nishantverse/topdock

1. Repository shape

- This is a small, mostly single-module Python CLI application.

- The core runtime appears to live in topdock.py, with packaging metadata in pyproject.toml.

- install.sh and install.ps1 provide convenience installation paths for Unix-like shells and Windows PowerShell.

2. Primary purpose

- TopDock is a terminal dashboard for live Docker container metrics.

- It presents per-container CPU, memory, network, and block I/O statistics in real time, and can also emit one-shot snapshot output.

3. Runtime boundary

- The app runs locally on the user’s machine and talks to a Docker daemon.

- It can connect either to the local Docker socket or to a remote Docker host via the --host option, so the Docker daemon is the main external dependency and data source.

4. Core data flow

- Docker container statistics are collected from the daemon.

- Those stats are transformed into a tabular view model for the terminal.

- The view model is then rendered into a live dashboard, or serialized into snapshot output such as JSON/CSV.

5. Main functional subsystems

- CLI/command parsing: handles flags such as --sort, --refresh, --alert, --snapshot, --format, --host, and --version.

- Docker metrics acquisition: fetches live container stats from Docker.

- Ranking/sorting: orders containers by CPU, memory, network, or block I/O.

- Terminal dashboard UI: displays a continuously updating table and responds to keyboard input.

- Export/output path: supports snapshot mode and export of CSV/JSON.

- Alert handling: tracks threshold-based alerting, with a command to clear alerts.

6. Interactive control boundary

- The live dashboard is event-driven through keyboard shortcuts.

- Navigation keys scroll the table; letter keys change sort mode, export data, clear alerts, or quit.

- This makes the UI stateful: the same underlying metric set can be viewed with different sort order and scroll position.

7. Modes of operation

- Live mode: continuous refresh, interactive terminal UI, default behavior.

- Snapshot mode: one-shot capture intended for machine-readable output.

- These are distinct output paths, which is important for an architecture diagram because rendering and serialization are alternate sinks from the same metrics source.

8. Output and integration points

- Human-facing output is the terminal dashboard.

- Machine-facing output is JSON and CSV export/snapshot data.

- There are no signs of a database, web server, message queue, or background service; the application seems self-contained aside from Docker access.

9. Packaging and distribution

- pyproject.toml indicates standard Python packaging, likely exposing a console script entrypoint.

- The install scripts suggest the repo supports direct local installation in addition to package-manager-based installation via pipx or pip.

10. Platform considerations

- The README claims Linux, macOS, and Windows support.

- That implies the architecture is designed around cross-platform Python code and a terminal interface rather than OS-specific GUI components.

- Remote Docker host support also suggests the Docker communication layer is configurable, not hardwired to a local socket.

11. Likely diagram framing

- A good architecture diagram would place the user terminal at the center, with an interactive dashboard UI consuming data from a Docker daemon.

- Beside it, show an alternate snapshot/export path for JSON/CSV.

- Surround those with a thin CLI/configuration layer and packaging/install scripts as the entry and distribution boundary.

12. What is notably absent

- No evidence of backend services, persistence, authentication, or multi-process coordination.

- No signs of separate frontend/backend separation; this is a single-process CLI tool with a clear external dependency on Docker.