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Merge pull request #8 from omercengiz/feat/richer-cli-reporting
Feat/richer cli reporting
2 parents ecd1382 + 0db5ce9 commit 30e0a0d

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‎README.md‎

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@@ -135,6 +135,11 @@ Then run:
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flowsense analyze <dag_id>
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```
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The CLI report includes a DAG summary and separate tables for task drift,
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handoff drift, change points, trends, propagation paths, and diagnostics.
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Results are ordered by severity or subject so repeated analyses remain easy to
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compare.
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## Library API
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FlowSense can also be used as a Python library through its supported top-level
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Planned areas include:
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- DAG-level analysis models
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- richer CLI reporting
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- broader Airflow compatibility testing
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## License

‎src/flowsense/cli/main.py‎

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import typer
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from rich.console import Console
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from rich.table import Table
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from flowsense.application import analyze_dag
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from flowsense.cli.report import render_analysis
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from flowsense.domain import AnalysisPolicy, MappedTaskAggregation
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from flowsense.infrastructure.airflow import AirflowApiError, AirflowClient
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@@ -63,91 +63,4 @@ def analyze(
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console.print(f"[bold red]Airflow request failed:[/bold red] {exc}")
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raise typer.Exit(code=1) from exc
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console.print(f"\n[bold]FlowSense Analysis — {analysis.dag_id}[/bold]\n")
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table = Table()
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table.add_column("Task")
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table.add_column("Baseline")
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table.add_column("Current")
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table.add_column("Deviation")
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table.add_column("Z-Score")
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table.add_column("Severity")
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table.add_column("Impact")
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for task_id, result in analysis.drift_results.items():
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impact = analysis.task_impacts.get(task_id)
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impact_label = impact.classification if impact else "-"
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table.add_row(
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task_id,
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f"{result.baseline:.2f}s",
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f"{result.current:.2f}s",
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f"{result.deviation_percent:+.1f}%",
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f"{result.robust_z_score:.2f}",
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result.severity,
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impact_label,
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)
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console.print(table)
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console.print(f"\nOverall Severity: [bold]{analysis.overall_severity}[/bold]")
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if analysis.change_point_results or analysis.handoff_change_point_results:
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console.print("\n[bold]Change Points[/bold]\n")
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for result in [
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*analysis.change_point_results.values(),
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*analysis.handoff_change_point_results.values(),
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]:
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change = (
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f"{result.change_percent:+.1f}%"
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if result.change_percent is not None
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else "n/a"
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)
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console.print(
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f"{result.subject_id}: {result.direction} at observation "
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f"{result.change_index + 1} ({change}, score={result.score:.2f})"
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)
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if analysis.trend_results or analysis.handoff_trend_results:
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console.print("\n[bold]Trends[/bold]\n")
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for result in [
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*analysis.trend_results.values(),
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*analysis.handoff_trend_results.values(),
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]:
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change = (
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f"{result.change_percent:+.1f}%"
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if result.change_percent is not None
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else "n/a"
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)
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console.print(
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f"{result.subject_id}: {result.direction} "
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f"({result.slope_per_observation:+.2f}/run, {change}, "
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f"score={result.score:.2f})"
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)
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if analysis.primary_origin:
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console.print(f"Primary Origin: [bold]{analysis.primary_origin.task_id}[/bold]")
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console.print(f"Reason: [bold]{analysis.primary_origin.classification}[/bold]")
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console.print(f"Severity: [bold]{analysis.primary_origin.severity}[/bold]")
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console.print(
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f"Propagation Score: {analysis.primary_origin.propagation_score:.2f}"
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)
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if analysis.propagation_results:
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console.print("\n[bold]Propagation Analysis[/bold]\n")
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for result in analysis.propagation_results:
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console.print(f"Origin: {result.origin_task}")
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console.print(f"Path: {' -> '.join(result.path)}")
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console.print(f"Propagation Score: {result.propagation_score:.2f}")
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if analysis.diagnostics:
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console.print("\n[bold yellow]Diagnostics[/bold yellow]\n")
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for diagnostic in analysis.diagnostics:
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console.print(
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f"[{diagnostic.code}] {diagnostic.subject_id}: {diagnostic.message}"
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)
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render_analysis(console, analysis)

‎src/flowsense/cli/report.py‎

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from __future__ import annotations
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from rich.console import Console
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from rich.panel import Panel
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from rich.table import Table
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from rich.text import Text
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from flowsense.domain import DAGAnalysis, Severity
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from flowsense.domain.enums import SEVERITY_SCORE
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_SEVERITY_STYLES = {
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Severity.NORMAL: "green",
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Severity.MEDIUM: "yellow",
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Severity.HIGH: "bright_red",
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Severity.CRITICAL: "bold red",
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}
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def _severity_text(severity: Severity) -> Text:
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return Text(str(severity), style=_SEVERITY_STYLES[severity])
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def _percent(value: float | None) -> str:
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return f"{value:+.1f}%" if value is not None else "n/a"
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def _render_summary(console: Console, analysis: DAGAnalysis) -> None:
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summary = Table.grid(padding=(0, 2))
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summary.add_column(style="bold")
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summary.add_column()
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summary.add_row("DAG", analysis.dag_id)
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summary.add_row("Runs analyzed", str(analysis.runs_analyzed))
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summary.add_row("Overall severity", _severity_text(analysis.overall_severity))
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if analysis.primary_origin is not None:
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summary.add_row("Primary origin", analysis.primary_origin.task_id)
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summary.add_row("Classification", str(analysis.primary_origin.classification))
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summary.add_row(
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"Propagation score",
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f"{analysis.primary_origin.propagation_score:.2f}",
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)
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console.print(Panel(summary, title="FlowSense Analysis", expand=False))
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def _render_task_drift(console: Console, analysis: DAGAnalysis) -> None:
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table = Table(title="Task Drift")
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table.add_column("Task")
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table.add_column("Baseline", justify="right")
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table.add_column("Current", justify="right")
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table.add_column("Deviation", justify="right")
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table.add_column("Z-Score", justify="right")
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table.add_column("Severity")
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table.add_column("Impact")
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ordered_results = sorted(
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analysis.drift_results.items(),
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key=lambda item: (-SEVERITY_SCORE[item[1].severity], item[0]),
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)
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for task_id, result in ordered_results:
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impact = analysis.task_impacts.get(task_id)
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table.add_row(
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task_id,
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f"{result.baseline:.2f}s",
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f"{result.current:.2f}s",
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f"{result.deviation_percent:+.1f}%",
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f"{result.robust_z_score:.2f}",
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_severity_text(result.severity),
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str(impact.classification) if impact else "-",
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)
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console.print(table)
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def _render_handoff_drift(console: Console, analysis: DAGAnalysis) -> None:
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if not analysis.handoff_drift_results:
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return
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table = Table(title="Handoff Drift")
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table.add_column("Edge")
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table.add_column("Baseline", justify="right")
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table.add_column("Current", justify="right")
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table.add_column("Deviation", justify="right")
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table.add_column("Z-Score", justify="right")
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table.add_column("Severity")
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ordered_results = sorted(
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analysis.handoff_drift_results.items(),
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key=lambda item: (-SEVERITY_SCORE[item[1].severity], item[0]),
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)
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for (upstream, downstream), result in ordered_results:
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table.add_row(
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f"{upstream} -> {downstream}",
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f"{result.baseline:.2f}s",
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f"{result.current:.2f}s",
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f"{result.deviation_percent:+.1f}%",
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f"{result.robust_z_score:.2f}",
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_severity_text(result.severity),
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)
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console.print(table)
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def _render_change_points(console: Console, analysis: DAGAnalysis) -> None:
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results = [
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*analysis.change_point_results.values(),
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*analysis.handoff_change_point_results.values(),
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]
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if not results:
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return
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table = Table(title="Change Points")
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table.add_column("Subject")
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table.add_column("Direction")
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table.add_column("Observation", justify="right")
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table.add_column("Before", justify="right")
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table.add_column("After", justify="right")
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table.add_column("Change", justify="right")
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table.add_column("Score", justify="right")
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for result in sorted(results, key=lambda item: item.subject_id):
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table.add_row(
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result.subject_id,
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str(result.direction),
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str(result.change_index + 1),
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f"{result.before_median:.2f}s",
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f"{result.after_median:.2f}s",
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_percent(result.change_percent),
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f"{result.score:.2f}",
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)
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console.print(table)
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def _render_trends(console: Console, analysis: DAGAnalysis) -> None:
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results = [
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*analysis.trend_results.values(),
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*analysis.handoff_trend_results.values(),
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]
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if not results:
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return
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table = Table(title="Trends")
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table.add_column("Subject")
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table.add_column("Direction")
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table.add_column("Slope / run", justify="right")
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table.add_column("Est. change", justify="right")
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table.add_column("Change", justify="right")
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table.add_column("Consistency", justify="right")
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table.add_column("Score", justify="right")
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for result in sorted(results, key=lambda item: item.subject_id):
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table.add_row(
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result.subject_id,
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str(result.direction),
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f"{result.slope_per_observation:+.2f}s",
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f"{result.estimated_change:+.2f}s",
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_percent(result.change_percent),
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f"{result.directional_consistency:.0%}",
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f"{result.score:.2f}",
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)
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console.print(table)
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def _render_propagation(console: Console, analysis: DAGAnalysis) -> None:
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if not analysis.propagation_results:
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return
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table = Table(title="Propagation")
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table.add_column("Origin")
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table.add_column("Path")
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table.add_column("Affected", justify="right")
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table.add_column("Score", justify="right")
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for result in sorted(
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analysis.propagation_results,
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key=lambda item: (item.origin_task, item.path),
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):
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table.add_row(
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result.origin_task,
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" -> ".join(result.path),
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str(len(result.affected_tasks)),
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f"{result.propagation_score:.2f}",
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)
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console.print(table)
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def _render_diagnostics(console: Console, analysis: DAGAnalysis) -> None:
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if not analysis.diagnostics:
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return
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table = Table(title="Diagnostics", title_style="bold yellow")
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table.add_column("Code", style="yellow")
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table.add_column("Subject")
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table.add_column("Message")
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for diagnostic in sorted(
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analysis.diagnostics,
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key=lambda item: (item.code, item.subject_id),
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):
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table.add_row(diagnostic.code, diagnostic.subject_id, diagnostic.message)
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console.print(table)
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def render_analysis(console: Console, analysis: DAGAnalysis) -> None:
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"""Render a complete human-readable analysis report."""
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_render_summary(console, analysis)
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_render_task_drift(console, analysis)
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_render_handoff_drift(console, analysis)
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_render_change_points(console, analysis)
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_render_trends(console, analysis)
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_render_propagation(console, analysis)
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_render_diagnostics(console, analysis)

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