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View All on GitHubAI Summary: Develop a FastAPI-based dashboard to visualize database query performance data tracked by pyquerytracker. This involves creating a new route, integrating a charting library (like Chart.js or ECharts) to display query execution times and counts, fetching data via a REST endpoint or WebSocket, and optionally adding filtering capabilities. The UI should be configurable and minimally styled.
A drop-in decorator to monitor and benchmark function or query performance with zero-vendor lock in.
AI Summary: Implement database persistence for tracked queries in the pyquerytracker library. This involves adding a configurable database writer (supporting SQLite and PostgreSQL initially), creating a SQLAlchemy schema for tracked query data, and potentially adding migration support. The feature should be togglable via configuration.
A drop-in decorator to monitor and benchmark function or query performance with zero-vendor lock in.
AI Summary: Write unit and integration tests for the WebSocket broadcasting feature of pyquerytracker, ensuring proper connection handling, message formatting, and edge case coverage (e.g., no connected clients). This involves mocking WebSocket connections and testing various scenarios.
A drop-in decorator to monitor and benchmark function or query performance with zero-vendor lock in.
AI Summary: Implement periodic flushing and atexit handling in the CsvExporter class of the pyquerytracker library to prevent data loss in case of program crashes or unexpected exits. This involves adding a configurable flush interval and using the `atexit` module to ensure a final flush before termination.
A drop-in decorator to monitor and benchmark function or query performance with zero-vendor lock in.
AI Summary: Add an empty file named `py.typed` to the root directory of the pyquerytracker project to enable type hint recognition by static type checkers like MyPy. This involves ensuring the file is included in the package distribution.
A drop-in decorator to monitor and benchmark function or query performance with zero-vendor lock in.
AI Summary: Develop a CLI tool or HTML dashboard to summarize query performance statistics from pyquerytracker logs. The tool should display total queries, slowest queries, average execution time, and optionally offer breakdowns by query type or function, along with export capabilities (CSV/JSON).
A drop-in decorator to monitor and benchmark function or query performance with zero-vendor lock in.
AI Summary: Implement colored logging output for the pyquerytracker library using the colorlog package as an optional dependency. This involves conditionally using colorlog's ColoredFormatter for enhanced console readability, falling back to the standard logging.Formatter if colorlog is not installed, and updating the README to document this feature.
A drop-in decorator to monitor and benchmark function or query performance with zero-vendor lock in.
AI Summary: Implement a command-line interface (CLI) for pyquerytracker, allowing users to configure settings (slow query threshold, log level), run tracked functions/scripts, and export logs in JSON or CSV format. This involves designing CLI commands, implementing them using a library like `argparse` or `click`, and adding comprehensive documentation and tests.
A drop-in decorator to monitor and benchmark function or query performance with zero-vendor lock in.