Compaction
In the mainframe context, compaction refers to the process of reducing the physical size of data, typically to save storage space on DASD, reduce I/O transfer times, or minimize network bandwidth usage. It achieves this by identifying and eliminating redundant information within data records or blocks, making it a form of data compression.
Key Characteristics
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- Algorithm-driven: Utilizes specific algorithms (e.g., run-length encoding, dictionary-based, null suppression) to identify and remove repetitive or unnecessary data patterns.
- Space Efficiency: The primary goal is to maximize the amount of logical data that can be stored within a given physical storage unit (e.g., DASD track, database block).
- CPU Overhead: Compacting and decompacting data requires CPU cycles, which must be balanced against the benefits of reduced I/O and storage consumption.
- Data Integrity: Ensures that the original data can be perfectly reconstructed (decompressed) without any loss or alteration.
- Variable-length Output: Often converts fixed-length input records into variable-length output records, requiring applications and utilities to handle variable-length formats.
- Hardware/Software Implementation: Can be performed by specialized hardware (e.g., zEDC for z/OS data compression) or by software utilities (e.g., DFSORT, database managers).
Use Cases
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- DASD Space Optimization: Storing large volumes of historical, archival, or infrequently accessed datasets in a compacted format to significantly reduce the required disk space.
- Batch Processing Efficiency: Using utilities like DFSORT with
COMPACTorZIPoptions to reduce the size of intermediate sort work files, thereby speeding up sort operations and reducing temporary storage requirements. - Database Storage: Compacting segments or records within databases like IMS or DB2 to fit more logical data per physical block, which reduces physical I/O operations and can improve query performance.
- Data Transmission: Reducing the size of data exchanged between systems or across networks (e.g., using
COMPRESSoptions in communication protocols or utilities) to improve throughput and shorten transmission times. - Backup and Recovery: Compacting backup files to minimize the storage footprint of backups and potentially decrease the time required for backup and restore operations.
Related Concepts
Compaction is a critical technique in Storage Management and Performance Tuning on z/OS, directly impacting DASD utilization and I/O subsystem efficiency. It is closely related to Data Compression, often used interchangeably, and works in conjunction with Data Set Organization (e.g., VSAM, QSAM) and Database Management Systems (DB2, IMS) to optimize physical storage. It also influences Application Performance by reducing I/O wait times and System Throughput by optimizing resource usage.
- Analyze Data Characteristics: Before implementing compaction, analyze the data to understand its redundancy and patterns; some data types (e.g., already compressed images, highly random data) may not benefit or could even increase in size.
- Monitor CPU Consumption: Continuously monitor the CPU overhead introduced by compaction/decompaction, especially for high-volume online transactions, to ensure the storage and I/O benefits outweigh the processing costs.
- Test Application Compatibility: Thoroughly test all applications and utilities that access compacted data to ensure they correctly handle the compacted format and any resulting variable-length records.
- Choose Appropriate Method: Select the compaction method (e.g., hardware compression via zEDC, software compression via DFSORT, database-specific compression) that best aligns with the data type, performance requirements, and storage medium.
- Regular Reorganization: For databases and large datasets, periodically reorganize compacted data to reclaim fragmented space and maintain optimal access performance.