mirror of
https://github.com/kvcache-ai/sglang.git
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978 lines
38 KiB
Python
978 lines
38 KiB
Python
"""
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SGLang CI Consecutive Failures Analyzer
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Monitors GitHub Actions workflows for consecutive test failures and runner issues.
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Detects failure streaks, tracks job health, identifies problematic runners, and generates alerts.
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Features:
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- Analyzes all jobs in PR Test workflow (excluding administrative jobs)
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- Tracks consecutive failure streaks for each job
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- Monitors runner health and failure rates
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- Identifies whether failures are code-related or infrastructure-related
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- Generates detailed reports with actionable recommendations
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Usage:
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python ci_failures_analysis.py --token <GITHUB_TOKEN> --limit 500 --threshold 3
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"""
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import argparse
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import json
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import os
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import sys
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import time
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from collections import defaultdict
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple
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import requests
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class SGLangFailuresAnalyzer:
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"""Analyzes consecutive failures in GitHub Actions workflows."""
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def __init__(self, token: str, alert_threshold: int = 3):
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self.token = token
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self.alert_threshold = alert_threshold
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self.base_url = "https://api.github.com"
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self.repo = "sgl-project/sglang"
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self.headers = {
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"Authorization": f"token {token}",
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"Accept": "application/vnd.github.v3+json",
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"User-Agent": "SGLang-Failures-Analyzer/1.0",
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}
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self.session = requests.Session()
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self.session.headers.update(self.headers)
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# Target workflows to monitor
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self.target_workflows = ["PR Test"]
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# Jobs to EXCLUDE from analysis (administrative/setup jobs, not actual tests)
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self.excluded_jobs = [
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"check-changes",
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"pr-test-finish",
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]
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def get_recent_runs(self, limit: int = 500) -> List[Dict]:
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"""Fetch recent workflow runs from GitHub API."""
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print(f"Fetching {limit} recent workflow runs...")
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all_runs = []
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page = 1
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per_page = 100
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while len(all_runs) < limit:
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url = f"{self.base_url}/repos/{self.repo}/actions/runs"
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params = {"per_page": min(per_page, limit - len(all_runs)), "page": page}
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try:
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response = self.session.get(url, params=params, timeout=30)
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response.raise_for_status()
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data = response.json()
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if not data.get("workflow_runs"):
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break
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all_runs.extend(data["workflow_runs"])
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print(f"Fetched {len(all_runs)} runs so far...")
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if len(data["workflow_runs"]) < per_page:
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break
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page += 1
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time.sleep(0.1)
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except requests.exceptions.RequestException as e:
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print(f"Error fetching workflow runs: {e}")
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break
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# Filter to target workflows only
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filtered_runs = [
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run
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for run in all_runs
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if run.get("name") in self.target_workflows
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and run.get("status") == "completed"
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]
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print(f"Filtered to {len(filtered_runs)} completed target workflow runs")
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return filtered_runs[:limit]
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def get_jobs_for_run(self, run_id: int) -> List[Dict]:
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"""Get all jobs for a specific workflow run."""
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try:
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url = f"{self.base_url}/repos/{self.repo}/actions/runs/{run_id}/jobs"
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response = self.session.get(url, timeout=30)
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response.raise_for_status()
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data = response.json()
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jobs = data.get("jobs", [])
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return jobs
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except requests.exceptions.RequestException as e:
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print(f"Error fetching jobs for run {run_id}: {e}")
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return []
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def analyze_runner_health(
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self, runs: List[Dict]
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) -> Tuple[Dict[str, Dict], Dict[str, Dict]]:
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"""
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Analyze runner health by tracking failures per runner.
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Returns:
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Tuple of (runner_stats, runner_job_failures)
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- runner_stats: Overall stats per runner (failure rate, total jobs, etc.)
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- runner_job_failures: Per-runner breakdown of which jobs failed
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"""
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print("\nAnalyzing runner health...")
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# Sort runs by created_at (oldest first)
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sorted_runs = sorted(runs, key=lambda x: x.get("created_at", ""))
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# Track runner statistics
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runner_total_jobs: Dict[str, int] = defaultdict(int)
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runner_failed_jobs: Dict[str, int] = defaultdict(int)
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runner_job_failures: Dict[str, Dict[str, int]] = defaultdict(
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lambda: defaultdict(int)
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)
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runner_job_totals: Dict[str, Dict[str, int]] = defaultdict(
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lambda: defaultdict(int)
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)
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# Track individual runner instances (runner_name + runner_id)
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runner_instance_stats: Dict[str, Dict] = defaultdict(
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lambda: {"total_jobs": 0, "failed_jobs": 0, "jobs_failed": defaultdict(int)}
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)
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total_runs_processed = len(sorted_runs)
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for i, run in enumerate(sorted_runs, 1):
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if i % 50 == 0 or i == total_runs_processed:
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print(
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f"Processing run {i}/{total_runs_processed} for runner analysis: #{run.get('run_number')}"
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)
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# Get jobs for this run
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jobs = self.get_jobs_for_run(run.get("id"))
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for job in jobs:
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job_name = job.get("name", "")
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# Skip excluded jobs (administrative/setup jobs)
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if any(
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job_name.startswith(excluded) for excluded in self.excluded_jobs
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):
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continue
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# Extract runner information
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# GitHub API might use different fields for runner info
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runner_name = (
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job.get("runner_name")
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or job.get("runner", {}).get("name")
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or "unknown"
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)
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runner_id = job.get("runner_id") or job.get("runner", {}).get("id")
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# Get runner labels (from runs-on field in workflow)
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runner_labels = job.get("labels", [])
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runner_labels_str = (
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", ".join(runner_labels) if runner_labels else "unknown"
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)
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# Skip jobs without runner information (likely skipped/queued jobs)
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if not runner_labels_str or runner_labels_str == "unknown":
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continue
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# Track by runner labels (primary identifier)
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# Use labels as the key since they're more informative than runner_name
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runner_key = runner_labels_str
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runner_total_jobs[runner_key] += 1
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runner_job_totals[runner_key][job_name] += 1
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# Track by specific runner instance
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if runner_id:
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runner_instance_key = f"{runner_labels_str}_{runner_id}"
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runner_instance_stats[runner_instance_key]["total_jobs"] += 1
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# Store runner name for reference
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runner_instance_stats[runner_instance_key][
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"runner_name"
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] = runner_name
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conclusion = job.get("conclusion")
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if conclusion == "failure":
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# Failure detected
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runner_failed_jobs[runner_key] += 1
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runner_job_failures[runner_key][job_name] += 1
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if runner_id:
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runner_instance_stats[runner_instance_key]["failed_jobs"] += 1
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runner_instance_stats[runner_instance_key]["jobs_failed"][
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job_name
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] += 1
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time.sleep(0.05)
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# Build final runner stats
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runner_stats = {}
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for runner_key in runner_total_jobs.keys():
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total = runner_total_jobs[runner_key]
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failed = runner_failed_jobs[runner_key]
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failure_rate = (failed / total * 100) if total > 0 else 0
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runner_stats[runner_key] = {
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"total_jobs": total,
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"failed_jobs": failed,
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"failure_rate": failure_rate,
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"unique_jobs_with_failures": len(runner_job_failures[runner_key]),
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"jobs_failed": dict(runner_job_failures[runner_key]),
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"jobs_total": dict(runner_job_totals[runner_key]),
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}
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# Convert runner instance stats to regular dicts
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runner_instance_data = {}
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for instance_key, stats in runner_instance_stats.items():
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runner_instance_data[instance_key] = {
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"total_jobs": stats["total_jobs"],
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"failed_jobs": stats["failed_jobs"],
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"failure_rate": (
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stats["failed_jobs"] / stats["total_jobs"] * 100
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if stats["total_jobs"] > 0
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else 0
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),
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"jobs_failed": dict(stats["jobs_failed"]),
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"runner_name": stats.get("runner_name", "unknown"),
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}
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return runner_stats, runner_instance_data
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def analyze_consecutive_failures(
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self, runs: List[Dict]
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) -> Tuple[Dict[str, Dict], Dict[str, int]]:
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"""
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Analyze consecutive failures for each job.
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Returns:
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Tuple of (job_streak_data, job_current_streaks)
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"""
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print("\nAnalyzing consecutive failures...")
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# Sort runs by created_at (oldest first) to track streaks chronologically
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sorted_runs = sorted(runs, key=lambda x: x.get("created_at", ""))
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# Track current streak for each job
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job_streaks: Dict[str, List[Dict]] = defaultdict(list)
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job_current_streak: Dict[str, int] = defaultdict(int)
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job_max_streak: Dict[str, int] = defaultdict(int)
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job_total_failures: Dict[str, int] = defaultdict(int)
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job_total_runs: Dict[str, int] = defaultdict(int)
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job_first_failure_in_streak: Dict[str, Optional[Dict]] = {}
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job_recovery_info: Dict[str, Optional[Dict]] = {}
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total_runs_processed = len(sorted_runs)
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for i, run in enumerate(sorted_runs, 1):
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if i % 50 == 0 or i == total_runs_processed:
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print(
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f"Processing run {i}/{total_runs_processed}: #{run.get('run_number')}"
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)
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run_info = {
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"run_number": run.get("run_number"),
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"run_id": run.get("id"),
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"created_at": run.get("created_at"),
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"head_sha": run.get("head_sha", "")[:8],
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"author": run.get("head_commit", {})
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.get("author", {})
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.get("name", "Unknown"),
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"url": f"https://github.com/{self.repo}/actions/runs/{run.get('id')}",
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}
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pull_requests = run.get("pull_requests", [])
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if pull_requests:
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run_info["pr_number"] = pull_requests[0].get("number")
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# Get jobs for this run
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jobs = self.get_jobs_for_run(run.get("id"))
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for job in jobs:
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job_name = job.get("name", "")
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# Skip excluded jobs (administrative/setup jobs)
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if any(
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job_name.startswith(excluded) for excluded in self.excluded_jobs
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):
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continue
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job_total_runs[job_name] += 1
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conclusion = job.get("conclusion")
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if conclusion == "failure":
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# Failure detected
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job_total_failures[job_name] += 1
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job_current_streak[job_name] += 1
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# Track if this is the first failure in a new streak
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if job_current_streak[job_name] == 1:
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job_first_failure_in_streak[job_name] = {
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**run_info,
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"job_name": job_name,
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"conclusion": conclusion,
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}
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# Update max streak
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if job_current_streak[job_name] > job_max_streak[job_name]:
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job_max_streak[job_name] = job_current_streak[job_name]
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elif conclusion == "success":
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# Success - streak broken
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if job_current_streak[job_name] > 0:
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# Record recovery
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job_recovery_info[job_name] = {
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**run_info,
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"job_name": job_name,
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"streak_length": job_current_streak[job_name],
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}
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job_current_streak[job_name] = 0
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job_first_failure_in_streak[job_name] = None
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time.sleep(0.05)
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# Build final results
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job_streak_data = {}
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for job_name in job_current_streak.keys():
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job_streak_data[job_name] = {
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"current_streak": job_current_streak[job_name],
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"max_streak": job_max_streak[job_name],
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"total_failures": job_total_failures[job_name],
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"total_runs": job_total_runs[job_name],
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"failure_rate": (
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job_total_failures[job_name] / job_total_runs[job_name] * 100
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if job_total_runs[job_name] > 0
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else 0
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),
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"first_failure_in_streak": job_first_failure_in_streak.get(job_name),
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"recovery_info": job_recovery_info.get(job_name),
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}
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return job_streak_data, job_current_streak
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def aggregate_matrix_jobs(
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self, job_streak_data: Dict[str, Dict]
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) -> Dict[str, Dict]:
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"""
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Aggregate matrix jobs (e.g., 'job-name (0)', 'job-name (1)') into a single entry.
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Returns:
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Dictionary with aggregated job data
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"""
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import re
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# Identify base job names (strip matrix suffix like " (0)", " (1)")
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base_jobs: Dict[str, List[Tuple[str, Dict]]] = defaultdict(list)
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for job_name, data in job_streak_data.items():
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# Match pattern like "job-name (0)" or "job-name (1)"
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match = re.match(r"^(.+?)\s*\((\d+)\)$", job_name)
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if match:
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base_name = match.group(1)
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base_jobs[base_name].append((job_name, data))
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else:
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# Not a matrix job, keep as-is
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base_jobs[job_name].append((job_name, data))
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# Aggregate stats for matrix jobs
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aggregated_data = {}
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for base_name, job_list in base_jobs.items():
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if len(job_list) == 1:
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# Single job, no aggregation needed
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job_name, data = job_list[0]
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aggregated_data[job_name] = data
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else:
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# Multiple matrix jobs - aggregate them
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total_runs = sum(data["total_runs"] for _, data in job_list)
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total_failures = sum(data["total_failures"] for _, data in job_list)
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# Current streak: take the max across all matrix jobs
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# (if any partition is broken, the whole job is considered broken)
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current_streak = max(data["current_streak"] for _, data in job_list)
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max_streak = max(data["max_streak"] for _, data in job_list)
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# Get the first failure from the job with the longest current streak
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first_failure_in_streak = None
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for _, data in job_list:
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if (
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data["current_streak"] == current_streak
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and data["first_failure_in_streak"]
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):
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first_failure_in_streak = data["first_failure_in_streak"]
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break
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# Recovery info from most recent recovery
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recovery_info = None
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for _, data in job_list:
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if data["recovery_info"]:
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recovery_info = data["recovery_info"]
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break
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aggregated_data[base_name] = {
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"current_streak": current_streak,
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"max_streak": max_streak,
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"total_failures": total_failures,
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"total_runs": total_runs,
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"failure_rate": (
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(total_failures / total_runs * 100) if total_runs > 0 else 0
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),
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"first_failure_in_streak": first_failure_in_streak,
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"recovery_info": recovery_info,
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"is_aggregated": True,
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"partition_count": len(job_list),
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"partitions": [job_name for job_name, _ in job_list],
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}
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return aggregated_data
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def detect_alerts(
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self,
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job_streak_data: Dict[str, Dict],
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job_current_streaks: Dict[str, int],
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runner_stats: Optional[Dict[str, Dict]] = None,
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runner_instance_data: Optional[Dict[str, Dict]] = None,
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) -> Tuple[List[Dict], List[Dict]]:
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"""
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Detect jobs and runners that need alerts based on thresholds.
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Returns:
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Tuple of (job_alerts, runner_alerts)
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"""
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job_alerts = []
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for job_name, data in job_streak_data.items():
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current_streak = data["current_streak"]
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# Alert condition: consecutive failures >= threshold
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if current_streak >= self.alert_threshold:
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job_alerts.append(
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{
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"job_name": job_name,
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"current_streak": current_streak,
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"max_streak": data["max_streak"],
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"failure_rate": data["failure_rate"],
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"first_failure": data["first_failure_in_streak"],
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"alert_type": "consecutive_failures",
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"severity": "high" if current_streak >= 5 else "medium",
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}
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)
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# Detect runner alerts
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runner_alerts = []
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if runner_stats:
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# Alert if runner has high failure rate (>30%) and multiple jobs failing
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for runner_labels, stats in runner_stats.items():
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if (
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stats["failure_rate"] > 50
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and stats["unique_jobs_with_failures"] >= 3
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):
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runner_alerts.append(
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{
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"runner_labels": runner_labels,
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"failure_rate": stats["failure_rate"],
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"total_jobs": stats["total_jobs"],
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"failed_jobs": stats["failed_jobs"],
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"unique_jobs_with_failures": stats[
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"unique_jobs_with_failures"
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],
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"alert_type": "runner_health",
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"severity": (
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"high" if stats["failure_rate"] > 50 else "medium"
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),
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}
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)
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# Check for specific runner instances with concerning patterns
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if runner_instance_data:
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for instance_key, stats in runner_instance_data.items():
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# Alert if a specific runner instance has >50% failure rate with >=3 jobs
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if stats["failure_rate"] > 50 and stats["total_jobs"] >= 3:
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runner_alerts.append(
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{
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"runner_instance": instance_key,
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"runner_name": stats.get("runner_name", "unknown"),
|
|
"failure_rate": stats["failure_rate"],
|
|
"total_jobs": stats["total_jobs"],
|
|
"failed_jobs": stats["failed_jobs"],
|
|
"jobs_failed": stats["jobs_failed"],
|
|
"alert_type": "runner_instance_health",
|
|
"severity": "high",
|
|
}
|
|
)
|
|
|
|
return job_alerts, runner_alerts
|
|
|
|
# print statements here mainly for local testing
|
|
def generate_failure_report(
|
|
self,
|
|
job_streak_data: Dict[str, Dict],
|
|
job_alerts: List[Dict],
|
|
runner_stats: Optional[Dict[str, Dict]] = None,
|
|
runner_instance_data: Optional[Dict[str, Dict]] = None,
|
|
runner_alerts: Optional[List[Dict]] = None,
|
|
output_file: Optional[str] = None,
|
|
):
|
|
"""Generate detailed failure analysis report."""
|
|
print("\n" + "=" * 80)
|
|
print("SGLang Consecutive Failures Analysis Report")
|
|
print("=" * 80)
|
|
|
|
# Sort jobs by current streak (descending)
|
|
sorted_jobs = sorted(
|
|
job_streak_data.items(),
|
|
key=lambda x: (x[1]["current_streak"], x[1]["failure_rate"]),
|
|
reverse=True,
|
|
)
|
|
|
|
print(
|
|
f"\nTotal (unique) jobs analyzed across PR Test workflows: {len(sorted_jobs)}"
|
|
)
|
|
print(
|
|
f"Jobs with active failure streaks: {sum(1 for j in sorted_jobs if j[1]['current_streak'] > 0)}"
|
|
)
|
|
print(
|
|
f"Job alerts triggered (>={self.alert_threshold} consecutive failures): {len(job_alerts)}"
|
|
)
|
|
|
|
if runner_stats:
|
|
print(f"Total runners analyzed: {len(runner_stats)}")
|
|
print(
|
|
f"Runner alerts triggered: {len(runner_alerts) if runner_alerts else 0}"
|
|
)
|
|
|
|
# Section 1: Currently Broken Jobs (Consecutive Failures) - URGENT
|
|
print("\n" + "=" * 100)
|
|
print("SECTION 1: Currently Broken Jobs (Active Consecutive Failures)")
|
|
print("=" * 100)
|
|
|
|
broken_jobs = [
|
|
(name, data) for name, data in sorted_jobs if data["current_streak"] > 0
|
|
]
|
|
|
|
if broken_jobs:
|
|
print(
|
|
f"\n{'Rank':<4} {'Job Name':<50} {'Current Streak':<16} {'Max Streak':<12}"
|
|
)
|
|
print("-" * 100)
|
|
for i, (job_name, data) in enumerate(broken_jobs[:20], 1):
|
|
print(
|
|
f"{i:<4} {job_name:<50} {data['current_streak']:<16} {data['max_streak']:<12}"
|
|
)
|
|
else:
|
|
print("\n✓ No jobs are currently in a failure streak!")
|
|
|
|
# Print job alerts
|
|
if job_alerts:
|
|
print("\n" + "!" * 40)
|
|
print("ALERTS: Jobs with Consecutive Failures Exceeding Threshold")
|
|
print("!" * 40)
|
|
|
|
for alert in sorted(
|
|
job_alerts, key=lambda x: x["current_streak"], reverse=True
|
|
):
|
|
print(f"\n {alert['job_name']}")
|
|
print(
|
|
f" Current Streak: {alert['current_streak']} consecutive failures"
|
|
)
|
|
print(f" Max Streak: {alert['max_streak']}")
|
|
print(f" Severity: {alert['severity'].upper()}")
|
|
|
|
if alert["first_failure"]:
|
|
first = alert["first_failure"]
|
|
print(
|
|
f" First Failure in Streak: Run #{first['run_number']} ({first['created_at']})"
|
|
)
|
|
print(f" Link: {first['url']}")
|
|
|
|
# Section 3: Runner Health Analysis
|
|
if runner_stats:
|
|
print("\n" + "=" * 100)
|
|
print("SECTION 2: Runner Health Analysis")
|
|
print("=" * 100)
|
|
|
|
# Sort runners by failure rate
|
|
sorted_runners = sorted(
|
|
runner_stats.items(),
|
|
key=lambda x: (x[1]["failure_rate"], x[1]["failed_jobs"]),
|
|
reverse=True,
|
|
)
|
|
|
|
print(f"\nTop 15 Runners by Failure Rate:")
|
|
print("-" * 100)
|
|
print(
|
|
f"{'Rank':<4} {'Runner Labels':<45} {'Fail Rate':<12} {'Failed':<10} {'Total':<10} {'Unique Jobs':<12}"
|
|
)
|
|
print("-" * 100)
|
|
|
|
for i, (runner_labels, stats) in enumerate(sorted_runners[:15], 1):
|
|
# Truncate labels if too long for display
|
|
display_labels = (
|
|
runner_labels
|
|
if len(runner_labels) <= 43
|
|
else runner_labels[:40] + "..."
|
|
)
|
|
print(
|
|
f"{i:<4} {display_labels:<45} {stats['failure_rate']:>10.1f}% "
|
|
f"{stats['failed_jobs']:<10} {stats['total_jobs']:<10} {stats['unique_jobs_with_failures']:<12}"
|
|
)
|
|
|
|
# Print runner alerts
|
|
if runner_alerts:
|
|
print("\n" + "!" * 40)
|
|
print("ALERTS: Runners with High Failure Rates")
|
|
print("!" * 40)
|
|
|
|
for alert in sorted(
|
|
runner_alerts, key=lambda x: x.get("failure_rate", 0), reverse=True
|
|
):
|
|
if alert["alert_type"] == "runner_health":
|
|
print(f"\n Runner Labels: {alert['runner_labels']}")
|
|
print(f" Failure Rate: {alert['failure_rate']:.1f}%")
|
|
print(
|
|
f" Failed Jobs: {alert['failed_jobs']} / {alert['total_jobs']}"
|
|
)
|
|
print(
|
|
f" Unique Jobs with Failures: {alert['unique_jobs_with_failures']}"
|
|
)
|
|
print(f" Severity: {alert['severity'].upper()}")
|
|
elif alert["alert_type"] == "runner_instance_health":
|
|
print(f"\n Runner Instance: {alert['runner_instance']}")
|
|
print(f" Runner Name: {alert['runner_name']}")
|
|
print(f" Failure Rate: {alert['failure_rate']:.1f}%")
|
|
print(
|
|
f" Failed Jobs: {alert['failed_jobs']} / {alert['total_jobs']}"
|
|
)
|
|
print(f" Jobs Failed: {list(alert['jobs_failed'].keys())}")
|
|
print(f" Severity: {alert['severity'].upper()}")
|
|
|
|
# Build report data (always needed for GitHub summary)
|
|
report_data = {
|
|
"summary": {
|
|
"total_jobs": len(sorted_jobs),
|
|
"jobs_with_streaks": sum(
|
|
1 for j in sorted_jobs if j[1]["current_streak"] > 0
|
|
),
|
|
"job_alerts_triggered": len(job_alerts),
|
|
"runner_alerts_triggered": len(runner_alerts) if runner_alerts else 0,
|
|
"total_runners": len(runner_stats) if runner_stats else 0,
|
|
"alert_threshold": self.alert_threshold,
|
|
"analysis_timestamp": datetime.now().isoformat(),
|
|
},
|
|
"job_streak_data": {
|
|
job_name: {
|
|
**data,
|
|
# Convert datetime objects to strings for JSON serialization
|
|
"first_failure_in_streak": data["first_failure_in_streak"],
|
|
"recovery_info": data["recovery_info"],
|
|
}
|
|
for job_name, data in sorted_jobs
|
|
},
|
|
"job_alerts": job_alerts,
|
|
"runner_stats": runner_stats if runner_stats else {},
|
|
"runner_instance_data": (
|
|
runner_instance_data if runner_instance_data else {}
|
|
),
|
|
"runner_alerts": runner_alerts if runner_alerts else [],
|
|
}
|
|
|
|
# Save to JSON only if output file is specified
|
|
if output_file:
|
|
with open(output_file, "w", encoding="utf-8") as f:
|
|
json.dump(report_data, f, ensure_ascii=False, indent=2)
|
|
print(f"\nDetailed report saved to: {output_file}")
|
|
|
|
print("=" * 80)
|
|
|
|
return report_data
|
|
|
|
def generate_github_summary(self, report_data: Dict):
|
|
"""Generate GitHub Actions Step Summary."""
|
|
try:
|
|
github_step_summary = os.environ.get("GITHUB_STEP_SUMMARY")
|
|
if not github_step_summary:
|
|
print("Not running in GitHub Actions, skipping summary generation")
|
|
return
|
|
|
|
print("Generating GitHub Actions summary...")
|
|
|
|
summary_lines = []
|
|
summary_lines.append("# SGLang Consecutive Failures Analysis")
|
|
summary_lines.append("")
|
|
summary_lines.append(
|
|
f"**Analysis Timestamp:** {report_data['summary']['analysis_timestamp']}"
|
|
)
|
|
summary_lines.append(
|
|
f"**Alert Threshold:** {report_data['summary']['alert_threshold']} consecutive failures"
|
|
)
|
|
summary_lines.append("")
|
|
|
|
# Summary stats
|
|
summary_lines.append("## Summary Statistics")
|
|
summary_lines.append("")
|
|
summary_lines.append("| Metric | Count |")
|
|
summary_lines.append("|--------|-------|")
|
|
summary_lines.append(
|
|
f"| Total (unique) jobs analyzed across PR Test workflows | {report_data['summary']['total_jobs']} |"
|
|
)
|
|
summary_lines.append(
|
|
f"| Jobs with Active Failure Streaks | {report_data['summary']['jobs_with_streaks']} |"
|
|
)
|
|
summary_lines.append(
|
|
f"| Job Alerts Triggered | {report_data['summary']['job_alerts_triggered']} |"
|
|
)
|
|
summary_lines.append(
|
|
f"| Total Runners Analyzed | {report_data['summary']['total_runners']} |"
|
|
)
|
|
summary_lines.append(
|
|
f"| Runner Alerts Triggered | {report_data['summary']['runner_alerts_triggered']} |"
|
|
)
|
|
summary_lines.append("")
|
|
|
|
# Job Alerts section
|
|
if report_data.get("job_alerts"):
|
|
summary_lines.append("## ALERTS: Critical Consecutive Job Failures")
|
|
summary_lines.append("")
|
|
summary_lines.append(
|
|
"| Job Name | Current Streak | Max Streak | First Failure | Link |"
|
|
)
|
|
summary_lines.append(
|
|
"|----------|----------------|------------|---------------|------|"
|
|
)
|
|
|
|
for alert in sorted(
|
|
report_data["job_alerts"],
|
|
key=lambda x: x["current_streak"],
|
|
reverse=True,
|
|
):
|
|
job_name = alert["job_name"]
|
|
if len(job_name) > 40:
|
|
job_name = job_name[:37] + "..."
|
|
|
|
first_failure = alert.get("first_failure")
|
|
first_failure_str = (
|
|
f"Run #{first_failure['run_number']}"
|
|
if first_failure
|
|
else "N/A"
|
|
)
|
|
first_failure_link = first_failure["url"] if first_failure else ""
|
|
|
|
summary_lines.append(
|
|
f"| `{job_name}` | {alert['current_streak']} | {alert['max_streak']} | "
|
|
f"{first_failure_str} | [View]({first_failure_link}) |"
|
|
)
|
|
|
|
summary_lines.append("")
|
|
|
|
# Runner Alerts section
|
|
if report_data.get("runner_alerts"):
|
|
summary_lines.append("## ALERTS: Runners with High Failure Rates")
|
|
summary_lines.append("")
|
|
summary_lines.append(
|
|
"| Runner Labels | Failure Rate | Failed Jobs | Total Jobs | Unique Jobs Failed | Severity |"
|
|
)
|
|
summary_lines.append(
|
|
"|---------------|--------------|-------------|------------|-------------------|----------|"
|
|
)
|
|
|
|
for alert in sorted(
|
|
report_data["runner_alerts"],
|
|
key=lambda x: x.get("failure_rate", 0),
|
|
reverse=True,
|
|
):
|
|
if alert["alert_type"] == "runner_health":
|
|
runner_labels = alert["runner_labels"]
|
|
if len(runner_labels) > 35:
|
|
runner_labels = runner_labels[:32] + "..."
|
|
|
|
summary_lines.append(
|
|
f"| `{runner_labels}` | {alert['failure_rate']:.1f}% | {alert['failed_jobs']} | "
|
|
f"{alert['total_jobs']} | {alert['unique_jobs_with_failures']} | {alert['severity'].upper()} |"
|
|
)
|
|
elif alert["alert_type"] == "runner_instance_health":
|
|
instance = alert["runner_instance"]
|
|
runner_name = alert["runner_name"]
|
|
if len(instance) > 35:
|
|
instance = instance[:32] + "..."
|
|
|
|
summary_lines.append(
|
|
f"| `{instance}` | {alert['failure_rate']:.1f}% | {alert['failed_jobs']} | "
|
|
f"{alert['total_jobs']} | {len(alert['jobs_failed'])} | {alert['severity'].upper()} |"
|
|
)
|
|
summary_lines.append(f"| (Runner: {runner_name}) | | | | | |")
|
|
|
|
summary_lines.append("")
|
|
|
|
# Section 1: Currently Broken Jobs
|
|
summary_lines.append(
|
|
"## Section 1: Currently Broken Jobs (Active Failures)"
|
|
)
|
|
summary_lines.append("")
|
|
|
|
sorted_jobs = sorted(
|
|
report_data["job_streak_data"].items(),
|
|
key=lambda x: (x[1]["current_streak"], x[1]["failure_rate"]),
|
|
reverse=True,
|
|
)
|
|
|
|
broken_jobs = [
|
|
(name, data) for name, data in sorted_jobs if data["current_streak"] > 0
|
|
]
|
|
|
|
if broken_jobs:
|
|
summary_lines.append(
|
|
"| Rank | Job Name | Current Streak | Max Streak |"
|
|
)
|
|
summary_lines.append(
|
|
"|------|----------|----------------|------------|"
|
|
)
|
|
for i, (job_name, data) in enumerate(broken_jobs[:20], 1):
|
|
display_name = (
|
|
job_name if len(job_name) <= 40 else job_name[:37] + "..."
|
|
)
|
|
summary_lines.append(
|
|
f"| {i} | `{display_name}` | {data['current_streak']} | {data['max_streak']} |"
|
|
)
|
|
else:
|
|
summary_lines.append("No jobs are currently in a failure streak!")
|
|
|
|
summary_lines.append("")
|
|
|
|
# Section 2: Runner Health Analysis
|
|
if report_data.get("runner_stats"):
|
|
summary_lines.append("## Section 2: Runner Health Analysis")
|
|
summary_lines.append("")
|
|
|
|
# Sort runners by failure rate
|
|
sorted_runners = sorted(
|
|
report_data["runner_stats"].items(),
|
|
key=lambda x: (x[1]["failure_rate"], x[1]["failed_jobs"]),
|
|
reverse=True,
|
|
)
|
|
|
|
summary_lines.append("### Top 15 Runners by Failure Rate")
|
|
summary_lines.append("")
|
|
summary_lines.append(
|
|
"| Rank | Runner Labels | Failure Rate | Failed Jobs | Total Jobs | Unique Jobs Failed |"
|
|
)
|
|
summary_lines.append(
|
|
"|------|---------------|--------------|-------------|------------|--------------------|"
|
|
)
|
|
|
|
for i, (runner_labels, stats) in enumerate(sorted_runners[:15], 1):
|
|
display_labels = (
|
|
runner_labels
|
|
if len(runner_labels) <= 35
|
|
else runner_labels[:32] + "..."
|
|
)
|
|
summary_lines.append(
|
|
f"| {i} | `{display_labels}` | {stats['failure_rate']:.1f}% | "
|
|
f"{stats['failed_jobs']} | {stats['total_jobs']} | {stats['unique_jobs_with_failures']} |"
|
|
)
|
|
|
|
summary_lines.append("")
|
|
|
|
# Write summary
|
|
with open(github_step_summary, "a", encoding="utf-8") as f:
|
|
f.write("\n".join(summary_lines))
|
|
|
|
print("GitHub Actions summary generated successfully")
|
|
|
|
except Exception as e:
|
|
print(f"Failed to generate GitHub Actions summary: {e}")
|
|
import traceback
|
|
|
|
traceback.print_exc()
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="SGLang Consecutive Failures Analyzer")
|
|
parser.add_argument("--token", required=True, help="GitHub Personal Access Token")
|
|
parser.add_argument(
|
|
"--limit",
|
|
type=int,
|
|
default=500,
|
|
help="Number of workflow runs to analyze (default: 500)",
|
|
)
|
|
parser.add_argument(
|
|
"--threshold",
|
|
type=int,
|
|
default=3,
|
|
help="Alert threshold for consecutive failures (default: 3)",
|
|
)
|
|
parser.add_argument(
|
|
"--output",
|
|
default=None,
|
|
help="Output JSON file (optional, only writes if specified)",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
analyzer = SGLangFailuresAnalyzer(args.token, alert_threshold=args.threshold)
|
|
|
|
try:
|
|
# Fetch recent runs
|
|
runs = analyzer.get_recent_runs(args.limit)
|
|
|
|
if not runs:
|
|
print("No workflow runs found")
|
|
return
|
|
|
|
# Analyze consecutive failures
|
|
job_streak_data, job_current_streaks = analyzer.analyze_consecutive_failures(
|
|
runs
|
|
)
|
|
|
|
if not job_streak_data:
|
|
print("No job data found")
|
|
return
|
|
|
|
# Aggregate matrix jobs (e.g., "job (0)", "job (1)" -> "job")
|
|
print("\nAggregating matrix jobs...")
|
|
job_streak_data = analyzer.aggregate_matrix_jobs(job_streak_data)
|
|
print(f"After aggregation: {len(job_streak_data)} unique jobs")
|
|
|
|
# Analyze runner health
|
|
runner_stats, runner_instance_data = analyzer.analyze_runner_health(runs)
|
|
|
|
# Detect alerts
|
|
job_alerts, runner_alerts = analyzer.detect_alerts(
|
|
job_streak_data, job_current_streaks, runner_stats, runner_instance_data
|
|
)
|
|
|
|
# Generate report
|
|
report_data = analyzer.generate_failure_report(
|
|
job_streak_data,
|
|
job_alerts,
|
|
runner_stats,
|
|
runner_instance_data,
|
|
runner_alerts,
|
|
args.output,
|
|
)
|
|
|
|
# Generate GitHub Actions summary
|
|
analyzer.generate_github_summary(report_data)
|
|
|
|
# Exit with error code if alerts triggered
|
|
total_alerts = len(job_alerts) + len(runner_alerts)
|
|
if total_alerts > 0:
|
|
print(
|
|
f"\n!!!!! {len(job_alerts)} job alert(s) and {len(runner_alerts)} runner alert(s) triggered!"
|
|
)
|
|
sys.exit(0) # Don't fail the workflow, just report
|
|
else:
|
|
print("\n No alerts triggered")
|
|
|
|
except Exception as e:
|
|
print(f"Error during analysis: {e}")
|
|
import traceback
|
|
|
|
traceback.print_exc()
|
|
sys.exit(1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|