Identical
In the mainframe context, "identical" refers to two or more entities (such as datasets, files, records, programs, or system configurations) having precisely the same content, structure, and attributes, byte-for-byte or character-for-character. It implies an exact match without any discrepancies, crucial for data integrity, validation, and replication.
Key Characteristics
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- Byte-for-byte/Character-for-character Match: The most stringent form of equality, ensuring every bit or character is exactly the same across the compared entities.
- Order Preservation: For sequential data, not only the content but also the exact order of records or bytes must be preserved for them to be considered identical.
- Attribute Matching: Beyond content, "identical" often implies matching dataset attributes such as
RECFM(Record Format),LRECL(Logical Record Length), andBLKSIZE(Block Size) for datasets, or compilation options for programs. - Context-Dependent Scope: The scope of "identical" can vary; it might apply to entire datasets, specific fields within records, or just the executable load module of a program.
- Immutability Implication: Often used in scenarios where data integrity or consistency requires an exact, unchanged copy, such as in disaster recovery or audit trails.
Use Cases
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- Data Validation and Reconciliation: Comparing two versions of a master file or transaction log to ensure consistency, identify unauthorized changes, or reconcile differences after processing.
- Disaster Recovery (DR) Verification: Confirming that replicated data on a recovery site is an identical, up-to-date copy of the production data volumes or datasets.
- Software Version Control: Comparing different versions of COBOL source code, JCL members, or load modules to track changes, verify deployments, or revert to a known good state.
- Migration and Conversion Verification: Ensuring that data migrated from one storage device, system, or format to another remains byte-for-byte identical to the source.
- Testing and Quality Assurance: Comparing actual program output with expected output to confirm correct functionality and identify regressions during testing cycles.
Related Concepts
The concept of "identical" is fundamental to data integrity and data consistency on z/OS. It is often achieved through data replication technologies like PPRC (Peer-to-Peer Remote Copy) or XRC (Extended Remote Copy), which strive to maintain identical copies of data volumes for high availability and disaster recovery. It's also closely related to checksums and hashing algorithms, which provide a quick way to *verify* if two large data sets are *likely* identical without a full byte-by-byte comparison, though a full comparison is needed for absolute certainty. In version control systems (e.g., CA Endevor), identifying identical source code files is crucial for managing changes and promoting code.
- Utilize Specialized Utilities: For comparing datasets, leverage z/OS utilities like
IDCAMS REPRO COMPARE,DFSMSdss DUMP/RESTOREwith comparison options, or third-party comparison tools (e.g., File-AID, ISPF Option 3.13). For source code,ISPF Option 3.13(Compare) is commonly used. - Define Comparison Scope: Clearly define what constitutes "identical" for a given scenario (e.g., ignore timestamps, compare only specific fields, compare entire files) to avoid false positives or negatives.
- Automate Comparisons: Integrate comparison steps into automated JCL procedures or scripts for routine validation, especially after data transfers, system updates, or during batch processing.
- Document Expected Identical States: Maintain clear documentation of expected identical states for critical data or configurations, particularly for disaster recovery plans, audit trails, and compliance requirements.
- Consider Performance for Large Datasets: For very large datasets, a full byte-for-byte comparison can be resource-intensive. Use checksums or sampling for initial checks, then targeted comparisons for identified discrepancies.