Incremental

Enhanced Definition

In the context of mainframe systems, "incremental" refers to a process or operation that deals only with changes or additions since a previous point in time, rather than processing the entire dataset or system state. This approach is primarily used to optimize resource usage, reduce processing time, and minimize storage requirements by focusing solely on modified components or data.

Key Characteristics

- Efficiency: Significantly reduces the amount of data processed, transferred, or stored compared to full operations, leading to faster execution.
- Speed: Operations complete more quickly because they only handle a subset of the total data, minimizing I/O and CPU cycles.
- Dependency: Requires a prior full or incremental baseline (e.g., a full backup or initial data load) to determine what has changed.
- Change Tracking: Relies on mechanisms to identify modifications, such as timestamps, log records (e.g., DB2 logs, IMS logs), or dataset attributes.
- Resource Optimization: Minimizes consumption of critical mainframe resources including CPU, I/O channels, network bandwidth, and storage capacity.
- Granularity: Can operate at various levels, such as individual records, data blocks, datasets, or entire volumes, depending on the specific implementation.

Use Cases

- Incremental Backups: Backing up only the datasets, volumes, or records that have been modified since the last full or incremental backup, commonly used for VSAM files, DB2 table spaces, or IMS databases to reduce backup windows.
- Incremental Data Loading/Updates: Applying only new or changed transaction data to a master file or database, a common pattern in batch processing for daily updates to large data repositories.
- Incremental Restores: Restoring only specific changed components or datasets from an incremental backup, often faster than a full restore for targeted data recovery.
- Software Maintenance (PTFs): Applying Program Temporary Fixes (PTFs) incrementally to z/OS or other IBM products, where each PTF addresses specific issues without requiring a full system reinstallation.
- Data Replication: Replicating only changed data blocks or records to a disaster recovery site or a secondary system, minimizing bandwidth usage and ensuring near real-time synchronization.

Related Concepts

Incremental processes are fundamentally linked to change data capture (CDC) mechanisms, which identify and track modifications to data. They often rely on system logs (like DB2 logs or IMS logs), timestamps on records or datasets, or application-level flags to determine what has changed. This approach contrasts sharply with full processing (e.g., full backups or full loads), which processes all data regardless of modification status. Incremental operations are a critical component of a robust disaster recovery strategy, data synchronization, and efficient batch processing workflows.

Best Practices:

Establish a Reliable Baseline: Always start with a full backup or initial data load to serve as the definitive reference point for all subsequent incremental operations.
Regular Full Operations: Periodically perform full backups or full data loads to simplify recovery processes, reduce the complexity of managing multiple incremental versions, and ensure data integrity.
Robust Change Tracking: Implement and verify reliable mechanisms for identifying and tracking changes, such as utilizing DB2's LOGREDO capabilities, VSAM REPLICATE utility, or application-specific change flags.
Thorough Recovery Testing: Regularly test incremental restore and recovery procedures to ensure data integrity, validate the recovery time objective (RTO), and confirm operational readiness.
Monitor Performance and Resources: Continuously monitor the performance of incremental operations and the resources they consume to ensure they remain efficient and do not introduce unexpected overhead or bottlenecks.