Compaction - Data Compression

Enhanced Definition

In the mainframe context, data compaction (often synonymous with data compression) refers to the process of reducing the physical size of data stored on disk or tape, or transmitted over a network, by encoding it more efficiently. This is achieved by identifying and eliminating redundant information, such as repeating characters or patterns, to save storage space, reduce I/O operations, and improve data transfer rates.

Key Characteristics

- Algorithm-driven: Utilizes various algorithms (e.g., Huffman coding, Lempel-Ziv variants, run-length encoding) to identify and eliminate data redundancy, often implemented in hardware or software.
- On-the-fly processing: Can be performed dynamically by hardware (e.g., disk controllers, tape drives, network adapters, zEDC for z/OS) or by software (e.g., database managers like DB2 or IMS, utility programs) during data writes or reads.
- Lossless: Mainframe data compression is typically lossless, meaning the original data can be perfectly reconstructed from the compressed version without any loss of information.
- Resource impact: While saving storage space and I/O, compression/decompression requires CPU cycles; modern hardware accelerators (like zEDC for z/OS) can offload this overhead.
- Variable compression ratios: The effectiveness of compression varies significantly depending on the nature and redundancy of the data; highly repetitive data compresses much better than random data.
- Transparency: Can be transparent to applications, where the operating system, hardware, or database system handles compression and decompression without requiring application code changes.

Use Cases

- Database storage: Compressing tablespaces or indexes in DB2 for z/OS or segments in IMS databases to reduce disk footprint, improve buffer pool efficiency, and enhance I/O performance.
- Tape archives and backups: Storing large volumes of historical or backup data on tape at a reduced size, extending tape cartridge capacity and speeding up backup/restore operations.
- Sequential files: Compressing large sequential datasets (PS or VSAM ESDS) used for batch processing, reporting, or data exchange to save DASD space and reduce I/O time.
- Network transmission: Reducing the size of data exchanged between mainframe applications or between mainframe and distributed systems to minimize network bandwidth usage and latency.
- Log files: Compressing system logs (e.g., SMF, SYSLOG, DB2 logs) to manage their growth and retention more efficiently, especially for long-term archiving.

Related Concepts

Data compaction is closely related to storage management as it directly impacts DASD and tape utilization, allowing more data to be stored in the same physical space. It often works in conjunction with data organization methods (e.g., VSAM, DB2 tablespaces) to optimize physical storage and access. It's a key component of performance tuning strategies, as reduced I/O and network traffic can significantly improve application response times and throughput. Furthermore, it's a fundamental technique used by backup and recovery utilities to manage the volume of data being protected and to shorten backup windows.

Best Practices:

Evaluate data characteristics: Analyze the data to be compressed to ensure it has sufficient redundancy to benefit from compression; highly random data may not compress well or could even slightly expand.
Monitor resource consumption: Keep an eye on CPU utilization for software-based compression and decompression, especially for high-volume transactions, to ensure it doesn't negatively impact system performance. Utilize hardware accelerators like zEDC where possible.
Choose appropriate compression methods: Select the right compression algorithm or hardware feature based on the data type, performance requirements, and storage medium (e.g., DB2 row compression, zEDC for GDGs or SMS-managed datasets).
Test thoroughly: Implement and test compression in non-production environments first to understand its impact on storage, performance, and application behavior before deploying to production.
Consider data access patterns: For frequently updated data, the overhead of compression/decompression on each update might outweigh the storage benefits; for read-heavy or archival data, it's often highly beneficial.