taosdata/TDengine
Feb 2025 – Mar 2025
2 Months active
Languages Used
CC++MarkdownPythonSQL
Technical Skills
API DesignC programmingData StructuresDatabase ManagementEvent windowingMemory management
54liuyao
PROFILE
54liuyao
Worked on the taosdata/TDengine repository to enhance time-series stream processing by implementing group-based state cleanup and a continuous window close trigger. Addressed memory leaks and improved event window integrity by refactoring resource management and ensuring deterministic cleanup of group states. Leveraged C and Python to optimize memory handling, state management, and concurrency control, while updating documentation and tests to support new features. Introduced configurable intervals for window recalculation, improving data timeliness and system reliability. These changes reduced the risk of stale data and runtime errors, resulting in more stable production deployments and streamlined maintenance for time-series database workloads.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
8Total
Bugs
2
Commits
8
Features
2
Lines of code
12,707
Activity Months2
Your Network
74 peopleShared Repositories
74
Work History
March 2025
6 Commits • 1 Features
Mar 1, 2025Month 2025-03 — TDengine streaming module improvements focused on stability, reliability, and operability. Delivered memory leak fixes in stream processing and introduced a continuous window close trigger with configurable intervals, supported by updated tests and documentation. These changes enhance streaming correctness, data timeliness, and deployment confidence for production workloads.
6 Commits • 1 Features
Mar 1, 2025Month 2025-03 — TDengine streaming module improvements focused on stability, reliability, and operability. Delivered memory leak fixes in stream processing and introduced a continuous window close trigger with configurable intervals, supported by updated tests and documentation. These changes enhance streaming correctness, data timeliness, and deployment confidence for production workloads.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025Month: 2025-02 Concise monthly summary focusing on business value and technical achievements. Key features delivered: - Group-Based State Cleanup in Time-Series Management Agent: introduced streamStateDelByGroupId to efficiently remove states associated with a specific group ID, enabling proper cleanup of invalid states in the tsma. Commits: 870fe1c071d3b9a8e6eb6901677945ef2944834c Major bugs fixed: - Memory leak fix and event window integrity for group lifecycle: fix memory leak and ensure the event window is rebuilt when the end flag changes from true to false; refactor resource cleanup in clearGroupResInfo to properly free resources and prevent data inconsistencies. Commits: 9d3a00920e5a2c2c78fcf31657865119f6a0a4e5 Overall impact and accomplishments: - Improved reliability and stability of the Time-Series Management Agent's group lifecycle handling; reduced risk of stale data and inconsistencies; better resource management and deterministic cleanup. Technologies/skills demonstrated: - State management, memory management, resource cleanup/refactoring, and traceable code changes via commits. Experience with TSMA, group lifecycle patterns, and performance-oriented fixes. Business value: - Eliminates invalid states and memory leaks, improving data integrity and system stability for time-series workloads, with measurable reductions in cleanup overhead and potential runtime errors.
February 2025
2 Commits • 1 Features
Feb 1, 2025Month: 2025-02 Concise monthly summary focusing on business value and technical achievements. Key features delivered: - Group-Based State Cleanup in Time-Series Management Agent: introduced streamStateDelByGroupId to efficiently remove states associated with a specific group ID, enabling proper cleanup of invalid states in the tsma. Commits: 870fe1c071d3b9a8e6eb6901677945ef2944834c Major bugs fixed: - Memory leak fix and event window integrity for group lifecycle: fix memory leak and ensure the event window is rebuilt when the end flag changes from true to false; refactor resource cleanup in clearGroupResInfo to properly free resources and prevent data inconsistencies. Commits: 9d3a00920e5a2c2c78fcf31657865119f6a0a4e5 Overall impact and accomplishments: - Improved reliability and stability of the Time-Series Management Agent's group lifecycle handling; reduced risk of stale data and inconsistencies; better resource management and deterministic cleanup. Technologies/skills demonstrated: - State management, memory management, resource cleanup/refactoring, and traceable code changes via commits. Experience with TSMA, group lifecycle patterns, and performance-oriented fixes. Business value: - Eliminates invalid states and memory leaks, improving data integrity and system stability for time-series workloads, with measurable reductions in cleanup overhead and potential runtime errors.
Activity
Loading activity data...
Quality Metrics
Correctness85.0%
Maintainability82.6%
Architecture81.2%
Performance80.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
CC++MarkdownPythonSQL
Technical Skills
API DesignBug FixingC ProgrammingC programmingCI/CDCode RefactoringConcurrency ControlData RecalculationData StructuresDatabaseDatabase InternalsDatabase ManagementDocumentationEvent windowingMemory Management
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline