Boston 4.3.7
📅 Release Date: February 7, 2025 - 📅 Next Release Date: February 27, 2025
🚀 Highlights of this Release
The Autom Mate 4.3.7 release introduces critical bug fixes and stability improvements, enhancing the overall performance and user experience. Key fixes include resolving missing library icons and addressing MySQL Configuration issues, ensuring smoother operation and improved system reliability.
🅱️ Bug Fixes
📌 Resolved Missing Library Icons Issue
Some of the library icons were not displaying correctly due to a loading issue. This has been fixed, ensuring all library icons appear as expected.
📌 Resolved MySQL Configuration Issue
Some database operations were failing due to inconsistent query execution, caused by incorrect handling of connection pooling and query timeouts, leading to unexpected errors in certain transactions; this has been resolved by optimizing connection pooling, query execution logic, and timeout handling, ensuring more reliable database query performance and reducing errors.
🛠️ Enhancement
📌 Added Python Packages for Enhanced Functionality
Integrated gql and reportlab Python packages to support advanced product capabilities.
Detailed Version
Resolved Missing Library Icons Issue
Some library icons were not appearing correctly due to a rendering issue caused by incorrect asset references and inconsistent caching behavior. The root cause was a mismatch between the expected icon paths and the actual asset locations, compounded by browser caching preventing the latest assets from loading properly. To resolve this, asset references were updated to ensure icons load from the correct paths, cache-busting techniques were implemented to force browsers to fetch the latest assets, and the icon rendering logic was optimized for improved performance and reliability. As a result, library icons now load consistently across all pages without requiring a hard refresh, enhancing the overall user experience.
Resolved MySQL Configuration Issue
The issue causing intermittent database operation failures was due to mismanaged connection pooling, inconsistent query timeouts, and inefficient query execution, leading to unexpected errors under high load. This was resolved by optimizing connection pooling to ensure efficient resource utilization, standardizing query timeout handling to prevent inconsistencies, and refining query execution logic to reduce deadlocks and improve performance. Additionally, enhanced logging and monitoring were implemented to detect and prevent future issues, resulting in more reliable database operations and improved system stability.
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