CFL - Compression Function Library
CFL, or Compression Function Library, is a set of IBM-provided routines on z/OS designed to efficiently compress and decompress data. It leverages specialized hardware instructions, where available, to optimize storage utilization and improve I/O performance for various data types.
Key Characteristics
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- Hardware Acceleration: CFL can exploit IBM z Systems hardware compression facilities (e.g., zEDC - zEnterprise Data Compression) for significant performance gains, offloading CPU cycles from the general-purpose processors.
- Software Implementation: When hardware acceleration is not available or not configured, CFL provides robust software-based compression algorithms, ensuring functionality across different z/OS environments.
- Multiple Algorithms: Supports various compression algorithms, allowing users to choose the best balance between compression ratio and CPU overhead for their specific data and application needs.
- Integration with System Components: Seamlessly integrated with key z/OS components like DFSMS (Data Facility Storage Management Subsystem) for managing compressed datasets, and can be invoked by applications.
- Data Integrity: Ensures data integrity during compression and decompression operations, preventing data corruption and maintaining the reliability of stored information.
Use Cases
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- Dataset Compression: Used by DFSMS to compress sequential (QSAM), VSAM, and PDS/PDSE datasets, significantly reducing disk space requirements and improving I/O performance for batch and online applications.
- Database Compression: Employed by database systems like DB2 for z/OS and IMS to compress tables, indexes, and segments, leading to smaller database footprints and faster query response times due to reduced I/O.
- Data Transfer Optimization: Compresses data before transmission over networks (e.g., between LPARs or to remote systems), reducing network bandwidth consumption and accelerating data transfers.
- Backup and Recovery: Compresses backup copies of datasets and databases, reducing the storage required for backups and potentially speeding up the backup and restore processes.
- Archiving: Used for long-term archival of data, minimizing storage costs for infrequently accessed information while maintaining data accessibility.
Related Concepts
CFL is fundamental to data management on z/OS, often working in conjunction with DFSMS to manage compressed datasets and volumes. It is a core component utilized by DB2 for z/OS and IMS for their internal data compression features, directly impacting their performance and storage efficiency. The effectiveness of CFL is greatly enhanced by zEDC (zEnterprise Data Compression) hardware, which provides dedicated compression engines, offloading the work from general-purpose CPUs and improving throughput for eligible workloads.
- Leverage Hardware Acceleration: Prioritize the use of zEDC hardware compression where available, as it offers superior performance and CPU offload compared to software-only compression.
- Monitor Performance: Regularly monitor CPU utilization, I/O rates, and compression ratios for CFL-enabled datasets and databases to ensure optimal performance and identify potential bottlenecks.
- Choose Appropriate Algorithms: Select compression algorithms based on data characteristics (e.g., highly repetitive vs. random data) and performance requirements. Test different options to find the best balance.
- Capacity Planning: Account for the CPU overhead associated with compression and decompression, even with hardware acceleration, when performing capacity planning for z/OS systems.
- Consider Data Access Patterns: For frequently updated or randomly accessed data, carefully evaluate the overhead of compression/decompression to ensure it doesn't negatively impact application response times.