COBOL Cloud Data Access via Extended Language Processor
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Solution Overview
Problem
Existing systems face significant challenges in integrating and communicating between COBOL programs, which use EBCDIC, and cloud databases, which use ASCII, requiring extensive and costly transformations to exchange data, making it difficult for mainframe applications to access cloud-based data sources.
Innovation Solution
A method and system utilizing a Service-Oriented Architecture with an Extended Language Processor and adapter to allow COBOL programs to interact with cloud databases like Cassandra, HANNA, Oracle, and Mongo, enabling SQL-like queries without needing to learn cloud database query languages, using REST service calls and JSON data format for efficient data retrieval and abstraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If COBOL programs directly access cloud databases, then data access capability is improved, but integration complexity increases due to EBCDIC-ASCII format differences
Solution Approach 1:
The patent introduces an intermediary translation layer that converts between EBCDIC and ASCII formats, allowing COBOL programs to access cloud databases without direct integration. This mediator handles the format conversion automatically, resolving the contradiction by enabling data access while managing integration complexity through abstraction.
Solution Approach 2:
The translation layer is designed to handle multiple database types (cloud databases, mainframe databases, flat files) and various COBOL program types (batch, online, interactive) through a single unified interface. This universal approach allows diverse systems to interoperate without increasing complexity for each specific integration scenario.
2Adaptability or versatility
If extensive transformations are implemented to exchange data between EBCDIC and ASCII systems, then data compatibility is improved, but implementation cost increases
Solution Approach 1:
The translation layer automatically performs EBCDIC-ASCII conversions without requiring manual intervention or extensive custom transformation logic for each integration scenario. The system self-configures and handles format conversions transparently, reducing implementation costs while maintaining data compatibility.
Solution Approach 2:
The patent changes the parameter of data format handling from manual transformation to automated conversion. By implementing a standardized translation mechanism that automatically adjusts format parameters, the system achieves data compatibility without the high costs associated with extensive custom transformation implementations.
3Productivity
If mainframe applications are modified to access cloud databases directly, then access efficiency is improved, but code modification workload increases
Solution Approach 1:
The translation layer is pre-configured with knowledge of both EBCDIC and ASCII formats, as well as various database schemas. This preliminary preparation allows COBOL programs to access cloud databases through standard interfaces without requiring code modifications, eliminating the trade-off between access efficiency and modification workload.
4Ease of operation
If a unified interface is created for COBOL programs to access multiple data sources, then ease of operation is improved, but system architecture complexity increases
Solution Approach 1:
The unified interface is implemented as an intermediary translation layer that abstracts the complexity of multiple data sources behind a single standardized API. COBOL programs interact with this simple interface, while the intermediary handles the complexity of translating between different formats and database types, thus improving ease of operation without exposing architecture complexity to users.
Data Source
AI summary
A method for facilitating a mainframe batch COBOL program to read from a cloud database is provided. The method includes receiving a first search predicate and a second search predicate from an extended language pre-compiler, the extended language pre-compiler parsing a user query written as a SQL query, building a cloud database format query based on the first search predicate and the second search predicate, transmitting the cloud database format query to the cloud database to extract data stored on the cloud database associated with the user query, and receiving the extracted data from the cloud database in a Java Script Object Notation (JSON) format.