pptx
2024 RoOUG Security model for the cloud.pptx2024 RoOUG Security model for the cloud.pptx
2024 RoOUG Security model for the cloud.pptx
 
How Recreation Management Software Can Streamline Your Operations.pptx
How Recreation Management Software Can Streamline Your Operations.pptxHow Recreation Management Software Can Streamline Your Operations.pptx
How Recreation Management Software Can Streamline Your Operations.pptx
 
How to Position Your Globus Data Portal for Success Ten Good Practices
How to Position Your Globus Data Portal for Success Ten Good PracticesHow to Position Your Globus Data Portal for Success Ten Good Practices
How to Position Your Globus Data Portal for Success Ten Good Practices
 
top nidhi software solution freedownload
top nidhi software solution freedownloadtop nidhi software solution freedownload
top nidhi software solution freedownload
 
A Sighting of filterA in Typelevel Rite of Passage
A Sighting of filterA in Typelevel Rite of PassageA Sighting of filterA in Typelevel Rite of Passage
A Sighting of filterA in Typelevel Rite of Passage
 
Prosigns: Transforming Business with Tailored Technology Solutions
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns: Transforming Business with Tailored Technology Solutions
Prosigns: Transforming Business with Tailored Technology Solutions
 

Datasets and catalogs

  • 2. Datasets • Collection of data records which are logically connected. • Every Dataset has an unique name which can be of 44 characters. • Maximum 22 name segments separated by ‘.’ can be used. Overall length is inclusive of the ‘.’. • First segment of the name is called High Level Qualifier (HLQ), Last segment is called Low Level Qualifier (LLQ) and the remaining are called Middle Level Qualifiers.
  • 3. Types of Datasets • VSAM and Non-VSAM. • VSAM Datasets – ESDS, KSDS, RRDS, and LDS • Non-VSAM Datasets – PS, PDS, and PDSE
  • 4. Non-VSAM Datasets • PS – Physical Sequential – Contains Data stored inside it. • PDS and PDSE – Partitioned Dataset and PDS(Extended) – Contains sequential datasets in which the data is stored. These datasets inside the PDS or PDSE are called PDS Members. • PDS and PDSE are also called Libraries.
  • 5. Non-VSAM Datasets (Contd..) – PS vs PDS • PS – The records are grouped into BLOCKS. A Block is a basic unit that can be read by the system into memory while processing the dataset. It is a good idea to group as more records as possible for efficient processing of data. • PDS – The members are grouped into Directory Blocks. This determines the number of members that can be created as each directory block can have approximately 6 members.
  • 6. Non-VSAM Datasets (Contd..) – PDS vs PDSE PDS PDSE Partitioned Dataset Partitioned Dataset Extended Dataset Organisation – PO Dataset Organisation – LIBRARY Can be stored on Tape and DASD Can be stored only on DASD Slower Directory searches Faster Directory searches Free space is not reused automatically, manual compression required Free space is automatically reused, no manual compression required Space is allocated in 16 extents Space is allocated in 123 extents
  • 7. Non –VSAM Datasets (Contd..) • Datasets are allocated space in extents of units (Cylinders, Tracks etc.). • Extents refer to the number of chunks of contiguous storage space allocated at a time. • If a dataset is allocated a space of 20 primary and 10 secondary quantities of Cylinders, then there will be 1*20 + 15*10 = 170 Cylinders of space allocated.
  • 8. VSAM Datasets • VSAM – Virtual Storage Access Method • There are 4 types of VSAM Datasets – KSDS, ESDS, RRDS, and LDS • Stored only on DASD for faster access. • VSAM datasets have components like Cluster, Control Interval and Control Area. • Cluster further has Data and Index components based on the type. • VSAM records are grouped into Control Intervals (CI). • CI is a smallest unit of data that can be swapped between DASD and memory for processing. • Cis are further grouped into Control Areas (CA). • IDCAMS is the IBM utility used to Define, Delete, Rename, Copy etc the VSAM datasets
  • 9. VSAM vs Non-VSAM Non-VSAM VSAM PS KSDS, ESDS, RRDS, LDS IEBGENER, IEFBR14 etc. IDCAMS Stored on DASD and Tape Stored only on DASD Records are grouped into Blocks Records are grouped into CI and CA
  • 10. VSAM Datasets - KSDS • KSDS – Key-Sequenced Dataset • It has data and index components. • Records in KSDS are uniquely identified by a key, arranged in key sequential order. • Data component contains records, where as the Index component is used for faster access of the data in Data component. • Records can be accessed sequentially or dynamically by supplying the key value. • Keyword ‘INDEXED’ is sued to create KSDS Datasets.
  • 11. VSAM Datasets – ESDS • Entry Sequential Dataset. • It has only Data component. • Records are arranged in the order they are inserted, identified by their physical address – Relative Byte Address (RBA). If record length is 20, then 0 is the RBA for first record(0-19), 20 is the RBA for second record(20-39). • Records can be accessed sequentially or dynamically by supplying the RBA value. • Keyword ‘NONINDEXED’ is used to create ESDS datasets. • Records in the ESDS cannot be deleted but can be marked inactive.
  • 12. VSAM Datasets - RRDS • Relative Record Dataset. • It has only Data component. • Space allocated is divided into slots of fixed length, records of fixed length or variable length can be inserted into the slots. • Each record is identified by RRN – Relative Record Number which is similar to serial numbers. • Records can be accessed sequentially or dynamically by supplying the RRN number. • Keyword ‘NUMBERED’ is sued to create RRDS datasets.
  • 13. Creation of VSAM through a JCL //EXAMPLE JOB(VSAMJCL,XXXXXX),CLASS=A,MSGCLASS=A //STEP1 EXEC PGM=IDCAMS //SYSPRINT DD SYSOUT=* //SYSIN DD * DEFINE CLUSTER (NAME(EXT1RRV.VSAM.DATASET) - INDEXED/NONINDEXED/NUMBERED - --> Specific to VSAM type <<<Other parameters specific to type of VSAM>>> DATA (NAME(EXT1RRV.VSAM.DATASET.DATA)) - --> For all types of VSAMs INDEX (NAME(EXT1RRV.VSAM.DATASET.INDEX)) --> Only for KSDS VSAM /*
  • 14. Catalogs • There are system critical datasets and user datasets in any environment. • Catalogs provide the facility of isolating system datasets from user datasets. • A Catalog is a VSAM dataset that maintains records of all the other datasets and the volumes in which they are stored. • VTOC - Every Volume has a Table Of Contents that tells the system about the physical address of the datasets stored inside it. • In order to access a dataset, system reads the catalog to find the volume in which it is stored and then reads the VTOC of that volume.
  • 15. Types of Catalogs • There are two types of catalogs – Master and User • Master catalog maintains all the system datasets. Master Catalog is created during the system generation and is stored in system volume. • User/Application datasets are separated from system datasets by creating User catalogs. Aliases for user datasets are mapped to a user catalog. • Alias is nothing but the HLQ of the datasets but can be multilevel as well. • LISTCAT in front of a user dataset displays the user catalog.