green-ecolution/green-ecolution-backend
Nov 2024 – Mar 2025
5 Months active
Languages Used
BashGoJavaScriptSQLyaml
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentCode CleanupCode RefactoringData Modeling
Mohammad Assaf
PROFILE
Mohammad Assaf
Worked on the green-ecolution-backend, delivering features that enhanced sensor data modeling, user management, and data querying for IoT-driven environmental monitoring. Focused on robust API development and integration, the work included refactoring sensor structures, implementing MQTT payload handling, and expanding test coverage to ensure data integrity. Leveraged Go, SQL, and the Fiber framework to optimize database queries, introduce flexible scheduling, and support multi-provider data access. Improvements to role-based access control and transactional integrity strengthened security and reliability. The approach emphasized maintainable code through regular refactoring, comprehensive testing, and clear documentation, enabling scalable, production-ready backend services for real-world deployments.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
120Total
Bugs
13
Commits
120
Features
35
Lines of code
18,301
Activity Months5
Your Network
8 peopleShared Repositories
8
Work History
March 2025
13 Commits • 4 Features
Mar 1, 2025March 2025 backend delivery focused on unlocking historical data access, improving data linkage, and fortifying cluster update reliability across the tree ecosystem. Delivered four core features spanning vehicle data access, sensor-to-tree linkage via TTN, enhanced tree querying capabilities, and robust multi-cluster update handling. These changes enable actionable analytics, faster data retrieval, and more dependable state across clusters, reducing manual reconciliation and improving operator confidence.
13 Commits • 4 Features
Mar 1, 2025March 2025 backend delivery focused on unlocking historical data access, improving data linkage, and fortifying cluster update reliability across the tree ecosystem. Delivered four core features spanning vehicle data access, sensor-to-tree linkage via TTN, enhanced tree querying capabilities, and robust multi-cluster update handling. These changes enable actionable analytics, faster data retrieval, and more dependable state across clusters, reducing manual reconciliation and improving operator confidence.
March 2025
February 2025
32 Commits • 9 Features
Feb 1, 2025February 2025 monthly summary for green-ecolution-backend. Focused on delivering robust data querying, multi-provider support, and reliability improvements, while aggressively reducing technical debt through code cleanup and refactors. Highlights include enhancements to tree cluster data access, expanded sensor management, and more flexible scheduling, all aimed at improving scalability and operational impact across the platform. Key features delivered: - Tree Clusters Filtering: added support for filtering tree clusters and retrieving filtered counts; tests added to verify filter behavior. - Get All Function Provider Argument Usage: enabled multi-provider retrieval by using the provider argument in the getAll function. - Tree Clusters Querying, Filtering and API Response Enhancements: introduced pagination and provider attributes for cluster filtering; multi-status/region querying; refined query struct and naming; improved server response for watering status. - Sensor Data Handling and Inactive Sensor Detection: ensured sensors remain updatable when TTN data arrives; improved detection of inactive sensors using the latest data. - TTN Sensor Compatibility Enhancement: broadened sensor acceptance to support all TTN sensors. - Scheduler Enhancements: made the generic scheduler more flexible to accommodate varying workloads. Major bugs fixed: - Use update function correctly: fixed incorrect usage of the update function to ensure data consistency. - Remove statusUpdater file: cleanup of deprecated maintenance artifact. - Scheduler Context Removal: simplified scheduler interface by removing context from its struct. - Filter pagination removal: simplified filtering logic by removing pagination in filters. - Misc cleanup: removed leftover print statements for cleaner logs. - MQTT Payload struct attribute names fix: corrected MQTT payload attribute naming for alignment with updated payload formats. - Other cleanups: removal of unused and deprecated code paths (flowerbedID usage, vehicleQuery annotations, and related cleanup) to improve maintainability. Overall impact and accomplishments: - Increased data accessibility and performance for tree clusters with richer query capabilities and multi-provider support. - Improved reliability and observability through context-based logging and test coverage. - Broadened TTN integration and sensor compatibility, enabling support for a wider range of real-world devices. - Reduced technical debt via targeted cleanups and refactors, paving the way for faster iterations and lower maintenance costs. - Prepared the backend for production scale with clearer API behavior and more robust data handling. Technologies and skills demonstrated: - Go/Backend development, SQL query enhancements, and API design improvements. - Test-driven development with added test coverage for new filters. - Context-based logging and scheduler design, enabling cleaner instrumentation and easier diagnostics. - Ecosystem-wide cleanup and refactors to streamline production deployments.
February 2025
32 Commits • 9 Features
Feb 1, 2025February 2025 monthly summary for green-ecolution-backend. Focused on delivering robust data querying, multi-provider support, and reliability improvements, while aggressively reducing technical debt through code cleanup and refactors. Highlights include enhancements to tree cluster data access, expanded sensor management, and more flexible scheduling, all aimed at improving scalability and operational impact across the platform. Key features delivered: - Tree Clusters Filtering: added support for filtering tree clusters and retrieving filtered counts; tests added to verify filter behavior. - Get All Function Provider Argument Usage: enabled multi-provider retrieval by using the provider argument in the getAll function. - Tree Clusters Querying, Filtering and API Response Enhancements: introduced pagination and provider attributes for cluster filtering; multi-status/region querying; refined query struct and naming; improved server response for watering status. - Sensor Data Handling and Inactive Sensor Detection: ensured sensors remain updatable when TTN data arrives; improved detection of inactive sensors using the latest data. - TTN Sensor Compatibility Enhancement: broadened sensor acceptance to support all TTN sensors. - Scheduler Enhancements: made the generic scheduler more flexible to accommodate varying workloads. Major bugs fixed: - Use update function correctly: fixed incorrect usage of the update function to ensure data consistency. - Remove statusUpdater file: cleanup of deprecated maintenance artifact. - Scheduler Context Removal: simplified scheduler interface by removing context from its struct. - Filter pagination removal: simplified filtering logic by removing pagination in filters. - Misc cleanup: removed leftover print statements for cleaner logs. - MQTT Payload struct attribute names fix: corrected MQTT payload attribute naming for alignment with updated payload formats. - Other cleanups: removal of unused and deprecated code paths (flowerbedID usage, vehicleQuery annotations, and related cleanup) to improve maintainability. Overall impact and accomplishments: - Increased data accessibility and performance for tree clusters with richer query capabilities and multi-provider support. - Improved reliability and observability through context-based logging and test coverage. - Broadened TTN integration and sensor compatibility, enabling support for a wider range of real-world devices. - Reduced technical debt via targeted cleanups and refactors, paving the way for faster iterations and lower maintenance costs. - Prepared the backend for production scale with clearer API behavior and more robust data handling. Technologies and skills demonstrated: - Go/Backend development, SQL query enhancements, and API design improvements. - Test-driven development with added test coverage for new filters. - Context-based logging and scheduler design, enabling cleaner instrumentation and easier diagnostics. - Ecosystem-wide cleanup and refactors to streamline production deployments.
January 2025
20 Commits • 3 Features
Jan 1, 2025January 2025 monthly summary for green-ecolution-backend. This month focused on delivering core features for RBAC, sensor status scheduling, and data layer improvements, with an emphasis on reliability, security, and developer productivity. Highlights include three major feature deliveries, robust endpoint enhancements and validations, and a set of tests and documentation updates to ensure sustainment.
20 Commits • 3 Features
Jan 1, 2025January 2025 monthly summary for green-ecolution-backend. This month focused on delivering core features for RBAC, sensor status scheduling, and data layer improvements, with an emphasis on reliability, security, and developer productivity. Highlights include three major feature deliveries, robust endpoint enhancements and validations, and a set of tests and documentation updates to ensure sustainment.
January 2025
December 2024
29 Commits • 11 Features
Dec 1, 2024December 2024 monthly summary for green-ecolution-backend focused on reliability, data ingestion quality, and test-driven improvements that drive business value in sensor data pipelines and user management APIs.
December 2024
29 Commits • 11 Features
Dec 1, 2024December 2024 monthly summary for green-ecolution-backend focused on reliability, data ingestion quality, and test-driven improvements that drive business value in sensor data pipelines and user management APIs.
November 2024
26 Commits • 8 Features
Nov 1, 2024November 2024 performance summary for green-ecolution-backend focused on strengthening sensor data modeling, expanding test coverage, and stabilizing the data lifecycle. Key work includes a comprehensive tree operations test suite, sensor ID refactor to string with streamlined structs, coordinates integrated into sensor payloads and repository functions, seed data alignment with the new structure, and foundational MQTT payload handling plus migration work. Notable fixes include removing an obsolete test for unlinking sensors from trees and addressing test errors introduced by sensor changes. Overall, these efforts improved reliability, data integrity, and developer velocity, enabling safer data ingestion and easier future refactors.
26 Commits • 8 Features
Nov 1, 2024November 2024 performance summary for green-ecolution-backend focused on strengthening sensor data modeling, expanding test coverage, and stabilizing the data lifecycle. Key work includes a comprehensive tree operations test suite, sensor ID refactor to string with streamlined structs, coordinates integrated into sensor payloads and repository functions, seed data alignment with the new structure, and foundational MQTT payload handling plus migration work. Notable fixes include removing an obsolete test for unlinking sensors from trees and addressing test errors introduced by sensor changes. Overall, these efforts improved reliability, data integrity, and developer velocity, enabling safer data ingestion and easier future refactors.
November 2024
Activity
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Quality Metrics
Correctness88.4%
Maintainability88.8%
Architecture84.4%
Performance84.2%
AI Usage21.2%
Skills & Technologies
Programming Languages
BashGoJavaScriptSQLyaml
Technical Skills
API DesignAPI DevelopmentAPI DocumentationAPI IntegrationAPI TestingAuthenticationBackend DevelopmentCode CleanupCode FormattingCode OrganizationCode RefactoringComponent ExtractionConcurrencyContext ManagementData Fetching
Repositories Contributed To
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