Fast Path

Enhanced Definition

**Fast Path** is an optional, high-performance component of **IMS DB/DC** designed to handle extremely high transaction volumes with very low response times. It achieves this by optimizing data access and processing, primarily through the use of in-memory databases and specialized data structures. Its primary goal is to provide rapid access to frequently used data for critical online applications.

Key Characteristics

- High Performance: Engineered for maximum transaction throughput and minimal response times, often measured in microseconds.
- Data Entry Databases (DEDBs): Uses a specialized database structure that stores data in VSAM ESDS datasets but manages it with OSAM buffers in virtual storage, minimizing physical I/O. DEDBs support multiple data areas and are highly available.
- Main Storage Databases (MSDBs): Stores entire databases in main storage (virtual storage backed by real memory), providing the fastest possible access. MSDBs are typically used for small, highly volatile, and frequently accessed data.
- Optimized Logging and Recovery: Employs a simplified logging and recovery mechanism compared to full-function IMS databases, contributing to its speed.
- Area-Level Availability: DEDBs can be managed and recovered at the area level, allowing specific portions of the database to be available while others are being recovered or reorganized.
- Reduced I/O: Achieves significant I/O reduction by keeping frequently accessed data in memory and using efficient buffer management techniques.

Use Cases

- High-Volume Online Transaction Processing (OLTP): Ideal for applications like banking ATM systems, point-of-sale systems, or real-time inventory management where sub-second response times and high concurrency are paramount.
- Real-time Data Collection: Used for capturing and processing large streams of event data, such as sensor readings or financial market updates, requiring immediate updates and availability.
- Frequently Accessed Reference Data: MSDBs are perfect for small, critical datasets that are constantly read and updated, like counter values, status indicators, or frequently checked reference tables.
- Critical Business Applications: Any application where even slight delays can lead to significant business impact or customer dissatisfaction.

Related Concepts

Fast Path is an integral part of the IMS DB/DC ecosystem, offering a specialized alternative to Full-Function Databases (FFDBs) (like HDAM, HIDAM, HISAM, OSAM) for performance-critical scenarios. While FFDBs offer greater data modeling flexibility and robust recovery, Fast Path sacrifices some of that flexibility for raw speed. It interacts seamlessly with the IMS Transaction Manager (IMS DC), allowing COBOL or Assembler applications to access Fast Path databases via standard IMS calls. DEDBs internally leverage VSAM ESDS for persistent storage, while MSDBs reside entirely in z/OS virtual storage, relying on efficient memory management.

Best Practices:

Database Design: Carefully design DEDB segment structures and area distribution to optimize data access patterns and minimize contention. Avoid large segments if possible.
Buffer Pool Sizing: Allocate sufficient DEDB buffer pools and MSDB buffer pools in the IMS control region to keep frequently accessed data in memory and achieve high buffer hit ratios.
Monitoring: Continuously monitor Fast Path performance metrics, including transaction rates, response times, buffer utilization, and I/O activity, using tools like IMS Performance Analyzer or SMF records.
Recovery Planning: Understand and regularly test the specialized recovery procedures for DEDBs (e.g., DBRC logs, image copies) and MSDBs (e.g., checkpoint records, system restart).
Data Volatility: Use MSDBs only for highly volatile data that can be quickly rebuilt or is not critical for long-term persistence, as their recovery differs significantly from DEDBs.
Area Organization: For DEDBs, consider using multiple areas to distribute data and I/O, improving concurrency and allowing for independent recovery or reorganization of specific data subsets.