Data Compression

Enhanced Definition

Data compression on z/OS is the process of reducing the physical size of data by encoding it more efficiently, primarily to optimize storage utilization, improve I/O performance, and decrease network transmission times. It achieves this by identifying and eliminating redundant information within the data stream. Data compression, often referred to as compaction in the mainframe context, is the process of encoding information using fewer bits than the original representation. Its primary purpose in z/OS is to reduce the physical storage space required for data, minimize I/O operations, and decrease data transmission times and costs. This is achieved through various algorithms that identify and eliminate redundancy within the data.

Key Characteristics

- Methods: Can be implemented at various levels: hardware (e.g., zEDC for z/OS Data Compression or IBM Z Integrated Accelerator for zlib), software (e.g., DFSMSdss, DB2, IMS, application-specific routines), or within specific utilities like ADRDSSU.
- Lossless Nature: Primarily utilizes lossless compression algorithms, ensuring that the original data can be perfectly reconstructed from the compressed version without any loss of information, which is critical for data integrity in enterprise systems.
- CPU vs. I/O Trade-off: While reducing I/O operations and storage footprint, the processes of compression and decompression consume CPU cycles, requiring careful balancing based on workload characteristics and available hardware accelerators.
- Transparency: Many mainframe compression techniques are transparent to applications; the operating system or subsystem handles the compression and decompression automatically, allowing applications to read and write data as if it were uncompressed.
- Variable Effectiveness: The degree of compression achieved (the compression ratio) varies significantly based on the data's characteristics; highly repetitive or structured data typically compresses much better than random or already compressed data.

Use Cases

- DASD and Tape Storage Optimization: Significantly reduces the amount of disk space (e.g., for VSAM datasets, sequential datasets) and tape volumes required for storing large volumes of data, leading to cost savings and improved storage management.
- Database Management Systems: Extensively used in DB2 and IMS to reduce the size of tablespaces, indexes, and segments, which improves buffer pool efficiency, reduces I/O operations, and enhances overall database performance.
- Data Transmission: Reduces the volume of data sent across network links (e.g., SNA, TCP/IP connections via IPSec or z/OS Communications Server features), lowering network bandwidth requirements and improving data transfer speeds between systems or LPARs.
- Backup and Recovery: Compressing backup datasets (e.g., using DFSMSdss or ADRDSSU) reduces the time and storage needed for backup operations and can speed up recovery processes by minimizing data transfer.
- Archiving: Essential for long-term data archiving, where data is stored for compliance or historical purposes, minimizing the physical storage footprint over extended periods while maintaining data integrity.

Related Concepts

Data compression is tightly integrated with storage management solutions like DFSMSdss and DFSMShsm, which provide utilities and policies for compressing datasets on DASD and tape. It directly impacts I/O performance by reducing the amount of data transferred, often leveraging specialized hardware like zEDC for efficient processing. In database systems such as DB2 and IMS, compression is a configurable feature that affects storage, buffer pool usage, and query performance. It also plays a role in network protocols and application design where data transfer efficiency is critical, potentially offloading CPU work to zIIP processors for eligible workloads.

Best Practices:

Evaluate CPU Overhead: Carefully assess the CPU cycles consumed by compression/decompression against the I/O and storage benefits, especially for high-transaction workloads, to ensure overall system performance isn't negatively impacted.
Choose Appropriate Methods: Select the right compression technique (hardware, software, utility-based) and algorithm based on data type,