Disk
A physical storage device that uses magnetic platters to store data persistently. In the mainframe context, this primarily refers to **Direct Access Storage Devices (DASD)**, which are high-speed, random-access storage units crucial for z/OS datasets, databases, and system files. DASD is the cornerstone of persistent data storage in the z/OS environment.
Key Characteristics
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- DASD (Direct Access Storage Device): The overarching term for disk storage on mainframes, allowing direct access to any data block, unlike sequential tape. Examples include IBM's 3390 series (emulated by modern storage arrays).
- Random Access: Enables data to be read or written directly to any location on the disk, providing rapid retrieval capabilities essential for online transaction processing and database operations.
- Persistent Storage: Data remains stored even when the system is powered off, ensuring data integrity and availability across system reboots.
- Volume: A single physical or logical unit of DASD, identified by a unique 6-character volser (volume serial number), which is used in JCL and system commands.
- Tracks and Cylinders: Data is physically organized into concentric tracks on platters, and cylinders represent the set of all tracks at the same radius across all platters, forming the smallest unit of allocation for some datasets.
- Storage Controllers: Modern DASD is managed by sophisticated storage controllers (e.g., IBM DS8000 series) that provide advanced features like caching, mirroring, replication, and virtualization, enhancing performance and availability.
Use Cases
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- System Datasets: Storing critical z/OS system files such as the Page Dataset, SYSRES (System Residence) volumes, IPL (Initial Program Load) volumes, and various system logs (
SYSLOG). - Application Data: Housing application-specific datasets (e.g., VSAM files, sequential files, PDS/PDSEs) used by COBOL programs, batch jobs, and online transaction managers like CICS.
- Database Storage: Providing the underlying storage for mainframe databases such as DB2 for z/OS and IMS DB, where data integrity, high-speed access, and concurrent access are paramount.
- Temporary Work Areas: Used for temporary datasets (
SORTWK,SYSOUTspooling) during batch job execution and for intermediate results. - Software Libraries: Storing program libraries (
LOADLIB,LINKLIB), JCL procedure libraries (PROCLIB), and source code libraries (SRCLIB) for development and production.
- System Datasets: Storing critical z/OS system files such as the Page Dataset, SYSRES (System Residence) volumes, IPL (Initial Program Load) volumes, and various system logs (
Related Concepts
DASD is fundamental to z/OS, serving as the primary repository for the operating system itself, its paging space, and all user data. It interacts closely with JCL (Job Control Language) through DD statements that define and allocate datasets on specific volumes. VSAM (Virtual Storage Access Method) and other access methods manage how data is organized, stored, and retrieved from DASD, while storage management software (like DFSMS) optimizes its utilization, performance, and availability across the enterprise.
- Efficient Dataset Allocation: Use appropriate
SPACEparameters in JCL (CYL,TRK,(primary,secondary)) to preventX37abends and optimize storage utilization, avoiding overallocation or underallocation. - Regular Monitoring: Continuously monitor DASD utilization, I/O performance metrics (e.g., response time, queue depth), and free space to anticipate capacity issues and performance bottlenecks.
- Data Placement: Strategically place frequently accessed datasets (e.g., critical database tables, high-volume transaction logs) on faster storage tiers or dedicated volumes to improve I/O performance and reduce contention.
- Backup and Recovery: Implement robust backup and disaster recovery strategies for all critical DASD volumes and datasets, utilizing technologies like FlashCopy and replication services.
- Storage Virtualization and Tiering: Leverage modern storage controllers (e.g., IBM DS8000) for features like thin provisioning, data compression, and automated data tiering to enhance efficiency, reduce costs, and improve resilience.