Regular Expression MapReduce Job Configuration
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Solution Overview
Problem
Hand coding MapReduce jobs in networked enterprise computing environments is costly, time-consuming, and error-prone, especially when dealing with diverse data types like XML and JSON, as it requires writing custom map and reduce functions for each specific case.
Innovation Solution
A method that uses regular expressions to specify data extraction criteria and applies functions to extracted data within a MapReduce framework, allowing users to define MapReduce jobs without writing code, by generating regular expressions and packaging them with function objects into a job configuration, which is then automatically submitted to the framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hand coding MapReduce jobs is used to extract and process data, then data extraction and processing can be performed, but it is costly, time consuming, and error prone
Solution Approach 1:
The patent introduces regular expressions as an intermediary mechanism between user requirements and MapReduce execution. Instead of directly coding MapReduce jobs, users specify extraction criteria through regular expressions, which are then automatically translated into executable MapReduce tasks by the system, eliminating manual coding while maintaining data extraction and processing capabilities
Solution Approach 2:
The system enables self-service by automatically generating MapReduce job configurations from user-provided regular expressions. The framework autonomously parses the regular expressions, creates appropriate map and reduce functions, and submits jobs without requiring users to manually write or maintain coding logic, thereby reducing time loss and errors
2Adaptability or versatility
If hand coded MapReduce jobs are used, then custom data extraction can be performed, but it requires writing map and reduce functions for each specific case
Solution Approach 1:
The patent creates a universal regular expression-based interface that can handle diverse data extraction requirements across different data types and formats. A single regular expression specification mechanism serves multiple purposes: extracting from XML, JSON, text files, and other formats without requiring separate coding approaches for each case, thereby maintaining adaptability while reducing complexity
Solution Approach 2:
The system changes the parameter representation from complex coded logic to simple regular expression patterns. By transforming extraction criteria into parameterized regular expressions with capture groups, the system maintains the ability to perform custom extractions while dramatically simplifying the interface and reducing the complexity of writing and maintaining extraction logic
3Ease of operation
If conventional MapReduce frameworks are used, then data processing can be performed, but it requires scripting language knowledge and manual job configuration
Solution Approach 1:
The framework performs self-service by automatically validating regular expressions, generating syntactically correct MapReduce job configurations, and handling error conditions. This automated validation and generation process eliminates manual configuration errors while maintaining ease of operation, as users only need to provide high-level extraction criteria without worrying about coding errors
Solution Approach 2:
Regular expressions serve as an intermediary validation layer between user intent and MapReduce execution. The system automatically checks regular expression syntax and semantics, providing immediate feedback on errors before job submission. This intermediary validation mechanism reduces errors while keeping the interface simple and easy to use
Data Source
AI summary
A system and method for facilitating extracting processing data, in a networked computing environment. An example method includes receiving a signal from a user input device specifying a regular expression. The regular expression includes criteria for extracting data from a database or file, e.g., a text, XML, JSON, or other type of file. A selected function is applied to data that is extracted in accordance with the regular expression via a MapReduce framework. In a more specific embodiment, the method further includes delivering reduced or processed data to a DataBase Management System (DBMS) of a networked enterprise computing environment. The database includes a file with data obtained from one or more databases distributed among one or more servers of the enterprise computing environment.