Caching

Enhanced Definition

Caching in the mainframe context refers to the process of storing frequently accessed data or instructions in a faster, smaller memory component closer to the processor or I/O device. Its primary purpose is to reduce latency and improve the performance of applications and the overall system by minimizing the need to access slower, larger storage media like DASD (Direct Access Storage Device).

Key Characteristics

- Hierarchical Implementation: Mainframes employ multiple levels of caching, from hardware-level CPU caches (L1, L2, L3) to storage controller caches and software-managed buffer pools within z/OS and applications (e.g., DB2, IMS, VSAM).
- Locality of Reference: Caching mechanisms leverage the principles of temporal locality (data recently accessed will likely be accessed again soon) and spatial locality (data near recently accessed data will likely be accessed soon).
- Read/Write Policies: Caches can implement various policies like write-through (data written to cache and main storage simultaneously) or write-back (data written only to cache initially, then flushed to main storage later). Write-back is common in storage controllers for performance.
- Cache Coherency: In multi-processor or sysplex environments, sophisticated hardware and software protocols ensure cache coherency, guaranteeing that all processors or LPARs see a consistent view of shared data.
- Performance Optimization: Effective caching significantly reduces I/O wait times, improves transaction response times, and lowers CPU utilization by minimizing physical I/O operations.
- Managed Resources: Caches are typically managed resources, with their size and behavior configurable at the hardware, operating system, or application level to optimize for specific workloads.

Use Cases

- DASD Storage Controllers: Modern IBM storage arrays (e.g., DS8000 series) utilize large amounts of cache memory to store frequently accessed data blocks, improving read and write performance to physical disk.
- DB2 Buffer Pools: DB2 uses large in-memory buffer pools (e.g., BP0, BP1) to cache database pages (data and index) in virtual storage, drastically reducing physical I/O to DASD for database operations.
- IMS Buffer Pools: Similar to DB2, IMS employs various buffer pools (e.g., DEDB, OSAM, VSAM) to cache database segments and indexes, enhancing transaction processing speed.
- VSAM Local/Global Shared Resources (LSR/GSR): VSAM files can be accessed using shared resource pools that cache control intervals and data records in virtual storage, improving access times for multiple users or applications.
- Processor Cache (L1/L2/L3): Hardware caches on the CPU chip store frequently used instructions and data for immediate access by the processor, minimizing delays from accessing main memory.
- z/OS Paging Subsystem: While primarily for virtual memory management, the paging subsystem effectively caches virtual storage pages in central storage, reducing the need to read them from external page datasets on DASD.

Related Concepts

Caching is fundamental to the I/O subsystem on z/OS, directly impacting the performance of DASD and tape operations by reducing physical access. It works in conjunction with virtual storage management, as many software-based caches (like buffer pools) reside within the z/OS address space. In a z/OS sysplex environment, robust cache coherency protocols are critical for data integrity when multiple LPARs share access to the same data (e.g., DB2 data sharing). Workload Manager (WLM) can implicitly benefit from efficient caching by allowing workloads to complete faster, thus meeting their performance goals.

Best Practices:

Right-sizing Buffer Pools: Analyze workload patterns and use performance monitoring tools (e.g., RMF, SMF, DB2 PM) to optimally size DB2, IMS, and VSAM buffer pools to achieve high cache hit ratios (ideally >95% for read-intensive workloads).
Monitor Cache Performance: Regularly monitor cache hit ratios and I/O rates for storage controllers and application buffer pools to identify bottlenecks or opportunities for further optimization.
Exploit Hardware Caching Features: Ensure storage controllers are configured with sufficient cache and appropriate caching policies (e.g., write-back for frequently updated, non-critical data) based on application requirements.
Data Locality in Design: Design applications and databases to promote data locality, organizing data so that frequently accessed items are stored together, which improves the effectiveness of hardware and software caches.
Understand Cache Coherency: Be aware of the implications of cache coherency, especially in shared data environments (e.g., DB2 data sharing), to prevent data integrity issues and ensure consistent data views across systems.