AMATERSE

Enhanced Definition

AMATERSE is an IBM utility program for z/OS that provides data compression and decompression capabilities. It is primarily used to reduce the size of data sets, particularly program libraries and sequential files, for efficient storage, transmission, and distribution.

Key Characteristics

- Dual Functionality: Supports both TERSE (compression) and UNTERSE (decompression) operations.
- Input/Output: Typically operates on sequential data sets (PS) or members of partitioned data sets (PDS/PDSE).
- Proprietary Algorithm: Uses a specific IBM-developed compression algorithm optimized for mainframe data.
- Data Integrity: Includes mechanisms to ensure the integrity of the compressed data, often involving checksums.
- SMP/E Integration: Crucial for the distribution of IBM program products, PTFs (Program Temporary Fixes), and APARs (Authorized Program Analysis Reports) which are often shipped in TERSE format.
- JCL Invocation: Executed via standard JCL, often using IEBGENER or similar utilities to copy the TERSEd file.

Use Cases

- Software Distribution: Packaging and distributing IBM program products, PTFs, and user-developed applications across z/OS systems or to customers.
- Efficient Data Transfer: Reducing the size of large data sets before transferring them via FTP, SFTP, or other network protocols, saving bandwidth and time.
- Archiving and Backup: Compressing infrequently accessed data or backup files to conserve disk space on DASD or tape.
- Inter-System Communication: Facilitating the exchange of large files or libraries between different z/OS LPARs or even to non-mainframe platforms.

Related Concepts

AMATERSE is intrinsically linked to SMP/E (System Modification Program/Extended), as it's the standard utility for packaging and un-packaging software components for SMP/E processing. It is often invoked within JCL (Job Control Language) streams, typically using a utility like IEBGENER to copy the TERSEd data set, which then gets UNTERSEd. The resulting data sets are often PDS/PDSEs containing load modules, source code, or control statements.

Best Practices:

Allocate Sufficient Space: Always ensure the target data set for an UNTERSE operation has ample space allocated, as the decompressed data will be significantly larger than the TERSEd input.
Verify Integrity: After transferring and UNTERSEing a file, perform checks to ensure the data integrity, especially for critical program libraries.
Understand Control Statements: Familiarize yourself with the TERSE and UNTERSE control statements and their parameters for precise control over the compression/decompression process.
Consider CPU vs. I/O: For extremely large data sets, weigh the CPU overhead of compression/decompression against the I/O and network bandwidth savings.
Standardize for Distribution: Use AMATERSE as the standard method for packaging and distributing software and large data sets within and outside your mainframe environment.