Files
xiaoxia-saas/scripts/ci/ci_dashboard.py
T
xiaoxia 6eac0b2cf2
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chore(ci): 同步main分支CI配置与scripts/ci脚本 - 与develop对齐
同步内容:
1. CI流水线配置(ci-pipeline.yml)与develop对齐
2. PR构建脚本docker_build_only.sh增加buildx→docker build回退
3. pre-build步骤worker基础镜像构建增加buildx回退
4. 单元测试脚本全量覆盖率改为仅报告不阻塞
5. diff-cover依赖加入requirements-dev.txt
6. worker base builder/runtime Dockerfile同步
7. test_config_oss.py clear=False→clear=True修复OSS污染
8. Frontend Lint增加prettier依赖
2026-07-24 10:36:29 +08:00

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#!/usr/bin/env python3
"""
CI 可观测性看板 - 从 Gitea Actions API 拉取数据并生成 Markdown/HTML 日报
用法:
python3 scripts/ci/ci_dashboard.py --days 7
python3 scripts/ci/ci_dashboard.py --days 30 --output ci_report.md
python3 scripts/ci/ci_dashboard.py --workflow ci-cd.yml --days 7
python3 scripts/ci/ci_dashboard.py --days 7 --html --html-output dashboard.html
环境变量:
GITEA_URL Gitea 地址 (默认 https://git.xiaoxiajianji.com)
GITEA_REPO 仓库 (默认 xiaoxia/xiaoxia-saas)
GITEA_TOKEN API Token (优先) 或 GITEA_USERNAME + GITEA_PASSWORD
"""
import argparse
import base64
import json
import math
import os
import statistics
import sys
import urllib.error
import urllib.request
from collections import defaultdict
from datetime import datetime, timedelta, timezone
# ── 配置 ──────────────────────────────────────────────
DEFAULT_GITEA_URL = "https://git.xiaoxiajianji.com"
DEFAULT_REPO = "xiaoxia/xiaoxia-saas"
DEFAULT_DAYS = 7
PAGE_LIMIT = 50 # 每页数量,最大50
# ── API 封装 ─────────────────────────────────────────
class GiteaActions:
def __init__(self, base_url, repo, token=None, username=None, password=None):
self.base_url = base_url.rstrip("/")
self.repo = repo
self.token = token
self.username = username
self.password = password
self.api_base = f"{self.base_url}/api/v1/repos/{self.repo}/actions"
def _request(self, path):
url = f"{self.api_base}/{path}"
req = urllib.request.Request(url)
if self.token:
req.add_header("Authorization", f"token {self.token}")
elif self.username and self.password:
auth = base64.b64encode(f"{self.username}:{self.password}".encode()).decode()
req.add_header("Authorization", f"Basic {auth}")
try:
with urllib.request.urlopen(req, timeout=30) as resp:
return json.loads(resp.read().decode())
except urllib.error.HTTPError as e:
print(f"[WARN] HTTP {e.code}: {url}", file=sys.stderr)
return None
except Exception as e:
print(f"[WARN] 请求失败 {url}: {e}", file=sys.stderr)
return None
def list_runs(self, status=None, branch=None, event=None, page=1, limit=PAGE_LIMIT):
"""获取 workflow runs 列表"""
params = []
if status:
params.append(f"status={status}")
if branch:
params.append(f"branch={branch}")
if event:
params.append(f"event={event}")
params.append(f"page={page}")
params.append(f"limit={limit}")
path = f"runs?{'&'.join(params)}"
data = self._request(path)
if not data:
return [], 0
runs = data.get("workflow_runs", [])
total = data.get("total_count", 0)
return runs, total
def get_run_jobs(self, run_id):
"""获取 run 的所有 job"""
data = self._request(f"runs/{run_id}/jobs")
if not data:
return []
return data.get("jobs", [])
def list_workflows(self):
"""获取所有 workflow"""
data = self._request("workflows")
if not data:
return []
return data.get("workflows", [])
# ── 工具函数 ─────────────────────────────────────────
def parse_datetime(s):
"""解析 ISO 格式时间字符串"""
if not s or s.startswith("1970") or s.startswith("0001"):
return None
try:
if s.endswith("Z"):
s = s[:-1] + "+00:00"
return datetime.fromisoformat(s)
except Exception:
return None
def to_shanghai(dt):
"""转换为上海时区"""
if dt is None:
return None
if dt.tzinfo is None:
dt = dt.replace(tzinfo=timezone.utc)
return dt.astimezone(timezone(timedelta(hours=8)))
def duration_seconds(start_str, end_str):
"""计算耗时(秒)"""
start = parse_datetime(start_str)
end = parse_datetime(end_str)
if not start or not end:
return None
return (end - start).total_seconds()
def fmt_duration(seconds):
"""格式化耗时显示"""
if seconds is None:
return "N/A"
seconds = int(seconds)
if seconds < 60:
return f"{seconds}s"
mins, secs = divmod(seconds, 60)
if mins < 60:
return f"{mins}m{secs:02d}s"
hours, mins = divmod(mins, 60)
return f"{hours}h{mins:02d}m"
def percentile(sorted_values, p):
"""计算百分位数"""
if not sorted_values:
return None
k = (len(sorted_values) - 1) * (p / 100)
f = math.floor(k)
c = math.ceil(k)
if f == c:
return sorted_values[int(k)]
return sorted_values[f] * (c - k) + sorted_values[c] * (k - f)
def classify_failure(job_name, step_name=None):
"""根据失败的 job/step 名称分类失败原因"""
name = f"{job_name} {step_name or ''}".lower()
if any(k in name for k in ["lint", "ruff", "flake8", "eslint", "prettier", "black", "mypy"]):
return "代码质量 / Lint"
if any(k in name for k in ["unit test", "pytest", "vitest", "jest"]):
return "单元测试失败"
if any(k in name for k in ["integration", "e2e"]):
return "集成测试 / E2E"
if any(k in name for k in ["build", "compile", "docker", "image"]):
return "构建失败"
if any(k in name for k in ["deploy", "preview", "release"]):
return "部署失败"
if any(k in name for k in ["setup", "checkout", "cache", "install", "deps"]):
return "环境 / 依赖"
if any(k in name for k in ["migrate", "migration", "schema"]):
return "数据库迁移"
return "其他"
# ── 数据收集 ─────────────────────────────────────────
def fetch_runs_in_range(ga, start_date, end_date, workflow_filter=None):
"""拉取指定日期范围内的所有 completed runs"""
all_runs = []
page = 1
print(f"[INFO] 拉取 {start_date} ~ {end_date} 的 CI runs...", file=sys.stderr)
while True:
runs, total = ga.list_runs(status="completed", page=page, limit=PAGE_LIMIT)
if not runs:
break
if workflow_filter:
runs = [r for r in runs if workflow_filter in r.get("path", "")]
in_range = []
out_range_old = False
for run in runs:
started = to_shanghai(parse_datetime(run.get("started_at")))
if not started:
continue
run_date = started.date()
if start_date <= run_date <= end_date:
in_range.append(run)
elif run_date < start_date:
out_range_old = True
all_runs.extend(in_range)
print(
f"[INFO] 第 {page} 页: {len(runs)} 条, 范围内 {len(in_range)} 条, 累计 {len(all_runs)} 条", file=sys.stderr
)
if out_range_old or len(runs) < PAGE_LIMIT:
break
page += 1
if page > 100:
print("[WARN] 超过100页,停止拉取", file=sys.stderr)
break
print(f"[INFO] 共获取 {len(all_runs)} 条 run 数据", file=sys.stderr)
return all_runs
def enrich_with_jobs(ga, runs, max_failures=50):
"""为 runs 补充 job 详情(失败原因分析 + runner 统计)
失败 run 按时间倒序取最近 N 个(避免 API 调用过多),
成功 run 采样用于 runner 分布统计。
"""
# 失败 run 取最近 N 个
failure_runs = [r for r in runs if r.get("conclusion") != "success"]
failure_runs = failure_runs[:max_failures] # 已经是时间倒序
print(f"[INFO] 为最近 {len(failure_runs)} 个失败 run 拉取 job 详情...", file=sys.stderr)
for i, run in enumerate(failure_runs):
jobs = ga.get_run_jobs(run["id"])
run["_jobs"] = jobs
if (i + 1) % 10 == 0:
print(f"[INFO] 已处理 {i+1}/{len(failure_runs)}", file=sys.stderr)
# 成功 run 采样用于 runner 分布
success_runs = [r for r in runs if r.get("conclusion") == "success"]
sample_size = min(50, len(success_runs))
if sample_size > 0:
sampled = success_runs[:: max(1, len(success_runs) // sample_size)]
print(f"[INFO] 采样 {len(sampled)} 个成功 run 用于 runner 统计...", file=sys.stderr)
for run in sampled:
if "_jobs" not in run:
jobs = ga.get_run_jobs(run["id"])
run["_jobs"] = jobs
return runs
# ── 统计分析 ─────────────────────────────────────────
def analyze_runs(runs):
"""对 runs 做全面统计分析"""
if not runs:
return {}
# 基础统计
total = len(runs)
success = sum(1 for r in runs if r.get("conclusion") == "success")
failure = sum(1 for r in runs if r.get("conclusion") == "failure")
cancelled = sum(1 for r in runs if r.get("conclusion") == "cancelled")
other = total - success - failure - cancelled
success_rate = (success / total * 100) if total > 0 else 0
# 耗时统计
durations = []
for r in runs:
d = duration_seconds(r.get("started_at"), r.get("completed_at"))
if d and d > 0:
durations.append(d)
durations.sort()
avg_dur = statistics.mean(durations) if durations else None
median_dur = percentile(durations, 50)
p95_dur = percentile(durations, 95)
# 按日期统计
daily_stats = defaultdict(lambda: {"total": 0, "success": 0, "failure": 0, "durations": []})
for r in runs:
started = to_shanghai(parse_datetime(r.get("started_at")))
if not started:
continue
day = started.date().isoformat()
daily_stats[day]["total"] += 1
if r.get("conclusion") == "success":
daily_stats[day]["success"] += 1
elif r.get("conclusion") == "failure":
daily_stats[day]["failure"] += 1
d = duration_seconds(r.get("started_at"), r.get("completed_at"))
if d and d > 0:
daily_stats[day]["durations"].append(d)
# 按 workflow 统计
wf_stats = defaultdict(lambda: {"total": 0, "success": 0, "failure": 0, "durations": []})
for r in runs:
path = r.get("path", "")
wf_name = path.split("@")[0] if "@" in path else path
wf_stats[wf_name]["total"] += 1
if r.get("conclusion") == "success":
wf_stats[wf_name]["success"] += 1
elif r.get("conclusion") == "failure":
wf_stats[wf_name]["failure"] += 1
d = duration_seconds(r.get("started_at"), r.get("completed_at"))
if d and d > 0:
wf_stats[wf_name]["durations"].append(d)
# 按触发事件统计
event_stats = defaultdict(lambda: {"total": 0, "success": 0, "failure": 0})
for r in runs:
evt = r.get("event", "unknown")
event_stats[evt]["total"] += 1
if r.get("conclusion") == "success":
event_stats[evt]["success"] += 1
elif r.get("conclusion") == "failure":
event_stats[evt]["failure"] += 1
# 失败原因 + runner + job 耗时(需要 _jobs 数据)
failure_categories = defaultdict(int)
failed_jobs_by_name = defaultdict(int)
job_success_stats = defaultdict(lambda: {"total": 0, "success": 0, "failure": 0})
runner_stats = defaultdict(lambda: {"jobs": 0, "success": 0, "failure": 0, "durations": []})
job_time_stats = defaultdict(list)
infra_failures = 0
business_failures = 0
other_failures_count = 0
# 基础设施关键词(与 ci_health_check.py 保持一致的分类逻辑)
infra_job_keywords = ["checkout", "build", "deploy", "cleanup", "setup", "cache", "install", "docker"]
business_job_keywords = [
"unit test",
"pytest",
"vitest",
"jest",
"lint",
"eslint",
"prettier",
"integration",
"e2e",
"validate",
"code quality",
"mypy",
"ruff",
"flake8",
]
for r in runs:
jobs = r.get("_jobs", [])
if not jobs:
continue
for job in jobs:
runner = job.get("runner_name", "unknown")
conclusion = job.get("conclusion", "unknown")
job_name = job.get("name", "unknown")
job_name_lower = job_name.lower()
runner_stats[runner]["jobs"] += 1
job_success_stats[job_name]["total"] += 1
if conclusion == "success":
runner_stats[runner]["success"] += 1
job_success_stats[job_name]["success"] += 1
elif conclusion == "failure":
runner_stats[runner]["failure"] += 1
job_success_stats[job_name]["failure"] += 1
jd = duration_seconds(job.get("started_at"), job.get("completed_at"))
if jd and jd > 0:
runner_stats[runner]["durations"].append(jd)
job_time_stats[job_name].append(jd)
if conclusion == "failure":
failed_jobs_by_name[job_name] += 1
failed_step = None
for step in job.get("steps", []):
if step.get("conclusion") == "failure":
failed_step = step.get("name")
break
category = classify_failure(job_name, failed_step)
failure_categories[category] += 1
# 基础设施 vs 业务代码分类
is_infra = any(k in job_name_lower for k in infra_job_keywords) and not any(
k in job_name_lower for k in business_job_keywords
)
is_business = any(k in job_name_lower for k in business_job_keywords)
if is_infra:
infra_failures += 1
elif is_business:
business_failures += 1
else:
other_failures_count += 1
return {
"total": total,
"success": success,
"failure": failure,
"cancelled": cancelled,
"other": other,
"success_rate": success_rate,
"avg_duration": avg_dur,
"median_duration": median_dur,
"p95_duration": p95_dur,
"durations": durations,
"daily_stats": dict(sorted(daily_stats.items())),
"workflow_stats": dict(wf_stats),
"event_stats": dict(event_stats),
"failure_categories": dict(failure_categories),
"failed_jobs_top": dict(sorted(failed_jobs_by_name.items(), key=lambda x: -x[1])[:15]),
"runner_stats": dict(runner_stats),
"job_time_stats": dict(job_time_stats),
"job_success_stats": dict(job_success_stats),
"infra_failures": infra_failures,
"business_failures": business_failures,
"other_failures_combined": other_failures_count,
}
# ── Markdown 报表生成 ────────────────────────────────
def generate_markdown(stats, start_date, end_date, repo):
"""生成 Markdown 格式的日报"""
lines = []
lines.append("# CI 运行状态看板")
lines.append("")
lines.append(f"> 统计周期: **{start_date} ~ {end_date}**")
lines.append(f"> 仓库: `{repo}`")
lines.append(f"> 生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
lines.append("")
# 概览
lines.append("## 📊 整体概览")
lines.append("")
lines.append("| 指标 | 数值 |")
lines.append("|------|------|")
lines.append(f"| 总构建次数 | **{stats['total']}** |")
lines.append(f"| ✅ 成功 | {stats['success']} |")
lines.append(f"| ❌ 失败 | {stats['failure']} |")
lines.append(f"| ⏹️ 取消 | {stats['cancelled']} |")
lines.append(f"| 📈 成功率 | **{stats['success_rate']:.1f}%** |")
lines.append(f"| ⏱️ 平均耗时 | {fmt_duration(stats['avg_duration'])} |")
lines.append(f"| ⏱️ P50 耗时 | {fmt_duration(stats['median_duration'])} |")
lines.append(f"| ⏱️ P95 耗时 | {fmt_duration(stats['p95_duration'])} |")
lines.append("")
# 每日趋势
lines.append("## 📈 每日趋势")
lines.append("")
lines.append("| 日期 | 总次数 | 成功 | 失败 | 成功率 | 平均耗时 | P95 耗时 |")
lines.append("|------|--------|------|------|--------|----------|----------|")
for day, s in stats["daily_stats"].items():
rate = (s["success"] / s["total"] * 100) if s["total"] > 0 else 0
durations = sorted(s["durations"])
avg = statistics.mean(durations) if durations else None
p95 = percentile(durations, 95) if durations else None
lines.append(
f"| {day} | {s['total']} | {s['success']} | {s['failure']} | {rate:.1f}% | {fmt_duration(avg)} | {fmt_duration(p95)} |"
)
lines.append("")
# 成功率趋势图
lines.append("### 成功率趋势图")
lines.append("")
lines.append("```")
max_bar = 40
days = list(stats["daily_stats"].keys())
if len(days) > 14:
days = days[-14:]
for day in days:
s = stats["daily_stats"][day]
rate = (s["success"] / s["total"] * 100) if s["total"] > 0 else 0
bar_len = int(rate / 100 * max_bar)
bar = "█" * bar_len + "░" * (max_bar - bar_len)
lines.append(f"{day} {bar} {rate:5.1f}% ({s['total']}次)")
lines.append("```")
lines.append("")
# 按 Workflow 统计
lines.append("## 🧩 各 Workflow 统计")
lines.append("")
wf_sorted = sorted(stats["workflow_stats"].items(), key=lambda x: -x[1]["total"])
lines.append("| Workflow | 次数 | 成功 | 失败 | 成功率 | 平均耗时 | P95 耗时 |")
lines.append("|----------|------|------|------|--------|----------|----------|")
for wf, s in wf_sorted:
rate = (s["success"] / s["total"] * 100) if s["total"] > 0 else 0
durations = sorted(s["durations"])
avg = statistics.mean(durations) if durations else None
p95 = percentile(durations, 95) if durations else None
wf_short = wf.split("/")[-1] if "/" in wf else wf
lines.append(
f"| `{wf_short}` | {s['total']} | {s['success']} | {s['failure']} | {rate:.1f}% | {fmt_duration(avg)} | {fmt_duration(p95)} |"
)
lines.append("")
# 失败原因分析
if stats["failure_categories"]:
lines.append("## ❌ 失败原因分析")
lines.append("")
lines.append("> ⚠️ 基于最近 N 个失败 run 采样分析,用于趋势参考")
lines.append("")
lines.append("### 按分类统计")
lines.append("")
total_failures = sum(stats["failure_categories"].values())
fc_sorted = sorted(stats["failure_categories"].items(), key=lambda x: -x[1])
lines.append("| 分类 | 次数 | 占比 |")
lines.append("|------|------|------|")
for cat, cnt in fc_sorted:
pct = (cnt / total_failures * 100) if total_failures > 0 else 0
lines.append(f"| {cat} | {cnt} | {pct:.1f}% |")
lines.append("")
lines.append("### Top 失败 Job")
lines.append("")
lines.append("| Job 名称 | 失败次数 |")
lines.append("|----------|----------|")
for job, cnt in stats["failed_jobs_top"].items():
lines.append(f"| `{job}` | {cnt} |")
lines.append("")
# Runner 利用率
if stats["runner_stats"]:
lines.append("## 🏃 Runner 利用率")
lines.append("")
runner_sorted = sorted(stats["runner_stats"].items(), key=lambda x: -x[1]["jobs"])
lines.append("| Runner | Job 数 | 成功 | 失败 | 成功率 | 平均耗时 |")
lines.append("|--------|--------|------|------|--------|----------|")
for runner, s in runner_sorted:
rate = (s["success"] / s["jobs"] * 100) if s["jobs"] > 0 else 0
avg = statistics.mean(s["durations"]) if s["durations"] else None
lines.append(
f"| `{runner}` | {s['jobs']} | {s['success']} | {s['failure']} | {rate:.1f}% | {fmt_duration(avg)} |"
)
lines.append("")
# Job 耗时排行
if stats["job_time_stats"]:
lines.append("## ⏱️ Job 耗时排行 (Top 20 by P95)")
lines.append("")
job_stats = []
for name, durs in stats["job_time_stats"].items():
if not durs:
continue
durs_sorted = sorted(durs)
job_stats.append(
{
"name": name,
"count": len(durs_sorted),
"avg": statistics.mean(durs_sorted),
"p50": percentile(durs_sorted, 50),
"p95": percentile(durs_sorted, 95),
}
)
job_stats.sort(key=lambda x: -x["p95"])
top_n = min(20, len(job_stats))
lines.append("| Job 名称 | 次数 | 平均 | P50 | P95 |")
lines.append("|----------|------|------|-----|-----|")
for j in job_stats[:top_n]:
lines.append(
f"| `{j['name']}` | {j['count']} | {fmt_duration(j['avg'])} | {fmt_duration(j['p50'])} | {fmt_duration(j['p95'])} |"
)
lines.append("")
# 触发事件分布
lines.append("## 📋 触发事件分布")
lines.append("")
evt_sorted = sorted(stats["event_stats"].items(), key=lambda x: -x[1]["total"])
lines.append("| 事件类型 | 次数 | 成功 | 失败 | 成功率 |")
lines.append("|----------|------|------|------|--------|")
for evt, s in evt_sorted:
rate = (s["success"] / s["total"] * 100) if s["total"] > 0 else 0
lines.append(f"| `{evt}` | {s['total']} | {s['success']} | {s['failure']} | {rate:.1f}% |")
lines.append("")
return "\n".join(lines)
# ── HTML 看板生成 ────────────────────────────────────
def generate_html(stats, start_date, end_date, repo):
"""生成 HTML 格式的可视化看板(内嵌 ECharts"""
# 准备图表数据
# 1. 每日成功率趋势
daily_dates = list(stats["daily_stats"].keys())
daily_success_rates = []
daily_run_counts = []
for day in daily_dates:
s = stats["daily_stats"][day]
rate = (s["success"] / s["total"] * 100) if s["total"] > 0 else 0
daily_success_rates.append(round(rate, 1))
daily_run_counts.append(s["total"])
# 2. 各 Workflow 耗时对比
wf_sorted = sorted(stats["workflow_stats"].items(), key=lambda x: -x[1]["total"])
wf_names = []
wf_avg_durations = []
for wf, s in wf_sorted:
wf_short = wf.split("/")[-1] if "/" in wf else wf
wf_names.append(wf_short)
avg = statistics.mean(s["durations"]) if s["durations"] else 0
wf_avg_durations.append(round(avg / 60, 1)) # 转为分钟
# 3. 失败原因分布(饼图数据 - 基础设施 vs 业务 vs 其他)
total_infra_biz = stats["infra_failures"] + stats["business_failures"] + stats["other_failures_combined"]
infra_rate = (stats["infra_failures"] / total_infra_biz * 100) if total_infra_biz > 0 else 0
failure_pie_data = [
{"value": stats["infra_failures"], "name": "基础设施问题"},
{"value": stats["business_failures"], "name": "业务代码问题"},
{"value": stats["other_failures_combined"], "name": "其他"},
]
# 4. 各 Job 成功率排行(横向柱状图,取成功率最低的 Top 15)
job_stats_list = []
for name, s in stats["job_success_stats"].items():
if s["total"] >= 3: # 至少有3次才统计
rate = (s["success"] / s["total"] * 100) if s["total"] > 0 else 0
job_stats_list.append(
{
"name": name,
"rate": round(rate, 1),
"total": s["total"],
"success": s["success"],
}
)
job_stats_list.sort(key=lambda x: x["rate"])
job_stats_list = job_stats_list[:15] # 取成功率最低的15个
job_names = [j["name"] for j in job_stats_list]
job_rates = [j["rate"] for j in job_stats_list]
# 核心指标
total_runs = stats["total"]
success_rate = round(stats["success_rate"], 1)
avg_dur_min = round(stats["avg_duration"] / 60, 1) if stats["avg_duration"] else 0
infra_fail_rate = round(infra_rate, 1)
now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
# 序列化数据为 JSON(供 JS 使用)
data_json = json.dumps(
{
"daily_dates": daily_dates,
"daily_success_rates": daily_success_rates,
"daily_run_counts": daily_run_counts,
"wf_names": wf_names,
"wf_avg_durations": wf_avg_durations,
"failure_pie_data": failure_pie_data,
"job_names": job_names,
"job_rates": job_rates,
},
ensure_ascii=False,
)
# HTML 模板(注意:不使用 f-string,避免与 CSS/JS 的大括号冲突)
html_parts = []
html_parts.append("<!DOCTYPE html>")
html_parts.append('<html lang="zh-CN">')
html_parts.append("<head>")
html_parts.append(' <meta charset="UTF-8">')
html_parts.append(' <meta name="viewport" content="width=device-width, initial-scale=1.0">')
html_parts.append(f" <title>CI 健康度看板 - {repo}</title>")
html_parts.append(' <script src="https://cdn.jsdelivr.net/npm/echarts/dist/echarts.min.js"></script>')
html_parts.append(" <style>")
html_parts.append(" * { margin: 0; padding: 0; box-sizing: border-box; }")
html_parts.append(" body {")
html_parts.append(
' font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "PingFang SC", "Hiragino Sans GB",'
)
html_parts.append(' "Microsoft YaHei", sans-serif;')
html_parts.append(" background: #f0f2f5;")
html_parts.append(" color: #333;")
html_parts.append(" padding: 20px;")
html_parts.append(" }")
html_parts.append(" .container { max-width: 1400px; margin: 0 auto; }")
html_parts.append(" .header {")
html_parts.append(" background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);")
html_parts.append(" color: white;")
html_parts.append(" padding: 24px 32px;")
html_parts.append(" border-radius: 12px;")
html_parts.append(" margin-bottom: 20px;")
html_parts.append(" }")
html_parts.append(" .header h1 { font-size: 24px; margin-bottom: 8px; }")
html_parts.append(" .header .subtitle { font-size: 14px; opacity: 0.9; }")
html_parts.append(" .header .meta { font-size: 12px; opacity: 0.8; margin-top: 8px; }")
html_parts.append(" .metrics-row {")
html_parts.append(" display: grid;")
html_parts.append(" grid-template-columns: repeat(4, 1fr);")
html_parts.append(" gap: 16px;")
html_parts.append(" margin-bottom: 20px;")
html_parts.append(" }")
html_parts.append(" .metric-card {")
html_parts.append(" background: white;")
html_parts.append(" border-radius: 12px;")
html_parts.append(" padding: 20px;")
html_parts.append(" box-shadow: 0 2px 8px rgba(0,0,0,0.06);")
html_parts.append(" transition: transform 0.2s;")
html_parts.append(" }")
html_parts.append(
" .metric-card:hover { transform: translateY(-2px); box-shadow: 0 4px 12px rgba(0,0,0,0.1); }"
)
html_parts.append(" .metric-card .label { font-size: 13px; color: #8c8c8c; margin-bottom: 8px; }")
html_parts.append(" .metric-card .value { font-size: 28px; font-weight: 600; }")
html_parts.append(" .metric-card .unit { font-size: 14px; color: #8c8c8c; margin-left: 4px; }")
html_parts.append(" .metric-card.success .value { color: #52c41a; }")
html_parts.append(" .metric-card.warning .value { color: #faad14; }")
html_parts.append(" .metric-card.danger .value { color: #ff4d4f; }")
html_parts.append(" .metric-card.info .value { color: #1890ff; }")
html_parts.append(" .charts-grid {")
html_parts.append(" display: grid;")
html_parts.append(" grid-template-columns: 1fr 1fr;")
html_parts.append(" gap: 16px;")
html_parts.append(" margin-bottom: 20px;")
html_parts.append(" }")
html_parts.append(" .chart-card {")
html_parts.append(" background: white;")
html_parts.append(" border-radius: 12px;")
html_parts.append(" padding: 20px;")
html_parts.append(" box-shadow: 0 2px 8px rgba(0,0,0,0.06);")
html_parts.append(" }")
html_parts.append(" .chart-card.full-width { grid-column: 1 / -1; }")
html_parts.append(" .chart-card h3 {")
html_parts.append(" font-size: 16px;")
html_parts.append(" margin-bottom: 12px;")
html_parts.append(" color: #262626;")
html_parts.append(" font-weight: 600;")
html_parts.append(" }")
html_parts.append(" .chart-container { width: 100%; height: 320px; }")
html_parts.append(" .chart-container.tall { height: 400px; }")
html_parts.append(" .footer {")
html_parts.append(" text-align: center;")
html_parts.append(" color: #8c8c8c;")
html_parts.append(" font-size: 12px;")
html_parts.append(" padding: 16px 0;")
html_parts.append(" }")
html_parts.append(" @media (max-width: 900px) {")
html_parts.append(" .metrics-row { grid-template-columns: repeat(2, 1fr); }")
html_parts.append(" .charts-grid { grid-template-columns: 1fr; }")
html_parts.append(" }")
html_parts.append(" @media (max-width: 600px) {")
html_parts.append(" .metrics-row { grid-template-columns: 1fr; }")
html_parts.append(" body { padding: 12px; }")
html_parts.append(" }")
html_parts.append(" </style>")
html_parts.append("</head>")
html_parts.append("<body>")
html_parts.append(' <div class="container">')
html_parts.append(' <div class="header">')
html_parts.append(" <h1>📊 CI 健康度看板</h1>")
html_parts.append(f' <div class="subtitle">仓库: {repo}</div>')
html_parts.append(f' <div class="meta">统计周期: {start_date} ~ {end_date} | 生成时间: {now_str}</div>')
html_parts.append(" </div>")
html_parts.append(' <div class="metrics-row">')
html_parts.append(' <div class="metric-card success">')
html_parts.append(' <div class="label">总成功率</div>')
html_parts.append(f' <div class="value">{success_rate}<span class="unit">%</span></div>')
html_parts.append(" </div>")
html_parts.append(' <div class="metric-card info">')
html_parts.append(' <div class="label">总 Run 数</div>')
html_parts.append(f' <div class="value">{total_runs}<span class="unit">次</span></div>')
html_parts.append(" </div>")
html_parts.append(' <div class="metric-card warning">')
html_parts.append(' <div class="label">平均耗时</div>')
html_parts.append(f' <div class="value">{avg_dur_min}<span class="unit">分钟</span></div>')
html_parts.append(" </div>")
html_parts.append(' <div class="metric-card danger">')
html_parts.append(' <div class="label">基础设施故障率</div>')
html_parts.append(f' <div class="value">{infra_fail_rate}<span class="unit">%</span></div>')
html_parts.append(" </div>")
html_parts.append(" </div>")
html_parts.append(' <div class="charts-grid">')
html_parts.append(' <div class="chart-card full-width">')
html_parts.append(" <h3>📈 CI 成功率趋势</h3>")
html_parts.append(' <div id="chart-success-rate" class="chart-container"></div>')
html_parts.append(" </div>")
html_parts.append(" </div>")
html_parts.append(' <div class="charts-grid">')
html_parts.append(' <div class="chart-card">')
html_parts.append(" <h3>⏱️ 各 Workflow 平均耗时</h3>")
html_parts.append(' <div id="chart-wf-duration" class="chart-container"></div>')
html_parts.append(" </div>")
html_parts.append(' <div class="chart-card">')
html_parts.append(" <h3>❌ 失败原因分布</h3>")
html_parts.append(' <div id="chart-failure-pie" class="chart-container"></div>')
html_parts.append(" </div>")
html_parts.append(" </div>")
html_parts.append(' <div class="charts-grid">')
html_parts.append(' <div class="chart-card full-width">')
html_parts.append(" <h3>📋 各 Job 成功率排行(最低 15 名)</h3>")
html_parts.append(' <div id="chart-job-success" class="chart-container tall"></div>')
html_parts.append(" </div>")
html_parts.append(" </div>")
html_parts.append(' <div class="charts-grid">')
html_parts.append(' <div class="chart-card full-width">')
html_parts.append(" <h3>📊 每日 Run 数量趋势</h3>")
html_parts.append(' <div id="chart-run-count" class="chart-container"></div>')
html_parts.append(" </div>")
html_parts.append(" </div>")
html_parts.append(' <div class="footer">')
html_parts.append(" 由 ci_dashboard.py 自动生成 | ECharts 可视化")
html_parts.append(" </div>")
html_parts.append(" </div>")
html_parts.append(" <script>")
html_parts.append(f" const DATA = {data_json};")
html_parts.append("")
# 图表 1: 成功率趋势
html_parts.append(" (function() {")
html_parts.append(' const chart = echarts.init(document.getElementById("chart-success-rate"));')
html_parts.append(" chart.setOption({")
html_parts.append(' tooltip: { trigger: "axis", formatter: function(params) {')
html_parts.append(' const p = params[0]; return p.name + "<br/>成功率: <b>" + p.value + "%</b>";')
html_parts.append(" }},")
html_parts.append(' grid: { left: "3%", right: "4%", bottom: "3%", containLabel: true },')
html_parts.append(' xAxis: { type: "category", boundaryGap: false, data: DATA.daily_dates,')
html_parts.append(" axisLabel: { rotate: 30, fontSize: 11 } },")
html_parts.append(' yAxis: { type: "value", min: 0, max: 100, axisLabel: { formatter: "{value}%" } },')
html_parts.append(
' series: [{ name: "成功率", type: "line", smooth: true, data: DATA.daily_success_rates,'
)
html_parts.append(' itemStyle: { color: "#52c41a" },')
html_parts.append(" areaStyle: { color: new echarts.graphic.LinearGradient(0, 0, 0, 1, [")
html_parts.append(' { offset: 0, color: "rgba(82, 196, 26, 0.3)" },')
html_parts.append(' { offset: 1, color: "rgba(82, 196, 26, 0.05)" }')
html_parts.append(" ])},")
html_parts.append(' markLine: { silent: true, data: [{ type: "average", name: "平均值",')
html_parts.append(' label: { formatter: "均值 {c}%" } }] }')
html_parts.append(" }]")
html_parts.append(" });")
html_parts.append(' window.addEventListener("resize", () => chart.resize());')
html_parts.append(" })();")
html_parts.append("")
# 图表 2: Workflow 耗时对比
html_parts.append(" (function() {")
html_parts.append(' const chart = echarts.init(document.getElementById("chart-wf-duration"));')
html_parts.append(" chart.setOption({")
html_parts.append(' tooltip: { trigger: "axis", formatter: function(params) {')
html_parts.append(
' const p = params[0]; return p.name + "<br/>平均耗时: <b>" + p.value + " 分钟</b>";'
)
html_parts.append(" }},")
html_parts.append(' grid: { left: "3%", right: "4%", bottom: "15%", containLabel: true },')
html_parts.append(' xAxis: { type: "category", data: DATA.wf_names,')
html_parts.append(" axisLabel: { rotate: 30, fontSize: 10, interval: 0 } },")
html_parts.append(' yAxis: { type: "value", name: "分钟", axisLabel: { formatter: "{value} min" } },')
html_parts.append(' series: [{ name: "平均耗时", type: "bar", data: DATA.wf_avg_durations,')
html_parts.append(" itemStyle: { color: new echarts.graphic.LinearGradient(0, 0, 0, 1, [")
html_parts.append(' { offset: 0, color: "#1890ff" },')
html_parts.append(' { offset: 1, color: "#096dd9" }')
html_parts.append(" ]), borderRadius: [4, 4, 0, 0] },")
html_parts.append(" barMaxWidth: 40")
html_parts.append(" }]")
html_parts.append(" });")
html_parts.append(' window.addEventListener("resize", () => chart.resize());')
html_parts.append(" })();")
html_parts.append("")
# 图表 3: 失败原因饼图
html_parts.append(" (function() {")
html_parts.append(' const chart = echarts.init(document.getElementById("chart-failure-pie"));')
html_parts.append(" chart.setOption({")
html_parts.append(' tooltip: { trigger: "item", formatter: "{b}: {c} 次 ({d}%)" },')
html_parts.append(' legend: { orient: "vertical", right: "5%", top: "center" },')
html_parts.append(
' series: [{ name: "失败原因", type: "pie", radius: ["40%", "70%"], center: ["35%", "50%"],'
)
html_parts.append(" avoidLabelOverlap: false,")
html_parts.append(' itemStyle: { borderRadius: 6, borderColor: "#fff", borderWidth: 2 },')
html_parts.append(' label: { show: false, position: "center" },')
html_parts.append(' emphasis: { label: { show: true, fontSize: 16, fontWeight: "bold" } },')
html_parts.append(" labelLine: { show: false },")
html_parts.append(" data: DATA.failure_pie_data,")
html_parts.append(' color: ["#ff4d4f", "#faad14", "#8c8c8c"]')
html_parts.append(" }]")
html_parts.append(" });")
html_parts.append(' window.addEventListener("resize", () => chart.resize());')
html_parts.append(" })();")
html_parts.append("")
# 图表 4: Job 成功率排行(横向柱状图)
html_parts.append(" (function() {")
html_parts.append(' const chart = echarts.init(document.getElementById("chart-job-success"));')
html_parts.append(" const barData = DATA.job_rates.map(function(rate, i) {")
html_parts.append(" return { value: rate, itemStyle: {")
html_parts.append(' color: rate >= 90 ? "#52c41a" : (rate >= 70 ? "#faad14" : "#ff4d4f")')
html_parts.append(" }};")
html_parts.append(" });")
html_parts.append(" chart.setOption({")
html_parts.append(' tooltip: { trigger: "axis", formatter: function(params) {')
html_parts.append(' const p = params[0]; return p.name + "<br/>成功率: <b>" + p.value + "%</b>";')
html_parts.append(" }},")
html_parts.append(' grid: { left: "3%", right: "8%", bottom: "3%", top: "3%", containLabel: true },')
html_parts.append(' xAxis: { type: "value", min: 0, max: 100, axisLabel: { formatter: "{value}%" } },')
html_parts.append(' yAxis: { type: "category", data: DATA.job_names, axisLabel: { fontSize: 11 } },')
html_parts.append(' series: [{ name: "成功率", type: "bar", data: barData, barWidth: "60%",')
html_parts.append(' label: { show: true, position: "right", formatter: "{c}%", fontSize: 11 }')
html_parts.append(" }]")
html_parts.append(" });")
html_parts.append(' window.addEventListener("resize", () => chart.resize());')
html_parts.append(" })();")
html_parts.append("")
# 图表 5: 每日 Run 数量趋势(面积图)
html_parts.append(" (function() {")
html_parts.append(' const chart = echarts.init(document.getElementById("chart-run-count"));')
html_parts.append(" chart.setOption({")
html_parts.append(' tooltip: { trigger: "axis", formatter: function(params) {')
html_parts.append(
' const p = params[0]; return p.name + "<br/>Run 数量: <b>" + p.value + " 次</b>";'
)
html_parts.append(" }},")
html_parts.append(' grid: { left: "3%", right: "4%", bottom: "3%", containLabel: true },')
html_parts.append(' xAxis: { type: "category", boundaryGap: false, data: DATA.daily_dates,')
html_parts.append(" axisLabel: { rotate: 30, fontSize: 11 } },")
html_parts.append(' yAxis: { type: "value", name: "次数" },')
html_parts.append(
' series: [{ name: "Run 数量", type: "line", smooth: true, data: DATA.daily_run_counts,'
)
html_parts.append(' itemStyle: { color: "#722ed1" },')
html_parts.append(" areaStyle: { color: new echarts.graphic.LinearGradient(0, 0, 0, 1, [")
html_parts.append(' { offset: 0, color: "rgba(114, 46, 209, 0.3)" },')
html_parts.append(' { offset: 1, color: "rgba(114, 46, 209, 0.05)" }')
html_parts.append(" ])},")
html_parts.append(' markLine: { silent: true, data: [{ type: "average", name: "平均值",')
html_parts.append(' label: { formatter: "均值 {c} 次" } }] }')
html_parts.append(" }]")
html_parts.append(" });")
html_parts.append(' window.addEventListener("resize", () => chart.resize());')
html_parts.append(" })();")
html_parts.append(" </script>")
html_parts.append("</body>")
html_parts.append("</html>")
return "\n".join(html_parts)
# ── 主函数 ───────────────────────────────────────────
def main():
parser = argparse.ArgumentParser(description="CI 可观测性看板 - 生成 Gitea Actions 运行状态报表")
parser.add_argument("--days", type=int, default=DEFAULT_DAYS, help=f"统计最近 N 天 (默认 {DEFAULT_DAYS})")
parser.add_argument("--output", "-o", type=str, help="输出文件路径 (默认输出到 stdout)")
parser.add_argument("--workflow", type=str, help="只统计指定 workflow (如 ci-cd.yml)")
parser.add_argument("--gitea-url", type=str, default=os.environ.get("GITEA_URL", DEFAULT_GITEA_URL))
parser.add_argument("--repo", type=str, default=os.environ.get("GITEA_REPO", DEFAULT_REPO))
parser.add_argument("--token", type=str, default=os.environ.get("GITEA_TOKEN"))
parser.add_argument("--username", type=str, default=os.environ.get("GITEA_USERNAME"))
parser.add_argument("--password", type=str, default=os.environ.get("GITEA_PASSWORD"))
parser.add_argument("--no-job-detail", action="store_true", help="不拉取 job 详情")
parser.add_argument("--max-failures", type=int, default=50, help="最多分析多少个失败 run 的 job 详情 (默认 50)")
# HTML 输出相关参数
parser.add_argument("--html", action="store_true", help="生成 HTML 可视化看板")
parser.add_argument("--html-output", type=str, help="HTML 输出文件路径 (默认 ci_dashboard.html)")
args = parser.parse_args()
ga = GiteaActions(
base_url=args.gitea_url,
repo=args.repo,
token=args.token,
username=args.username,
password=args.password,
)
end_date = datetime.now().date()
start_date = end_date - timedelta(days=args.days - 1)
runs = fetch_runs_in_range(ga, start_date, end_date, args.workflow)
if not runs:
print("[ERROR] 未获取到任何数据", file=sys.stderr)
sys.exit(1)
if not args.no_job_detail:
runs = enrich_with_jobs(ga, runs, max_failures=args.max_failures)
stats = analyze_runs(runs)
# HTML 模式
if args.html:
html = generate_html(stats, start_date, end_date, args.repo)
html_output = args.html_output or args.output or "ci_dashboard.html"
with open(html_output, "w", encoding="utf-8") as f:
f.write(html)
print(f"[INFO] HTML 看板已保存到 {html_output}", file=sys.stderr)
return
# 默认 Markdown 模式(向后兼容)
md = generate_markdown(stats, start_date, end_date, args.repo)
if args.output:
with open(args.output, "w", encoding="utf-8") as f:
f.write(md)
print(f"[INFO] 报表已保存到 {args.output}", file=sys.stderr)
else:
print(md)
if __name__ == "__main__":
main()