Data Compaction

Reducing Size

Enhanced Definition

Data compaction, in the context of z/OS, refers to the process of reducing the physical storage size of data. This is achieved by applying algorithms to remove redundancy, thereby minimizing the amount of disk space, tape space, or memory required to store the data. Its primary purpose is to optimize storage utilization, improve I/O performance, and reduce data transfer times.

Key Characteristics

- Algorithm-Driven: Utilizes various compression algorithms (e.g., dictionary-based, run-length encoding) to identify and eliminate repetitive patterns or redundant information within the data.
- CPU vs. I/O Trade-off: While compaction saves storage and can reduce I/O time by transferring less data, it consumes CPU cycles for the compression and decompression processes.
- Transparency: Can be transparent to applications, especially when implemented at the hardware level (e.g., zEDC) or within database systems (e.g., DB2 row compression).
- Data Type Dependency: The effectiveness of compaction varies significantly based on the characteristics of the data; highly repetitive data (e.g., logs, fixed-format records with many blanks) typically achieves higher compression ratios.
- Hardware and Software Implementations: Can be performed by dedicated hardware accelerators like the zEDC (zEnterprise Data Compression) Express card or through software utilities and features within z/OS components (e.g., DB2, IMS, VSAM).

Use Cases

- Database Storage Optimization: Compressing DB2 tablespaces, IMS segments, or VSAM KSDS files to significantly reduce DASD consumption for large transactional or historical databases.
- Archival and Backup: Compacting historical data, log files, or full system backups to minimize the storage footprint on tape or disk, reducing costs and improving backup/restore windows.
- Data Transmission: Reducing the volume of data transferred over networks (e.g., between LPARs, to remote systems, or for distributed applications) to improve network performance and reduce latency.
- Log File Management: Compressing system logs (e.g., SMF, SYSLOG, CICS journals) to manage their growth and retain more historical data online or on near-line storage.
- Application Datasets: Applying compression to large sequential datasets or PDS/PDSE members that are frequently accessed or stored for long periods.

Related Concepts

Data compaction is intrinsically linked to Storage Management on z/OS, directly impacting DASD and tape utilization. It plays a crucial role in Performance Tuning by potentially reducing I/O operations, though it introduces CPU overhead. It is often implemented via z/OS Utilities like IDCAMS for VSAM, DFSORT, or database utilities like DSNUTILB for DB2. Modern implementations leverage Hardware Acceleration through zEDC to offload CPU-intensive compression tasks. Compaction must maintain Data Integrity throughout the compression and decompression cycles, ensuring no data loss or corruption.

Best Practices:

Analyze Data Characteristics: Before implementing compaction, analyze the data to understand its compressibility and estimate potential savings, as not all data benefits equally.
Monitor Performance Impact: Carefully monitor CPU utilization and I/O performance after implementing compaction to ensure the benefits (storage savings, faster I/O) outweigh the CPU overhead.
Utilize Transparent Compression: Whenever possible, leverage built-in, transparent compression features like DB2 row compression or VSAM compression, which are optimized for their respective data structures.
Consider zEDC for High Volume: For very high-volume, repetitive data, explore using zEDC hardware acceleration to achieve high compression ratios with minimal impact on general-purpose CPUs.
Plan for Decompression: Ensure that the chosen compaction method allows for efficient and reliable decompression, especially for critical data that needs to be quickly restored or accessed.