Unified File Watcher Script for Predictive Scheduling
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Solution Overview
Problem
Existing scheduler tools are inefficient for managing multiple files, requiring multiple watcher scripts, lacking historical data analysis, resource-intensive, and unable to validate files or calendar events, leading to bottlenecks and debugging challenges.
Innovation Solution
A unified platform using Shell or PowerShell scripts to monitor file arrivals, trigger jobs, and validate files within a single thread, enabling predictive analysis, scheduling, and historical data utilization to optimize file loading and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple watcher scripts are created to monitor multiple files, then file monitoring capability is improved, but system complexity and maintenance burden increase
Solution Approach 1:
The patent combines multiple watcher scripts into a single unified watcher script that can monitor multiple files simultaneously. This single script uses a configuration file to define which files to watch and what actions to take, eliminating the need to create and maintain separate watcher scripts for each file while preserving comprehensive file monitoring capability.
Solution Approach 2:
The unified watcher script serves multiple functions: it monitors multiple files, reads configuration from a config file, determines file actions based on patterns, and triggers appropriate jobs. This multi-functional approach replaces the need for specialized single-file watcher scripts while maintaining reliable monitoring across all files.
2Reliability
If multiple watcher scripts are created for each file, then individual file monitoring is improved, but resource consumption increases
Solution Approach 1:
Multiple watcher scripts are merged into a single unified watcher script that monitors all files through one process. This consolidation reduces the total number of running processes and threads, thereby decreasing CPU usage, memory consumption, and other system resources while maintaining individual file monitoring capability through configuration-based file tracking.
3Ease of manufacture
If scheduler tools are used without predictive analysis, then simple scheduling is achieved, but efficiency and bottleneck prevention are reduced
Solution Approach 1:
The system performs predictive analysis on file arrival patterns using historical data to forecast when files are likely to arrive. This preliminary action allows the scheduler to proactively prepare and allocate resources before files actually arrive, preventing bottlenecks and improving processing efficiency while maintaining simple scheduling implementation through automated predictions.
4Reliability
If general alarm alerts are used for file issues, then alerting capability is provided, but debugging efficiency is reduced
Solution Approach 1:
The system enhances general alarm alerts by incorporating specific feedback information about file issues, such as which file caused the problem, what the specific error is, and relevant contextual details. This enriched feedback mechanism maintains reliable alerting capability while significantly improving debugging efficiency by providing actionable information that directs users to the root cause rather than requiring broad script debugging.
Data Source
AI summary
A method for creating a common platform graphical user interface is provided. The interface may enable a user to trigger a data load job from a tool. The tool may monitor file upload events, trigger jobs and identify lists of missing or problematic file names. The tool may run on a single thread, thereby consuming relatively less system resources than a multi-thread program to perform its capabilities. The tool may enable selection of file names using wildcard variables or keyword variables. The tool may validate a list of files received against a master file list for each data load job. The tool may receive user input relating to each data load job. The tool may generate a loop within the single thread to receive information. The tool may analyze the received information and use the received information to predict future metadata associated with future data load jobs.